Steel structure product full-process life cycle management system and method

Abstract

The invention relates to the technical field of data management, in particular to a steel structure product full-process life cycle management system and method, and the system comprises a production data optimization module which calculates the ratio of material consumption to energy consumption in a working procedure based on data collected in a production process, evaluates the resource utilization rate of each link, and outputs the data to a database; and extracting abnormal deviation values of materials and energy consumption in the production process, screening links influencing the production efficiency, and generating production resource allocation data. According to the method, the resource utilization rate of each link is accurately evaluated by calculating the ratio of material consumption to energy consumption in production. In this way, abnormal deviation existing in production is extracted, key steps influencing efficiency are recognized, and refined distribution of production resources is achieved. And further calculating the variation amplitude of the equipment operation state and the production speed, accurately analyzing the association between the core parameters and the yield, and determining the core parameters which have the most influence on the production quality and the yield.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a full-process life cycle management system and method for steel structure products. Background Art

[0002] The technical field of data management mainly studies and applies technologies and methods for various aspects such as data collection, storage, processing, analysis, and maintenance. The field includes database management systems, data warehouses, big data processing, data analysis and mining, etc. The goal of data management is to efficiently organize, store, and utilize data to support enterprise decision-making, improve production efficiency, optimize resource allocation, and achieve intelligent management.

[0003] Among them, the full-process life cycle management system for steel structure products is a data management system applied to the steel structure industry, which conducts full-life cycle tracking and management on each link of steel structure products from design, production, transportation, installation, use to scrapping. By means of digitalization, it collects and analyzes the data information generated in each link, realizes data integration and management within the life cycle of steel structure products, and its uses include monitoring product quality, optimizing resource allocation, and supporting decision-making through data analysis.

[0004] In the prior art for the full-process management of steel structure products, it is difficult to deeply analyze the subtle deviations of material consumption and energy consumption in the production process, resulting in the inability to conduct refined management of resource allocation, and there are problems of material waste and excessive energy consumption. In addition, in terms of the correlation analysis between the equipment operation status and the output, it is difficult to accurately identify and adjust the key influencing factors in production, reducing production efficiency. The monitoring of the full life cycle stays more at the level of data recording, which is not conducive to accurately predicting the failure risk of products under different working conditions and affects decision-making. In addition, it is difficult to accurately estimate material fatigue and life, as well as the material failure time, resulting in potential safety hazards during use. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a full-process life cycle management system and method for steel structure products.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A full-process life cycle management system for steel structure products includes:

[0007] Based on the data collected during the production process, the production data optimization module calculates the ratio of material consumption to energy consumption in the process, evaluates the resource utilization rate of each link, extracts the abnormal deviation values of materials and energy consumption in the production process, screens the links affecting production efficiency, and generates production resource allocation data;

[0008] Based on the production resource allocation data, the key production variable extraction module calculates the change ranges of the equipment operation status and production speed, analyzes the correlation between the core parameters and the output, selects the core parameters that have the greatest impact on production quality and output, and generates a set of production key variables;

[0009] The full-life cycle risk assessment module compares the influence degrees of environmental conditions on the core parameters in the set of production key variables, calculates the failure risks of steel structure products under multiple simulated usage scenarios, analyzes the fluctuation conditions of the core parameters under the current environmental conditions, and generates a full-life cycle risk assessment result;

[0010] Based on the full-life cycle risk assessment result, the material fatigue and life prediction module calls the stress distribution and fatigue accumulation data of the steel structure product, calculates the fatigue accumulation speed of the material under the current working conditions to estimate the failure time of the material, and generates steel structure product life prediction data.

[0011] As a further solution of the present invention, the acquisition steps of the resource utilization rate for each link are specifically as follows:

[0012] Based on the energy consumption and material consumption data collected during the production process, combined with the energy efficiency conversion rate parameters obtained from the energy efficiency detection of the equipment, the formula is used:

[0013]

[0014] Calculate the ratio R of energy consumption to material consumption;

[0015] where E is the total energy consumed during the production process, η is the equipment energy efficiency coefficient, M is the mass of the material consumed during the production process, k is the time consumption adjustment parameter, and T is the time of the process during the production process;

[0016] Based on the ratio of energy consumption to material consumption, call this ratio to conduct a comparative analysis with the reference data, analyze the resource utilization efficiency of the production link, compare the energy consumption fluctuation and material consumption trend, and conduct an efficiency assessment in combination with the equipment operation status to generate a resource utilization rate assessment result.

[0017] As a further solution of the present invention, the acquisition steps of the link that affects the production efficiency are specifically as follows:

[0018] Based on the ratio of material consumption to energy consumption, compare the past data with the current data, perform a deviation analysis of material and energy consumption, and use the formula:

[0019]

[0020] Calculate the abnormal deviation value D of material consumption and energy consumption, and generate the abnormal deviation value of material consumption and energy consumption;

[0021] Among them, D is used to reflect the difference between the current process and the past standard, and R avg is the past average ratio, and F is the process adjustment factor;

[0022] Based on the abnormal deviation values of the material consumption and energy consumption, by setting a conventional deviation range, comparing with the reference range, analyzing the equipment performance and efficiency, and generating a list of links affecting production efficiency;

[0023] According to the list of links affecting production efficiency, obtain the equipment resource occupancy rate, material usage rate and energy consumption allocation information, perform comparison and classification analysis, and refine the process in combination with the deviation data to generate production resource allocation data.

[0024] As a further solution of the present invention, the specific steps for obtaining the correlation between the analysis core parameters and the output are as follows:

[0025] Based on the production resource allocation data, extract the equipment operation status and production speed, and use the formula:

[0026]

[0027] The change amplitude ΔV of the equipment operation status or production speed is generated to obtain the change amplitude data of the equipment operation status and production speed;

[0028] Among them, V final is the speed indicating the equipment or production line at the end of the monitoring period, V initial is the speed indicating the equipment or production line at the start of the monitoring, T is the total time of the monitoring period, η1 is the equipment efficiency coefficient, and α1 is the adjustment parameter;

[0029] Based on the change amplitude data of the equipment operation status and production speed, by calling the output data and equipment operation records, perform correlation analysis on the output and speed change trends, combine the finished product qualification rate data, identify the key variables, and perform data comparison to generate a set of production key variables.

[0030] As a further solution of the present invention, the specific steps for obtaining the influence degree of the comparison environmental conditions on the core parameters in the set of production key variables are as follows:

[0031] Compare the influence degree of the environmental conditions on the core parameters in the set of production key variables, and use the formula:

[0032]

[0033] Calculate the failure risk value R′ f ;

[0034] Among them, K i is the i-th core parameter in the set of production key variables, Ci is the influence coefficient of environmental conditions on the i-th core parameter, β i is the material deterioration coefficient, E′ is the comprehensive value of environmental conditions, γ1 is the external load correction coefficient, and n is the total number of production key variables;

[0035] Based on the failure risk value, analyze the risk level of the product in the current environment, judge the risk state of the product in combination with the risk value range, and perform data induction to generate a risk assessment result.

[0036] As a further solution of the present invention, the obtaining step of analyzing the fluctuation of the core parameter under the current environmental conditions is specifically as follows:

[0037] Referring to the failure risk value, use the formula:

[0038]

[0039] Calculate the fluctuation amount ΔP of the equipment operation state or production speed to obtain equipment performance fluctuation data;

[0040] where P0 is the initial reference value of the core parameter, R′ max is the maximum allowable failure risk value, T′ is the temperature under the current environment, T′ norm is the standard temperature of the equipment design, L is the current load, obtained through a load sensor, L max is the maximum design load of the equipment;

[0041] According to the equipment performance fluctuation data, correlate the environmental data with the equipment operation state data, analyze the impact of environmental changes on the equipment performance, and perform trend evaluation to establish a quantitative relationship between environmental changes and equipment performance, and generate a full-life cycle risk assessment result.

[0042] As a further solution of the present invention, the obtaining step of estimating the failure time of the material is specifically as follows:

[0043] According to the full-life cycle risk assessment result, use the formula:

[0044]

[0045] Calculate the fatigue accumulation speed v of the material under the current working conditions to obtain fatigue accumulation speed information;

[0046] where ΔN is the stress cycle number increment, Δt is the time increment, S is the stress borne by the current material, S max is the maximum load-bearing capacity of the material, G is the fatigue residual strength of the material, G ref is the reference fatigue strength of the material;

[0047] Based on the fatigue accumulation rate information, use the formula:

[0048]

[0049] Calculate the remaining failure time T of the material f , and generate life prediction data for steel structure products;

[0050] where B is the fatigue cumulative damage value, and S res is the remaining load-bearing capacity of the material.

[0051] A full-process life cycle management method for steel structure products, which is executed based on the above full-process life cycle management system for steel structure products, and includes the following steps:

[0052] S1: Based on the production data of steel structure products, calculate the ratio of material consumption to energy consumption, evaluate the resource utilization rate, extract abnormal deviation values, screen the links affecting efficiency, and generate production resource allocation data;

[0053] S2: Based on the production resource allocation data, calculate the changes in the equipment operation status and production speed, analyze the correlation between the core parameters and the output, select the key parameters, and generate a set of production key variables;

[0054] S3: Based on the set of production key variables, compare the influence of environmental conditions on the core parameters, calculate the failure risks under multiple scenarios, analyze the parameter fluctuations, and generate a full-life cycle risk assessment result;

[0055] S4: Based on the full-life cycle risk assessment result, call the stress distribution and fatigue data, calculate the fatigue accumulation rate of the material, estimate the failure time, and obtain the life prediction data of the steel structure product;

[0056] S5: Based on the life prediction data of the steel structure product, optimize the material consumption and energy consumption allocation, adjust the production process, and establish an optimized result of production resource allocation.

[0057] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0058] In the present invention, by calculating the ratio of material consumption to energy consumption during production, the resource utilization rate of each link is accurately evaluated. Based on this, the abnormal deviations existing in production are extracted, the key steps affecting efficiency are identified, and the refined allocation of production resources is achieved. Further, by calculating the change range of the equipment operation state and production speed, the correlation between the core parameters and the output is accurately analyzed, and the core parameters that have the greatest influence on production quality and output are determined. In addition, by comparing the changes in the core parameters under different environmental conditions, the failure conditions of the product in various usage scenarios are comprehensively analyzed, and the full-life cycle risks are effectively predicted. Subsequently, by combining the product stress distribution and fatigue accumulation data, the fatigue accumulation speed of the material under actual working conditions is evaluated, and the failure time of the material is accurately estimated. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is the system flow chart of the present invention;

[0060] Figure 2 is the acquisition step flow chart for the present invention to evaluate the resource utilization rate of each link;

[0061] Figure 3 is the acquisition step flow chart for the present invention to screen the links affecting production efficiency;

[0062] Figure 4 is the acquisition step flow chart for the present invention to analyze the correlation between core parameters and output;

[0063] Figure 5 is the acquisition step flow chart for the present invention to compare the influence degree of environmental conditions on the core parameters in the set of production key variables;

[0064] Figure 6 is the acquisition step flow chart for the present invention to analyze the fluctuation of core parameters under the current environmental conditions;

[0065] Figure 7 is the acquisition step flow chart for the present invention to estimate the failure time of the material. DETAILED DESCRIPTION OF THE INVENTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0068] Please refer to Figure 1 , the present invention provides a technical solution: a full-process life cycle management system for steel structure products includes:

[0069] The production data optimization module calculates the ratio of material consumption to energy consumption in the process based on the material input, energy consumption, equipment status, and production output data collected during the production process, evaluates the resource utilization rate of each link, extracts the abnormal deviation values of materials and energy consumption during the production process, screens the links affecting production efficiency, obtains the resource allocation information of each production step, and generates production resource allocation data;

[0070] The key production variable extraction module calls the core parameters associated with production efficiency based on the production resource allocation data. The core parameters are the equipment operation status or production speed, calculates the change range of the equipment operation status and production speed, analyzes the correlation between the core parameters and the output, selects the core parameters that have the greatest impact on production quality and output, and generates a set of production key variables;

[0071] The full life cycle risk assessment module combines the external environmental conditions during the use stage of the product. The external environmental conditions are temperature, humidity, or load, compares the influence degree of the environmental conditions on the core parameters in the set of production key variables, calculates the failure risk of the steel structure product under multiple simulated use scenarios, analyzes the fluctuation of the core parameters under the current environmental conditions, establishes the relationship information between environmental changes and product performance degradation, and generates the full life cycle risk assessment result;

[0072] The material fatigue and life prediction module calculates the fatigue accumulation rate of the material under the current working conditions according to the full life cycle risk assessment result, referring to the stress distribution and fatigue accumulation data of the steel structure product under various stress and load conditions, extracts the fatigue damage data of the material in the stress concentration area, combines the environmental conditions and the load changes during the use stage, estimates the failure time of the material, and generates the steel structure product life prediction data;

[0073] The production resource allocation data includes the material consumption ratio, equipment energy consumption distribution, and time utilization rate of each process. The set of production key variables includes the operating frequency of production equipment, product qualification rate, and process time fluctuation range. The full-life cycle risk assessment results include the impact of environmental factors on structural strength, the change trend of product performance, and failure warning information. The steel structure product life prediction data includes the life estimation of stress concentration points, fatigue accumulation rate, and remaining service time under different loads.

[0074] Please refer to Figure 2 , and the specific steps for obtaining the resource utilization rate of each link are as follows:

[0075] Based on the energy consumption and material consumption data collected during the production process, combined with the energy efficiency conversion rate parameters obtained from the energy efficiency detection of the equipment, use the formula:

[0076]

[0077] Calculate the ratio R of energy consumption to material consumption;

[0078] Among them, E is the total energy consumed during the production process, obtained through the energy consumption monitoring of the equipment. The specific equipment records the actual energy consumption during the production process by installing an electric energy monitoring instrument. η is the equipment energy efficiency coefficient, used to describe the energy efficiency conversion rate of the equipment, with a range of 0 to 1, reflecting the efficiency of converting energy input into effective production power, obtained through the evaluation and detection of the operation efficiency and energy consumption of the equipment. M is the mass of materials consumed during the production process, obtained through the material sensors on the production line or the actual consumption of materials recorded by production management. k is the time consumption adjustment parameter, used to correct the impact of time on the material-to-energy consumption ratio, obtained through the regression analysis of past production data or a machine learning model to analyze the impact of time consumption on the resource utilization rate and obtain a reasonable adjustment value. T is the time of each process during the production process, obtained through production scheduling or time monitoring equipment (such as production line scheduling software) to obtain the actual working time of each process.

[0079] If the total energy consumed in a certain production process is 500 kWh, the material consumption is 2,500 kg, the equipment energy efficiency coefficient η is 0.85, the process time is 4 hours, and the time consumption adjustment parameter k is 0.05, calculate the ratio of energy consumption to material consumption:

[0080]

[0081] The results show that in this production process, for every 1 kg of material consumed, the energy consumption is 0.17 kWh.

[0082] Based on the ratio of energy consumption to material consumption, call this ratio and compare it with the benchmark data for analysis, analyze the resource utilization efficiency in the production process, compare the energy consumption fluctuations and material consumption trends, and combine the equipment operation status to conduct efficiency evaluation, generating the resource utilization rate evaluation results;

[0083] Evaluate the resource utilization rate of each link, analyze the resource efficiency through the ratio R of energy consumption to material consumption, use the comparative analysis method to compare with the industry benchmark data, and screen out the links with relatively low resource utilization rate. Next, relevant data of the process need to be obtained, including information such as the material consumption, total energy consumption, and production time of each link. Automatically collect these values and classify them, gradually call the material consumption and energy consumption data, and through time series analysis, analyze the trend and fluctuation of the ratio R, find out the links with excessive energy consumption or serious material waste, and then combine the equipment operation status and production speed in each link to conduct efficiency evaluation. Evaluate the time consumption of each process through the time utilization analysis algorithm, and compare the time utilization rates of each link. The steps or processes with low efficiency can be identified by correlating the changes in the ratio R with the energy efficiency and material consumption of each process. Subsequently, optimization plans can be proposed based on these evaluation results, thereby effectively improving the resource utilization efficiency in production.

[0084] Please refer to Figure 3 , and the specific steps for screening the links that affect production efficiency are as follows:

[0085] Based on the ratio of material consumption to energy consumption, compare the past data with the current data, perform deviation analysis of materials and energy consumption, and use the formula:

[0086]

[0087] Calculate the abnormal deviation value D of material consumption and energy consumption, generating the abnormal deviation value of material consumption and energy consumption;

[0088] Among them, D is used to reflect the difference between the current process and the past standard, R avg is the past average ratio, obtained by statistical calculation of the data in the production process, and is used as the benchmark ratio. F is the process adjustment factor, used to consider the complexity and resource consumption characteristics of different processes, with a value between 0 and 1, and is obtained based on the analysis of all operation data and process complexity of the process.

[0089] If the ratio R of the current production process is 0.17 kWh / kg and the past average value R avg is 0.15 kWh / kg, set the process adjustment factor F to 0.9 and substitute it into the formula for calculation:

[0090]

[0091] The results show that the ratio of material consumption to energy consumption in the current process deviates from the past average value by 0.12. Through this deviation value, it is possible to further determine whether there are problems such as material waste, low equipment efficiency, or abnormal energy consumption.

[0092] Based on the abnormal deviation values of material consumption and energy consumption, by setting a conventional deviation range, comparing with the benchmark range, analyzing the equipment performance and efficiency, and generating a list of links affecting production efficiency;

[0093] Set a benchmark range to determine the normal deviation range of materials and energy consumption. According to the analysis of past production data, statistically obtain the upper and lower limits of normal deviation, and then compare the deviation value D with this benchmark range to screen out the production links with larger deviations. Next, it is necessary to further call the equipment operation status and material consumption data, combine with the process execution time, analyze the equipment operation efficiency and material utilization rate in each process, and through equipment logs and time monitoring, deeply analyze the equipment performance in each link to determine whether the equipment is in the best state or there is a situation of reduced energy efficiency. Finally, conduct a correlation analysis between material consumption and energy consumption deviation and equipment status, screen out the links that have a greater impact on production efficiency, and record and analyze the detailed operation processes of these links to complete the screening of the links affecting production efficiency.

[0094] According to the list of links affecting production efficiency, obtain the equipment resource occupancy rate, material utilization rate, and energy consumption allocation information, perform comparison and classification analysis, and refine the processing in combination with the deviation data to generate production resource allocation data;

[0095] Collect data on the equipment operation status, material utilization rate, and energy consumption allocation of each production step, call the resource allocation data in production scheduling, combine with material consumption and energy consumption deviation, conduct a detailed analysis of process resource allocation, obtain the equipment resource occupancy rate and material allocation ratio of each process through resource monitoring, classify and process these data, gradually conduct comparison analysis, identify the differences in resource allocation of each process, and generate the resource allocation data of each process through analyzing the distribution of materials and energy consumption for subsequent production strategy adjustment and optimization.

[0096] Please refer to Figure 4 , the specific steps for obtaining the correlation between the core parameters and the output are as follows:

[0097] Based on the production resource allocation data, extract the equipment operation status and production speed, and use the formula:

[0098]

[0099] The change range ΔV of the equipment operation status or production speed, and generate the change range data of the equipment operation status and production speed;

[0100] Among them, V final represents the speed of the device or production line at the end of the monitoring period, recorded by a speed sensor, and V initial represents the speed of the device or production line at the start of the monitoring, also obtained through the monitoring device. T represents the total time of the monitoring period, obtained through production scheduling or a time recording device. η1 is the equipment efficiency coefficient, reflecting the efficiency of the equipment during actual operation, collected based on equipment efficiency monitoring, with a numerical range between 0 and 1. α1 is an adjustment parameter used to account for fluctuations in equipment load during the production process, obtained through data statistics or equipment load monitoring analysis, and is used to calibrate the impact of equipment load on speed changes.

[0101] If in a certain production process, the initial production speed V initial is 100 pieces per hour, the end speed V final is 130 pieces per hour, the monitoring time T is 2 hours, the equipment efficiency coefficient η1 is set to 0.9, and the adjustment parameter α1 is 1.2. Substitute these values into the formula for calculation:

[0102]

[0103] The results show that the production speed of the device or production line increases by 11 pieces per hour. This provides a basis for subsequent equipment scheduling and optimization of the production rhythm, and can help identify fluctuations in equipment operation.

[0104] Based on the data on the change amplitude of the equipment operation state and production speed, by calling the production data and equipment operation records, conduct a correlation analysis on the production volume and speed change trend, combine the finished product qualification rate data, identify key variables, and conduct data comparison to generate a set of production key variables;

[0105] Extract the production volume data for each production stage, and conduct a correlation analysis on these data and the change parameters of the equipment operation speed. By comparing all the data records, compare the production volume and production speed change trends in different time periods, and gradually clarify the impact of the change in production speed on the overall production volume. Next, it is necessary to introduce the equipment operation records in each production step, combine the qualified product data collected by quality inspection, and analyze the correlation between production quality and equipment speed. During this process, it is necessary to call the equipment operation logs and production data for comparison to identify the impact of speed changes in each production stage on quality. Finally, through multi-dimensional data comparison, determine the core variables that are highly correlated with production volume and quality, and generate a set of production key variables, providing basic data and a basis for subsequent optimization of the production process.

[0106] Please refer to Figure 5 for the specific steps to obtain the degree of influence of environmental conditions on the core parameters in the set of production key variables:

[0107] Compare the influence degree of environmental conditions on the core parameters in the set of key production variables, and use the formula:

[0108]

[0109] Calculate the failure risk value R′ f ;

[0110] Among them, R′ f represents the failure probability of the steel structure product under specific external environmental conditions. The higher the failure risk value, the greater the failure probability of the product. K i is the i-th core parameter in the set of key production variables, such as the load strength of the steel or the operating state of the equipment, which is obtained through equipment sensors and production monitoring. C i is the influence coefficient of environmental conditions on the i-th core parameter, indicating the fluctuation range of the core parameter under different environmental conditions (such as temperature, humidity, etc.), which is quantified based on experimental data and usage data. β i is the material deterioration coefficient, indicating the performance degradation of the steel structure material due to external environmental influences (such as corrosion, fatigue, etc.), which is obtained based on laboratory durability tests and material aging data analysis. E′ is the comprehensive value of environmental conditions, indicating the influence of the current environment (temperature, humidity, load, etc.) on materials and equipment, which is obtained through real-time environmental monitoring. γ1 is the external load correction coefficient, indicating the influence of special or dynamic loads on the failure risk, which is obtained through load sensors and production data analysis. n is the total number of key production variables.

[0111] If in a certain process, the core parameter is the load K1 = 5000N, set the temperature influence coefficient C1 = 0.8, the material deterioration coefficient β1 = 0.7, the current comprehensive value of environmental conditions E′ = 1.2, and the external load correction coefficient γ1 = 1.1. Substitute into the formula for calculation:

[0112]

[0113] According to industry experience and the full life cycle data of steel structure products, the failure risk R′ f is generally divided into the following levels: R′ f <1500: Low risk, the failure probability of the steel structure in this environment is extremely low; 1500 ≤ R′ f ≤ 2500: Medium risk, regular monitoring and maintenance of the product are required; R′ f > 2500: High risk, the product has a high failure probability, and preventive measures or structural optimization are required. The results show that the current failure risk value of the steel structure product is 2121, belonging to the medium risk level. It is recommended to conduct regular inspections and environmental monitoring of the product to reduce the failure risk.

[0114] Analyze the risk level of the product in the current environment based on the failure risk value, determine the risk status of the product by combining the risk value range, and perform data induction to generate the risk assessment result;

[0115] In the simulated usage scenario, first, key parameters such as temperature, humidity, and load need to be set for different environmental conditions. These parameters are collected through real-time environmental monitoring equipment and matched with the core parameters of the steel structure product (such as load, material strength, etc.). Next, these environmental data are input into the risk assessment model to perform data correlation analysis. Combining the material degradation coefficient and the external load correction coefficient, calculate the failure risk of the steel structure product in different scenarios. During the process, carefully evaluate the fluctuations of each environmental condition and analyze its impact on the degradation of the steel structure performance. Finally, generate a risk assessment report for different environmental scenarios through data aggregation and record the failure risk value of each scenario for subsequent product optimization and usage decision-making.

[0116] Please refer to Figure 6 , the specific steps for obtaining the fluctuations of the core parameters under the current environmental conditions are as follows:

[0117] Refer to the failure risk value and use the formula:

[0118]

[0119] Calculate the fluctuation amount ΔP of the equipment operation status or production speed to obtain the equipment performance fluctuation data;

[0120] Among them, P0 is the initial reference value of the core parameter, usually obtained through equipment design parameters or past operation data, R′ max is the maximum allowable failure risk value, obtained through equipment failure and failure data analysis, T′ is the actual temperature under the current environment, collected in real time through a temperature sensor, T′ norm is the standard temperature designed for the equipment, usually obtained through data provided in the equipment specifications or operation manuals, L is the current load, obtained through a load sensor, L max is the maximum design load of the equipment, determined through equipment specification parameters.

[0121] For example, analyze the efficiency of a cutting machine. The standard operating speed of this machine can be measured by the length of the steel plate cut per hour (m / h) or the weight of the steel (t / h). If the standard production rate P0 = 200 t / h, the failure risk value R′ f = 2333.1, the maximum failure risk value R′ max = 3000, the current temperature T′ = 45 °C, the standard temperature T′ norm = 25 °C, the current load L = 5000 N (or in relevant unit conversions), the maximum load L max= 10000 N.

[0122] Substituting into the formula gives:

[0123]

[0124] ΔP = 200×(1 - 0.7777×1.8×0.5) = 200×0.3001 = 60.02 tons per hour

[0125] This means that under the current environmental and load conditions, the productivity of the cutting machine is expected to decline to 60.02 tons per hour of the original productivity.

[0126] According to the equipment performance fluctuation data, associate the environmental data with the equipment operation status data, analyze the impact of environmental changes on equipment performance, and conduct trend assessment, establish a quantitative relationship between environmental changes and equipment performance, and generate a full - life - cycle risk assessment result;

[0127] First, obtain the real - time data of the current temperature, humidity, and load from the environmental monitoring equipment, use temperature sensors, humidity sensors, and pressure sensors to record these environmental parameters, then associate these environmental parameters with the core operation status data of the equipment, use data analysis tools to quantitatively analyze the impact of environmental changes on the equipment operation status (such as speed or power), further combine the equipment's past operation data and the material's aging data, calculate the impact of each environmental variable on the material performance decline, use regression analysis tools to evaluate the relationship between environmental changes and equipment performance decline, generate a model of the impact of the environment on equipment decline, and finally summarize these data to generate a quantitative relationship report between environmental changes and equipment performance decline. This report provides basic data for the full - life - cycle risk assessment and is ultimately used to formulate a more reliable equipment operation and maintenance plan.

[0128] Please refer to Figure 7 , and the specific steps for obtaining the estimated material failure time are as follows:

[0129] According to the full - life - cycle risk assessment result, use the formula:

[0130]

[0131] Calculate the fatigue cumulative speed v of the material under the current working conditions to obtain the fatigue cumulative speed information;

[0132] Among them, ΔN is the increment of the number of stress cycles, representing the number of stress cycles that the material bears under the current working conditions, obtained by counting the cyclic stress of the steel structure under the action of the load. This value can be affected by the fluctuation ΔP of the equipment operation state or production speed. Because under different operation speeds or load changes, the number of stress cycles of the material will change. Δt is the time increment, obtained by monitoring the cycle or the service time of the equipment. S is the stress borne by the current material, measured on the steel structure by a stress sensor or a strain gauge, S max is the maximum load-bearing capacity of the material, determined by the material property test, usually obtained according to the experimental data of the yield strength or ultimate strength of the material. G is the fatigue residual strength of the material, obtained by the fatigue life test of the material, measured under different cyclic stress and load conditions, G ref is the reference fatigue strength of the material, determined according to the standard fatigue strength value of the material, usually given by the material design manual or relevant standards.

[0133] Set the stress cycle number increment ΔN = 2500, the time increment Δt = 5 hours, the stress S borne by the current material = 300×10 6 Pa, the maximum load-bearing capacity S of the material max = 500×10 6 Pa, the fatigue residual strength G of the material = 200×10 6 Pa, the reference fatigue strength G of the material ref = 250×10 6 Pa.

[0134] Substitute into the formula:

[0135]

[0136] v = 500×0.48 = 240 times / hour

[0137] The result shows that the fatigue accumulation speed of the material under the current working conditions is 240 times / hour.

[0138] According to the fatigue accumulation speed information, use the formula:

[0139]

[0140] Calculate the remaining failure time T of the material f , and generate the life prediction data of the steel structure product;

[0141] Among them, B is the fatigue cumulative damage value, obtained by the fatigue damage monitoring and data analysis of the material, representing the damaged proportion of the material, S res is the remaining load-bearing capacity of the material, obtained by measuring the current remaining strength of the material.

[0142] Set the fatigue cumulative damage value B = 0.3, and the remaining load-bearing capacity S of the material res = 150×10 6 Pa, and the fatigue cumulative speed v = 240 times per hour.

[0143] Substitute into the formula:

[0144]

[0145] The results show that under the current environmental and load conditions, the remaining failure time of the material is approximately 437,500 hours. By considering the remaining load-bearing capacity, the life prediction is more in line with the actual working conditions.

[0146] A full-process life cycle management method for steel structure products. The full-process life cycle management method for steel structure products is executed based on the above-mentioned full-process life cycle management system for steel structure products, and includes the following steps:

[0147] S1: Based on the production data of steel structure products, calculate the ratio of material consumption to energy consumption, evaluate the resource utilization rate, extract abnormal deviation values, screen the links affecting efficiency, and generate production resource allocation data;

[0148] S2: Based on the production resource allocation data, calculate the changes in the equipment operation status and production speed, analyze the correlation between the core parameters and the output, select the key parameters, and generate a set of production key variables;

[0149] S3: Based on the set of production key variables, compare the influence of environmental conditions on the core parameters, calculate the failure risks in multiple scenarios, analyze the parameter fluctuations, and generate the full-life cycle risk assessment results;

[0150] S4: Based on the full-life cycle risk assessment results, call the stress distribution and fatigue data, calculate the fatigue cumulative speed of the material, estimate the failure time, and obtain the life prediction data of the steel structure product;

[0151] S5: Based on the life prediction data of the steel structure product, optimize the material consumption and energy consumption allocation, adjust the production links, and establish the optimized results of production resource allocation.

[0152] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A full-process life cycle management system for steel structure products, characterized in that, The system includes: The production data optimization module calculates the ratio of material consumption to energy consumption in the process based on the data collected during the production process, evaluates the resource utilization rate of each link, extracts the abnormal deviation values of materials and energy consumption in the production process, screens the links that affect production efficiency, and generates production resource allocation data. The key production variable extraction module calculates the change range of the equipment operation status and production speed based on the production resource allocation data, analyzes the correlation between the core parameters and the output, selects the core parameters that have the greatest impact on production quality and output, and generates a set of production key variables. The full-life cycle risk assessment module compares the influence degree of environmental conditions on the core parameters in the set of production key variables, calculates the failure risk of steel structure products under multiple simulated usage scenarios, analyzes the fluctuation of core parameters under the current environmental conditions, and generates the full-life cycle risk assessment result. The material fatigue and life prediction module calls the stress distribution and fatigue accumulation data of the steel structure product according to the full-life cycle risk assessment result, calculates the fatigue accumulation speed of the material under the current working conditions to estimate the failure time of the material, and generates the steel structure product life prediction data.

2. The full-process life cycle management system for steel structure products according to claim 1, characterized in that, The specific acquisition steps of the resource utilization rate evaluation of each link are as follows: Based on the energy consumption and material consumption data collected during the production process, combined with the energy efficiency conversion rate parameter obtained from the energy efficiency detection of the equipment, the formula is used: Calculate the ratio R of energy consumption to material consumption. Where E is the total energy consumed during the production process, η is the equipment energy efficiency coefficient, M is the mass of materials consumed during the production process, k is the time consumption adjustment parameter, and T is the time of the process during the production process. Based on the ratio of energy consumption to material consumption, call the ratio to compare and analyze with the reference data, analyze the resource utilization efficiency of the production link, compare the energy consumption fluctuation and material consumption trend, and conduct efficiency evaluation in combination with the equipment operation status to generate the resource utilization rate evaluation result.

3. The full-process life cycle management system for steel structure products according to claim 2, wherein, The specific acquisition steps of screening the links that affect production efficiency are as follows: Based on the ratio of material consumption to energy consumption, compare the past data with the current data, perform deviation analysis of materials and energy consumption, and use the formula: Calculate the abnormal deviation value D of material consumption and energy consumption to generate the abnormal deviation value of material consumption and energy consumption. Among them, D is used to reflect the difference between the current process and the past standard, and R avg is the past average ratio, and F is the process adjustment factor; Based on the abnormal deviation values of material consumption and energy consumption, by setting the conventional deviation range, perform comparison with the reference range, analyze the equipment performance and efficiency, and generate a list of links that affect production efficiency. According to the list of links that affect production efficiency, obtain the equipment resource occupancy rate, material usage rate, and energy consumption allocation information, perform comparison and classification analysis, and conduct refinement processing in combination with the deviation data to generate production resource allocation data.

4. The full-process life cycle management system for steel structure products according to claim 3, characterized in that The specific acquisition steps of analyzing the correlation between core parameters and output are as follows: Based on the production resource allocation data, extract the equipment operation status and production speed, and use the formula: The change range ΔV of the equipment operation status or production speed is generated to obtain the change range data of the equipment operation status and production speed. Among them, V final represents the speed of the device or production line at the end of the monitoring period, V initial represents the speed of the device or production line at the start of the monitoring, T represents the total time of the monitoring period, η1 is the device efficiency coefficient, and α1 is the adjustment parameter; Based on the variation amplitude data of the device operating state and production speed, by invoking the production data and device operation records, the production volume and speed change trends are correlated and analyzed. Combining with the finished product qualification rate data, key variables are identified, and data comparison is carried out to generate a set of production key variables.

5. The full-process life cycle management system for steel structure products according to claim 4, characterized in that, The specific steps for obtaining the influence degree of the comparison environmental conditions on the core parameters in the set of production key variables are as follows: The influence degree of the comparison environmental conditions on the core parameters in the set of production key variables is calculated using the formula: Calculate the failure risk value R' f ; Among them, K i is the i-th core parameter in the set of production key variables, C i is the influence coefficient of environmental conditions on the i-th core parameter, β i is the material deterioration coefficient, E′ is the comprehensive value of environmental conditions, γ1 is the external load correction coefficient, and n is the total number of production key variables; Based on the failure risk value, the risk level of the product in the current environment is analyzed. Combining with the risk value range, the risk state of the product is judged, and data induction is carried out to generate a risk assessment result.

6. The full-process life cycle management system for steel structure products according to claim 5, characterized in that The specific steps for obtaining the fluctuation situation of the analysis core parameters under the current environmental conditions are as follows: Referring to the failure risk value, using the formula: Calculate the fluctuation amount ΔP of the device operating state or production speed to obtain device performance fluctuation data; Among them, P0 is the initial reference value of the core parameter, R′ max is the maximum allowable failure risk value, T′ is the temperature in the current environment, T′ norm is the standard temperature of the device design, L is the current load, obtained through the load sensor, L max is the maximum design load of the device; According to the device performance fluctuation data, the environmental data and device operating state data are correlated, the influence of environmental changes on device performance is analyzed, and trend evaluation is carried out to establish a quantitative relationship between environmental changes and device performance, generating a full-life cycle risk assessment result.

7. The full-process life cycle management system for steel structure products according to claim 6, characterized in that, The specific steps for estimating the failure time of the material are as follows: According to the full-life cycle risk assessment result, using the formula: Calculate the fatigue accumulation speed v of the material under the current working conditions to obtain fatigue accumulation speed information; where ΔN is the increment of stress cycle times, Δt is the time increment, S is the stress borne by the current material, and S max is the maximum load-bearing capacity of the material, G is the fatigue residual strength of the material, and G ref is the reference fatigue strength of the material; According to the fatigue accumulation speed information, using the formula: Calculate the remaining failure time T of the material f to generate life prediction data for steel structure products; Among them, B is the fatigue cumulative damage value, and S res is the remaining load-bearing capacity of the material.

8. A full-process life cycle management method for steel structure products, characterized in that, Execute according to the steel structure product full-process life cycle management system described in any one of claims 1-7, including the following steps: Based on the steel structure product production data, calculate the ratio of material consumption to energy consumption, evaluate the resource utilization rate, extract abnormal deviation values, screen the links affecting efficiency, and generate production resource allocation data; Based on the production resource allocation data, calculate the changes in the device operating state and production speed, analyze the correlation between the core parameters and production volume, select key parameters, and generate a set of production key variables; Based on the set of production key variables, compare the influence of environmental conditions on the core parameters, calculate the failure risks in multiple scenarios, analyze parameter fluctuations, and generate a full-life cycle risk assessment result; Based on the full-life cycle risk assessment result, invoke the stress distribution and fatigue data, calculate the fatigue accumulation speed of the material, estimate the failure time, and obtain the steel structure product life prediction data; Based on the steel structure product life prediction data, optimize the material consumption and energy consumption allocation, adjust the production links, and establish an optimized result of production resource allocation.

Images