Visual production management system for steel structure intelligent manufacturing

Abstract

The invention discloses a visual production management system for intelligent manufacturing of a steel structure, relates to the technical field of intelligent manufacturing management, and solves the technical problems that the production progress is not monitored in time, the analysis is not accurate enough, and the production efficiency is low due to the lack of scientific and effective analysis means for the arrangement of production tasks and the allocation of resources. According to the method, the production progress conditions in different time periods can be displayed through the established histogram, the real-time production progress state is judged, the abnormal recognition result and the normal recognition result are generated in time, under the normal progress, the completeness of the remaining progress is analyzed based on the real-time production progress, and the progress time and the remaining time are calculated and compared, so that the real-time production progress can be identified. And if the progress time is greater than the remaining time, adjustment is carried out and analysis completion information is generated, so that a production plan is reasonably arranged and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing management, and in particular to a visual production management system for intelligent manufacturing of steel structures. Background Art

[0002] With the development of the manufacturing industry, the steel structure manufacturing industry faces many challenges such as improving production efficiency, ensuring product quality, and optimizing production management.

[0003] The patent application with publication number CN113901039A discloses a three-dimensional visualization monitoring method, device, storage medium and terminal for a steel structure factory. The method includes: constructing a three-dimensional visualization monitoring model of the steel structure factory; acquiring and preprocessing the operation data of the automation equipment in the steel structure factory in real time according to a preset cycle; performing data analysis on the preprocessed operation data to generate the production data of the steel structure factory; generating a product report sheet based on the production data of the steel structure factory, and sending the product report sheet to the factory production data screen for display; obtaining equipment operating parameters according to the equipment identification code preset and entered on the three-dimensional visualization monitoring model; mapping and associating the equipment operating parameters with the three-dimensional visualization monitoring model to generate visualization data of the automation equipment.

[0004] However, some existing intelligent manufacturing visualization management systems have problems with untimely monitoring of production progress and inaccurate analysis when in use. At the same time, there is a lack of scientific and effective analysis methods for the arrangement of production tasks and allocation of resources, resulting in low production efficiency. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a visual production management system for intelligent manufacturing of steel structures, which solves the problems of untimely monitoring of production progress, inaccurate analysis, lack of scientific and effective analysis methods for the arrangement of production tasks and allocation of resources, resulting in low production efficiency.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A visual production management system for intelligent manufacturing of steel structures, comprising: The production task information analysis module is used to obtain the steel structure production task information and draw a histogram of the production progress in different time periods. At the same time, the real-time production progress status is identified according to the histogram to generate abnormal identification results and normal identification results, and then the abnormal identification results are transmitted to the abnormal progress processing module, and the normal identification results are transmitted to the normal progress analysis module; The normal progress analysis module is used to analyze the normal recognition results obtained, analyze the completion of the remaining progress based on the real-time production progress, generate corresponding completion analysis information, and transmit it to the visual analysis information output module; The abnormal progress processing module is used to analyze the abnormal identification results obtained, generate a larger abnormal result or a smaller abnormal result by analyzing the specific abnormalities of the real-time progress task, and analyze the production quality corresponding to the larger abnormal result, generate the corresponding manufacturing analysis information, and transmit it to the visual analysis information output module, and then transmit the smaller abnormal result to the progress management analysis module; The progress management and analysis module is used to analyze the obtained abnormal results, determine the abnormal elements by comparing and analyzing the production elements corresponding to the real-time progress tasks, and perform management analysis based on the properties of the abnormal elements themselves, and generate management information, which is then transmitted to the visual analysis information output module.

[0007] As a further solution of the present invention, it also includes a visual analysis information output module for displaying the acquired completion analysis information, manufacturing analysis information and management information to corresponding operators.

[0008] As a further solution of the present invention, the specific method in which the production task information analysis module analyzes the steel structure production task information is as follows: Obtain steel structure production task information, and use T as the time demarcation point to obtain the corresponding production progress in different time periods. At the same time, use time as the horizontal axis and production progress as the vertical axis to establish a time-production progress histogram. Then obtain the real-time production progress and plot the real-time production progress through the histogram. At the same time, judge the real-time production progress status according to the height of the histogram. Calculate the mean of the corresponding heights of the histograms of all different time periods, and calculate the difference between the obtained mean and the height of the real-time production progress histogram, and compare the calculated difference with the preset value. If the difference is greater than the preset value, an abnormal recognition result is generated. Otherwise, if the difference is less than the preset value, a normal recognition result is generated.

[0009] As a further solution of the present invention, the specific manner in which the normal progress analysis module analyzes the normal recognition result is: Obtain the real-time production progress and the remaining progress at the same time, and calculate the progress time corresponding to the remaining progress based on the real-time production progress, then obtain the corresponding remaining time of the production task, and compare the progress time with the remaining time. If the progress time is greater than the remaining time, generate a progress analysis signal and analyze it. If the progress time is less than the remaining time, generate a normal monitoring signal; The obtained progress analysis signal is processed to obtain the remaining progress and the corresponding remaining time, and the corresponding planned progress is calculated at the same time, where planned progress = remaining progress / remaining time. The real-time production progress is adjusted based on the planned progress, and completion analysis information is generated at the same time.

[0010] As a further solution of the present invention, the specific manner in which the abnormal progress processing module analyzes the abnormal identification result is: Obtain the abnormal identification result, and at the same time obtain the difference corresponding to the real-time progress task, and judge the size of the difference. If the difference is greater than zero, it means that the real-time progress task is getting larger, and an abnormal result of increasing is generated. Otherwise, if the difference is less than zero, it means that the real-time progress task is getting smaller, and an abnormal result of decreasing is generated. At the same time, the abnormal result of increasing is analyzed.

[0011] As a further solution of the present invention, the specific method of analyzing the abnormal progress processing module on the abnormal result is: The generated enlarged abnormal results are analyzed to obtain the steel structure products corresponding to the real-time progress tasks. At the same time, the raw material quality and appearance quality corresponding to the steel structure products are analyzed. If the raw material quality and appearance quality meet the production requirements, normal monitoring results are generated. Otherwise, if any group does not meet the production requirements, quality abnormal results are generated, and the normal monitoring results and quality abnormal results are combined to generate manufacturing analysis information, which is then transmitted to the visual analysis information output module.

[0012] As a further solution of the present invention, the specific method of analyzing the abnormal result of reduction by the progress management analysis module is as follows: The production elements corresponding to the real-time progress task are obtained and recorded as the elements to be analyzed. At the same time, the obtained production elements are compared with the production elements corresponding to the production progress of different cycles. The elements to be analyzed that are different from the production elements are screened out and recorded as secondary analysis elements. The properties of the secondary analysis elements themselves are judged, and they are classified into fixed elements and active elements, and then the two are analyzed separately.

[0013] As a further solution of the present invention, the specific manner in which the progress management analysis module analyzes the fixed elements and the active elements is: Analyze the fixed elements, obtain all the fixed elements, and obtain the total fixed elements corresponding to the remaining progress, and divide the total fixed elements equally based on the time period T. Then compare the average fixed elements obtained by the average division with the preset value. If the average fixed element is greater than the preset value, generate management information. Otherwise, if the corresponding production demand is not met, obtain the maximum fixed element in the completed progress task as the standard to generate management information. The analysis method for active elements is similar to that for fixed elements, and management information is generated, which is then transmitted to the visual analysis information output module.

[0014] The present invention provides a visual production management system for intelligent manufacturing of steel structures. Compared with the prior art, it has the following beneficial effects: The present invention establishes a time-production progress histogram with time as the horizontal axis and the production progress as the vertical axis, which can intuitively display the production progress status of different time periods, and by calculating the difference between the histogram height mean and the real-time production progress histogram height and comparing it with the preset value, the real-time production progress status can be accurately judged, and abnormal recognition results and normal recognition results can be generated in time.

[0015] When an abnormal identification result appears, the system can further analyze whether it is a larger abnormality or a smaller abnormality. For larger abnormalities, the raw material quality and appearance quality of the steel structure products are deeply analyzed; for smaller abnormalities, the abnormal elements are determined through detailed comparative analysis of production elements, and management analysis is performed in combination with their properties. The root cause of the problem can be quickly located and effective measures can be taken, thereby improving the efficiency and accuracy of production abnormality handling.

[0016] Under normal progress, the completion of the remaining progress is analyzed based on the real-time production progress. By calculating and comparing the progress time and the remaining time, if the progress time is greater than the remaining time, adjustments are made and completion analysis information is generated, which helps to reasonably arrange production plans and improve production efficiency.

[0017] Through the visual analysis information output module, the completion analysis information, manufacturing analysis information and management information are intuitively displayed to the operators, making various key information in the production process clear at a glance, which is convenient for operators and managers to understand the production status in a timely manner and make scientific decisions, thereby improving the efficiency and coordination of production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0020] For example, see Figure 1 The present application provides a visual production management system for intelligent manufacturing of steel structures, including a production task information analysis module, a normal progress analysis module, an abnormal progress processing module, a progress management analysis module and a visual analysis information output module. Figure 1 It can be known that the above functional modules are electrically connected in a unidirectional manner.

[0021] Production task information analysis module, which is used to obtain steel structure production task information and draw histograms of production progress in different time periods. At the same time, it identifies the real-time production progress status according to the histogram to generate abnormal identification results and normal identification results, and then transmits the abnormal identification results to the abnormal progress processing module, and transmits the normal identification results to the normal progress analysis module.

[0022] Obtain steel structure production task information, and use T as the time demarcation point to obtain the corresponding production progress in different time periods. At the same time, use time as the horizontal axis and production progress as the vertical axis to establish a time-production progress histogram. Then obtain the real-time production progress and plot the real-time production progress through the histogram. At the same time, judge the real-time production progress status according to the height of the histogram. Calculate the mean of the corresponding heights of the histograms of all different time periods, and the calculation here does not include the real-time production progress. At the same time, calculate the difference between the obtained mean and the height of the real-time production progress histogram, and compare the calculated difference with the preset value. The specific value of the preset value is set by the operator. If the difference is greater than the preset value, it means that the real-time production progress is abnormal, and an abnormal recognition result is generated. Otherwise, if the difference is less than the preset value, it means that the real-time production progress is normal, and a normal recognition result is generated.

[0023] Assume that we are monitoring a steel structure production project with a total construction period of 10 days. We use a time window T of 2 days to analyze the production progress.

[0024] Step 1: Data acquisition and preprocessing: Get production task information, assuming that the production progress data is as follows (unit: %): Day 1-2: [30,32,28,31] (may be the progress of different subtasks or different production lines, data retained after preprocessing) Day 3-4: [40,42,38,41] Day 5-6: [50,52,48,51] Real-time production progress (Day 7): 60 Assume the default value is 10%.

[0025] Step 2: Histogram drawing: Use Python's matplotlib library to draw histograms.

[0026] The horizontal axis is the time window (1-2 days, 3-4 days, 5-6 days), and the vertical axis is the production progress.

[0027] Draw a histogram of historical data while using different colors to mark real-time production progress.

[0028] Step 3: Progress status judgment: Calculate the weighted average of historical production progress (assuming that recent data has a higher weight): Weighted average = (31*0.3+41*0.4+51*0.3) = 41.6 Calculate the difference: Difference = 60-41.6 = 18.4 Compare the difference with the preset value: Since 18.4>10, an abnormal recognition result is generated.

[0029] Normal progress analysis module, which is used to analyze the normal recognition results obtained, analyze the completion of the remaining progress based on the real-time production progress, generate corresponding completion analysis information, and transmit it to the visual analysis information output module.

[0030] Get the real-time production progress and the remaining progress at the same time, and get the real-time production progress and remaining progress at certain time intervals (such as every 1 hour). The real-time production progress can obtain the completed workload through sensors, equipment monitoring systems, etc. The remaining progress can be obtained by subtracting the completed workload from the total task volume, and the progress time corresponding to the remaining progress is calculated based on the real-time production progress. Then, the corresponding remaining time for the production task is obtained. For example, assuming that the historical average production efficiency is to complete 10 units of tasks per hour, and the current remaining progress is 50 units, then the progress time = 50 / 10 = 5 hours, and the progress time is compared with the remaining time. If the progress time is greater than the remaining time, it means that the construction is carried out according to the real-time production progress, and the production task requirements cannot be met, and a progress analysis signal is generated. If the progress time is less than the remaining time, it means that the construction is carried out according to the real-time production progress, and the production task requirements can be met, and a normal monitoring signal is generated; Suppose a steel structure manufacturing project has a total task volume of 100 steel beams. Currently, 30 steel beams have been produced, and there are 5 hours left before the task is delivered. The real-time production progress: 30 steel beams have been produced, and the remaining progress: 100-30=70 steel beams. The historical average production efficiency is 20 steel beams per hour, and the progress time = 70 / 20=3.5 hours. The remaining time is 5 hours. Because 3.5 hours < 5 hours, a normal monitoring signal is generated, indicating that the current progress is normal and production can continue as it is. However, if in another case, the historical average production efficiency drops to 10 steel beams per hour, the progress time = 70 / 10=7 hours. Because 7 hours > 5 hours, a progress analysis signal is generated.

[0031] The obtained progress analysis signal is processed to obtain the remaining progress and the corresponding remaining time, and the corresponding planned progress is calculated at the same time. The specific formula is planned progress = remaining progress / remaining time. The real-time production progress is adjusted based on the planned progress, and completion analysis information is generated at the same time.

[0032] The remaining progress is recalculated to be 70 steel beams, and the remaining time is 5 hours. The planned progress = 70 / 5 = 14 steel beams / hour. To achieve the planned progress, it may be necessary to increase the equipment speed, deploy more workers, etc., and generate completion analysis information: "In order to meet the task requirements, the equipment speed will be increased by 20%, and 3 workers will be deployed from other departments to participate in production. It is expected that the production efficiency will be increased to 14 steel beams / hour in the next 2 hours, and the production progress will be continuously monitored in the future." Visual analysis information output module, which is used to display the acquired normal monitoring signals and completed analysis information to the corresponding operators.

[0033] Embodiment 2: This embodiment is implemented on the basis of Embodiment 1, and the difference from Embodiment 1 is as follows: The abnormal progress processing module is used to analyze the acquired abnormal identification results, generate a larger abnormal result or a smaller abnormal result by analyzing the specific abnormalities of the real-time progress task, and analyze the production quality corresponding to the larger abnormal result, generate the corresponding manufacturing analysis information, and transmit it to the visual analysis information output module, and then transmit the smaller abnormal result to the progress management analysis module.

[0034] Obtain the abnormal recognition result, and at the same time obtain the difference value corresponding to the real-time progress task, and judge the size of the difference value. If the difference value is greater than zero, it means that the real-time progress task is increasing, and an abnormal result of increasing is generated. Otherwise, if the difference value is less than zero, it means that the real-time progress task is decreasing, and an abnormal result of decreasing is generated. Analyze the generated abnormal results of enlargement, obtain the steel structure products corresponding to the real-time progress tasks, and analyze the raw material quality and appearance quality corresponding to the steel structure products. Specifically, for the analysis of raw material quality, spectral analysis, chemical titration and other methods are used to detect the main chemical components such as carbon, silicon, manganese, phosphorus, and sulfur in steel to ensure that their content is within the specified range. For the analysis of appearance quality, check the flatness, verticality, cutting depth and other indicators of the cut surface. You can use rulers, calipers, squares and other tools to measure and detect whether the angle of the groove, blunt edge size, surface roughness, etc. meet the welding process requirements. If the raw material quality and appearance quality meet the production requirements, it means that the production quality is normal, and normal monitoring results are generated at the same time. On the contrary, if any group does not meet the production requirements, it means that the production quality is abnormal, and quality abnormality results are generated at the same time. The normal monitoring results and quality abnormality results are combined to generate manufacturing analysis information, which is then transmitted to the visual analysis information output module; The progress management and analysis module is used to analyze the obtained abnormal results, determine the abnormal elements by comparing and analyzing the production elements corresponding to the real-time progress tasks, and perform management analysis based on the properties of the abnormal elements themselves, and generate management information, which is then transmitted to the visual analysis information output module.

[0035] For example, if the steel structure product being produced is a steel beam, the raw material quality standard of the steel beam can be obtained from the quality standard library: the carbon content of the steel should be 0.2%-0.25%, the silicon content should be 0.15%-0.35%, etc.

[0036] Test results: The carbon content of the steel is 0.23%, the silicon content is 0.32%, and other components are also within the standard range. The flatness error of the cutting surface is 0.1mm (the standard is ±0.2mm), and the verticality error is 0.15° (the standard is ±0.3°).

[0037] Appearance quality inspection: groove angle deviation is 1° (standard is ±2°), surface roughness is Ra3.2 (standard is Ra3.0-Ra6.0).

[0038] The production elements corresponding to the real-time progress task are obtained and recorded as elements to be analyzed. The production elements here include production personnel, production equipment, and production raw materials, etc. At the same time, the obtained production elements are compared with the production elements corresponding to the production progress of different cycles, and the elements to be analyzed that are different from the production elements are screened out and recorded as secondary analysis elements. The properties of the secondary analysis elements themselves are judged, and they are classified into fixed elements and active elements. Specifically, the fixed elements here are represented by non-variable factors such as production equipment and production raw materials in the production process, while the active elements are represented by the number of personnel in the production process and some other variable factors.

[0039] Analyze the fixed elements, obtain all the fixed elements, and obtain the total fixed elements corresponding to the remaining progress, and divide the total fixed elements equally based on the time period T. Then compare the average fixed elements obtained by the average division with the preset value, and the preset value is expressed as the demand element corresponding to the corresponding periodic progress task. The specific value is obtained according to the average value calculated by the completed progress task. If the average fixed element is greater than the preset value, it means that the corresponding production demand is met, and management information is generated. Otherwise, if the corresponding production demand is not met, the maximum fixed element in the completed progress task is taken as the standard to generate management information. The analysis method for active elements is similar to that for fixed elements, and management information is generated, which is then transmitted to the visual analysis information output module.

[0040] Assume a steel structure manufacturing project, currently in the middle of production: Step 1: Get the element to be analyzed: Elements to be analyzed: Production staff: 20 people (including 15 skilled workers and 5 apprentices); Production equipment: 5 welding machines, 3 cutting machines, raw material inventory is 10 tons.

[0041] Step 2: Element comparison and screening: Assume that the production elements of the previous cycle are: Production staff: 18 people (including 14 skilled workers and 4 apprentices).

[0042] Production equipment: 4 welding machines, 3 cutting machines, raw material inventory is 8 tons.

[0043] Secondary analysis elements: Fixed elements: 1 new welding machine and 2 tons of raw materials.

[0044] Activity elements: 2 new workers (1 skilled worker, 1 apprentice).

[0045] Step 3: Fixed element analysis (taking welding machine as an example): Assume that the remaining progress is 50% and the time period T is 10 days.

[0046] Total fixed elements: 5 welding machines.

[0047] Evenly divided fixed elements: 5 / 10 = 0.5 welding machine / day.

[0048] Default value: Based on the historical data of completed progress tasks, it is assumed that an average of 0.4 welding machines per day were needed to meet the demand in the past.

[0049] Since 0.5>0.4, the management information is generated: "The current number of welding machines is sufficient to ensure the smooth progress of subsequent production. It is expected that the production task can be completed ahead of schedule or the production efficiency can be improved." Step 4: Activity element analysis (taking production staff as an example): Assuming that the skill level and work efficiency of the personnel are taken into consideration, the personnel are converted into equivalent numbers according to their work efficiency, and 20 people are equivalent to 18 skilled workers.

[0050] Assume that the remaining progress is 50% and the time period T is 10 days.

[0051] Total activity elements: Equivalent to 18 skilled workers.

[0052] Evenly divided activity elements: 18 / 10=1.8 equivalent skilled workers / day.

[0053] Default value: Based on the historical data of completed progress tasks, it is assumed that an average of 2 equivalent skilled workers were needed per day to meet the demand in the past.

[0054] Since 1.8<2, management information is generated: "The current staffing may be insufficient, which will affect the production progress. It is recommended to deploy more skilled workers or provide skills training to existing personnel to improve production efficiency." Visual analysis information output module, which is used to display the acquired management information and manufacturing analysis information to the corresponding operators.

[0055] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0056] Some of the data in the above formulas are numerically calculated by taking their dimensions. At the same time, the contents not described in detail in this specification belong to the existing technologies well known to those skilled in the art.

[0057] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A visual production management system for intelligent manufacturing of steel structures, characterized in that: include: The production task information analysis module is used to obtain the steel structure production task information and draw a histogram of the production progress in different time periods. At the same time, the real-time production progress status is identified according to the histogram to generate abnormal identification results and normal identification results, and then the abnormal identification results are transmitted to the abnormal progress processing module, and the normal identification results are transmitted to the normal progress analysis module; The normal progress analysis module is used to analyze the normal recognition results obtained, analyze the completion of the remaining progress based on the real-time production progress, generate corresponding completion analysis information, and transmit it to the visual analysis information output module; The abnormal progress processing module is used to analyze the abnormal identification results obtained, generate a larger abnormal result or a smaller abnormal result by analyzing the specific abnormalities of the real-time progress task, and analyze the production quality corresponding to the larger abnormal result, generate the corresponding manufacturing analysis information, and transmit it to the visual analysis information output module, and then transmit the smaller abnormal result to the progress management analysis module; The progress management and analysis module is used to analyze the obtained abnormal results, determine the abnormal elements by comparing and analyzing the production elements corresponding to the real-time progress tasks, and perform management analysis based on the properties of the abnormal elements themselves, and generate management information, which is then transmitted to the visual analysis information output module.

2. According to claim 1, a visual production management system for intelligent manufacturing of steel structures is characterized in that: It also includes a visual analysis information output module for displaying the acquired completion analysis information, manufacturing analysis information and management information to corresponding operators.

3. According to claim 1, a visual production management system for intelligent manufacturing of steel structures is characterized in that: The specific method in which the production task information analysis module analyzes the steel structure production task information is as follows: Obtain steel structure production task information, and use T as the time demarcation point to obtain the corresponding production progress in different time periods. At the same time, use time as the horizontal axis and production progress as the vertical axis to establish a time-production progress histogram. Then obtain the real-time production progress and plot the real-time production progress through the histogram. At the same time, judge the real-time production progress status according to the height of the histogram. Calculate the mean of the corresponding heights of the histograms of all different time periods, and calculate the difference between the obtained mean and the height of the real-time production progress histogram, and compare the calculated difference with the preset value. If the difference is greater than the preset value, an abnormal recognition result is generated. Otherwise, if the difference is less than the preset value, a normal recognition result is generated.

4. According to claim 1, a visual production management system for intelligent manufacturing of steel structures is characterized in that: The specific method in which the normal progress analysis module analyzes the normal recognition result is as follows: Obtain the real-time production progress and the remaining progress at the same time, and calculate the progress time corresponding to the remaining progress based on the real-time production progress, then obtain the corresponding remaining time of the production task, and compare the progress time with the remaining time. If the progress time is greater than the remaining time, generate a progress analysis signal and analyze it. If the progress time is less than the remaining time, generate a normal monitoring signal; The obtained progress analysis signal is processed to obtain the remaining progress and the corresponding remaining time, and the corresponding planned progress is calculated at the same time, where planned progress = remaining progress / remaining time. The real-time production progress is adjusted based on the planned progress, and completion analysis information is generated at the same time.

5. According to claim 1, a visual production management system for intelligent manufacturing of steel structures is characterized in that: The specific method in which the abnormal progress processing module analyzes the abnormal identification result is as follows: Obtain the abnormal identification result, and at the same time obtain the difference corresponding to the real-time progress task, and judge the size of the difference. If the difference is greater than zero, it means that the real-time progress task is getting larger, and an abnormal result of increasing is generated. Otherwise, if the difference is less than zero, it means that the real-time progress task is getting smaller, and an abnormal result of decreasing is generated. At the same time, the abnormal result of increasing is analyzed.

6. The visual production management system for intelligent manufacturing of steel structures according to claim 5 is characterized in that: The specific method for the abnormal progress processing module to analyze the abnormal result is as follows: The generated enlarged abnormal results are analyzed to obtain the steel structure products corresponding to the real-time progress tasks. At the same time, the raw material quality and appearance quality corresponding to the steel structure products are analyzed. If the raw material quality and appearance quality meet the production requirements, normal monitoring results are generated. Otherwise, if any group does not meet the production requirements, quality abnormal results are generated, and the normal monitoring results and quality abnormal results are combined to generate manufacturing analysis information, which is then transmitted to the visual analysis information output module.

7. The visual production management system for intelligent manufacturing of steel structures according to claim 1 is characterized in that: The specific method in which the progress management analysis module analyzes the abnormal result of reduction is as follows: The production elements corresponding to the real-time progress task are obtained and recorded as the elements to be analyzed. At the same time, the obtained production elements are compared with the production elements corresponding to the production progress of different cycles. The elements to be analyzed that are different from the production elements are screened out and recorded as secondary analysis elements. The properties of the secondary analysis elements themselves are judged, and they are classified into fixed elements and active elements, and then the two are analyzed separately.

8. The visual production management system for intelligent manufacturing of steel structures according to claim 7 is characterized in that: The specific method in which the progress management analysis module analyzes fixed elements and active elements is as follows: Analyze the fixed elements, obtain all the fixed elements, and obtain the total fixed elements corresponding to the remaining progress, and divide the total fixed elements equally based on the time period T. Then compare the average fixed elements obtained by the average division with the preset value. If the average fixed element is greater than the preset value, generate management information. Otherwise, if the corresponding production demand is not met, obtain the maximum fixed element in the completed progress task as the standard to generate management information. The analysis method for active elements is similar to that for fixed elements, and management information is generated, which is then transmitted to the visual analysis information output module.

Images