Method, system and equipment for adjusting tip thickness of upper leg of steel rail and medium

Abstract

The invention relates to the field of steel rolling, and provides a steel rail upper leg tip thickness adjusting method, system and device and a medium, and the method comprises the steps: obtaining historical data and multi-source information of steel rail production, constructing a knowledge graph, and combining the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph; the upper leg tip thickness is detected in real time; when the deviation of the specification size of the upper leg tip thickness exceeds a first preset value in response, an upper horizontal roller of the semi-universal rolling mill is adjusted based on the expert empirical model introduced into the knowledge graph; and in response to the fact that the deviation of the specification size of the upper leg tip thickness exceeds a second preset value, upper horizontal rollers of the universal rolling mill and the semi-universal rolling mill are adjusted based on the expert empirical model introduced into the knowledge graph. According to the method, manual adjustment experience is integrated and summarized, an automatic adjustment method with the manual experience as the basis, the knowledge graph as the assistance and the computer model as the framework is constructed, common interference is effectively eliminated, and the steel rail specification control precision of the model is improved.

Description

Technical Field

[0001] The invention relates to the field of steel rolling, and in particular to a method, system, equipment and medium for adjusting the thickness of a leg tip on a rail. Background Art

[0002] There are usually two methods for the production of heavy rails: the traditional two-roller mode and the four-roller universal mode. The surface quality, cross-sectional dimension accuracy and fullness of the rail crown of the heavy rails produced by the two-roller mode cannot meet the requirements of modern high-speed heavy rails. Modern advanced heavy rail production lines all use the four-roller universal mode for production. The universal method of rolling rails is very different from the traditional hole-type rolling method. The rolling of the rolled pieces with a preliminary rail shape is produced by two blanking mills, and the proportional relationship between the head, waist and bottom is established. This is the basis for the universal rolling mill to roll rails. The universal unit generally adopts three or five frames. The five-frame has higher control accuracy. It consists of a universal roughing mill (UR1), an edge rolling mill (E1), a universal intermediate rolling mill (UR2), an edge rolling mill (UF), and a universal finishing mill (UF). After multiple rolling passes, the finished product is finally rolled out. In addition to the correlation between the head width and the upper leg tip thickness, the bottom width and the upper leg tip thickness can be adjusted separately without affecting each other. There are also many adjustment methods, which are more flexible and easier to adjust than the traditional rolling method. The main advantage of the rolling method is that when the head and bottom are directly pressed down by the vertical rollers, the rolling of the rail head and rail bottom is carried out by the vertical rollers, and the rolling direction is the same as the rotation direction of the rollers.

[0003] In rail production, there are different methods for adjusting the thickness of the rail leg tip, and there are many problems such as cross-influence between adjustment parameters and parameter accuracy, which result in the analysis results of equipment parameters and specification parameters being inconsistent with the actual deformation law and actual adjustment methods. How to refine and reconstruct manual experience, extract its essence, remove its dross, and build a unified adjustment plan is the problem of ensuring high-precision control of the thickness of the rail leg tip. Summary of the invention

[0004] Based on the above purpose, the present invention proposes a method for adjusting the thickness of a rail leg tip, comprising: Obtain historical data and multi-source information on rail production, build a knowledge graph, and combine the knowledge graph with the preset expert experience model to generate an expert experience model that introduces the knowledge graph; Real-time detection of upper leg tip thickness; In response to the deviation of the specification size of the upper leg tip thickness exceeding a first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness exceeding a second preset value, the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill are adjusted based on the expert experience model introduced into the knowledge graph.

[0005] In some embodiments, in response to the deviation of the specification size of the upper leg tip thickness exceeding a first preset value, the step of adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the first scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than the first preset value, the upper horizontal roll of the semi-universal rolling mill is adjusted based on the second scheme of the expert experience model introducing the knowledge graph.

[0006] In some embodiments, in response to the deviation of the specification size of the upper leg tip thickness exceeding the second preset value, the step of adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a second preset value, adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the third scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than a second preset value, the fourth scheme based on the expert experience model introducing the knowledge graph adjusts the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill.

[0007] In some embodiments, the step of constructing the preset expert experience model includes: The historical leg tip thickness specifications of each team were extracted from the historical data; Analyze the distribution of the historical leg tip thickness specification size values, rank them according to the historical control effect of the leg tip thickness specifications, and use the teams whose ranking exceeds the threshold as expert knowledge; Generate an expert experience model based on specification size value distribution and expert knowledge.

[0008] In some embodiments, the step of acquiring historical data and multi-source information on rail production and constructing a knowledge graph includes: Obtain historical data and multi-source information on rail production, and perform pre-processing operations on the historical data and multi-source information; Identify entities related to rail production and quality control, analyze associations and interactions between entities, and extract relationships between them; Organize entities and relationships in a structured way to form a knowledge graph; Among them, multi-source information includes industry standard information and scientific research information.

[0009] In some embodiments, the step of combining the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph includes: Identify the correspondence between entities in the knowledge graph and parameters in the expert experience model; Use relations in the knowledge graph as constraints; Use historical data to validate the integrated model; The verified integrated model is used as the expert experience model introduced into the knowledge graph.

[0010] In some embodiments, the step of detecting the thickness of the upper leg tip in real time comprises: Set the measurement range and accuracy for wireless track detection; Monitor the upper leg tip thickness in real time and record the real-time value of the upper leg tip thickness.

[0011] The present invention proposes a system for adjusting the thickness of rail leg tips, comprising: A construction unit configured to obtain historical data and multi-source information of rail production, construct a knowledge graph, and combine the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph; A detection unit configured to detect the thickness of the upper leg tip in real time; A first adjustment unit is configured to adjust an upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to a deviation of a specification dimension of the upper leg tip thickness exceeding a first preset value; The second adjustment unit is configured to adjust the upper horizontal rollers of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to the deviation of the specification size of the upper leg tip thickness exceeding a second preset value.

[0012] The present invention provides a computer device, comprising: At least one processor; and a memory, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, the steps of the method for adjusting the thickness of the leg tip on the rail are performed.

[0013] The present invention proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for adjusting the thickness of a leg tip on a rail are executed.

[0014] The present invention has at least the following beneficial technical effects: The present invention proposes a method, system, equipment and medium for adjusting the thickness of a rail upper leg tip. The method comprises: acquiring historical data and multi-source information of rail production, constructing a knowledge graph, combining the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph; detecting the thickness of the upper leg tip in real time; in response to a deviation in the specification size of the upper leg tip thickness exceeding a first preset value, adjusting the upper horizontal roller of a semi-universal rolling mill based on the expert experience model introduced into the knowledge graph; in response to a deviation in the specification size of the upper leg tip thickness exceeding a second preset value, adjusting the upper horizontal rollers of a universal rolling mill and a semi-universal rolling mill based on the expert experience model introduced into the knowledge graph.

[0015] The present invention integrates and summarizes manual adjustment experience, constructs an automated adjustment method based on manual experience, assisted by knowledge graphs, and based on computer models. It realizes automated adjustment by accurately processing the incremental data of specification difference, significantly improving the accuracy of adjustment. By adopting empirical formula modeling, common interference is effectively eliminated, further improving the accuracy of the model's control of rail specifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for adjusting the thickness of a rail leg tip provided by the present invention; Figure 2 A system module diagram of adjusting the thickness of the upper leg tip of a rail provided by the present invention; Figure 3 A flow chart of an embodiment of a method for adjusting rail bottom width provided by the present invention; Figure 4 A process diagram of a universal rolling rail pass profile of an embodiment of a method for adjusting rail bottom width provided by the present invention; Figure 5 A schematic diagram of a rail cross section and bottom width of an embodiment of a method for adjusting the bottom width of a rail provided by the present invention; Figure 6 A schematic diagram of a rail cross section and rail crown height of an embodiment of a rail bottom width adjustment method provided by the present invention; Figure 7 A schematic diagram of a rail cross section and belly cavity of an embodiment of a method for adjusting rail bottom width provided by the present invention; Figure 8A schematic diagram of a rail cross section and an upper leg tip of an embodiment of a method for adjusting the bottom width of a rail provided by the present invention; Fig. 9 A schematic diagram of a rail cross section and a lower leg tip of an embodiment of a method for adjusting the bottom width of a rail provided by the present invention; Fig.10 A schematic diagram of a hole pattern of an adjustment scheme for a rail bottom width adjustment method provided by the present invention, in which the upper leg tip of 60kg / m exceeds the specification by 0.1mm; Fig.11 A schematic diagram of a hole pattern of an adjustment scheme for increasing the specification of 0.3mm-0.4mm on the upper leg tip of 60kg / m in one embodiment of a method for adjusting the bottom width of a rail provided by the present invention; Fig.12 A schematic diagram of the structure of an embodiment of a computer device provided by the present invention; Fig.13 A schematic diagram of the structure of an embodiment of a computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0019] It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are for distinguishing two non-identical entities with the same name or non-identical parameters. It can be seen that "first" and "second" are only for the convenience of expression and should not be understood as limitations on the embodiments of the present invention. The subsequent embodiments will not explain this one by one.

[0020] The present invention proposes a method for adjusting the thickness of the upper leg tip of a rail. Figure 1 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Fig. 9 ,include: S1: Obtain historical data and multi-source information on rail production, build a knowledge graph, and combine the knowledge graph with the preset expert experience model to generate an expert experience model that introduces the knowledge graph; S2: Real-time detection of upper leg tip thickness; S3: in response to the deviation of the specification size of the upper leg tip thickness exceeding the first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph; S4: In response to the deviation of the specification size of the upper leg tip thickness exceeding the second preset value, the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill are adjusted based on the expert experience model introduced into the knowledge graph.

[0021] Universal rolling rail pass technology Figure 4 As shown: Production line: semi-universal production line, equipment UR1 (universal rolling mill), E1, UR2 (universal rolling mill), E2, UF (semi-universal rolling mill); adjusted production situation: normal steel output, normal rolling force, normal temperature, reasonable rolling speed, normal temperature.

[0022] Figure 5 , Figure 6 , Figure 7 , Figure 8 and Fig. 9 The different parameters of the rail section are shown. Among them, Figure 5 The bottom width, Figure 6 For the rail crown, Figure 7 For the abdominal cavity, Figure 8 For the upper leg tip, Fig. 9 It is the tip of the lower leg.

[0023] Rail section Figure 8 As shown, the upper leg tip thickness is an important specification of the rail. In actual production, the adjustment of the upper leg tip thickness of the rail is mainly aimed at controlling the inclination of the upper horizontal roller to adjust the metal plasticity change, so as to achieve the purpose of controlling the thickness of the upper leg tip of the rail.

[0024] Design method of the present invention: According to the production data of each team, a ranking table of the historical leg tip thickness specification control is compiled, and the best team is selected as the object of expert knowledge extraction; According to the specification deviation value, combined with expert experience, the optimal adjustment plan is formed, and an empirical data model for upper leg tip thickness specification control is established; Check the actual specifications, compare them with the standard values, retrieve the adjustment plan from the experience database based on the deviation value, and participate in issuing it.

[0025] The adjustment method of the present invention: Set the measured steel upper leg tip thickness as X; the standard steel upper leg tip thickness as X0; By sampling and measuring the specifications, determine whether the size deviation Δ between the steel upper leg tip thickness X and the standard value X0 is greater than the control deviation Δ0. , needs to be adjusted.

[0026] Determine the adjustment Δ and Δ0, , determine the corresponding adjustment plan through the adjustment value.

[0027] The present invention provides an automated solution for adjusting the thickness of the upper leg of the rail by combining the adjustment experience of experts with a computer model. By collecting and summarizing manual adjustment experience, using expert experience extraction and dataization methods, a model that can recommend adjustment plans in real time is constructed. The method not only improves the timeliness and standardization of the adjustment, but also reduces the errors of manual calculations and the fluctuations in product specifications between teams, ensuring the stability of the control accuracy of the upper leg thickness of the rail.

[0028] The present invention can make full use of existing resources and avoid data redundancy and waste by integrating historical data and multi-source information of rails. The construction of the knowledge graph can connect scattered data points to form a complete knowledge system, which is convenient for subsequent analysis and application. In the process of constructing the knowledge graph, through the steps of entity recognition and relationship extraction, the data can be further cleaned and normalized to ensure the accuracy of the data. The knowledge graph can intuitively display the status and change trend of the rails, providing a fast and accurate basis for decision-making. Combined with the expert experience model, the reliability and scientificity of the decision can be further improved.

[0029] The knowledge graph provides rich structured knowledge for the expert experience model, which helps the model better understand and handle complex problems. By introducing the knowledge graph, the model's parameter settings and reasoning process can be optimized, and the model's performance and accuracy can be improved. The knowledge graph can clearly display the key information and steps in the model reasoning process and enhance the model's interpretability. Users can better understand the model's output results and decision-making basis, and improve their trust in the model. As a carrier of structured knowledge, the knowledge graph facilitates the sharing and inheritance of knowledge. By constructing a knowledge graph, expert experience can be saved in a structured form, which facilitates subsequent research and application.

[0030] Real-time detection of the thickness of the upper leg tip can promptly detect problems such as deformation in rail production, providing a timely basis for adjusting production. Real-time monitoring data can help reduce maintenance costs and improve maintenance efficiency.

[0031] In some embodiments, see Figure 1 , Figure 3 and Fig.10 In response to the deviation of the specification size of the upper leg tip thickness exceeding the first preset value, the step of adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the first scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than the first preset value, the upper horizontal roll of the semi-universal rolling mill is adjusted based on the second scheme of the expert experience model introducing the knowledge graph.

[0032] The first preset value is 0.1 mm to 0.2 mm, the first solution is to increase it by 0.1 mm to 0.2 mm, and the second solution is to reduce it by 0.1 mm to 0.2 mm.

[0033] The adjustment range is the first preset value: 0.1mm (exceeding / under standard): Adjusting roller: UF upper horizontal roller is tilted; Adjustment logic description: UF semi-universal mill is equipped with finished holes, which has the most obvious impact on the adjustment of specifications and dimensions, such as Fig.10 The UF horizontal roll downward pressure shown changes the specifications of the rolled product.

[0034] The impact of the adjustment scheme: UF upper horizontal roll tilting Positive tilting operation will make the bottom metal flow to the bottom center and also flow to the upper leg tip. The flow effect will increase the bottom width and symmetrical size, while the thickness of the upper leg tip will decrease, making the upper leg part more slender. On the contrary, if the negative tilting operation is performed, the effect will be opposite to the positive operation, that is, reducing the bottom width and symmetrical size and increasing the thickness of the upper leg tip.

[0035] Adjustment plan: If the specification is less than the standard lower limit, use the first option; When the upper leg tip thickness specification is less than the standard lower limit of 0.1mm, by adjusting the tilt of the upper horizontal roller in the first pass of UF and adjusting the roller gap by 0.3mm in the positive direction, the upper leg tip thickness can be increased by 0.1mm, the upper symmetry can be reduced by 0.1mm, and the bottom width can be reduced by 0.1mm.

[0036] If the specification exceeds the upper limit of the standard, use the second option; When the upper leg tip thickness exceeds the standard upper limit by 0.1mm, by adjusting the UF first pass upper horizontal roller tilt negatively adjusting the roller gap by 0.3mm, the upper leg tip thickness can be reduced by 0.1mm, the upper symmetry can be increased by 0.1mm, and the bottom width can be increased by 0.1mm.

[0037] In some embodiments, see Figure 1 , Figure 3 and Fig.11 In response to the deviation of the specification size of the upper leg tip thickness exceeding the second preset value, the step of adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a second preset value, adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the third scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than a second preset value, the fourth scheme based on the expert experience model introducing the knowledge graph adjusts the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill.

[0038] The second preset value is 0.2mm~0.5mm, the third option is to increase 0.2mm~0.3mm, and the fourth option is to reduce 0.2mm~0.3mm.

[0039] Adjustment interval second preset value: 0.2mm~0.3mm (exceeding / under standard); Adjusting roller: UF upper horizontal roller is tilted, UR2 upper horizontal roller is tilted; Adjustment logic description: For the case where the upper leg tip thickness is smaller than the standard, it is unreasonable to adjust UF alone, which will cause large fluctuations in the rolling force of the UF horizontal roll, resulting in steel twisting and serious impact on the head deviation specifications. Therefore, a more reasonable adjustment idea is to adjust the inclination of the UR2 upper horizontal roll and the UF upper horizontal roll synchronously, such as Fig.11 The specification changes and impacts after the adjustment of the scheme shown. If the upper leg tip thickness specification is lower than the standard, the adjustment idea is opposite to the adjustment that exceeds the standard.

[0040] The impact of the adjustment plan: Adjusting the roll gap value of the upper horizontal roll inclination of UR2 and the upper horizontal roll inclination of UF will change the metal flow at the bottom of the rolled piece and affect the specifications of the bottom metal (upper symmetry, upper leg tip thickness, bottom width). The processing of the upper horizontal roll inclination also affects the size of the head deviation, but the synchronous adjustment of UR2 and UF reduces the impact of the specifications. The above comprehensive adjustment can effectively control the specifications of the upper leg tip thickness.

[0041] Adjustment plan: If the specification is less than the standard lower limit, use the third option; When the upper leg tip thickness specification is less than the standard lower limit of 0.2mm, adjust the upper horizontal roller tilt of UF in the first pass to adjust the roller gap by 0.3mm, and adjust the upper horizontal roller tilt of UR2 in the first pass to adjust the roller gap by 0.8mm. This can increase the upper leg tip thickness by 0.2mm, reduce the bottom width by 0.2mm, reduce the upper symmetry by 0.2mm, and deflect the head downward by 0.1.

[0042] When the upper leg tip thickness specification is less than the standard lower limit of 0.3mm, adjust the upper horizontal roller tilt of UF in the first pass to positively adjust the roller gap by 0.3mm, and adjust the upper horizontal roller tilt of UR2 in the first pass to positively adjust the roller gap by 0.8mm. This can increase the upper leg tip thickness by 0.3mm, reduce the bottom width by 0.3mm, reduce the upper symmetry by 0.3mm, and deflect the head downward by 0.2.

[0043] If the specification exceeds the upper limit of the standard, use the fourth option; When the upper leg tip thickness exceeds the standard upper limit by 0.2mm, adjust the upper horizontal roller tilt of UF in the first pass to negatively adjust the roller gap by 0.3mm, and adjust the upper horizontal roller tilt of UR2 in the first pass to positively adjust the roller gap by 0.8mm. This can reduce the upper leg tip thickness by 0.2mm, increase the bottom width by 0.2mm, increase the upper symmetry by 0.2mm, and deflect the head upward by 0.1.

[0044] When the upper leg tip thickness exceeds the standard upper limit of 0.3mm, adjust the upper horizontal roller tilt of UF in the first pass to negatively adjust the roller gap by 0.3mm, and adjust the upper horizontal roller tilt of UR2 in the first pass to positively adjust the roller gap by 0.8mm. This can reduce the upper leg tip thickness by 0.3mm, increase the bottom width by 0.3mm, increase the upper symmetry by 0.3mm, and deflect the head upward by 0.2.

[0045] In some embodiments, see Figure 1 The steps of constructing the preset expert experience model include: The historical leg tip thickness specifications of each team were extracted from the historical data; Analyze the distribution of the historical leg tip thickness specification size values, rank them according to the historical control effects of the leg tip thickness specifications, and use the teams whose rankings exceed the threshold as expert knowledge; Generate an expert experience model based on specification size value distribution and expert knowledge.

[0046] By extracting the historical leg tip thickness specifications of each team from historical data, we can clearly understand the operating habits and production quality of each team. The management can better grasp the production status and provide data support for subsequent decision-making. By analyzing the distribution of historical leg tip thickness specification values, potential specification deviations or quality problems can be discovered. These problems may be difficult to detect in daily operations, but through data analysis, they can be discovered in time and measures can be taken to correct them. By analyzing the data, bottlenecks or inefficient links in the production process can be discovered, so as to carry out targeted optimization. Improve production efficiency and reduce production costs.

[0047] The expert experience model generated based on the distribution of specification size values ​​and expert knowledge can provide a more accurate and scientific basis for production decisions, reduce decision-making errors, and improve production efficiency and product quality.

[0048] In some embodiments, see Figure 1 The steps of obtaining historical data and multi-source information of rail production and constructing a knowledge graph include: Obtain historical data and multi-source information on rail production, and perform pre-processing operations on the historical data and multi-source information; Identify entities related to rail production and quality control, analyze associations and interactions between entities, and extract relationships between them; Organize entities and relationships in a structured way to form a knowledge graph; Among them, multi-source information includes industry standard information and scientific research information.

[0049] By acquiring historical data on rail production and information from multiple sources (such as industry standard information, scientific research information, etc.), the data set can be enriched and the comprehensiveness and accuracy of the data can be improved. Preprocessing operations (such as data cleaning, deduplication, format conversion, etc.) can further eliminate noise and redundancy in the data and improve the quality of the data. The integrated data is easier to perform subsequent analysis and mining. The preprocessed data format is unified, which is conducive to batch processing and automated analysis.

[0050] Identifying entities related to rail production and quality control (such as rail materials, production processes, quality indicators, etc.) helps to clarify the key elements of analysis. These entities are the basis for building knowledge graphs and are also an important basis for subsequent analysis and decision-making. Analyzing the associations and interactions between entities can reveal the inherent connections and laws between them. Understanding the complex factors in the rail production and quality control process provides guidance for optimizing production processes and improving product quality.

[0051] Through relationship extraction technology, structured information (such as relationships and attributes between entities) can be extracted from unstructured data such as text. This structured information is easier to store, query and analyze, providing strong support for the construction of knowledge graphs. Knowledge graphs display the relationship between entities in a graphical way, making the information more intuitive and easy to understand. Users can quickly obtain key information and improve decision-making efficiency.

[0052] In some embodiments, see Figure 1 The steps of combining the knowledge graph and the preset expert experience model to generate the expert experience model introduced into the knowledge graph include: Identify the correspondence between entities in the knowledge graph and parameters in the expert experience model; Use relations in the knowledge graph as constraints; Use historical data to validate the integrated model; The verified integrated model is used as the expert experience model introduced into the knowledge graph.

[0053] By identifying the correspondence between entities in the knowledge graph and parameters in the expert experience model, we can ensure that the parameters in the model have clear physical or business meanings, reduce the arbitrariness and uncertainty of parameter settings, and improve the accuracy and reliability of the model.

[0054] By using the relationships in the knowledge graph as constraints, the value range or relationship pattern of the model parameters can be further restricted. These constraints help ensure that the results of the model output are consistent with the business logic and actual situation, and improve the practicality and credibility of the model. The knowledge graph graphically displays the relationship between entities, making the parameters and relationships in the model more intuitive and easy to understand. Non-professionals or decision makers can better understand the working principle and output results of the model, and enhance the interpretability and comprehensibility of the model. By introducing the expert experience model, the knowledge and experience of domain experts can be integrated into the model. This makes the model not only data-driven, but also supported by expert wisdom, which improves the practicality and credibility of the model.

[0055] In some embodiments, see Figure 1 The step of real-time detection of the thickness of the upper leg tip comprises: Set the measurement range and accuracy for wireless track detection; Monitor the upper leg tip thickness in real time and record the real-time value of the upper leg tip thickness.

[0056] The present invention proposes a system for adjusting the thickness of the leg tip on the rail, please refer to Figure 2 ,include: A construction unit 100 is configured to obtain historical data and multi-source information of rail production, construct a knowledge graph, and combine the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph; The detection unit 200 is configured to detect the thickness of the upper leg tip in real time; A first adjustment unit 300 is configured to adjust the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to the deviation of the specification size of the upper leg tip thickness exceeding a first preset value; The second adjustment unit 400 is configured to adjust the upper horizontal rollers of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to the deviation of the specification size of the upper leg tip thickness exceeding a second preset value.

[0057] In one embodiment, please refer to Table 1 and Table 2. The implementation of the solution is described below using 60 heavy rail as an example: Obtain the rail specifications, as shown in Table 1, and determine whether the rail upper leg tip thickness specification needs to be adjusted.

[0058] Table 1 Sampling data table before adjustment

[0059] Input sampling data, and the rest of the specifications are in standard internal control. The upper leg tip thickness needs to be reduced by 0.2mm. Click calculate to generate a solution: the upper horizontal roller tilt gap of the first pass of UF is negatively adjusted by 0.3mm, and the upper horizontal roller tilt gap of the first pass of UR2 is negatively adjusted by 0.8mm. The impact values ​​are: reduce the upper leg tip thickness by 0.2mm, increase the bottom width by 0.2mm, increase the upper symmetry by 0.2mm, and the head is tilted upward by 0.1mm.

[0060] The model calculation plan is sent down, the actual roll gap value is modified, and then the next roll is sampled. As shown in Table 2.

[0061] Table 2 Adjusted sampling data table

[0062] Analysis of the sampling data after adjustment shows that the issued plan has obvious changes in the specifications of the upper leg tip thickness, and the impact on the specifications of the upper leg tip thickness is also in line with the trend of changes in the plan simulation.

[0063] In terms of feasibility, the present invention is based on the rolling adjustment experience of mature products, and after careful screening and optimization, a set of high-efficiency, high-standard and high-precision rolling adjustment schemes have been formed. These schemes are integrated into the computer model to achieve automated adjustment. In the automated test conducted by the second line of the rail beam plant, the feasibility of the adjustment scheme reached 97.74%, and the adjustment success rate was 98.87%, proving the high practicality and reliability of the present invention.

[0064] Looking into the future, the present invention has broad application prospects in steel and vanadium rail beam factories. Rich experience in manual adjustment provides a solid foundation for the present invention, which is expected to form more efficient, high-standard, and high-precision rolling adjustment schemes. This will help improve rolling efficiency and accuracy, while saving time and reducing production costs, bringing revolutionary changes to the rail manufacturing industry.

[0065] The present invention proposes an innovative method for constructing a rail upper leg thickness adjustment model, the core innovation of which is: Digitalization of expert experience: Through expert experience extraction, rich practical operation experience is converted into quantifiable data values, providing accurate input parameters for the model.

[0066] Expert knowledge modeling: Using the expert knowledge data value method, combined with advanced solution optimization algorithms, an expert experience model is constructed to achieve precise control of the rail leg tip thickness.

[0067] Real-time specification control: The model can recommend the specification control scheme of the rail leg thickness in real time to ensure timely and accurate specification adjustment during the production process.

[0068] Real-time adjustment of abnormal status: Through the integrated expert adjustment experience database and computer control system, the abnormal status of the leg tip thickness on the online rail is monitored and adjusted in real time to achieve deviation correction.

[0069] Stable internal control standards: Ensure that the rail leg tip thickness specifications always remain within the internal control standard range, improving product quality and production efficiency.

[0070] In summary, the present invention realizes the automated and intelligent adjustment of the rail leg tip thickness by combining expert experience with advanced algorithms, thereby improving the accuracy and stability of rail production.

[0071] Based on the same inventive concept, according to another aspect of the present invention, Fig.12 As shown, an embodiment of the present invention further provides a computer device 30, which includes a processor 310 and a memory 320. The memory 320 stores a computer program 321 that can be run on the processor. When the processor 310 executes the program, the steps of the above method are performed.

[0072] Based on the same inventive concept, according to another aspect of the present invention, Fig.13 As shown, an embodiment of the present invention further provides a computer-readable storage medium 40, which stores a computer program 410 for executing the above method when executed by a processor.

[0073] The embodiment of the present invention may also include a corresponding computer device. The computer device includes a memory, at least one processor, and a computer program stored in the memory and executable on the processor, and the processor executes any one of the above methods when executing the program.

[0074] The memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules in the embodiments of the present application. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions and modules stored in the memory, that is, implementing the above method.

[0075] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In an embodiment, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the local module via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0076] Finally, it should be noted that a person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program can be a disk, an optical disk, a read-only storage memory (ROM) or a random access memory (RAM), etc. The above-mentioned computer program embodiments can achieve the same or similar effects as the corresponding above-mentioned arbitrary method embodiments.

[0077] It will also be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits and algorithm steps described in conjunction with the disclosure herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, a general description has been given of the functions of various schematic components, blocks, modules, circuits and steps. Whether the function is implemented as software or hardware depends on the specific application and the design constraints imposed on the entire system. Those skilled in the art can implement the function in various ways for each specific application, but the implementation decision should not be interpreted as causing a departure from the scope disclosed in the embodiments of the present invention.

[0078] The above are exemplary embodiments disclosed in the present invention, but it should be noted that various changes and modifications may be made without departing from the scope of the embodiments disclosed in the present invention as defined in the claims. The functions, steps and / or actions of the method claims according to the disclosed embodiments described herein do not need to be performed in any particular order. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments. In addition, although the elements disclosed in the embodiments of the present invention may be described or required in individual form, they may also be understood as multiple unless explicitly limited to the singular.

[0079] It should be understood that, as used herein, the singular forms "a", "an" are intended to include the plural forms as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations including one or more of the associated listed items.

[0080] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the above embodiments of the present invention, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for adjusting the thickness of a rail leg tip, characterized in that: include: Obtain historical data and multi-source information on rail production, build a knowledge graph, and combine the knowledge graph with the preset expert experience model to generate an expert experience model that introduces the knowledge graph; Real-time detection of upper leg tip thickness; In response to the deviation of the specification size of the upper leg tip thickness exceeding a first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness exceeding a second preset value, the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill are adjusted based on the expert experience model introduced into the knowledge graph.

2. A method for adjusting the thickness of a rail upper leg tip according to claim 1, characterized in that: In response to the deviation of the specification size of the upper leg tip thickness exceeding the first preset value, the step of adjusting the upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a first preset value, adjusting the upper horizontal roll of the semi-universal rolling mill based on the first scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than the first preset value, the upper horizontal roll of the semi-universal rolling mill is adjusted based on the second scheme of the expert experience model introducing the knowledge graph.

3. The method for adjusting the thickness of the upper leg tip of a rail according to claim 1, characterized in that: In response to the deviation of the specification size of the upper leg tip thickness exceeding the second preset value, the step of adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph includes: In response to the deviation of the specification size of the upper leg tip thickness being less than a second preset value, adjusting the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill based on the third scheme of the expert experience model introduced into the knowledge graph; In response to the deviation of the specification size of the upper leg tip thickness being greater than a second preset value, the fourth scheme based on the expert experience model introducing the knowledge graph adjusts the upper horizontal rolls of the universal rolling mill and the semi-universal rolling mill.

4. A method for adjusting the thickness of a rail upper leg tip according to claim 1, characterized in that: The steps of constructing the preset expert experience model include: The historical leg tip thickness specifications of each team were extracted from the historical data; Analyze the distribution of the historical leg tip thickness specification size values, rank them according to the historical control effect of the leg tip thickness specifications, and use the teams whose ranking exceeds the threshold as expert knowledge; Generate an expert experience model based on specification size value distribution and expert knowledge.

5. The method for adjusting the thickness of the upper leg tip of a rail according to claim 1, characterized in that: The steps of obtaining historical data and multi-source information of rail production and constructing a knowledge graph include: Obtain historical data and multi-source information on rail production, and perform pre-processing operations on the historical data and multi-source information; Identify entities related to rail production and quality control, analyze associations and interactions between entities, and extract relationships between them; Organize entities and relationships in a structured way to form a knowledge graph; Among them, multi-source information includes industry standard information and scientific research information.

6. A method for adjusting the thickness of a rail upper leg tip according to claim 5, characterized in that: The steps of combining the knowledge graph and the preset expert experience model to generate the expert experience model introduced into the knowledge graph include: Identify the correspondence between entities in the knowledge graph and parameters in the expert experience model; Use relations in the knowledge graph as constraints; Use historical data to validate the integrated model; The verified integrated model is used as the expert experience model introduced into the knowledge graph.

7. A method for adjusting the thickness of a rail upper leg tip according to claim 1, characterized in that: The step of real-time detection of the thickness of the upper leg tip comprises: Set the measurement range and accuracy for wireless track detection; Monitor the upper leg tip thickness in real time and record the real-time value of the upper leg tip thickness.

8. A system for adjusting the thickness of rail leg tip, characterized in that: include: A construction unit configured to obtain historical data and multi-source information of rail production, construct a knowledge graph, and combine the knowledge graph with a preset expert experience model to generate an expert experience model introduced into the knowledge graph; A detection unit configured to detect the thickness of the upper leg tip in real time; A first adjustment unit is configured to adjust an upper horizontal roll of the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to a deviation of a specification dimension of the upper leg tip thickness exceeding a first preset value; The second adjustment unit is configured to adjust the upper horizontal rollers of the universal rolling mill and the semi-universal rolling mill based on the expert experience model introduced into the knowledge graph in response to the deviation of the specification size of the upper leg tip thickness exceeding a second preset value.

9. A computer device comprising: at least one processor; and a memory storing a computer program executable on the processor, wherein the processor executes the steps of a method for adjusting the thickness of a leg tip on a rail as claimed in any one of claims 1 to 7 when executing the program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for adjusting the thickness of a rail leg tip are performed as described in any one of claims 1 to 7.

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