Cold-rolled steel sheet material control system and cold-rolled steel sheet material prediction model generation method

Abstract

The cold-rolled steel sheet material control system according to one embodiment of the present invention comprises: a programmable logic controller for acquiring process data of a produced cold-rolled steel sheet; the process controller is used for controlling the manufacturing process of the cold-rolled steel sheet according to the process data of the programmable logic controller; a model generation unit that generates a prediction model for predicting the longitudinal material of the cold-rolled steel sheet to be produced by learning the learning data set in advance; and a production management system that transmits and receives information to and from the process controller, receives the predicted value of the prediction model generated by the model generation unit, and manages the production of the cold-rolled steel sheet to be produced. Comprising the following steps: a model generation part generates a learning data set; a preset model is trained through the learning data set, so that a prediction model for predicting the length direction material of the cold-rolled steel sheet to be produced is generated, and the learning data set comprises multiple pieces of data including hot rolling process data in process data provided by a programmable logic controller; material data and component data provided from a production management system; and mapping data of measurement positions between cold rolling process data and annealing process data in the process data from the programmable logic controller, and the process data provided by the programmable logic controller is the process data of the produced cold-rolled steel sheet acquired by a sensor in advance.

Description

Technical Field

[0001] The present invention relates to a cold-rolled steel sheet material control system and a method for generating a cold-rolled steel sheet material prediction model. Background Art

[0002] Recently, various countries around the world have introduced strict guidelines for environmental regulations such as carbon dioxide (CO2) emission restrictions and energy usage regulations. Therefore, improving fuel efficiency and durability has become an important issue that automotive companies need to address. For this purpose, if thin high-strength steel is used, various problems such as the environment, fuel efficiency, collision resistance, and durability can be improved simultaneously. In order to achieve vehicle body lightweighting, the development of high-strength steel is continuously underway. However, in order to exhibit high-strength steel, it is necessary to increase the content of components such as carbon (C), manganese (Mn), and silicon (Si). The more components there are within the steel type, the greater the material sensitivity (material change) to process temperature changes. Therefore, the more severe the material deviation within the coil is for gigapascal high-strength steel. If material deviation occurs, springback deviation within a single coil will occur during customer forming, resulting in continuous poor forming quality problems.

[0003] (Patent Document 1) Korean Registered Patent Bulletin No. 10-0858902 Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] According to an embodiment of the present invention, there is provided a cold-rolled steel sheet material control system and a method for generating a cold-rolled steel sheet material prediction model that use artificial intelligence to predict the material according to the total length of a cold-rolled steel sheet.

[0006] (II) Technical Solutions

[0007] To solve the above problems of the present invention, a cold-rolled steel sheet material control system according to an embodiment of the present invention may include: a programmable logic controller that acquires process data of the produced cold-rolled steel sheet; a process controller that controls the manufacturing process of the cold-rolled steel sheet according to the process data of the programmable logic controller; a model generation unit that generates a prediction model for predicting the material in the length direction of the cold-rolled steel sheet to be produced by pre-learning a learning data set; and a production management system that sends and receives information with the process controller and receives the prediction value of the prediction model generated by the model generation unit to manage the production of the cold-rolled steel sheet to be produced. The learning data set may include: hot rolling process data in the process data from the programmable logic controller; material data and composition data from the production management system; and mapping data of the measurement position between the hot rolling process data and the cold rolling and annealing process data in the process data from the programmable logic controller.

[0008] A method for generating a cold-rolled steel sheet material prediction model according to an embodiment of the present invention may include the following steps: a model generation unit generates a learning data set; and a preset model is trained through the learning data set to generate a prediction model for predicting the material in the length direction of the cold-rolled steel sheet to be produced. The multiple data included in the learning data set may include: hot rolling process data in the process data provided by a programmable logic controller; material data and composition data provided by a production management system; and mapping data of the measurement position between the hot rolling process data and the cold rolling and annealing process data in the process data from the programmable logic controller. The process data provided by the programmable logic controller may be the process data of the produced cold-rolled steel sheet obtained in advance through sensors.

[0009] (III) Beneficial effects

[0010] According to an embodiment of the present invention, it can exhibit precise and accurate prediction capabilities, and has the effect of being able to help solve factors that reduce production capacity, such as die modification and forming condition changes caused by the sheet material part inside the coiled sheet during forming. Description of the drawings

[0011] Figure 1 is a schematic configuration diagram of a cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0012] Figure 2 is a diagram showing process data detected in a cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0013] Figure 3 is a schematic flowchart of a method for generating a cold-rolled steel sheet material prediction model according to an embodiment of the present invention.

[0014] Figure 4 is a diagram showing learning data variables of a method for generating a cold-rolled steel sheet material prediction model according to an embodiment of the present invention.

[0015] Figure 5 and Figure 6 is a diagram showing the setting of weights of a method for generating a cold-rolled steel sheet material prediction model according to an embodiment of the present invention.

[0016] Figure 7a shows a flowchart of the total length material control of a cold-rolled steel sheet material control system according to an embodiment of the present invention, Figure 7b is a diagram showing an example of giving the optimal annealing temperature according to the total length in a cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0017] Figure 8FIG. 1 is a diagram showing an exemplary computing environment capable of implementing a cold-rolled steel plate material control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement the present invention.

[0019] First, in a cold-rolled steel sheet material control system and a cold-rolled steel sheet material prediction model generation method according to an embodiment of the present invention, a cold-rolled steel sheet, for example, a cold-rolled steel sheet for automobiles, is heated after manufacturing a steel billet, and the steel billet contains, by weight%, C: 0.001-0.4%, Si: 0.1-2.5%, Mn: 1-3%, Al: 0.1% or less, Cr: 1.0% or less, Ti: 0.2% or less, Nb: 0.1% or less, V: 0.2% or less, Mo: 0.5% or less, B: 30ppm or less, the balance of Fe and other inevitable impurities. This is to smoothly perform the subsequent hot rolling process and obtain a material corresponding to the target, preferably heated to a temperature range of 1100-1300°C. Hot rolling is to perform finish rolling after rough rolling, and enter the coiling (CT) step after finish rolling, and will undergo a cooling process before this. A cold-rolled steel sheet can be manufactured by cold rolling the above-mentioned coiled hot-rolled steel sheet at a predetermined reduction rate at room temperature. The cold-rolled steel sheet is preferably subjected to continuous annealing. Continuous annealing may consist of a heating section (HS), a soaking section (SS), a slow cooling section (SCS), a rapid cooling section (RCS), a reheating section (RHS), an overaging section (OAS), and a final cooling section (FCS).

[0020] Figure 1 Schematic configuration diagram of a cold-rolled steel plate material control system according to an embodiment of the present invention.

[0021] Reference Figure 1, a cold-rolled steel sheet material control system according to an embodiment of the present invention may include a sensor 110, a Programmable Logic Controller (PLC) 120, a Process Controller (PC) 130, a Manufacturing Execution System (MES) 140, and a model generation unit 150.

[0022] The sensor 110 may be respectively disposed in the above-mentioned manufacturing process of the cold-rolled steel sheet to detect process data. The programmable logic controller 120 may obtain the process data from the sensor 110 and transmit it to the process controller 130. The process controller 130 may communicate with the manufacturing execution system 140 and control the programmable logic controller 120 according to the control of the manufacturing execution system 140 and the obtained process data, so as to control the above-mentioned manufacturing process of the cold-rolled steel sheet. The programmable logic controller 120 may transmit the process data obtained from the sensor 110 to the model generation unit 150.

[0023] The model generation unit 150 may generate a learning data set according to the process data and train a preset model, so as to generate a prediction model capable of predicting the material according to the length direction (total length) of the cold-rolled steel sheet.

[0024] The model generation unit 150 may include a first Database (DB) 151, a second database 152, and a model generator 153.

[0025] Figure 2 FIG. is a diagram showing process data detected in a cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0026] Refer to Figure 2 , the shown process data are data for measuring the yield strength (YP), tensile strength (TS), and elongation (EL) in the length direction from the head to the tail of the cold-rolled steel sheet.

[0027] The learning data selects factors that have a greater impact on the material and loads the component data for each roll of cold-rolled steel sheet, and the data related to the length direction (total length) in the hot rolling, cold rolling, and annealing processes may be stored in the first database 151. Refer to Figure 2, more specifically, the temperature before hot rolling finish rolling (FET), the temperature after hot rolling finish rolling (FDT), and the coiling temperature (CT) can be measured and collected, and this longitudinal data can be used as hot rolling data for learning data. The data of the hot rolling process can be collected at predetermined intervals for the total length of the coil, for example, data measured at equal intervals within 10 m.

[0028] By connecting the data between the hot rolling process and the subsequent cold rolling annealing process, as shown in Table 1 below, compared with the case of using the average value in each process as each variable of the hot rolling process, the matching of the material prediction model may be more excellent. That is, as variables related to hot rolling, the data of the material measurement part is used instead of the average value of the total length, so that the prediction matching can be excellent.

[0029] [Table 1]

[0030]

[0031] Figure 3 is a schematic flowchart of a method for generating a cold rolled steel sheet material prediction model according to an embodiment of the present invention.

[0032] Referring to Figure 1 and Figure 3 , the model generator 153 generates a learning data set based on the process data stored in the first database 151, where the process data stored in the first database 151 can be queried (S1), merged with the material data, and then a learning data set is generated (S2), and a preset model is trained to generate a prediction model (S3).

[0033] First, the steps of generating learning data may include the following steps: querying the coil composition data of the slab (S1a); querying the collected temperature before hot rolling finish rolling (FET), the temperature after hot rolling finish rolling (FDT), and the coiling temperature (CT) (S1b); querying the rolling force value during cold rolling of the coil (S1c); and querying the annealing temperature value of the total length of the coil (S1d), and this total length data can be used as hot rolling data for learning data. To elaborate on the cold rolling related data, first, the data related to the rolling force generated during cold rolling will be measured and collected, and the passing speed of the steel sheet in the continuous annealing furnace from the heating section (HS) to the final cooling section (FCS) will be measured and collected. Since the dimensional change of the coil between the hot rolling process and the cold rolling process is too large, a measurement position mapping operation between the hot rolling data and the cold rolling / annealing data is required. Therefore, the hot rolling data can be extended by the length caused by cold rolling and merged with the cold rolling and annealing data.

[0034] Regarding the detailed description of cold rolling related data, first, data related to the rolling force generated during cold rolling will be measured and collected, and the passing speed of the steel plate in the continuous annealing furnace from the heating section to the final cooling section and in the annealing furnace will be measured and collected. Since the lengths of hot-rolled steel plates and cold-rolled steel plates are different, a position mapping step for each data of hot rolling / cold rolling and annealing can be further performed.

[0035] Figure 4 FIG. is a diagram showing learning data variables of a method for generating a cold-rolled steel plate material prediction model according to an embodiment of the present invention. Figure 5 and Figure 6 FIG. is a diagram showing setting weights of a method for generating a cold-rolled steel plate material prediction model according to an embodiment of the present invention.

[0036] First, with reference to both Figure 1 and Figure 4 , it includes the step of querying the measured and collected data from the first database 151 through the model generator 153, and this is data related to process variables, corresponding to the X data in the model. The output, that is, the data related to Y, is the material measurement value, corresponding to the yield strength, tensile strength, and elongation. For each coil, the above process variables will be stored in the second database 152. Information on newly produced coils is periodically stored in the first database 152 (for the variables as described above), and the period can be one week or one month. The learning data stored in the second database 152 as described above is re-query through the model generator 153, and an outlier removal logic for the loaded data is applied. The outlier logic is a way to exclude data below or above the 1st quartile and 3rd quartile using the common 1.5*IQR standard, or functions to filter error data when collecting data.

[0037] As Figure 4 shown, for the XY pairs in the generated learning data, a training set and a test set are separately made, and the training of the model is only performed on the learning data. The trained model is evaluated through the test set, and the ways to evaluate the matching of the model can be the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE).

[0038] With reference to both Figure 1 , Figure 3 and Figure 4, the generated data is merged with the material data (output variable) to form a learning dataset, and then the preset model of the model generator 153 is trained. The model can be an integrated model between a neural network algorithm consisting of 3 hidden layers and extreme gradient boosting (XGBoost) and random forest (RandomForest) algorithms as machine learning methods. The form of the learning data can be formed into a table as shown in Figure 4 in each length direction. The material values are obtained in the length direction, and the process data and material data in the length direction are used, so that models can be formed separately in each length direction, and prediction models can be generated using the learning dataset (S3).

[0039] More specifically, it is a method of separately generating regression models with extreme gradient boosting (XGBoost) algorithm, random forest (RandomForest) algorithm, and neural network algorithm using learning data, and then using the average value of the output values derived (predicted) in each model as the final predicted value. It shows that the matching performance is further improved by more than 10% compared with the existing when using this model. More specifically, in the case of steel grades with a yield strength of 1000 MPa, as shown in Table 2 below. (Example of yield strength prediction performance)

[0040] In addition, referring to Figure 1 , Figure 2 and Figure 5 , Figure 6 , when integrating extreme gradient boosting (XGBoost) algorithm, random forest (RandomForest) algorithm, and neural network (Neural network) algorithm, if the weights of each model are different, the matching performance of the model can be improved. The logic at this time is to derive the weights of each model through the gradient boosting method, that is, initially assign arbitrary weights, and train until the root mean square error (RMSE) decreases as shown in Figure 6 . Among them, when initializing the weights, if 1 is input for each model respectively, the training speed will be further improved.

[0041] [Table 2]

[0042]

[0043] Figure 7a Shows the flowchart of the total length material control of the cold-rolled steel sheet material control system according to an embodiment of the present invention. Figure 7bIt is a diagram showing an example of giving the optimal annealing temperature according to the total length in the cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0044] Refer to simultaneously Figure 1 , Figure 3 and Figure 7a , Figure 7b , including the step (S4) where the model generator 153 derives the optimal annealing temperature for finding the target material through the Particle Swarm Optimization (PSO) algorithm based on the prediction model.

[0045] That is, after the above prediction model is completed, the next step will be to create a model for deriving the annealing temperature among the X variables required to obtain the target material based on the prediction model. More specifically, it is a method of taking the composition, hot-rolled variables, and cold-rolled variables as fixed variables and only taking the annealing temperature as a variable variable to find the annealing temperature required to obtain the optimal material. The variable of the annealing temperature can use one or more variables as variable variables. At this time, the algorithm will search for the optimal annealing temperature based on particle swarm optimization. The total length data from hot rolling to annealing used can be, for example, 100 to 2000 for each coil (which can vary according to the production length and production speed of the coil), and the optimal annealing indication value is derived separately for each point (i.e., data) of the total length of the coil. Therefore, for the material deviation according to the total length generated in the hot rolling process, the material deviation caused by the hot rolling process can be reduced by setting different annealing temperatures according to the total length in the continuous annealing furnace, and more stable materials can be ensured by matching the central value of the material specifications. As Figure 5 shown, the annealing temperature value is given for each point of the total length. As described above, when the optimal annealing temperature is derived, this value can be stored in the second database 152, and the optimal annealing temperature value stored in the second database 152 can be transmitted to the production management system 140. The optimal annealing temperature transmitted to the production management system 140 can be reflected in the factory design value.

[0046] This operation can form a prediction model and an optimal annealing temperature derivation model respectively at points separated by a preset distance along the length direction at a predetermined unit, for example, for every 5 to 15 points.

[0047] Figure 8 It is a diagram showing an exemplary computing environment capable of implementing the cold-rolled steel sheet material control system according to an embodiment of the present invention.

[0048] In Figure 8FIG. 0 shows an example of a system 1000 including a computing device 1100 configured to implement a cold-rolled steel sheet material control system according to an embodiment of the present invention. For example, the computing device 1100 includes a personal computer, a server computer, a handheld or notebook device, a mobile device (mobile phone, PDA, media player, etc.), a multiprocessor system, a consumer electronic device, a microcomputer, a mainframe computer, a distributed computing environment including any of the above systems or devices, etc., but is not limited thereto.

[0049] The computing device 1100 may include at least one processing unit 1110 and a memory 1120. Among them, the processing unit 1110 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a custom semiconductor (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field Programmable Gate Arrays, FPGA), etc., and may include multiple cores. The memory 1120 may be a volatile memory (e.g., RAM, etc.), a non-volatile memory (e.g., ROM, flash memory, etc.), or a combination thereof.

[0050] In addition, the computing device 1100 may include a separate storage device 1130. The storage device 1130 includes a magnetic storage device, an optical storage device, etc., but is not limited thereto. In the storage device 1130, computer-readable commands for implementing one or more embodiments improved in this specification may be stored, and other computer-readable commands for implementing an operating system, an application program, etc. may be stored. The computer-readable commands stored in the storage device 1130 may be loaded into the memory 1120 for execution by the processing unit 1110.

[0051] In addition, the computing device 1100 may include (a plurality of) input devices 1140 and (a plurality of) output devices 1150. Among them, the (a plurality of) input devices 1140 may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, or any other input device, etc. In addition, the (a plurality of) output devices 1150 may include, for example, one or more displays, speakers, printers, or any other output device, etc. In addition, the computing device 1100 may use an input device or an output device provided in another computing device as the (a plurality of) input devices 1140 or the (a plurality of) output devices 1150.

[0052] In addition, the computing device 1100 may include one or more communication connections 1160 for communicating with another device (e.g., the computing device 1300) via a network such as the Internet 1200. Among them, the one or more communication connections 1160 may include a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection, or other interfaces for connecting the computing device 1100 to another computing device. In addition, the one or more communication connections 1160 may include a wired connection or a wireless connection.

[0053] Each configuration of the above-described computing device 1100 may be connected via various interconnections such as a bus (e.g., Peripheral Component Interconnect (PCI), USB, FireWire (IEEE 1394), optical bus architecture, etc.), or may be interconnected via a network.

[0054] Terms such as "programmable logic controller (PLC)", "process controller (PC)", "manufacturing execution system (MES)", etc. used in this specification generally refer to hardware, a combination of hardware and software, software, or a computer-related object in the form of software in execution. For example, components such as "programmable logic controller (PLC)", "process controller (PC)", "manufacturing execution system (MES)", etc. may be a process in execution on a processor, a processor, an object, an executable, an execution thread, a program, and / or a computer, but are not limited thereto. For example, an application program driven on a controller and the controller itself may both be components. One or more components may exist within a process and / or an execution thread, and the components may be localized on one computer or distributed between two or more computers.

[0055] As described above, according to the present invention, due to the use of an artificial intelligence model, a wider and larger amount of data can be utilized compared to existing physical models, thereby exhibiting more precise and accurate prediction capabilities. In addition, in the configuration of the online model, since the system continuously drives from data generation to calculation, it helps to realize an intelligent factory. In addition, in the case of cold-rolled products, the hot-rolling process is a pre-process, and in the entire process from hot-rolling to cold-rolling, since the data at each position within the coil is matched in a through-type manner, a more accurate prediction model is provided. In addition, according to the configuration of the present invention as described above, as Figure 2 shown, material information related to the total length can be obtained, and the reasons related to the material differences at each position within the coil can be known. By providing such total-length material data, factors that reduce production capacity, such as die modification and forming condition changes caused by the sheet metal part within the coil during forming, can be solved.

[0056] The present invention described above is not limited to the above embodiments and drawings, but is limited by the appended claims. Those of ordinary skill in the art to which the present invention pertains can easily understand that the configuration of the present invention can be variously changed and modified within the scope not exceeding the technical idea of the present invention.

Claims

1. A cold-rolled steel sheet material control system, comprising: A programmable logic controller that acquires process data of the produced cold-rolled steel sheet; A process controller that controls the manufacturing process of the cold-rolled steel sheet according to the process data of the programmable logic controller; A model generation unit that generates a prediction model for predicting the material in the length direction of the cold-rolled steel sheet to be produced by pre-learning a learning data set; And A production management system that sends and receives information with the process controller and receives the prediction value of the prediction model generated by the model generation unit to manage the production of the cold-rolled steel sheet to be produced, The learning data set includes: Hot rolling process data in the process data from the programmable logic controller; Material data and composition data from the production management system; and Mapping data of the measurement positions between the hot rolling process data and the cold rolling and annealing process data in the process data from the programmable logic controller.

2. The cold-rolled steel sheet material control system according to claim 1, wherein The model generation unit extends the length extended due to cold rolling of the hot rolling process data and merges it with the cold rolling and annealing process data to correct the learning data set.

3. The cold-rolled steel sheet material control system according to claim 1, wherein The data acquired by the programmable logic controller includes: material data measured at each preset predetermined unit along the length direction of the cold-rolled steel sheet.

4. The cold-rolled steel sheet material control system according to claim 3, wherein The model generation unit generates the prediction model at each of the predetermined units where the material data is measured along the length direction of the cold-rolled steel sheet.

5. The cold-rolled steel sheet material control system according to claim 1, wherein Based on the prediction model, the model generation unit further generates an annealing temperature derivation model for deriving the optimal annealing temperature for finding the target material through a preset particle swarm optimization algorithm, and the production management system controls the process controller according to the optimal annealing temperature of the annealing temperature derivation model to control the annealing temperature of the cold-rolled steel sheet.

6. The cold-rolled steel sheet material control system according to claim 1, wherein The model generation unit generates the prediction model including a preset extreme gradient boosting algorithm, random forest algorithm, and neural network algorithm, and averages the prediction values of the extreme gradient boosting algorithm, the random forest algorithm, and the neural network algorithm of the prediction model to output a final prediction value.

7. The cold-rolled steel sheet material control system according to claim 6, wherein The model generation unit assigns weights to the prediction values of the extreme gradient boosting algorithm, the random forest algorithm, and the neural network algorithm of the prediction model according to a preset gradient boosting.

8. A method for generating a cold-rolled steel sheet material prediction model, comprising the following steps: The model generation unit generates a learning data set; And Training a preset model through the learning data set to generate a prediction model for predicting the material in the length direction of the cold-rolled steel sheet to be produced, The multiple data included in the learning data set include: Hot rolling process data from the process data provided by the programmable logic controller; Material data and composition data provided by the production management system; and Mapping data of the measurement positions between the hot rolling process data and the cold rolling and annealing process data in the process data from the programmable logic controller, The process data provided by the programmable logic controller is the process data of the cold rolled steel sheet produced in advance through sensors.

9. The method for generating a cold rolled steel sheet material prediction model according to claim 8, wherein, In the step of generating the learning data set, The model generation unit extends the length of the hot rolling process data extended due to cold rolling and merges it with the cold rolling and annealing process data to correct the learning data set.

10. The method for generating a cold rolled steel sheet material prediction model according to claim 8, wherein, In the step of generating the learning data set, The data obtained by the programmable logic controller from the sensor includes: material data measured at each preset predetermined unit along the length direction of the cold rolled steel sheet.

11. The method for generating a cold rolled steel sheet material prediction model according to claim 10, wherein, In the step of generating the prediction model, The model generation unit generates the prediction model at each of the predetermined units where the material data is measured along the length direction of the cold rolled steel sheet.

12. The method for generating a cold rolled steel sheet material prediction model according to claim 8, further comprising the following steps: Based on the prediction model, the model generation unit further generates an annealing temperature derivation model for deriving the optimal annealing temperature for finding the target material through a preset particle swarm optimization algorithm, and the production management system controls the process controller according to the optimal annealing temperature of the annealing temperature derivation model to control the annealing temperature of the cold rolled steel sheet.

13. The method for generating a cold rolled steel sheet material prediction model according to claim 8, wherein, In the step of generating the prediction model, The model generation unit generates the prediction model including a preset extreme gradient boosting algorithm, random forest algorithm, and neural network algorithm, and averages the prediction values of the extreme gradient boosting algorithm, the random forest algorithm, and the neural network algorithm of the prediction model to output a final prediction value.

14. The method for generating a cold rolled steel sheet material prediction model according to claim 13, wherein, In the step of generating the prediction model, The model generation unit assigns weights to the prediction values of the extreme gradient boosting algorithm, the random forest algorithm, and the neural network algorithm of the prediction model according to a preset gradient boost.

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