Method and device for predicting energy consumption of tube push bench

Abstract

The invention belongs to the technical field of hot-rolled seamless steel tube production, and discloses a method and device for predicting energy consumption of a tube push bench, and the method comprises the steps: collecting data of various factors in a tube push production process; establishing a mechanism model and a time lag model of the pipe jacking production process; establishing a regression prediction model for predicting the energy consumption of the tube push bench according to the mechanism model and the time lag model, wherein the regression prediction model comprises a TCN network layer and an attention layer; the TCN layer is used for extracting spatial features influencing rolling energy consumption; the attention layer is used for extracting time sequence feature data; and predicting the energy consumption of the tube push bench through the regression prediction model. According to the method, dynamic changes in the production process can be captured more comprehensively through fusion of spatial-temporal characteristics, so that the precision of energy consumption prediction is improved. The production data can be monitored in real time, the response is quick, the production efficiency is improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of hot-rolled seamless steel pipe production, and specifically relates to a method and a device for predicting energy consumption of a pipe jacking machine. Background Art

[0002] Among the many energy optimization strategies, accurate prediction and effective management of electricity consumption in the process of seamless steel pipe jacking has become a key research direction. Accurate prediction of electricity consumption not only helps enterprises optimize production processes and improve energy efficiency, but also lays an important foundation for achieving green manufacturing and reducing carbon emissions. The pipe jacking machine is one of the key production equipment in the hot-rolled pipe jacking unit production line. The CPE (Cross roll piercing and elongating mills) hot-rolled seamless steel pipe process, namely oblique rolling piercing + pipe jacking extension, is a highly automated process that can produce high-quality precision pipes. In addition, it has the advantages of high productivity and low cost. The wall thickness of the product can reach 3mm and the rolling speed can reach 7 meters per second.

[0003] Although machine learning technology has been widely used in the hot-rolled steel prediction process, the currently used machine learning technology only focuses on its highly nonlinear characteristics when predicting the hot-rolled energy consumption task with spatial sequence characteristics, while ignoring its complex spatial characteristics. In addition, traditional methods have difficulty capturing these complex spatial data-related characteristics, resulting in high energy consumption of equipment and difficulty in predicting and warning of power consumption, thereby increasing the risk of equipment failure.

[0004] The Chinese patent publication number is CN116882560A, and the name is a patent application for a method and system for predicting product energy consumption based on accurate calculation of hot-rolled product energy consumption, which includes: obtaining the energy consumption data of materials at every moment; taking materials as the accounting object, by coupling the process position of the materials with the consumption of various energy meters, establishing a material energy consumption allocation model to achieve accurate calculation of material energy consumption; according to the material energy consumption calculation results, grouping the energy data of materials of different steel grades and specifications according to the grouping rules, and establishing a material energy consumption sample database; according to the normal distribution mathematical model, calculating the mean and variance of the energy consumption data of the same steel grade and the same specification after grouping, obtaining the characteristic value of the energy consumption of the product sample as the standard data of the energy consumption of the product; matching the energy consumption sample database with the planned material specifications to predict the planned material production energy consumption; dynamically adjusting the product energy consumption sample database according to the actual situation of the unit to improve the accuracy of product energy consumption prediction. This patent application is difficult to capture the complex spatial data related characteristics in the process of seamless steel pipe jacking. Summary of the invention

[0005] In order to overcome the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method and device for predicting energy consumption of a pipe jacking machine, which can more comprehensively capture the dynamic changes in the production process through the fusion of temporal and spatial features, thereby improving the accuracy of energy consumption prediction.

[0006] To achieve the above object, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a method for predicting energy consumption of a pipe jacking machine, comprising the following steps: S1: Collect data on various factors in the pipe jacking production process; S2: Establish the mechanism model and time lag model of pipe jacking production process; S3: A regression prediction model for predicting the energy consumption of the pipe jacking machine is established according to the mechanism model and the time lag model, wherein the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract spatial features that affect rolling energy consumption; and the attention layer is used to extract time series feature data; S4: Predict the energy consumption of pipe jacking machine through regression prediction model.

[0007] Optionally, in step S1, the collected pipe jacking production data is subjected to outlier detection using the 3 sigma criterion, and the missing values ​​are interpolated using an interpolation method.

[0008] Optionally, in step S2, the mechanism model is combined with actual field data to perform Pearson correlation analysis on relevant variables affecting energy consumption to identify positive correlation features and eliminate negative correlation features.

[0009] Optionally, in step S3, the regression prediction model is a TCN-Transformer-Attention model, and the spatial feature data is first weighted through the CroosAttention mechanism, and then the weights of the temporal feature data are weighted, and the weights of the temporal feature data and the spatial feature data are concatenated.

[0010] Optionally, in step S1, data is collected by setting a temperature sensor, a pressure sensor, a speed sensor and a thickness gauge at a fixed roller mold of a frame of the pipe jacking machine equipment.

[0011] Optionally, based on the collected historical data of various factors in the pipe jacking production process, machine learning is performed on normal patterns and abnormal patterns in the historical data to obtain a fault detection model.

[0012] In a second aspect, the present invention provides an energy consumption prediction system for a pipe jacking machine, comprising: Data acquisition module, used to collect data on various factors during pipe jacking production; The first model building module is used to build a mechanism model and a time lag model of the pipe jacking production process; The second model building module is used to establish a regression prediction model for predicting the energy consumption of the pipe jacking machine according to the mechanism model and the time lag model, and the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract the spatial features that affect the rolling energy consumption; the attention layer is used to extract the time series feature data; The calculation module is used to predict the energy consumption of the pipe jacking machine through a regression prediction model.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for predicting energy consumption of a pipe jacking machine when executing the computer program.

[0014] In a fourth aspect, the present invention provides 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 method for predicting energy consumption of a pipe jacking machine is implemented.

[0015] In a fifth aspect, the present invention provides a computer program product including a computer-readable medium, wherein the computer-readable medium contains a computer-readable program code, and the program code executes the method for predicting energy consumption of the pipe jacking machine.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention can more comprehensively capture the dynamic changes in the production process through the fusion of spatiotemporal features, thereby improving the accuracy of energy consumption prediction. The present invention can monitor production data in real time and respond quickly, thereby improving production efficiency and reducing energy consumption. The present invention can adaptively adjust the prediction model according to different production conditions, thereby enhancing the flexibility of the system.

[0017] At the same time, the method proposed in the present invention can meet the rolling power consumption prediction needs of various products, provide a basis for equipment energy consumption management and equipment failure warning, and has important theoretical and practical significance. It can improve the generalization ability of the energy consumption prediction model and integrate it with the energy management and control system to improve work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

[0019] In the attached picture: Figure 1 This is a flow chart of energy consumption prediction for pipe jacking according to the present invention; Figure 2 It is a schematic diagram of the pipe jacking process of the present invention, wherein Figure 2 a is before the jacking starts. Figure 2 b is after the pipe jacking is completed. Figure 2c is during the pipe jacking process; Figure 3 The prediction results of Example 2 of the present invention are compared; Figure 4 This is a schematic diagram of data transmission in accordance with Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of data fusion of the present invention; Figure 6 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 7 A block diagram of an electronic device provided according to an embodiment of the present invention; Among them, 60, computer equipment; 61, processor; 62, memory; 63, computer program; 600, electronic equipment; 610, processing unit; 620, storage unit; 6201, random access storage unit; 6202, cache storage unit; 6203, read-only storage unit; 6204, utility; 6205, program module; 630, bus; 640, display unit; 650, input / output (I / O) interface; 660, network adapter; 700, external device. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined. The present invention is described in detail below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, a method for predicting energy consumption of a pipe jacking machine of the present invention comprises the following steps: S1: Collect data on various factors in the pipe jacking production process; S2: Establish the mechanism model and time lag model of pipe jacking production process; S3: A regression prediction model for predicting the energy consumption of the pipe jacking machine is established according to the mechanism model and the time lag model, wherein the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract spatial features that affect rolling energy consumption; and the attention layer is used to extract time series feature data; S4: Predict the energy consumption of pipe jacking machine through regression prediction model.

[0024] The energy consumption prediction method of a pipe jacking machine of the present invention can more comprehensively capture the dynamic changes in the production process through the fusion of temporal and spatial features, thereby improving the accuracy of energy consumption prediction. The present invention can monitor production data in real time and respond quickly, thereby improving production efficiency and reducing energy consumption. The present invention can adaptively adjust the prediction model according to different production conditions, thereby enhancing the flexibility of the system.

[0025] Example 1 A method for predicting energy consumption of a pipe jacking machine of the present invention comprises the following steps: 1) Data collection and preprocessing S101: Collect multi-source data, including design time characteristics, spatial characteristics, process parameters and product quality data in the production process.

[0026] S102: Preprocess the collected data, use the 3 sigma criterion to detect outliers, and use interpolation to fill in missing values ​​to ensure data integrity 2) Rolling mechanism analysis S201. Study the rolling mechanism in the actual production process of seamless steel pipes and identify the main factors affecting the energy consumption of pipe jacking.

[0027] S202, determine the influence of time characteristic data and space characteristic data on energy consumption. Specifically, the time characteristic data includes rolling time and intermittent time. The space characteristic data includes stand spacing and pass area.

[0028] Specifically, according to the formula Calculate the required rolling load when passing through each stand during the pipe jacking process. Since the pipe jacking process is an intermittent rolling process, there is a time lag between each process of each billet from piercing to pipe jacking and then to diameter reduction.

[0029] According to the process of pipe jacking from the first frame to the i-th frame, the number of steel biting frames is constantly changing. The initial rough pipe gradually becomes thinner and longer with the hole type of decreasing diameter, following the principle of constant volume: F1V1=F2V2=…=FiVi=…=FnVn In the formula, Fi represents the hole area of ​​each rack, Vi represents the jacking speed when reaching each rack, and V represents the distance between each rack. Therefore, the drastic change of load during the rolling process (i.e., the jacking process) leads to high energy consumption. A method for predicting the energy consumption of the jacking machine is helpful for the energy scheduling and load distribution of the entire production line. It can improve production efficiency, reduce production costs, and predict equipment failure alarms in advance.

[0030] The electric energy consumed in the production process of a seamless steel pipe can be expressed as:

[0031] Among them, Uec is the voltage during the pipe jacking process, Iec is the current during the pipe jacking process, t0 is the beginning of the pipe jacking, and t1 is the end of the pipe jacking. In order to explain the reasons that affect energy consumption more specifically, the jacking force and the jacking load during the pipe jacking process can be converted into M=Pa, where M is the torque, P is the jacking force, and a is the radius of the machine base gear. The load required to reach each frame is expressed as: .

[0032] The mechanism model is used to conduct correlation analysis on the relevant variables affecting energy consumption factors, and the positive correlation features are determined and the negative correlation features are discarded for model training.

[0033] Through the equivalent conversion of rolling force Pi and rolling load Ci, the relevant process parameters and equipment parameters in the rolling process can be determined, including hole area, steel deformation resistance, wall thickness of rough pipe and rough pipe, mandrel diameter and other related data, avoiding the collection of redundant data.

[0034] 3) Establishing spatiotemporal feature model S301. Calculate the rolling load requirement when passing through each stand during the pipe jacking process according to the mechanism model.

[0035] S302, considering the intermittent characteristics of the pipe jacking production process, establishing a time lag model to describe the load change from the first rack to the i-th rack.

[0036] 4) Construction of energy consumption prediction model S401. Combining the mechanism model with the actual field data, performing Pearson correlation analysis on the relevant variables affecting energy consumption to identify positive correlation features and eliminate negative correlation features.

[0037] S402, establish a TCN-Transformer-Attention model for predicting the energy consumption of the pipe jacking machine, in which the TCN network is used to extract the spatial features that affect the rolling energy consumption, and the Transformer network is used to extract the temporal features, and finally the cross-attention mechanism is used to perform feature fusion, and the cross-attention machine assigns different weights to the spatial and temporal features. Finally, the energy consumption of the pipe jacking machine is predicted through the regression predictor.

[0038] Temporal Convolutional Network (TCN),Since the spatial features of the pipe jacking process vary with time (such as,spatial features such as roll size and product specifications), a temporal,convolutional network (TCN) is used to extract multi-channel spatial features,in order to effectively capture its local spatial features.

[0039] The TCN network structure includes input layer, causal convolution layer, residual connection, activation function layer and fully connected layer as model output. Each convolution layer uses multiple convolution kernels (filters) to extract spatial features through convolution operation. The size of the convolution kernel can be adjusted according to the characteristics of the input data. Residual connections are added between convolution layers to improve information flow and gradient propagation. This helps prevent information loss in deep networks. The activation function uses the relu activation function.

[0040] Transformer is a deep learning model with self-attention mechanism, which mainly consists of two parts, encoder and decoder. The present invention uses the encoder of Transformer to extract the time features in the top-pipe process, the position encoding layer, and replaces the single-head attention mechanism with a multi-head attention mechanism in the encoder, which allows the model to learn information in parallel in different subspaces, so that different features of the data can be better captured.

[0041] As a time series feature extractor, the Transformer encoder has an input layer, a position encoding layer, a multi-head self-attention mechanism, a normalization layer, a residual connection, a feedforward network, a normalization layer, and an output layer. The entire model maps the input sequence to a vector representation of a fixed dimension, and uses position encoding to provide the position information of each element in the sequence. The multi-head self-attention mechanism focuses on the different position information in the sequence, and the model learns the relevant information of each sequence feature in parallel in different subspaces.

[0042] The role of Croos Attention is to first assign weights to the features extracted by the spatial feature extractor and the temporal features, and then concatenate the weights of the two.

[0043] Croos Attention (CA) is a method to extend the self-attention mechanism, which is often used to process multimodal input or combine information from different sources. Its main idea is to consider information from two different input sequences simultaneously when calculating the attention weights.

[0044] The input source in this invention includes two parts: spatial features and temporal features. By calculating the temporal feature query (Query), spatial feature key (key) and value (value), the attention score is finally calculated. ,The input features that affect the energy consumption of pipe jacking are determined by calculating ,weights assigned to spatial features and temporal features.

[0045] 5) Construction of energy consumption control system for pipe jacking machine In order to solve the data compatibility problem between different devices, an efficient data standardization module is designed.

[0046] Multi-protocol support: The module can support multiple communication protocols, such as CAN (Controller Area Network), RS-232, Modbus, etc., so that various types of devices can be connected to the system.

[0047] Unified data format: Through standardized interfaces, data from different sources are converted into a unified format (JSON or XML) to facilitate subsequent data processing and analysis. In this way, data from various devices can be integrated on the same platform, effectively improving data utilization.

[0048] In order to achieve comprehensive real-time monitoring of the steel rolling process, ensure that the working status of the equipment and production process information can be obtained in a timely manner.

[0049] Sensor layout: Various sensors are deployed on the fixed roller mold of the pipe jacking machine, including: Temperature sensor: monitors the temperature changes of materials and equipment during the rolling process to prevent overheating.

[0050] Pressure sensor: monitors the working pressure of the hydraulic system in real time to ensure that the system operates within a safe range.

[0051] Speed ​​sensor: detects rolling speed to ensure that the equipment operates according to the set parameters.

[0052] Thickness gauge: detect the uniformity of wall thickness during steel pipe rolling to ensure that the quality of the produced products meets the standards Wireless data transmission: Using wireless communication technologies such as LoRa (Long-Range Low-Power Wide Area Network), the collected data is transmitted to the central control unit in real time to ensure the timeliness and accuracy of the data. The collection frequency can be set to once per second to achieve dynamic monitoring of the production process.

[0053] Through intelligent analysis of real-time monitoring data, potential faults and abnormal conditions can be identified to improve the reliability of equipment operation.

[0054] Data analysis algorithm: Based on machine learning, historical data is used for model training to build a fault detection model. The model can analyze the collected monitoring data in real time and quickly determine whether the current state is abnormal by learning the normal and abnormal patterns in the historical data. Figure 2 shown.

[0055] Abnormal detection: When the monitored parameter motor load exceeds the set threshold, the system will sound an alarm and automatically take measures, such as adjusting operating parameters (reducing rolling speed, hydraulic pressure reduction).

[0056] Automatic feedback mechanism: The system can not only identify anomalies, but also achieve self-regulation through intelligent control systems, thereby keeping the equipment in optimal working condition and reducing the risk of failure.

[0057] Example 2 In order to verify the accuracy of the energy consumption prediction of the TTCA network model, the relevant production lines were collected for model training and testing to obtain the prediction results. The absolute value error MAE, mean square error RMSE, average percentage error MAPE, and regression coefficient R-square were used for evaluation. Compared with the prediction results of the traditional model, the prediction results of the present invention are better and have guiding significance for actual production. The error results are shown in Table 1.

[0058] Table 1

[0059] The evaluation index in Table 1 of the experimental results shows that the MAE of the TTCA model proposed in the present invention is 60.25% lower than that of the Transformer and TCN which only focus on time features and only focus on space features, respectively, and 74.5% lower than that of the TCN-Transformer model, which is 51% lower, and its R2 reaches nearly 99.6%. It can be seen that the model proposed in the present invention is more suitable for equipment energy consumption prediction of steel rolling production lines with multi-source data fusion.

[0060] Example 3 Based on the energy consumption prediction method of the pipe jacking machine in Example 1, a pipe jacking machine energy consumption prediction system is disclosed, including: Data acquisition module, used to collect data on various factors during pipe jacking production; The first model building module is used to build a mechanism model and a time lag model of the pipe jacking production process; The second model building module is used to establish a regression prediction model for predicting the energy consumption of the pipe jacking machine according to the mechanism model and the time lag model, and the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract the spatial features that affect the rolling energy consumption; the attention layer is used to extract the time series feature data; The calculation module is used to predict the energy consumption of the pipe jacking machine through a regression prediction model.

[0061] Example 4 In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the energy consumption prediction method of the pipe jacking machine, including: Collect data on various factors in the pipe jacking production process; establish a mechanism model and a time lag model of the pipe jacking production process; establish a regression prediction model for predicting the energy consumption of the pipe jacking machine based on the mechanism model and the time lag model, the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract spatial features that affect rolling energy consumption; the attention layer is used to extract time series feature data; and the energy consumption of the pipe jacking machine is predicted by the regression prediction model.

[0062] See also Figure 6 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the energy consumption prediction method of the pipe jacking machine in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the energy consumption prediction system of the pipe jacking machine in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0063] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 6 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0064] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0065] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0066] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0067] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0068] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0069] See also Figure 7The terminal device is an electronic device 600, and the electronic device is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0070] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0071] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0072] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0073] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0074] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0075] Example 5 In one embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0076] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0077] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0078] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for predicting energy consumption of a pipe jacking machine in the above embodiment; the processor may load and execute the following steps: Collect data on various factors in the pipe jacking production process; establish a mechanism model and a time lag model of the pipe jacking production process; establish a regression prediction model for predicting the energy consumption of the pipe jacking machine based on the mechanism model and the time lag model, the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract spatial features that affect rolling energy consumption; the attention layer is used to extract time series feature data; and the energy consumption of the pipe jacking machine is predicted by the regression prediction model.

[0079] Example 6 The purpose of this embodiment is to provide a computer program product including a computer-readable medium, on which a computer-readable program code is contained, and the program code executes the energy consumption prediction method of the pipe jacking machine.

[0080] The steps involved in the devices of the above embodiments 3, 4, 5 and 6 correspond to those of the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0081] It should be understood by those skilled in the art that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] Unless otherwise specified, the working modes or control modes involved in the above embodiments are all conventional working modes or control modes in the art.

[0083] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Other modifications or equivalent substitutions made to the technical solution of the present invention by ordinary technicians in the field should be included in the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for predicting energy consumption of a pipe jacking machine, characterized in that: The following steps are involved: S1: Collect data on various factors in the pipe jacking production process; S2: Establish the mechanism model and time lag model of pipe jacking production process; S3: A regression prediction model for predicting the energy consumption of the pipe jacking machine is established according to the mechanism model and the time lag model, wherein the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract spatial features that affect rolling energy consumption; and the attention layer is used to extract time series feature data; S4: Predict the energy consumption of pipe jacking machine through regression prediction model.

2. A method for predicting energy consumption of a pipe jacking machine according to claim 1, characterized in that: In step S1, the collected pipe jacking production data is subjected to outlier detection using the 3 sigma criterion, and the missing values ​​are interpolated using the interpolation method.

3. A method for predicting energy consumption of a pipe jacking machine according to claim 1, characterized in that: In step S2, the mechanism model is combined with the actual field data to perform Pearson correlation analysis on the relevant variables affecting energy consumption to identify positive correlation features and eliminate negative correlation features.

4. A method for predicting energy consumption of a pipe jacking machine according to claim 1, characterized in that: In step S3, the regression prediction model is a TCN-Transformer-Attention model. The Croos Attention mechanism is used to first assign weights to the spatial feature data, then to the temporal feature data, and then the weights of the temporal feature data and the spatial feature data are concatenated.

5. A method for predicting energy consumption of a pipe jacking machine according to claim 1, characterized in that: In step S1, data is collected by setting a temperature sensor, a pressure sensor, a speed sensor and a thickness gauge at the fixed roller mold of the frame of the pipe jacking machine equipment.

6. A method for predicting energy consumption of a pipe jacking machine according to claim 1, characterized in that: Based on the collected historical data of various factors in the pipe jacking production process, machine learning is performed on the normal and abnormal patterns in the historical data to obtain a fault detection model.

7. A pipe jacking machine energy consumption prediction system, characterized in that: include: Data acquisition module, used to collect data on various factors during pipe jacking production; The first model building module is used to build a mechanism model and a time lag model of the pipe jacking production process; The second model building module is used to establish a regression prediction model for predicting the energy consumption of the pipe jacking machine according to the mechanism model and the time lag model, and the regression prediction model includes a TCN network layer and an attention layer; the TCN network layer is used to extract the spatial features that affect the rolling energy consumption; the attention layer is used to extract the time series feature data; The calculation module is used to predict the energy consumption of the pipe jacking machine through a regression prediction model.

8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting energy consumption of a pipe jacking machine according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting energy consumption of a pipe jacking machine according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer readable medium, characterized in that The computer-readable medium contains a computer-readable program code, and the program code executes the energy consumption prediction method of the pipe jacking machine according to any one of claims 1-6.

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