Hob load decoupling method, system and equipment based on BP neural network

Abstract

In the disclosed embodiment, a method, system and device for hob load decoupling based on BP neural network are provided, which belongs to the field of data processing technology. A sensor is installed correspondingly at multiple marked points of the hob force transmission path, wherein the method includes: obtaining strain data of multiple groups of sensors under different experimental conditions; using BP neural network to establish a strain-load decoupling model with strain sum and strain difference as input, and vertical force and rolling force as output; performing load test on the hob to obtain voltage data under simulated working conditions; establishing voltage-strain function according to voltage data and strain data; substituting voltage-strain function into strain-load decoupling model to obtain voltage data as input, and vertical force and rolling force as output. The scheme disclosed in the present invention is suitable for load decoupling in the case of strong coupling between voltage and load in the hob load monitoring method, and improves decoupling efficiency, measurement efficiency and accuracy.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of data processing technology, and in particular to a method, system and device for decoupling a hob load based on a BP neural network. Background Art

[0002] At present, disc cutters are often used in large-scale tunneling equipment such as shield machines and TBMs as a tool for digging hard rocks. The working performance of the cutters directly affects the excavation effect and construction speed of the tunnel boring machine. Real-time monitoring of the cutter load helps to adjust the tunneling parameters of the tunnel boring machine (such as thrust, torque, speed, and penetration) in a timely manner, which helps to improve rock breaking efficiency and speed up tunneling. At the same time, the rock parameters of the face can be understood through the cutter load conditions at different cutter positions. In addition, obtaining the cutter load information can also optimize the arrangement of the cutters on the cutterhead to make the cutterhead load more uniform.

[0003] In the aspect of roller cutter operation monitoring, domestic and foreign scholars have carried out a lot of research. Yin Qian, Ma Qiang and others proposed a roller cutter monitoring system for normal pressure cutter change shield, which is used to monitor information such as roller cutter speed, temperature and wear, but failed to obtain roller cutter load information; Gong Qiuming, Ding Shipeng and others found that the roller cutter load and the monitored acoustic emission energy rate have similar change rules by monitoring the acoustic emission characteristics during the roller cutter rock breaking experiment, and this method cannot directly obtain the roller cutter load; Zhang Xiaobo, Xu Jingfei and others proposed a non-destructive detection method for roller cutter wear and speed using eddy current sensors, but could not obtain the roller cutter load; Wang Shaohua and others used the method of built-in strain gauges in the roller cutter pads and surface-pasted strain gauges to monitor the roller cutter load. Although this method achieves the acquisition of roller cutter load, the measurement accuracy is difficult to guarantee. Based on this, although there are many related studies, there are still problems such as the inability to guarantee measurement accuracy, complex measurement methods, and poor applicability.

[0004] Therefore, many scholars at home and abroad have developed various unique real-time monitoring systems for TBM cutter stress in response to the above problems. In most cutter stress monitoring systems, each sensor (generally a resistive strain sensor) generally only measures one force (vertical force, rolling force or lateral force). There is no complex coupling relationship between the measurement data and the load, and generally there is a one-to-one relationship, so the cutter force calibration test can be carried out directly. When there is a more complex coupling relationship between the measurement data and the load, the above method will no longer be applicable.

[0005] It can be seen that there is an urgent need for a hob load decoupling method based on BP neural network that is suitable for the situation where there is a strong coupling between measurement data and load. Summary of the invention

[0006] In view of this, the embodiments of the present disclosure provide a method, system and device for roller cutter load decoupling based on BP neural network, which at least partially solve the problems of poor decoupling efficiency, measurement efficiency and accuracy in the prior art.

[0007] In a first aspect, an embodiment of the present disclosure provides a hob load decoupling method based on a BP neural network, wherein a sensor is correspondingly installed at multiple marked points on a hob force transmission path, and the method comprises:

[0008] Numerical simulation is performed on the strain of the hob stress analysis assembly, and two of the sensors are used as a group to obtain strain data of multiple groups of sensors under different experimental conditions, wherein the strain data includes the strain sum and strain difference between the two sensors;

[0009] A strain-load decoupling model is established using a BP neural network, with the strain sum and the strain difference as input and the vertical force and the rolling force as output;

[0010] Performing a load test on the hob to obtain measured voltage data under simulated working conditions, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors;

[0011] Establishing a voltage-strain function according to the voltage data and the strain data;

[0012] Substituting the voltage-strain function into the strain-load decoupling model, a voltage-load decoupling model is obtained, which takes voltage data as input and vertical force and rolling force as output.

[0013] According to a specific implementation of the embodiment of the present disclosure, the rolling force F r and the vertical force F v The calculation formula is as follows;

[0014]

[0015] Among them, the contact angle between the roller and the rock The calculation formula is as follows;

[0016]

[0017] In the formula, F v Represents the vertical force, F r represents the rolling force, M represents the bending moment, R represents the radius of the hob, h represents the penetration of the hob, Fv represents the vertical force, R represents the radius of the hob, T represents the width of the top edge of the hob cutter ring, is the pressure distribution coefficient of the top edge of the cutter ring, σ c Uniaxial compressive strength of rock, σ t is the tensile strength of rock.

[0018] According to a specific implementation of the embodiment of the present disclosure, the experimental working conditions include the vertical force acting alone, the bending moment acting alone, and the vertical force and the bending moment acting simultaneously.

[0019] According to a specific implementation of the embodiment of the present disclosure, the simulatable working conditions include the vertical force acting alone, and the vertical force and the rolling force acting simultaneously.

[0020] According to a specific implementation of the embodiment of the present disclosure, the step of establishing a voltage-strain function according to the voltage data and the strain data includes:

[0021] The voltage data is linearly fitted with the strain sum and strain difference under the corresponding working condition to obtain the voltage-strain function.

[0022] In a second aspect, the present disclosure provides a hob load decoupling system based on a BP neural network, comprising:

[0023] A simulation module, used for numerically simulating the strain of the hob stress analysis assembly, sequentially taking two of the sensors as a group, and obtaining strain data of multiple groups of sensors under different experimental conditions, wherein the strain data includes the strain sum and strain difference between the two sensors;

[0024] A training module, used for establishing a strain-load decoupling model using a BP neural network, with the strain sum and the strain difference as inputs and the vertical force and the rolling force as outputs;

[0025] An experimental module, used for performing a load test on the hob to obtain voltage data under a simulated working condition, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors;

[0026] An establishing module, used for establishing a voltage-strain function according to the voltage data and the strain data;

[0027] The prediction module is used to substitute the voltage-strain function into the strain-load decoupling model to obtain a voltage-load decoupling model with voltage data as input and vertical force and rolling force as output.

[0028] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0029] at least one processor;

[0030] and, a memory communicatively coupled to the at least one processor;

[0031] Among them, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the roller cutter load decoupling method based on BP neural network in the aforementioned first aspect or any implementation of the first aspect.

[0032] The hob load decoupling scheme based on BP neural network in the embodiment of the present disclosure includes: numerically simulating the strain of the hob stress analysis assembly, controlling the two sensors to obtain strain data of the two sensors under different experimental conditions, wherein the strain data includes the strain sum and strain difference between the two sensors; using the BP neural network to establish a strain-load decoupling model with the strain sum and the strain difference as input, and vertical force and rolling force as output; performing a load test on the hob to obtain voltage data under simulated conditions, wherein the voltage data includes the voltage sum and voltage difference between the two sensors; establishing a voltage-strain function based on the voltage data and the strain data; substituting the voltage-strain function into the strain-load decoupling model to obtain a voltage-load decoupling model with the voltage data as input, and vertical force and rolling force as output.

[0033] The beneficial effects of the embodiments of the present disclosure are as follows: through the scheme of the present disclosure, the BP neural network is combined with the cutter load decoupling, which is suitable for load decoupling in the case of strong coupling between voltage and load in the TBM cutter load monitoring method, thereby improving the decoupling efficiency, measurement efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A schematic flow chart of a method for decoupling a hob load based on a BP neural network provided in an embodiment of the present disclosure;

[0036] Figure 2 A schematic diagram of a hob marking point provided in an embodiment of the present disclosure;

[0037] Figure 3 A schematic diagram of voltage data under different simulatable working conditions provided by an embodiment of the present disclosure;

[0038] Figure 4 A schematic diagram of a voltage-strain function provided in an embodiment of the present disclosure;

[0039] Figure 5A schematic diagram of the structure of a hob load decoupling system based on a BP neural network provided in an embodiment of the present disclosure;

[0040] Figure 6 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0042] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0043] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0044] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0045] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0046] The embodiment of the present disclosure provides a roller cutter load decoupling method based on BP neural network, and the method can be applied to the roller cutter performance prediction process.

[0047] See also Figure 1 , is a flow chart of a method for decoupling a hob load based on a BP neural network provided by an embodiment of the present disclosure. Figure 1 As shown, a sensor is installed correspondingly at multiple marked points on the hob force transmission path, and the method mainly includes the following steps:

[0048] S101, numerically simulating the strain of the hob stress analysis assembly, sequentially taking two of the sensors as a group, and obtaining strain data of multiple groups of sensors under different experimental conditions, wherein the strain data includes the strain sum and strain difference between the two sensors;

[0049] Optionally, the rolling force F r and the vertical force F v The calculation formula is as follows;

[0050]

[0051] Among them, the contact angle between the roller and the rock The calculation formula is as follows;

[0052]

[0053] In the formula, F v Represents the vertical force, F r represents the rolling force, M represents the bending moment, R represents the radius of the hob, h represents the penetration of the hob, Fv represents the vertical force, R represents the radius of the hob, T represents the width of the top edge of the hob cutter ring, is the pressure distribution coefficient of the top edge of the cutter ring, σ c Uniaxial compressive strength of rock, σ t is the tensile strength of rock.

[0054] Optionally, the experimental conditions include the vertical force acting alone, the bending moment acting alone, and the vertical force and the bending moment acting simultaneously.

[0055] When implementing it, Figure 2 As shown, a sensor can be installed at a plurality of marked points on the hob force transmission path. According to the installation position of the sensor, two sensors are selected as a group. The strain data of each group can indirectly reflect one or two forces. The nth group of sensors is represented by Vn1 and Vn2. Figure 2As shown, if two sensors installed on the left and right sides of position 1 are used as a group, their strain sum and strain difference can indirectly reflect the vertical force and rolling force of the two rollers, respectively; if two sensors installed at positions 2 and 3 are used as a group, their strain difference can indirectly reflect the rolling force of the two rollers; if two sensors installed at positions 4 and 5 are used as a group, their strain difference can indirectly reflect the lateral force of the two rollers. This embodiment briefly describes the case where n is 1, and the method is the same when n is greater than 1.

[0056] In this embodiment, two sensors are installed at the middle beam position of the roller cutter pressure block to perform a roller cutter load test. For example, the joint seat is nested on the lower side of the middle beam of the pressure block, the electrical control box and the joint seat are fastened with bolts, the sensor is embedded in the center position of the middle beam of the pressure block, and the two sensors are symmetrically installed on both sides of the joint seat.

[0057] The strain sum and strain difference data of the two sensors are processed and analyzed to predict the vertical force and rolling force on the hob. There is a relatively complex coupling relationship between the strain sum, strain difference and the load. Then, sufficient strain data is obtained to establish a BP neural network. Currently, there is no device for loading rolling force. Based on the conversion relationship between rolling force and bending moment, the loading of rolling force is considered in the form of loading bending moment.

[0058] The hob performs finite element calculation of strain, mainly recording strain data at the positions of two strain gauges. The experimental working conditions are still three types: vertical force, bending moment acting alone and acting simultaneously. However, in order to explore the variation law of the strain sum and strain difference of the two strain groups with the load, the strain-load data should try to include all types of working conditions and reflect their variation law. Based on the above requirements, in this embodiment, a total of 7 series of data are included, and a total of 35 groups of data are obtained.

[0059] S102, using a BP neural network to establish a strain-load decoupling model with the strain sum and the strain difference as inputs and the vertical force and the rolling force as outputs;

[0060] During specific implementation, considering that the roller cutter load test can only load the bending moment under the premise of loading the vertical force, and it is impossible to obtain the voltage data under the action of the bending moment alone, the strain sum, strain difference, vertical force and bending moment of the two sensors can be used to establish a BP neural network decoupling model. The input of the strain-load decoupling model is the strain sum and the strain difference, and the output of the decoupling model is the vertical force and rolling force.

[0061] For example, the 35 groups of data obtained in the above steps are used as the training set of the BP neural network prediction model to establish a strain-load decoupling model corresponding to the strain-load. Since there are two prediction values, there are two schemes for establishing the prediction model: establishing a dual-input dual-output prediction model; establishing two dual-input single-output prediction models. Through comparative analysis, the prediction results of scheme two are more accurate. The network training target of the two prediction models is 0.001, the learning rate is set to 0.01, the maximum number of iterations is 1000 times, and the transfer function, output function, and training function use the default values. The range of the determination coefficient is in [0,1]. The closer it is to one, the better the performance of the model; conversely, the closer it is to zero, the worse the performance of the model. In the disclosed embodiment, the determination coefficient of the vertical force prediction model is 0.99229, and the determination coefficient of the bending moment prediction model is 0.98796. Therefore, the performance of the two models is better.

[0062] S103, performing a load test on the hob to obtain voltage data under a simulated working condition, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors;

[0063] Optionally, the simulatable working conditions include the vertical force acting alone, and the vertical force and rolling force acting simultaneously.

[0064] In specific implementation, a hob force test under corresponding working conditions is carried out according to actual needs. The simulatable working conditions include the vertical force O point loading acting alone, and the vertical force and the A point loading bending moment acting simultaneously, and then the voltage data under different simulatable working conditions are obtained, such as Figure 3 As shown,

[0065] S104, establishing a voltage-strain function according to the voltage data and the strain data;

[0066] Furthermore, in step S104, establishing a voltage-strain function according to the voltage data and the strain data includes:

[0067] The voltage data is linearly fitted with the strain sum and strain difference under the corresponding working condition to obtain the voltage-strain function.

[0068] In a specific implementation, a plurality of voltage-strain relationship curves can be established according to the voltage sum, the voltage difference, the strain sum and the strain difference. Considering that the plurality of voltage-strain relationship curves are substantially linearly correlated, the voltage sum and the voltage difference, and the corresponding strain sum and strain difference can be linearly fitted to obtain the voltage-strain function. The voltage-strain function is obtained as follows: Figure 4 shown.

[0069] S105, substituting the voltage-strain function into the strain-load decoupling model to obtain a voltage-load decoupling model with voltage data as input and vertical force and rolling force as output.

[0070] In specific implementation, after obtaining the voltage-strain function, the voltage-strain function can be substituted into the strain-load decoupling model to obtain a voltage-load decoupling model with voltage data as input and vertical force and rolling force as output. In the actual use of the hob, when it is necessary to predict its performance, the measured voltage of the hob can be collected and input into the voltage-load decoupling model. The voltage-strain fitting curve is used to convert the measured voltage data into strain, and then the vertical force and rolling force are obtained according to the strain. When it is inconvenient to load the rolling force in the experimental device, the rolling force can be loaded in the form of loading bending moment, that is, the bending moment is converted into rolling force using the formula Fr=M / 2R, that is, the vertical force and rolling force of the hob are obtained from the measured voltage. In this embodiment, the error of the vertical force predicted by the established voltage-load decoupling model is controlled within 17.62%, and the error of the rolling force is controlled within 19.78%, so the overall error of the voltage-load decoupling model can be controlled within 20%.

[0071] The cutter load decoupling method based on BP neural network provided in this embodiment combines BP neural network with cutter load decoupling. It is suitable for load decoupling in the case where there is strong coupling between voltage and load in TBM cutter load monitoring method, thereby improving decoupling efficiency, measurement efficiency and accuracy.

[0072] Corresponding to the above method embodiment, see Figure 5 The embodiment of the present disclosure further provides a hob load decoupling system 50 based on a BP neural network, comprising:

[0073] A simulation module 501 is used to perform numerical simulation on the strain of the hob stress analysis assembly, and sequentially use two of the sensors as a group to obtain strain data of multiple groups of sensors under different experimental conditions, wherein the strain data includes the strain sum and strain difference between the two sensors;

[0074] A training module 502 is used to establish a strain-load decoupling model using a BP neural network, with the strain sum and the strain difference as inputs and the vertical force and the rolling force as outputs;

[0075] The experimental module 503 is used to perform a load test on the hob to obtain voltage data under a simulated working condition, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors;

[0076] An establishing module 504, configured to establish a voltage-strain function according to the voltage data and the strain data;

[0077] The prediction module 505 is used to substitute the voltage-strain function into the strain-load decoupling model to obtain a voltage-load decoupling model with voltage data as input and vertical force and rolling force as output.

[0078] Figure 5 The system shown can correspondingly execute the contents of the above method embodiment. For the parts not described in detail in this embodiment, refer to the contents recorded in the above method embodiment and will not be repeated here.

[0079] See also Figure 6 The embodiment of the present disclosure further provides an electronic device 60, which includes: at least one processor and a memory connected to the at least one processor. The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the hob load decoupling method based on the BP neural network in the above method embodiment.

[0080] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the roller cutter load decoupling method based on BP neural network in the aforementioned method embodiment.

[0081] The embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the roller cutter load decoupling method based on the BP neural network in the aforementioned method embodiment.

[0082] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device 60 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0083] like Figure 6As shown, the electronic device 60 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0084] Typically, the following devices may be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 609. The communication device 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 60 with various devices is shown in the figure, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0085] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0086] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer 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. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0087] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0088] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can execute the relevant steps of the method embodiment.

[0089] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device can perform the relevant steps of the method embodiment.

[0090] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0091] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0092] The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware.

[0093] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0094] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A hob load decoupling method based on BP neural network, characterized in that: A sensor is installed correspondingly at a plurality of marked points on the hob force transmission path, and the method comprises: The strain of the hob stress analysis assembly is numerically simulated. According to the installation position of the sensor, two sensors are selected as a group to obtain strain data of multiple groups of sensors under different experimental conditions. The strain data of each group achieves the goal of indirectly reflecting one or two forces, wherein the strain data includes the strain sum and strain difference between the two sensors. A strain-load decoupling model is established using a BP neural network, with the strain sum and the strain difference as input and the vertical force and the rolling force as output; Performing a load test on the hob to obtain voltage data under a simulated working condition, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors; Establishing a voltage-strain function according to the voltage data and the strain data; Substituting the voltage-strain function into the strain-load decoupling model, a voltage-load decoupling model is obtained, which takes voltage data as input and vertical force, rolling force and lateral force as output.

2. The method according to claim 1, characterized in that , the rolling force F r and the vertical force F v The calculation formula is as follows; Among them, the contact angle between the roller and the rock The calculation formula is as follows; In the formula, F v Represents the vertical force, F r represents the rolling force, M represents the bending moment, R represents the radius of the hob, h represents the penetration of the hob, Fv represents the vertical force, R represents the radius of the hob, T represents the top edge width of the hob cutter ring, is the pressure distribution coefficient of the top edge of the cutter ring, σ c Uniaxial compressive strength of rock, σ t is the tensile strength of rock.

3. The method according to claim 1, characterized in that ,The experimental conditions include the vertical force acting alone, the bending moment acting alone, and ,the vertical force and bending moment acting simultaneously.

4. The method according to claim 1, characterized in that ,The simulatable working conditions include the action of vertical force alone and the action of vertical force and rolling force simultaneously.

5. The method according to claim 1, characterized in that The step of establishing a voltage-strain function according to the voltage data and the strain data comprises: The voltage data is linearly fitted with the strain sum and strain difference under the corresponding working condition to obtain the voltage-strain function.

6. A hob load decoupling system based on BP neural network, characterized in that: A sensor is installed correspondingly at multiple marked points on the hob force transmission path, and the system includes: A simulation module is used to perform numerical simulation on the strain of the hob stress analysis assembly. According to the installation position of the sensor, two sensors are selected as a group to obtain strain data of multiple groups of sensors under different experimental conditions. The strain data of each group can indirectly reflect one or two forces, wherein the strain data includes the strain sum and strain difference between the two sensors. A training module, used for establishing a strain-load decoupling model using a BP neural network, with the strain sum and the strain difference as inputs and the vertical force and the rolling force as outputs; An experimental module, used for performing a load test on the hob to obtain voltage data under a simulated working condition, wherein the voltage data includes a voltage sum and a voltage difference between the two sensors; An establishing module, used for establishing a voltage-strain function according to the voltage data and the strain data; The prediction module is used to substitute the voltage-strain function into the strain-load decoupling model to obtain a voltage-load decoupling model with voltage data as input and vertical force and rolling force as output.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the hob load decoupling method based on the BP neural network as described in any one of claims 1 to 5.

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