Intelligent shield tunneling early warning and evaluation method based on dynamic early warning threshold boundary curve

Abstract

The invention provides an intelligent shield tunneling early warning and evaluation method based on a dynamic early warning threshold boundary curve. The method is applied to the technical field of shield construction, and comprises the following steps: respectively acquiring parameter data sets of shield tunneling of an (i-1) th ring, an (i-2) th ring,..., and an (i-n) th ring; i is a positive integer greater than 0; n is a positive integer greater than 0; i > n; according to the parameter data set, calculating to obtain a trajectory value corresponding to shield tunneling of the ith ring; fitting trajectory values corresponding to shield tunneling of the ith ring, the (i + 1) th ring,..., and the (i + m) th ring to obtain a dynamic early warning threshold boundary curve corresponding to shield tunneling of the (i + m + 1) th ring; and according to the dynamic early warning threshold boundary curve, early warning is carried out on shield tunneling parameters of the (i + m + 1) th ring. In this way, the accuracy of shield tunneling early warning can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of shield construction, and in particular to an intelligent shield tunneling early warning and evaluation method based on a dynamic early warning threshold boundary curve. Background Art

[0002] As an important construction equipment for modern underground engineering, shield machines are widely used in urban rail transit, tunnel construction and other fields. With the acceleration of urbanization, the demand for shield tunneling is increasing. However, the shield construction environment is complex and changeable, and the potential risks and uncertainties are increasing. Intelligent early warning systems have emerged as the times require, becoming an important means to improve the safety and efficiency of shield construction. During shield tunneling, unexpected situations such as geological mutations, groundwater influx, and deformation of surrounding structures are often encountered, which may pose a threat to construction safety. Through the intelligent early warning system, the construction team can monitor the surrounding environment and the operating status of the shield machine in real time. Once an abnormality occurs, the system can quickly issue an alarm to prompt the operator to take corresponding measures and adjust the construction plan in time, thereby effectively avoiding accidents. The intelligent early warning system can provide a scientific basis for shield construction through real-time analysis of a large amount of data, help decision makers choose appropriate shield parameters and tunneling speed, improve construction efficiency, reduce resource waste, and thus reduce project costs. It can combine various sensors with data analysis technology, and through accurate data collection and analysis, it can achieve comprehensive monitoring of the construction site, improve the transparency of the construction process, and help managers grasp the construction dynamics in real time, find problems in time and make adjustments. When an emergency occurs, it can quickly collect and analyze on-site data to assist decision makers in formulating emergency plans. Through the analysis of historical accident data, the system can identify potential risks and make early warnings in advance to ensure that the construction team can respond to various emergencies efficiently and orderly, reducing the losses and impacts caused by construction accidents. Therefore, early warning for shield tunneling is of great significance.

[0003] At present, the existing shield tunneling early warning process usually relies on manual observation and experience judgment, resulting in low early warning accuracy. Summary of the invention

[0004] The present invention provides an intelligent shield tunneling early warning and evaluation method based on a dynamic early warning threshold boundary curve.

[0005] According to the first aspect of the present disclosure, a method for early warning and evaluation of intelligent shield tunneling based on a dynamic early warning threshold boundary curve is provided. The method comprises:

[0006] Obtain parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data sets, calculate the trajectory value corresponding to the shield tunneling of the i-th ring;

[0007] Fitting the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain the dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring;

[0008] According to the dynamic warning threshold boundary curve, the parameters of the i+m+1th ring shield tunneling are warned and evaluated.

[0009] Further, the parameter data set includes at least one of the following: a propulsion speed data set, a cutter head rotation speed data set, a cutter head torque data set and a total thrust data set.

[0010] Furthermore, the track value includes: an upper track value, a middle track value and a lower track value.

[0011] Further, the parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring are respectively obtained; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, the trajectory value corresponding to the shield tunneling of the i-th ring is calculated, including:

[0012] According to the parameter data sets of the shield tunneling of the i-1th ring, the i-2th ring, ..., the inth ring, the corresponding parameter median is determined;

[0013] According to the average value of the median of each parameter, the middle track value corresponding to the i-th ring shield tunneling is determined;

[0014] According to the warning limit threshold and the standard deviation of the median of each parameter, the upper track value and the lower track value corresponding to the i-th ring shield tunneling are determined.

[0015] Further, the determining of the upper track value and the lower track value corresponding to the i-th ring shield tunneling according to the warning limit threshold and the standard deviation of the median of each parameter includes:

[0016] Determine the maximum and minimum values ​​of the parameters corresponding to the i-th ring shield tunneling according to the standard deviation of the medians of the parameters and the middle track value corresponding to the i-th ring shield tunneling;

[0017] According to the warning limit threshold and the maximum value of the parameter corresponding to the i-th ring shield tunneling, the upper track value corresponding to the i-th ring shield tunneling is determined;

[0018] According to the warning limit threshold and the minimum value of the parameter corresponding to the i-th ring shield tunneling, the lower track value corresponding to the i-th ring shield tunneling is determined.

[0019] Furthermore: the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring are fitted by means of polynomial fitting.

[0020] Furthermore, the step of giving an early warning of the parameters of the shield tunneling of the (i+m+1)th ring according to the dynamic early warning threshold boundary curve includes:

[0021] The dynamic warning threshold boundary curve includes an upper boundary curve and a lower boundary curve;

[0022] If the parameters of the shield tunneling of the i+m+1th ring exceed the upper boundary curve or the lower boundary curve by more than a preset number within the preset time, an early warning will be issued.

[0023] Furthermore, the warning and evaluation of the parameters of the shield tunneling of the i+m+1th ring according to the dynamic warning threshold boundary curve includes:

[0024] If 80% of the parameters of the i+m+1th ring shield tunneling are between the upper boundary curve and the lower boundary curve, the state of the i+m+1th ring shield tunneling is evaluated to be a stable state; otherwise, the state of the i+m+1th ring shield tunneling is evaluated to be a fluctuating state.

[0025] According to a second aspect of the present disclosure, a smart shield tunneling early warning and assessment device based on a dynamic early warning threshold boundary curve is provided. The device comprises:

[0026] A calculation module is used to obtain parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, the trajectory value corresponding to the shield tunneling of the i-th ring is calculated;

[0027] A fitting module is used to fit the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain a dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring;

[0028] The early warning module is used to warn and evaluate the parameters of the i+m+1th ring shield tunneling according to the dynamic early warning threshold boundary curve.

[0029] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, wherein a computer program is stored in the memory, and the method is implemented when the processor executes the program.

[0030] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and the program implements the method when executed by a processor.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. By acquiring the parameter data of the i-1th ring, the i-2th ring to the inth ring, a more comprehensive historical data set can be established. This multi-level data accumulation helps to capture trends and changes in the tunneling process and effectively improve the accuracy of early warning;

[0033] 2. By calculating the trajectory value corresponding to the i-th ring, the subsequent warning is based on the current actual status rather than relying solely on historical data, which can reflect the real-time performance of the shield machine during the excavation process and reduce the impact of time delay;

[0034] 3. By fitting the trajectory values ​​of the i-th ring, the i+1-th ring, etc., mathematical models can be used to extract potential laws and trends, generate more accurate dynamic warning threshold boundary curves, enhance the scientific nature of the prediction, and adapt to different geological conditions.

[0035] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0037] Figure 1 A flow chart of an intelligent shield tunneling early warning and evaluation method based on a dynamic early warning threshold boundary curve according to an embodiment of the present disclosure is shown;

[0038] Figure 2 A block diagram of an intelligent shield tunneling early warning and evaluation device based on a dynamic early warning threshold boundary curve according to an embodiment of the present disclosure is shown;

[0039] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0040] In order to achieve the purpose, technical solutions and

[0041] The advantages are clearer. The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0042] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0043] Figure 1 A flow chart of an intelligent shield tunneling early warning and evaluation method based on a dynamic early warning threshold boundary curve according to an embodiment of the present disclosure is shown, and the method includes:

[0044] S101, respectively obtain parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, calculate the trajectory value corresponding to the i-th ring shield tunneling. By obtaining the parameter data of the i-1th ring, the i-2th ring to the inth ring, a more comprehensive historical data set can be established. This multi-level data accumulation helps to capture trends and changes in the tunneling process and effectively improve the accuracy of early warnings. By calculating the trajectory value corresponding to the i-th ring, subsequent early warnings are based on the current actual state, rather than relying solely on historical data, which can reflect the immediate performance of the shield machine during the tunneling process and reduce the impact of time delays.

[0045] In some embodiments, the parameter data set may be a propulsion speed data set, a cutter head rotation speed data set, a cutter head torque data set, or a total thrust data set.

[0046] In some embodiments, the trajectory values ​​include an upper trajectory value, a middle trajectory value, and a lower trajectory value.

[0047] In some embodiments, the parameter data sets of the i-1th, i-2th, ..., and inth ring shield tunneling are respectively obtained; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data sets, the trajectory value corresponding to the i-th ring shield tunneling is calculated, including: according to the parameter data sets of the i-1th, i-2th, ..., and inth ring shield tunneling, the corresponding parameter median is determined; according to the average value of each of the medians of the parameters, the middle trajectory value corresponding to the i-th ring shield tunneling is determined; according to the warning limit threshold and the standard deviation of each of the medians of the parameters, the upper trajectory value and the lower trajectory value corresponding to the i-th ring shield tunneling are determined.

[0048] For example, obtain the advance speed data set of the 90th to 100th shield tunneling rings; determine the median of the parameters corresponding to each ring, that is, the parameter median of the advance speed data set of the 90th shield tunneling ring is 7.95; the parameter median of the advance speed data set of the 91st shield tunneling ring is 11.95; the parameter median of the advance speed data set of the 92nd shield tunneling ring is 10.06; the parameter median of the advance speed data set of the 93rd shield tunneling ring is 7; the parameter median of the advance speed data set of the 94th shield tunneling ring is 6.26; the parameter median of the advance speed data set of the 95th shield tunneling ring is 7.62; the parameter median of the advance speed data set of the 96th shield tunneling ring is 7.95; the parameter median of the advance speed data set of the 96th shield tunneling ring is 7.95; the parameter median of the advance speed data set of the 97th shield tunneling ring is 7.96; the parameter median of the advance speed data set of the 98th shield tunneling ring is 7.96; the parameter median of the advance speed data set of the 99th shield tunneling ring is 7.95; the parameter median of the advance speed data set of the 91st shield tunneling ring is 11.95; the parameter median of the advance speed data set of the 92nd shield tunneling ring is 10.06; the parameter median of the advance speed data set of the 93rd shield tunneling ring is 7; the parameter median of the advance speed data set of the 94th shield tunneling ring is 6.26; the parameter median of the advance speed data set of the 95th shield tunneling ring is 7.62; the parameter median of the advance speed data set of the 96th shield tunneling ring is 7.95. The parameter median of the speed data set is 11.45; the parameter median of the speed data set of the 97th shield tunneling is 12.33; the parameter median of the speed data set of the 98th shield tunneling is 13.61; the parameter median of the speed data set of the 99th shield tunneling is 8.85; the parameter median of the speed data set of the 100th shield tunneling is 15.54; then the center track value corresponding to the 101st shield tunneling = (7.95+11.95+10.06+7+6.26+7.62+11.45+12.33+13.61+8.85+15.54) / 11≈10.24.

[0049] In some embodiments, determining the upper track value and the lower track value corresponding to the i-th ring shield tunneling according to the warning limit threshold and the standard deviation of the median of each parameter includes:

[0050] Determine the maximum and minimum values ​​of the parameters corresponding to the i-th ring shield tunneling according to the standard deviation of the medians of the parameters and the middle track value corresponding to the i-th ring shield tunneling;

[0051] According to the warning limit threshold and the maximum value of the parameter corresponding to the i-th ring shield tunneling, the upper track value corresponding to the i-th ring shield tunneling is determined;

[0052] According to the warning limit threshold and the minimum value of the parameter corresponding to the i-th ring shield tunneling, the lower track value corresponding to the i-th ring shield tunneling is determined.

[0053] For example, the maximum parameter value corresponding to the i-th shield tunneling ring = the middle track value corresponding to the i-th shield tunneling ring + 1.2 times the standard deviation; the minimum parameter value corresponding to the i-th shield tunneling ring = the middle track value corresponding to the i-th shield tunneling ring - 1.2 times the standard deviation; the upper track value corresponding to the i-th shield tunneling ring = the maximum parameter value + the maximum parameter value * the warning limit threshold; the lower track value corresponding to the i-th shield tunneling ring = the minimum parameter value - the minimum parameter value * the warning limit threshold.

[0054] S102, fitting the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain the dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring. By fitting the trajectory values ​​of the i-th ring, the i+1-th ring, etc., the mathematical model can be used to extract potential laws and trends, generate a more accurate dynamic warning threshold boundary curve, enhance the scientific nature of the prediction, and adapt to different geological conditions.

[0055] In some embodiments, the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring,..., and the i+m-th ring are fitted by polynomial fitting to obtain a dynamic warning threshold boundary curve corresponding to the i+m+1-th ring shield tunneling.

[0056] S103, issuing an early warning on the parameters of the shield tunneling of the (i+m+1)th ring according to the dynamic early warning threshold boundary curve.

[0057] In some embodiments, the parameters of the i+m+1th ring shield tunneling are warned based on the dynamic warning threshold boundary curve, including: the dynamic warning threshold boundary curve includes an upper boundary curve and a lower boundary curve; if the parameters of the i+m+1th ring shield tunneling exceed the upper boundary curve or the lower boundary curve by more than a preset number within a preset time, a warning is issued.

[0058] For example, if the advancing speed parameter (or cutter head speed parameter or cutter head torque parameter or total thrust parameter) of the 200th shield tunneling ring exceeds the upper boundary curve or the lower boundary curve more than three times within one minute, an early warning will be issued.

[0059] In some embodiments, it also includes: evaluating the state of the i+m+1th ring shield tunneling according to the dynamic warning threshold boundary curve and the parameters of the i+m+1th ring shield tunneling, specifically, if 80% of the parameters of the i+m+1th ring shield tunneling are between the upper boundary curve and the lower boundary curve, then the state of the i+m+1th ring shield tunneling is evaluated to be a stable state; otherwise, the state of the i+m+1th ring shield tunneling is evaluated to be a fluctuating state. The cause of the fluctuating state may be wear of the cutter head of the shield tunneling machine, operating errors, geological changes, etc.

[0060] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0061] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0062] Figure 2 A block diagram of intelligent shield tunneling warning and evaluation based on a dynamic warning threshold boundary curve according to an embodiment of the present disclosure is shown, and the device includes:

[0063] The calculation module 201 is used to obtain parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, the trajectory value corresponding to the shield tunneling of the i-th ring is calculated;

[0064] A fitting module 202 is used to fit the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain a dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring;

[0065] The early warning module 203 is used to warn and evaluate the parameters of the i+m+1th ring shield tunneling according to the dynamic early warning threshold boundary curve.

[0066] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0067] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0068] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0069] Figure 3 A schematic block diagram of an electronic device that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0070] The electronic device includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM 302 or a computer program loaded from a storage unit 308 into a RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0071] A number of components in the electronic device are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0072] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the intelligent shield tunneling early warning and evaluation method based on the dynamic early warning threshold boundary curve. For example, in some embodiments, the intelligent shield tunneling early warning and evaluation method based on the dynamic early warning threshold boundary curve can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM302 and / or a communication unit 309. When the computer program is loaded into RAM303 and executed by the computing unit 301, one or more steps of the intelligent shield tunneling early warning and evaluation method based on the dynamic early warning threshold boundary curve described above can be executed. Alternatively, in other embodiments, the computing unit 301 may be configured in any other appropriate manner (eg, by means of firmware) to execute the intelligent shield tunneling warning and assessment method based on the dynamic warning threshold boundary curve.

[0073] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0074] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0075] In the context of the present disclosure, a readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of readable storage media may include an electrical connection based on one or more lines, 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 foregoing.

[0076] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0077] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0078] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0079] It should be understood that the above-mentioned various forms of processes can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document does not limit this.

[0080] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An intelligent shield tunneling early warning and assessment method based on a dynamic early warning threshold boundary curve, characterized in that: include: Obtain parameter data sets of shield tunneling of the i-1th ring, i-2th ring, ..., inth ring respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, the trajectory value corresponding to the i-th ring shield tunneling is calculated; Fitting the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain the dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring; According to the dynamic warning threshold boundary curve, the parameters of the i+m+1th ring shield tunneling are warned and evaluated.

2. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 1 is characterized in that: The parameter data set includes at least one of the following: a propulsion speed data set, a cutter head rotation speed data set, a cutter head torque data set and a total thrust data set.

3. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 2 is characterized in that: The track value includes: an upper track value, a middle track value and a lower track value.

4. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 3 is characterized in that: The parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring are obtained respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; According to the parameter data set, the trajectory value corresponding to the i-th ring shield tunneling is calculated, including: According to the parameter data sets of the shield tunneling of the i-1th ring, the i-2th ring, ..., the inth ring, the corresponding parameter median is determined; According to the average value of the median of each parameter, the middle track value corresponding to the i-th ring shield tunneling is determined; According to the warning limit threshold and the standard deviation of the median of each parameter, the upper track value and the lower track value corresponding to the i-th ring shield tunneling are determined.

5. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 4 is characterized in that: Determining the upper track value and the lower track value corresponding to the i-th ring shield tunneling according to the warning limit threshold and the standard deviation of the median of each parameter includes: Determine the maximum and minimum values ​​of the parameters corresponding to the i-th ring shield tunneling according to the standard deviation of the medians of the parameters and the middle track value corresponding to the i-th ring shield tunneling; According to the warning limit threshold and the maximum value of the parameter corresponding to the i-th ring shield tunneling, the upper track value corresponding to the i-th ring shield tunneling is determined; According to the warning limit threshold and the minimum value of the parameter corresponding to the i-th ring shield tunneling, the lower track value corresponding to the i-th ring shield tunneling is determined.

6. The intelligent shield tunneling early warning and assessment method based on the dynamic early warning threshold boundary curve according to claim 5 is characterized in that: The trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., and the i+m-th ring are fitted by polynomial fitting.

7. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 6 is characterized in that: The step of providing an early warning for the parameters of the shield tunneling of the i+m+1th ring according to the dynamic early warning threshold boundary curve includes: The dynamic warning threshold boundary curve includes an upper boundary curve and a lower boundary curve; If the parameters of the shield tunneling of the i+m+1th ring exceed the upper boundary curve or the lower boundary curve by more than a preset number within the preset time, an early warning will be issued.

8. The intelligent shield tunneling early warning and assessment method based on dynamic early warning threshold boundary curve according to claim 7 is characterized in that: The step of providing early warning and evaluation on the parameters of the shield tunneling of the i+m+1th ring according to the dynamic early warning threshold boundary curve includes: If 80% of the parameters of the i+m+1th ring shield tunneling are between the upper boundary curve and the lower boundary curve, the state of the i+m+1th ring shield tunneling is evaluated to be a stable state; otherwise, the state of the i+m+1th ring shield tunneling is evaluated to be a fluctuating state.

9. An intelligent shield tunneling warning and assessment device based on a dynamic warning threshold boundary curve, characterized in that: include: A calculation module is used to obtain parameter data sets of shield tunneling of the i-1th ring, the i-2th ring, ..., and the inth ring respectively; i is a positive integer greater than 0; n is a positive integer greater than 0; i>n; according to the parameter data set, the trajectory value corresponding to the i-th ring shield tunneling is calculated; A fitting module is used to fit the trajectory values ​​corresponding to the shield tunneling of the i-th ring, the i+1-th ring, ..., the i+m-th ring, to obtain a dynamic warning threshold boundary curve corresponding to the shield tunneling of the i+m+1-th ring; The early warning module is used to warn and evaluate the parameters of the i+m+1th ring shield tunneling according to the dynamic early warning threshold boundary curve.

10. An electronic device, characterized in that: include: at least one processor; A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute the method of any one of claims 1-8.

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