Control strategy allocation method and system for a tunneling machine control

Abstract

The control strategy allocation method and system for tunnel boring machine (TBM) control provided in this invention combines TBM state anomaly events with TBM state anomaly characteristics loaded during the TBM control process, schedules multiple corresponding prior TBM state anomaly events, and determines the TBM problem characteristics corresponding to the prior TBM state anomaly events. When the problem parameter value corresponding to the TBM problem characteristics is greater than a set value, the construction problem category characteristics corresponding to the multiple prior TBM state anomaly events are determined. Combining the construction problem category characteristics and the prior TBM state anomaly events, corresponding construction problem control nodes are scheduled to allocate construction problem control strategies for the prior TBM state anomaly events. In this way, construction problem control strategies can be allocated for TBM state anomaly events with TBM state anomaly characteristics.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring machine technology, and more specifically, to a control strategy allocation method and system for tunnel boring machine control. Background Technology

[0002] How to allocate construction problem control strategies for shield machine status anomaly events with characteristics of shield machine status anomaly is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0003] Based on the above description, embodiments of the present invention provide a control strategy allocation method for tunnel boring machine control, comprising:

[0004] Combining shield machine state change events with shield machine state change characteristics loaded in the shield machine control process, schedule multiple corresponding prior shield machine state change events.

[0005] By combining the corresponding multiple prior shield machine status anomaly events, the shield machine problem characteristics corresponding to the prior shield machine status anomaly events are determined. The shield machine problem characteristics are analyzed with the pre-configured shield machine construction problem characteristics to determine the problem parameter values ​​corresponding to the shield machine problem characteristics.

[0006] When the problem parameter value corresponding to the tunnel boring machine problem feature is greater than the set value, the construction problem category feature corresponding to the multiple prior tunnel boring machine status anomaly events is determined; combining the construction problem category feature and the prior tunnel boring machine status anomaly events, the corresponding construction problem control nodes are scheduled to allocate construction problem control strategies for the prior tunnel boring machine status anomaly events.

[0007] The step of determining the tunnel boring machine (TBM) problem characteristics corresponding to the prior TBM state anomaly events by combining the corresponding multiple prior TBM state anomaly events includes:

[0008] The prior shield machine state anomaly event is transmitted to the shield machine problem analysis network, and the shield machine problem analysis network analyzes the prior shield machine state anomaly event to obtain the corresponding shield machine problem characteristics.

[0009] Specifically, when the problem parameter value corresponding to the tunnel boring machine problem feature is greater than a set value, the construction problem category feature corresponding to the multiple prior tunnel boring machine state anomaly events is determined, including:

[0010] When the problem parameter value corresponding to the tunnel boring machine problem feature is greater than the set value, the tunnel boring machine construction problem category variable corresponding to the tunnel boring machine problem feature is determined;

[0011] Based on the tunnel boring machine construction problem category variables, search for construction problem category features corresponding to the tunnel boring machine construction problem category variables from the preset category feature mapping relationship.

[0012] Specifically, combining the tunnel boring machine (TBM) construction problem category variables, the search for construction problem category features corresponding to the TBM construction problem category variables from a preset category feature mapping relationship includes:

[0013] Combining the tunnel boring machine construction problem category variables, multiple problem feature knowledge graphs corresponding to the tunnel boring machine construction problem category variables are determined from the preset category feature mapping relationship;

[0014] The knowledge graphs of the multiple problem features are used to classify the tunnel boring machine problem features into construction problem categories using a construction problem category discrimination model.

[0015] Based on the discrimination results of the construction problem category discrimination model on the knowledge graph of multiple problem features, the construction problem category features corresponding to the tunnel boring machine construction problem category variables are determined.

[0016] The method further includes: generating construction problem control strategy allocation data by combining the above-mentioned scheduled construction problem control nodes and the prior shield machine state anomaly events, and transmitting the construction problem control strategy allocation data to the shield machine problem analysis network, so that the shield machine problem analysis network can optimize the control data of the construction problem control nodes by combining the received construction problem control strategy allocation data.

[0017] The present invention also provides a control strategy allocation system for tunnel boring machine control, comprising:

[0018] The first scheduling module is used to schedule multiple prior shield machine state anomaly events that are loaded in the shield machine control process and have shield machine state anomaly characteristics.

[0019] The analysis module is used to combine the corresponding multiple prior shield machine status anomaly events to determine the shield machine problem characteristics corresponding to the prior shield machine status anomaly events, analyze the shield machine problem characteristics with pre-configured shield machine construction problem characteristics, and determine the problem parameter values ​​corresponding to the shield machine problem characteristics.

[0020] The second scheduling module is used to determine the construction problem category characteristics corresponding to the multiple prior shield machine state anomaly events when the problem parameter value corresponding to the problem characteristics of the shield machine is greater than a set value; and to schedule the corresponding construction problem control nodes to allocate construction problem control strategies for the prior shield machine state anomaly events by combining the construction problem category characteristics and the prior shield machine state anomaly events.

[0021] Specifically, the analysis module is used to transmit the prior shield machine state anomaly event to the shield machine problem analysis network, and to analyze the prior shield machine state anomaly event through the shield machine problem analysis network to obtain the corresponding shield machine problem characteristics.

[0022] Specifically, the scheduling module is used to determine the tunnel boring machine (TBM) construction problem category variable corresponding to the TBM problem feature when the problem parameter value corresponding to the TBM problem feature is greater than a set value; and to search for the construction problem category feature corresponding to the TBM construction problem category variable from a preset category feature mapping relationship based on the TBM construction problem category variable.

[0023] Specifically, the scheduling module is further used for:

[0024] Combining the tunnel boring machine construction problem category variables, multiple problem feature knowledge graphs corresponding to the tunnel boring machine construction problem category variables are determined from the preset category feature mapping relationship;

[0025] The knowledge graphs of the multiple problem features are used to classify the tunnel boring machine problem features into construction problem categories using a construction problem category discrimination model.

[0026] Based on the discrimination results of the construction problem category discrimination model on the knowledge graph of multiple problem features, the construction problem category features corresponding to the tunnel boring machine construction problem category variables are determined.

[0027] The system also includes an optimization module, which is used to generate construction problem control strategy allocation data by combining the above-mentioned scheduled construction problem control nodes and the prior shield machine state anomaly events, and to transmit the construction problem control strategy allocation data to the shield machine problem analysis network, so that the shield machine problem analysis network can optimize the control data of the construction problem control nodes by combining the received construction problem control strategy allocation data.

[0028] In summary, the control strategy allocation method and system for tunnel boring machine (TBM) control provided in this embodiment of the invention firstly combines TBM state anomaly events with TBM state anomaly characteristics loaded in the TBM control process, schedules multiple corresponding prior TBM state anomaly events, determines the TBM problem characteristics corresponding to the prior TBM state anomaly events based on the multiple prior TBM state anomaly events, analyzes the TBM problem characteristics with pre-configured TBM construction problem characteristics, and determines the problem parameter values ​​corresponding to the TBM problem characteristics; when the problem parameter values ​​corresponding to the TBM problem characteristics are greater than a set value, determines the construction problem category characteristics corresponding to the multiple prior TBM state anomaly events, and schedules corresponding construction problem control nodes to allocate construction problem control strategies for the prior TBM state anomaly events based on the construction problem category characteristics and the prior TBM state anomaly events. Therefore, by combining prior shield machine state change events with shield machine state change characteristics with shield machine problem analysis network to allocate construction problem control strategies, it is possible to allocate construction problem control strategies for shield machine state change events with shield machine state change characteristics.

[0029] To make the above-mentioned objects, features and advantages of the embodiments of the present invention more apparent and understandable, a detailed description will be given below in conjunction with the embodiments and the accompanying drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings are only some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other corresponding drawings can be obtained in conjunction with these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the control strategy allocation method for tunnel boring machine control provided in an embodiment of the present invention;

[0032] Figure 2 This is a functional block diagram of the control strategy allocation system for tunnel boring machine control provided in an embodiment of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art in conjunction with the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0034] Figure 1 This is a flowchart illustrating the control strategy allocation method for tunnel boring machine control provided in this embodiment of the invention. The risk behavior alarm method can be executed by a security monitoring device set up within a local area network.

[0035] The detailed steps of the control strategy allocation method used for tunnel boring machine control are described below.

[0036] Step S11: Combine the shield machine state change events with shield machine state change characteristics loaded in the shield machine control process, and schedule the corresponding multiple prior shield machine state change events.

[0037] Step S12: Combining the corresponding multiple prior shield machine status anomaly events, determine the shield machine problem characteristics corresponding to the prior shield machine status anomaly events, analyze the shield machine problem characteristics with the pre-configured shield machine construction problem characteristics, and determine the problem parameter values ​​corresponding to the shield machine problem characteristics.

[0038] Step S13: When the problem parameter value corresponding to the problem feature of the tunnel boring machine is greater than the set value, determine the construction problem category feature corresponding to the multiple prior tunnel boring machine state change events; combine the construction problem category feature and the prior tunnel boring machine state change events, and schedule the corresponding construction problem control nodes to allocate construction problem control strategies for the prior tunnel boring machine state change events.

[0039] Preferably, for step S12, determining the shield machine problem characteristics corresponding to the prior shield machine state anomaly events by combining the corresponding multiple prior shield machine state anomaly events includes:

[0040] The prior shield machine state anomaly event is transmitted to the shield machine problem analysis network, and the shield machine problem analysis network analyzes the prior shield machine state anomaly event to obtain the corresponding shield machine problem characteristics.

[0041] Preferably, for step S13, when the problem parameter value corresponding to the tunnel boring machine problem feature is greater than a set value, the construction problem category feature corresponding to the multiple prior tunnel boring machine state anomaly events is determined, including:

[0042] When the problem parameter value corresponding to the tunnel boring machine problem feature is greater than a set value, the tunnel boring machine construction problem category variable corresponding to the tunnel boring machine problem feature is determined; combined with the tunnel boring machine construction problem category variable, the construction problem category feature corresponding to the tunnel boring machine construction problem category variable is searched from the preset category feature mapping relationship.

[0043] Preferably, for step S13, combining the tunnel boring machine construction problem category variable, searching for construction problem category features corresponding to the tunnel boring machine construction problem category variable from a preset category feature mapping relationship, including:

[0044] Combining the tunnel boring machine construction problem category variables, multiple problem feature knowledge graphs corresponding to the tunnel boring machine construction problem category variables are determined from the preset category feature mapping relationship;

[0045] The knowledge graphs of the multiple problem features are used to classify the tunnel boring machine problem features into construction problem categories using a construction problem category discrimination model.

[0046] Based on the discrimination results of the construction problem category discrimination model on the knowledge graph of multiple problem features, the construction problem category features corresponding to the tunnel boring machine construction problem category variables are determined.

[0047] Preferably, the control strategy allocation method for tunnel boring machine control described in this embodiment further includes:

[0048] Step S14: Combine the above-mentioned scheduled construction problem control nodes and the prior shield machine status anomaly events to generate construction problem control strategy allocation data, and transmit the construction problem control strategy allocation data to the shield machine problem analysis network, so that the shield machine problem analysis network can optimize the control data of the construction problem control nodes based on the received construction problem control strategy allocation data.

[0049] Figure 2 This is a functional block diagram of a control strategy allocation system for tunnel boring machine (TBM) control provided in an embodiment of the present invention. The functions implemented by this control strategy allocation system for TBM control correspond to the steps performed in the above-described method. This control strategy allocation system for TBM control can be understood as the aforementioned safety monitoring device, or the processor of the safety monitoring device, or it can be understood as a component that implements the functions of the present invention under the control of the safety monitoring device, independent of the aforementioned safety monitoring device or processor.

[0050] The security monitoring device may include one or more processors, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The security monitoring device may also include any storage medium for storing any kind of information, such as code, settings, data, etc. Non-limitingly, for example, the storage medium may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any storage medium can use any technology to store information. Furthermore, any storage medium may provide volatile or non-volatile retention of information. Furthermore, any storage medium may represent a fixed or removable component of the security monitoring device. In one case, when the processor executes associated instructions stored in any storage medium or combination of storage media, the security monitoring device can perform any operation of the associated instructions. The security monitoring device also includes one or more drive units for interacting with any storage medium, such as hard disk drive units, optical disk drive units, etc.

[0051] The security monitoring equipment also includes input / output (I / O) for receiving various inputs (via input units) and providing various outputs (via output units). A specific output mechanism may include a presentation device and an associated graphical user interface (GUI). The security monitoring equipment may also include one or more network interfaces for exchanging data with other devices via one or more communication units. One or more communication buses couple the components described above together.

[0052] The communication unit can be implemented in any way, such as via a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a point-to-point connection, or any combination thereof. The communication unit may include any combination of hardwired links, wireless links, routers, gateway functions, etc., governed by any protocol or combination of protocols.

[0053] like Figure 2 As shown below, the functions of each functional module of the control strategy allocation system used for tunnel boring machine control will be described in detail.

[0054] The shield machine status anomaly event scheduling 11 is used to combine shield machine status anomaly events with shield machine status anomaly characteristics loaded in the shield machine control process and schedule multiple corresponding prior shield machine status anomaly events.

[0055] Risk Analysis 12 is used to combine the corresponding multiple prior shield machine status anomaly events to determine the shield machine problem characteristics corresponding to the prior shield machine status anomaly events, analyze the shield machine problem characteristics with pre-configured shield machine construction problem characteristics, and determine the problem parameter values ​​corresponding to the shield machine problem characteristics.

[0056] Strategy scheduling 13 is used to determine the construction problem category characteristics corresponding to the multiple prior shield machine state anomaly events when the problem parameter value corresponding to the shield machine problem characteristics is greater than a set value; and to schedule the corresponding construction problem control nodes to allocate construction problem control strategies for the prior shield machine state anomaly events in combination with the construction problem category characteristics and the prior shield machine state anomaly events.

[0057] Preferably, the analysis module is specifically used for:

[0058] The prior shield machine state anomaly event is transmitted to the shield machine problem analysis network, and the shield machine problem analysis network analyzes the prior shield machine state anomaly event to obtain the corresponding shield machine problem characteristics.

[0059] Preferably, the scheduling module is specifically used for:

[0060] When the problem parameter value corresponding to the tunnel boring machine problem feature is greater than a set value, the tunnel boring machine construction problem category variable corresponding to the tunnel boring machine problem feature is determined; combined with the tunnel boring machine construction problem category variable, the construction problem category feature corresponding to the tunnel boring machine construction problem category variable is searched from the preset category feature mapping relationship.

[0061] Preferably, the scheduling module is further configured to:

[0062] Combining the tunnel boring machine construction problem category variables, multiple problem feature knowledge graphs corresponding to the tunnel boring machine construction problem category variables are determined from the preset category feature mapping relationship;

[0063] The knowledge graphs of the multiple problem features are used to classify the tunnel boring machine problem features into construction problem categories using a construction problem category discrimination model.

[0064] Based on the discrimination results of the construction problem category discrimination model on the knowledge graph of multiple problem features, the construction problem category features corresponding to the tunnel boring machine construction problem category variables are determined.

[0065] Preferably, the system further includes an optimization module 14, which is used to generate construction problem control strategy allocation data by combining the above-mentioned scheduled construction problem control nodes and the prior shield machine state anomaly events, and to transmit the construction problem control strategy allocation data to the shield machine problem analysis network, so that the shield machine problem analysis network can optimize the control data of the construction problem control nodes by combining the received construction problem control strategy allocation data.

[0066] In summary, the control strategy allocation method and system for tunnel boring machine (TBM) control provided in this embodiment of the invention firstly combines TBM state anomaly events with TBM state anomaly characteristics loaded in the TBM control process, schedules multiple corresponding prior TBM state anomaly events, determines the TBM problem characteristics corresponding to the prior TBM state anomaly events based on the multiple prior TBM state anomaly events, analyzes the TBM problem characteristics with pre-configured TBM construction problem characteristics, and determines the problem parameter values ​​corresponding to the TBM problem characteristics; when the problem parameter values ​​corresponding to the TBM problem characteristics are greater than a set value, determines the construction problem category characteristics corresponding to the multiple prior TBM state anomaly events, and schedules corresponding construction problem control nodes to allocate construction problem control strategies for the prior TBM state anomaly events based on the construction problem category characteristics and the prior TBM state anomaly events. Therefore, by combining prior shield machine state change events with shield machine state change characteristics with shield machine problem analysis network to allocate construction problem control strategies, it is possible to allocate construction problem control strategies for shield machine state change events with shield machine state change characteristics.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] In the various embodiments of the present invention, the functional modules can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0069] It can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a security monitoring device or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0070] It should be noted that, in this document, the terms "comprising," "having," or any other variations thereof are intended to cover non-exclusive accompaniment, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No drawings in the claims should be construed as limiting the scope of the claims.

Claims

1. A control strategy allocation method for a tunneling machine control, characterized by, The method comprises the following steps: In combination with the shield machine state abnormality event loaded in the shield machine control process and having the shield machine state abnormality characteristics, a plurality of prior shield machine state abnormality events corresponding to the shield machine state abnormality event are scheduled; In combination with the plurality of prior shield machine state abnormality events, a shield machine problem characteristic corresponding to the prior shield machine state abnormality event is determined, the shield machine problem characteristic is analyzed with a preconfigured shield machine construction problem characteristic, and a problem parameter value corresponding to the shield machine problem characteristic is determined; When the problem parameter value corresponding to the shield machine problem characteristic is greater than a set value, a construction problem category characteristic corresponding to the plurality of prior shield machine state abnormality events is determined, and in combination with the construction problem category characteristic and the prior shield machine state abnormality event, a corresponding construction problem control node is scheduled to perform construction problem control strategy distribution on the prior shield machine state abnormality event. The method further comprises the following steps: In combination with the scheduled construction problem control node and the prior shield machine state abnormality event, construction problem control strategy distribution data is generated, and the construction problem control strategy distribution data is transmitted to the shield machine problem analysis network, so that the shield machine problem analysis network performs control data optimization on the construction problem control node in combination with the construction problem control strategy distribution data. The method comprises the following steps: The method further comprises the following steps: In combination with the shield machine construction problem category variable, a construction problem category characteristic corresponding to the shield machine construction problem category variable is searched from a preset category characteristic mapping relationship.

2. The control strategy assignment method for a tunneling machine control of claim 1, wherein, In combination with the shield machine construction problem category variable, a construction problem category characteristic corresponding to the shield machine construction problem category variable is searched from a preset category characteristic mapping relationship. The method further comprises the following steps: In combination with the shield machine construction problem category variable, a plurality of problem characteristic knowledge graphs corresponding to the shield machine construction problem category variable are determined from the preset category characteristic mapping relationship; The plurality of problem characteristic knowledge graphs are subjected to construction problem category discrimination on the shield machine problem characteristic through a construction problem category discrimination model; 3. A control strategy allocation method for a tunneling machine control according to claim 1 or 2, characterized in that, In combination with the discrimination results of the plurality of problem characteristic knowledge graphs by the construction problem category discrimination model, a construction problem category characteristic corresponding to the shield machine construction problem category variable is determined. The method further comprises the following steps:

4. A control strategy allocation system for a tunneling machine control, characterized by, In combination with the scheduled construction problem control node and the prior shield machine state abnormality event, construction problem control strategy distribution data is generated, and the construction problem control strategy distribution data is transmitted to the shield machine problem analysis network, so that the shield machine problem analysis network performs control data optimization on the construction problem control node in combination with the construction problem control strategy distribution data. The method comprises the following steps: The first scheduling module is configured to schedule a plurality of prior shield machine state change events in combination with a shield machine state change event having a shield machine state change feature loaded in a shield machine control process; The analysis module is configured to determine a shield machine problem feature corresponding to the prior shield machine state change event in combination with the plurality of prior shield machine state change events, analyze the shield machine problem feature and a preconfigured shield machine construction problem feature, and determine a problem parameter value corresponding to the shield machine problem feature; The second scheduling module is configured to determine a construction problem category feature corresponding to the plurality of prior shield machine state change events when the problem parameter value corresponding to the shield machine problem feature is greater than a set value, and schedule a corresponding construction problem control node to perform construction problem control strategy distribution on the prior shield machine state change event in combination with the construction problem category feature and the prior shield machine state change event. The analysis module is specifically configured to: pass the prior shield machine state change event to a shield machine problem analysis network, and analyze the prior shield machine state change event through the shield machine problem analysis network to obtain a corresponding shield machine problem feature; The scheduling module is specifically configured to: determine a shield machine construction problem category variable corresponding to the shield machine problem feature when the problem parameter value corresponding to the shield machine problem feature is greater than a set value, and search for a construction problem category feature corresponding to the shield machine construction problem category variable from a preconfigured category feature mapping relationship in combination with the shield machine construction problem category variable.

5. The control strategy assignment system for a tunneling machine control of claim 4, wherein, The scheduling module is specifically further configured to: determine a plurality of problem feature knowledge graphs corresponding to the shield machine construction problem category variable from the preconfigured category feature mapping relationship in combination with the shield machine construction problem category variable; perform construction problem category discrimination on the shield machine problem feature through a construction problem category discrimination model using the plurality of problem feature knowledge graphs; determine a construction problem category feature corresponding to the shield machine construction problem category variable in combination with a discrimination result of the plurality of problem feature knowledge graphs using the construction problem category discrimination model.

6. A control strategy allocation system for a tunneling machine control according to claim 4 or 5, characterized in that, The system further includes: An optimization module configured to generate construction problem control strategy distribution data in combination with the scheduled construction problem control node and the prior shield machine state change event, and pass the construction problem control strategy distribution data to the shield machine problem analysis network, so that the shield machine problem analysis network performs control data optimization on the construction problem control node in combination with the received construction problem control strategy distribution data.

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