WO2024108871A1 - 基于边缘计算架构的盾构施工预警系统及预警方法 - Google Patents

基于边缘计算架构的盾构施工预警系统及预警方法 Download PDF

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Publication number
WO2024108871A1
WO2024108871A1 PCT/CN2023/087545 CN2023087545W WO2024108871A1 WO 2024108871 A1 WO2024108871 A1 WO 2024108871A1 CN 2023087545 W CN2023087545 W CN 2023087545W WO 2024108871 A1 WO2024108871 A1 WO 2024108871A1
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Prior art keywords
data
sub
terminal
early warning
shield
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PCT/CN2023/087545
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English (en)
French (fr)
Inventor
刘四进
马浴阳
王军
王华伟
史林肯
刘鹏
付浩
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中铁十四局集团有限公司
中铁十四局集团大盾构工程有限公司
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Publication of WO2024108871A1 publication Critical patent/WO2024108871A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
    • G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/22—Arrangements for detecting or preventing errors in the information received using redundant apparatus to increase reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/22—Adaptations for optical transmission
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/30—Arrangements in telecontrol or telemetry systems using a wired architecture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q2209/00—Arrangements in telecontrol or telemetry systems
    • H04Q2209/40—Arrangements in telecontrol or telemetry systems using a wireless architecture
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present invention relates to the field of intelligent monitoring of shield machines, and in particular to a shield construction early warning system and an early warning method based on an edge computing architecture.
  • the shield machine is a complex underground construction equipment with super-large electromechanical and hydraulic integration. During the excavation process, a large amount of data will be generated, including construction environment, geological information, construction status parameters, etc. Using big data analysis methods and visualization technology to obtain, identify and analyze the massive data sets generated by the shield machine construction and excavation, and building a comprehensive shield monitoring and early warning platform is an important method to ensure the safe and efficient excavation of the shield machine.
  • Shield data collection requires connecting the shield machine PLC to the collection terminal to collect data in real time, transmit it to the remote monitoring host for analysis and calculation, and monitor and warn through visual display.
  • Purpose of the invention To propose a shield construction early warning system based on edge computing architecture, and further propose an early warning method based on the above early warning system to solve the above problems existing in the prior art.
  • a shield construction early warning system based on edge computing architecture which includes a collection and early warning module, a transmission module, and a monitoring and display center.
  • the collection and early warning module includes a directly connected collection terminal and an early warning interactive terminal; the collection terminal is directly connected to the shield machine programmable logic controller or the shield machine operation host.
  • the transmission module establishes a communication connection with the collection and warning module; the transmission module includes a directly connected data forwarding terminal and a monitoring video gateway; the data forwarding terminal is connected to the collection terminal through a router public network; the monitoring video gateway terminal is connected to the work area video monitoring equipment through a router gateway.
  • the monitoring and display center establishes a communication connection with the transmission module;
  • the monitoring and display center includes a directly connected communication module and a data computing and storage server;
  • the communication module is connected to a router, and the router receives data sent by the data forwarding terminal of the transmission module through a public network.
  • the edge computing architecture includes central roles and edge roles.
  • the monitoring display center is set as the central
  • the collection and warning module and the transmission module are set as edge roles.
  • the edge computing architecture includes central hardware and edge hardware.
  • the data computing and storage server and supporting communication modules are the central hardware, and the collection terminal, early warning interaction terminal, data forwarding terminal, and monitoring video gateway terminal are the edge hardware.
  • the collection terminal is deployed in the cab of a slurry balance shield machine in a construction tunnel, and is directly connected to the programmable logic controller of the shield machine through a network cable;
  • the early warning interaction terminal is deployed in the cab of a slurry balance shield machine in the construction tunnel, and is directly connected to the collection terminal through a cable;
  • the data forwarding terminal is deployed in a ground computer room, and is connected to the collection terminal in the tunnel through a router public network;
  • the monitoring video gateway terminal is deployed in the ground computer room, directly connected to the data forwarding terminal, and connected to the work area video monitoring equipment through a router gateway;
  • the communication module is deployed at a remote end, connected to a router, receives data sent by a transmission module through a public network, and forwards it to a computing storage server;
  • the computing storage server is deployed at a remote location, and is directly connected to the communication module.
  • the acquisition terminal acts as edge hardware, participates in the preliminary computational processing of the shield machine's collected data, and forwards the results to the monitoring display center;
  • the monitoring video gateway terminal acts as edge hardware, automatically modulating and encoding the monitoring video connected to the same gateway.
  • the communication module receives data sent by the data forwarding terminal and forwards it to the data computing and storage server; the data computing and storage server automatically stores the received data, generates excavation parameter reports, material consumption reports, hazard warning reports, and construction quality reports, and performs weighted comparison between the generated report data and the theoretically calculated values, automatically issues warnings of different levels based on value fluctuations and deviations, and accesses the expert system database to match solutions to assist technicians in driving the shield machine.
  • the data forwarding terminal has two data forwarding modes: optical fiber fixed network and mobile network; under normal circumstances, data is sent by optical fiber directly connected to the router fixed network; when the fixed network is interrupted or the network speed fluctuation range is greater than a predetermined value, it automatically switches to the mobile network to send data.
  • the collection terminal performs multiple data collection operations:
  • the collection terminal filters the data of some data points and directly forwards them to the remote monitoring display center through the transmission module;
  • the collection terminal extracts the data of the set data point and compares it with the data collected for the Nth time. If the data has changed, the new data is forwarded to the remote monitoring and display center through the transmission module. If the data has not changed, the new data is not sent. At this time, the computing storage server of the remote monitoring and display center maintains the original data unchanged by default;
  • the tunneling parameter report E1 includes the following characteristic fields: cutter head propulsion torque i1 , rotation speed i2 , thrust i3 , penetration i4 , total extrusion pressure i5 ;
  • the material consumption report E2 includes the following characteristic fields: synchronous grouting volume i6 , shield tail grease injection volume i7 , main bearing seal lubricating grease i8 ;
  • the danger warning report E3 includes the following characteristic fields: automatic recognition and capture of construction area monitoring video i9 , automatic recognition and capture of shield machine trolley monitoring video i10 , automatic recognition and capture of synchronous construction monitoring video inside the tunnel i11 , automatic recognition and capture of cockpit monitoring video i12 , automatic recognition and capture of air cushion chamber monitoring video i13 ;
  • the construction quality report E4 includes the following characteristic fields: surface settlement i14 , segment misalignment i15 , segment floating i16 , shield tail gap i17 , shield attitude guidance i18 ; All feature fields are constructed into an N
  • a shield construction early warning method is proposed, which is implemented based on the shield construction early warning system described in the first aspect, and the steps are as follows:
  • Step 1 The shield machine starts excavation construction, and the operator controls the early warning interactive terminal on site or remotely, sets the collection parameters of the collection terminal, and issues instructions to start collection;
  • Step 2 The collection terminal collects the construction data and sensor monitoring data generated by the shield machine
  • Step 3 The acquisition terminal stores the collected construction data and sensor monitoring data to form a data point table, and uses edge computing capabilities to perform preliminary calculations to obtain processed data, which is forwarded to the data forwarding terminal in real time. At the same time, the original data stored in the acquisition terminal is regularly transmitted to the data forwarding terminal;
  • Step 4 The monitoring video gateway terminal uniformly encodes the video monitoring connected to the same gateway and sends it to the data forwarding terminal of the transmission module;
  • Step 5 The data forwarding terminal packages the transmitted shield machine data and video surveillance and transmits them to the monitoring display center via the optical fiber fixed network.
  • the optical fiber fixed network cannot be used, it automatically switches to the cellular mobile network for transmission;
  • Step 6 The communication module of the monitoring and display center receives the processed shield construction data and video monitoring data, performs analysis and processing on the data computing and storage server, outputs the results, and directly stores the received original shield construction data on the server;
  • Step 7 The results of the data calculation and storage server analysis and processing are transmitted in real time to the early warning interactive terminal of the acquisition and early warning module for display, providing auxiliary decision-making for the shield machine driver.
  • the sound and light alarm function of the early warning interactive terminal is activated and an alarm is sounded.
  • the shield machine operator and the technicians of the monitoring and display center can communicate remotely through video calls for solutions;
  • Step 8 After the alarm is extinguished, the sound and light alarm will automatically turn off.
  • step 3 the collection terminal performs multiple data collection operations:
  • the collection terminal filters the data of some data points and directly forwards them to the remote monitoring display center through the transmission module;
  • the collection terminal extracts the data of the set data point and compares it with the data collected for the Nth time. If the data has changed, the new data is forwarded to the remote monitoring and display center through the transmission module. If the data has not changed, the new data is not sent. At this time, the computing storage server of the remote monitoring and display center maintains the original data unchanged by default;
  • the process of analyzing and processing by the data computing and storage server in step 6 further includes:
  • Step 6-1 calculate the tunneling parameters of the shield machine: obtain the maximum, mean, median, and fluctuation coefficient of the cutter head propulsion torque, speed, thrust, penetration, and total extrusion pressure data within a predetermined period of time, and generate a tunneling parameter report;
  • Calculate the material consumption parameters of the shield machine obtain the mean, ring value, and difference between the theoretical consumption of the synchronous grouting volume, shield tail grease injection volume, and main bearing seal lubricating grease data within a predetermined period, and generate a material consumption report;
  • Analyze automatically captured surveillance videos automatically identify and capture surveillance videos of each construction area, shield machine trolley, synchronous construction inside the tunnel, cockpit, and air cushion chamber, and generate danger warning reports;
  • Automatic measurement of construction quality data automatic measurement and recording of ground settlement, segment misalignment, segment floating, shield tail gap, shield attitude guidance data, and generation of construction quality reports;
  • Step 6-2 The data calculation and storage server imports the generated report data into the weight model, calculates the score by the weight model, and compares it with the built-in standard database. If the threshold is exceeded, different levels of warnings are automatically issued. The warning level is connected to the expert system database to match the solution.
  • the construction rules of the weight model are as follows:
  • Each sub-weight Wi 2 corresponds to the maximum, mean, median and fluctuation coefficient of the speed within the predetermined period, respectively.
  • weights Wi 2max , Wi 2average , Wi 2median , Wi 2fluctuation ; wherein Wi 2max +Wi 2average +Wi 2median +Wi 2fluctuation 1;
  • Step 6-2C construct the third weight model: define the total weight of the automatically captured surveillance video as W E3 ; the total weight is the weight proportion of each sub-data under W E3 ;
  • Step 6-2D construct the fourth weight model: define the total weight of the measured construction quality data as W E4 ; the total weight is the weight proportion of each sub-data under W E4 divided equally;
  • Step 6-2E The data computing and storage server imports the generated report data into the first weight model, the second weight model, the third weight model, and the fourth weight model, and outputs sub-score A, sub-score B, sub-score C, Sub-score D, total score G; compare the above scores with a preset standard database, in which the sub-alarm limit W, sub-alarm limit X, sub-alarm limit Y, sub-alarm limit Z, and total alarm limit S correspond respectively;
  • sub-score A ⁇ sub-alarm limit W, then further judge sub-score B, sub-score C, and sub-score D. If any of the scores among sub-score B, sub-score C, and sub-score D exceeds its corresponding sub-alarm limit, a third-level warning is issued;
  • sub-score B, sub-score C, and sub-score D all exceed sub-alarm limit X, sub-alarm limit Y, and sub-alarm limit Z, a level 1 warning is issued;
  • the early warning results are connected to the expert system database to match solutions and assist technicians in driving the shield machine.
  • the shield construction data collection and early warning terminal system of the present invention adopts an edge computing architecture, sinking part of the data calculation to the collection terminal, which not only reduces the computing pressure of the data calculation storage server of the monitoring display center, but also greatly reduces the amount of data calculated by the collection terminal. According to calculations, 50%-80% of the point data of the shield machine in the excavation state changes in real time, while in the shutdown and splicing state, only 20%-50% of the point data of the shield machine changes in real time, so it can reduce about 30%-50% of the data throughput and network transmission delay of the data calculation storage server.
  • the data forwarding terminal of the shield construction data collection and early warning terminal system of the present invention has two transmission modes: optical fiber fixed network and 5G mobile network, and can switch automatically to ensure stable data transmission.
  • the shield construction data collection and early warning method of the present invention innovatively integrates data collection and parameter early warning, and has multiple functions such as construction data collection, parameter early warning, and auxiliary decision-making, thereby improving construction efficiency and ensuring construction safety.
  • FIG1 is a functional block diagram of the system of the present invention.
  • FIG. 2 is a functional block diagram of a terminal device according to the present invention.
  • FIG3 is an architecture diagram of the edge computing system of the present invention.
  • FIG4 is a typical application flow chart of edge computing of the present invention.
  • FIG5 is a flow chart of data forwarding by a data forwarding terminal of the present invention.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • This embodiment proposes a complete set of shield data collection and early warning terminal system adopting edge computing architecture, as shown in Figure 1, the system includes a collection and early warning module, a transmission module, and a monitoring and display center.
  • the collection and early warning module includes a collection terminal and an early warning interaction terminal.
  • the transmission module includes a data forwarding terminal and a monitoring video gateway terminal.
  • the monitoring and display center includes a data computing and storage server and a communication module.
  • the acquisition terminal is connected to the shield machine PLC or control host, the data forwarding terminal, and the early warning interaction terminal.
  • the data forwarding terminal is connected to the acquisition terminal, the monitoring video gateway terminal, and the communication module.
  • the data computing and storage server is connected to the communication module and the early warning interaction terminal.
  • the monitoring video gateway terminal is connected to multiple monitoring videos.
  • the edge computing architecture is shown in FIG3 , in which the data computing and storage server of the monitoring display center is set as the central hardware, and the acquisition terminal and the monitoring video gateway terminal are set as the edge hardware, which have certain computing capabilities and can participate in the preprocessing, data analysis, data distribution and policy execution of shield data.
  • the acquisition terminal is deployed in the cab of the slurry balance shield machine in the construction tunnel and is directly connected to the shield machine PLC through a network cable.
  • the acquisition terminal uses an automation interface based on the OPC industrial software standard, follows the OPC data template specification, and supplies a communication channel through the RSLinuxOPC server to realize automatic acquisition of PLC data by the acquisition terminal.
  • the data of some data points are screened according to the pre-settings and directly forwarded to the remote monitoring and display center via the transmission module; after the second collection, the data of the set data points are extracted and the difference operation is performed with the data collected last time. If the operation result is not zero, the new data is forwarded to the remote monitoring and display center via the transmission module. If the operation result is zero, the new data is not sent, and the computing storage server of the remote monitoring and display center maintains the original data unchanged by default; the above process is repeated for subsequent collections; a certain frequency (such as every minute) is set to package the collected raw data and transmit it to the remote monitoring and display center.
  • a certain frequency such as every minute
  • the early warning interactive terminal is deployed in the cab of the slurry balance shield machine in the construction tunnel and is connected to the collection terminal via an electric The cable is directly connected.
  • the early warning interactive terminal has the functions of remote control, collection setting, data display, sound and light alarm, and video call: the monitoring and display center can remotely control the early warning interactive terminal, query and adjust the collection parameters of the collection terminal (such as collection frequency and collection points); the monitoring and display center will also display the results of data processing on the early warning interactive terminal.
  • the alarm mechanism When there are abnormal parameters or crossing risk sources, the alarm mechanism will be triggered and sound and light alarm will be issued; the technical staff of the monitoring and display center can directly video call with the shield machine driver through the early warning interactive terminal to realize "expert direct access to the heading face".
  • the data forwarding terminal is deployed in a ground machine room and is connected to the collection terminal in the tunnel through a router public network.
  • the data forwarding terminal has multiple data forwarding modes, including optical fiber fixed network and 4G/5G mobile network.
  • the data forwarding terminal is connected to the public network of the ground machine room and uses an industrial-grade gigabit optical fiber direct-connected router to send encrypted shield data; when the public network of the ground machine room is interrupted, the data forwarding terminal automatically switches to the 4G/5G mobile network in milliseconds, completes data integrity verification, and continues to send data.
  • the monitoring video gateway terminal is deployed in a ground machine room, directly connected to the data forwarding terminal, and connected to the work area video monitoring equipment through a router gateway.
  • the surveillance video gateway terminal has the function of automatic modulation and demodulation of video encoding. It can automatically modulate the surveillance video connected to the same gateway, unify it into the specified H.264 or H.265 encoding format, encrypt and compress the video, and send it to the monitoring display center through the data forwarding terminal.
  • the communication module is deployed at a remote end, connected to a router, receives data sent by the transmission module through a public network, and forwards the data to a computing storage server.
  • the computing storage server is deployed at a remote location and is directly connected to the communication module.
  • the data computing and storage server receives the original construction data of the shield machine through the communication module for automatic storage, receives the point data pre-processed by the acquisition terminal for intelligent analysis and calculation, and generates alarm information: such as real-time monitoring and statistics of the shield machine's cutter thrust, penetration, total extrusion pressure, propulsion speed, rotation speed, and torque to form a tunneling parameter report; real-time monitoring and statistics of the shield machine's material consumption, synchronous grouting volume, shield tail grease injection volume, main bearing sealing grease (HBW), and main bearing lubricating grease (EP2) to form a material consumption report; real-time monitoring and statistics of the shield machine's horizontal offset, shield machine's vertical offset, segment floating volume, segment misalignment, surface settlement monitoring value, and shield tail gap to form a construction quality report; real-time monitoring and capture of construction monitoring videos including monitoring videos of various construction areas, shield machine trolleys, synchronous construction inside the tunnel, cockpits, air cushion chambers and other important places to form a danger warning report.
  • alarm information such as real-time
  • the numerical values in the report are subtracted from the theoretically calculated values based on geological surveys and construction conditions, and the differences of the above parameters are calculated. According to the importance in different working conditions, different weights are multiplied and accumulated to obtain nonlinear fluctuation values. Different levels of warnings are issued according to the size of the fluctuation values, and data from the expert database is automatically retrieved to propose solutions to assist shield technicians in decision-making and driving.
  • the weight distribution and scoring method are as follows:
  • weight models are constructed, namely the first weight model, the second weight model, the third weight model, and the fourth weight model.
  • the first weight model The total weight of the shield machine excavation parameters is defined as W E1 , and W E1 accounts for 0.4.
  • the weight W E1 is further divided into the following sub-weights: cutter head propulsion torque, speed, thrust, penetration, and total extrusion pressure, with corresponding weights of 0.2, 0.2, 0.2, and 0.2, respectively.
  • each sub-weight Wi 1 corresponds to the maximum, mean, median, and fluctuation coefficient of the cutter head propulsion torque within a predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 2 corresponds to the maximum, mean, median, and fluctuation coefficient of the speed within a predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 3 corresponds to the maximum, mean, median, and fluctuation coefficient of the thrust within a predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 4 corresponds to the maximum, mean, median, and fluctuation coefficient of penetration within a predetermined period, corresponding to weights 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 5 corresponds to the maximum, mean, median, and fluctuation coefficient of total extrusion pressure within a predetermined period, corresponding to weights 0.25, 0.25, 0.25, and 0.25, respectively.
  • the second weight model The total weight of the material consumption parameters of the shield machine is defined as W E2 , where the synchronous grouting volume, shield tail grease injection volume, and main bearing seal lubricating grease data correspond to weights of 0.3, 0.3, and 0.4, respectively.
  • each sub-weight Wi 6 corresponds to the maximum, mean, median, and fluctuation coefficient of the synchronous grouting volume in the predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 7 corresponds to the maximum, mean, median, and fluctuation coefficient of the shield tail grease injection volume in the predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • Each sub-weight Wi 8 corresponds to the maximum, mean, median, and fluctuation coefficient of the main bearing seal lubricating grease data in the predetermined period, corresponding to weights of 0.25, 0.25, 0.25, and 0.25, respectively.
  • the third weight model the total weight of the automatically captured surveillance video is defined as WE3 ; the total weight WE3 is further divided into the following sub-weights: construction area surveillance video accounts for 0.2, shield machine trolley surveillance video accounts for 0.2, tunnel internal synchronous construction surveillance video accounts for 0.2, cockpit surveillance video accounts for 0.2, and air cushion chamber surveillance video accounts for 0.2.
  • the fourth weight model the total weight of the measured construction quality data is defined as WE4 ; the total weight WE4 is further divided into the following sub-weights: surface settlement data accounts for 0.2, segment misalignment data accounts for 0.2, segment floating data accounts for 0.2, shield tail gap data accounts for 0.2, and shield attitude guidance data accounts for 0.2.
  • the data computing and storage server imports the generated report data into the first weight model, the second weight model, the third weight model, and the fourth weight model, and outputs sub-score A, sub-score B, sub-score C, sub-score D, and total score G respectively; compares the above scores with a preset standard database, and the standard database corresponds to sub-alarm limit W, sub-alarm limit X, sub-alarm limit Y, sub-alarm limit Z, and total alarm limit S respectively;
  • sub-score A ⁇ sub-alarm limit W, then further judge sub-score B, sub-score C, and sub-score D. If any of the scores among sub-score B, sub-score C, and sub-score D exceeds its corresponding sub-alarm limit, a third-level warning is issued;
  • sub-score B, sub-score C, and sub-score D all exceed sub-alarm limit X, sub-alarm limit Y, and sub-alarm limit Z, a level 1 warning is issued;
  • the early warning results are connected to the expert system database to match solutions and assist technicians in driving the shield machine.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • This embodiment is based on the shield construction early warning system mentioned in the first embodiment, and proposes a shield construction data collection and early warning method using an edge computing architecture, and the steps are as follows:
  • Step 1 The shield machine starts excavation construction.
  • the technicians in the monitoring and display center remotely control the early warning interactive terminal or the technicians in the shield machine cab control the early warning interactive terminal on site, set the data collection frequency, data collection points and other parameters of the data collection terminal, and issue instructions to start collection.
  • Step 2 The data collection terminal automatically collects data on the shield machine cutter head excavation, mud and water circulation, guide system, grease grouting, water and soil pressure sensors, etc.
  • Step 3 The acquisition terminal stores the collected raw data and performs preliminary calculations using edge computing capabilities: First, set the required data point table, as shown in Table 1.
  • the data in the data point table comes from the acquisition terminal.
  • the acquisition terminal collects real-time data from various types of sensors installed on the shield machine.
  • the sensors installed on the shield machine include pressure sensors, liquid level sensors, temperature sensors, displacement sensors, optical sensors, etc. These sensors are mainly used to measure the water and soil pressure, propulsion stroke, propulsion speed, and cutter head rotation speed of the shield machine during excavation. Speed, sealing grease pressure, shield tail grease injection volume, mud pipeline pump pressure, speed, flow rate and other important data that require human attention.
  • the rough set theory in data mining technology is used to remove redundant information in the data that does not play a role in early warning decision-making, and the data of some data points that play a role in early warning decision-making are directly forwarded to the remote monitoring and display center through the transmission module; after the second collection, the data of the data points that are required to be set first are extracted, and compared with the data collected for the first time. If the data changes, the new data is forwarded to the remote monitoring and display center through the transmission module.
  • the computing storage server of the remote monitoring and display center maintains the original data unchanged by default; the subsequent collection repeats the above process; set the collected raw data to be packaged and transmitted to the remote monitoring and display center every minute.
  • Step 4 The surveillance video gateway terminal automatically modulates the surveillance video connected to the same gateway, unifies it into the specified H.264 or H.265 encoding format, encrypts and compresses the video, and sends it to the surveillance display center through the data forwarding terminal.
  • Step 5 The data forwarding terminal is connected to the public network of the ground machine room, encrypts and packages the transmitted shield machine data and video surveillance, and transmits them to the monitoring display center using an industrial-grade gigabit fiber-optic direct-connected router.
  • the data forwarding terminal automatically switches to the 5G mobile network in milliseconds, completes the data integrity check, and continues to send data.
  • Step 6 The communication module of the monitoring and display center receives the processed shield construction data and video monitoring data, and performs intelligent analysis while storing them on the data computing and storage server.
  • the shield machine cutterhead mud cake working condition warning as an example, under the premise that there is no sudden change in the formation, the cutterhead advancement speed of the shield machine continues to decrease, the cutterhead torque continues to increase, the cutterhead speed increases, the penetration rate decreases, and the temperature of the cutterhead center panel rises abnormally (generally exceeding 35 degrees Celsius), and this
  • the intelligent monitoring program of the data computing and storage server monitors the above-mentioned related and coupled parameters, mines the data, extracts feature changes, matches the expert database established by machine learning big data, determines the mud cake condition, demarcates the first to third level mud cake warning, and issues an alarm.
  • Step 7 The results of the data computing and storage server analysis and processing are transmitted in real time to the early warning interactive terminal of the acquisition and early warning module for display, providing auxiliary decision-making for the shield machine driver.
  • the sound and light alarm function of the early warning interactive terminal automatically starts the alarm and warns the management personnel step by step.
  • the technical experts of the monitoring and display center call the driver of the shield machine cab with one click, communicate through remote video calls, and propose solutions in time.
  • Step 8 After the risks and parameter abnormalities are eliminated, the warning is eliminated and the sound and light alarms are automatically turned off.
  • Step 9 Perform machine recognition on surveillance videos to automatically capture dangerous actions of construction personnel and equipment and issue safety warnings for operations.

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Abstract

本发明提供了一种基于边缘计算架构的盾构施工预警系统及预警方法，属于盾构智能监控领域。该系统包括采集预警模块、传输模块、监控展示中心，采集预警模块包括采集终端、预警交互终端，传输模块包括数据转发终端、监控视频网关终端，监控展示中心包括通讯模块、数据计算存储服务器。在系统架构中，数据计算存储服务器设定为中心硬件，采集终端、监控视频网关终端设定为边缘硬件，可以进行边缘计算，参与盾构数据的预处理、数据分析、数据分发与策略执行。本发明系统和方法能够实现盾构机施工数据的高效、稳定、安全采集，并且具备参数预警与远程安全监控功能。

Description

基于边缘计算架构的盾构施工预警系统及预警方法 技术领域
本发明涉及盾构智能监控领域，尤其涉及一种基于边缘计算架构的盾构施工预警系统及预警方法。
背景技术
盾构机是一种超大型机电液一体化的复杂地下施工设备，在掘进施工的过程中，会产生大量的数据，包括施工环境、地质信息以及施工状态参数等信息。利用大数据分析方法与可视化技术对盾构机施工掘进产生的海量数据集进行信息获取、识别与分析，构建盾构综合监控与预警平台，是保障盾构机安全、高效掘进的重要方法。
盾构数据采集需要通过采集终端连接盾构机PLC实时采集数据，传输至远程监控主机进行解析运算，通过可视化展示进行监测及预警。目前主要面临两个方面的困难：(1)数据传输仅基于工区公共网络，经常出现网络断开、停电、网速波动的现象，造成数据传输频繁中断。(2)数据计算功能集中在主机云端，盾构海量实时数据涌入容易造成拥塞、丢包，对主机多线程并行处理计算能力提出了挑战，制约了数据查询的实时性与安全性。
发明内容
发明目的：提出一种基于边缘计算架构的盾构施工预警系统，并进一步提出一种基于上述预警系统的预警方法，以解决现有技术存在的上述问题。
第一方面，提出一种基于边缘计算架构的盾构施工预警系统，该系统包括采集预警模块、传输模块、监控展示中心。
采集预警模块包括直连的采集终端和预警交互终端；采集终端直接与盾构机可编程逻辑控制器或盾构机操作主机连接。
传输模块与采集预警模块建立通信连接；传输模块包括直连的数据转发终端和监控视频网关；数据转发终端通过路由器公网与所述采集终端连接；监控视频网关终端与工区视频监控设备通过路由器网关相连。
监控展示中心与传输模块建立通信连接；监控展示中心包括直连的通讯模块和数据计算存储服务器；通讯模块与路由器连接，路由器通过公共网络接收传输模块的数据转发终端发送的数据。
在系统架构层面，边缘计算架构包含中心角色和边缘角色，监控展示中心设定为中 心角色，采集预警模块和传输模块设定为边缘角色。
在硬件终端层面，边缘计算架构包含中心硬件和边缘硬件，数据计算存储服务器及配套通讯模块为中心硬件，采集终端、预警交互终端、数据转发终端、监控视频网关终端为边缘硬件。
在第一方面的一些可实现方式中，采集终端部署在施工隧道的泥水平衡盾构机驾驶室内，通过网线直接与盾构机可编程逻辑控制器连接；预警交互终端部署在施工隧道的泥水平衡盾构机驾驶室内，与采集终端通过电缆直接相连；数据转发终端部署在地面机房，通过路由器公网接入隧道内的采集终端；监控视频网关终端部署在地面机房，与数据转发终端直接相连，与工区视频监控设备通过路由器网关相连；通讯模块部署在远端，与路由器连接，通过公共网络接收传输模块发送的数据，并转发至计算存储服务器；计算存储服务器部署在远端异地，与通讯模块直接相连。
在第一方面的一些可实现方式中，采集终端作为边缘硬件，参与盾构机采集数据的初步运算处理，并将结果转发至所述监控展示中心；监控视频网关终端作为边缘硬件，对接入同一网关的监控视频进行自动调制编码。
在第一方面的一些可实现方式中，通讯模块接收所述数据转发终端发送的数据，并转发至所述数据计算存储服务器；数据计算存储服务器自动存储接收到的数据，分别生成掘进参数报表、物料消耗报表、危险预警报表、施工质量报表，并将生成的报表数据与理论计算的数值进行加权对比，根据数值波动和偏差自动发出不同等级的预警，并且接入专家系统数据库，匹配解决方案，辅助技术人员驾驶盾构机。
在第一方面的一些可实现方式中，数据转发终端具有光纤固网与移动网络两种数据转发模式；普通情况下采用光纤直连路由器固网的方式发送数据；当固网中断或网速波动范围大于预定值时，自动切换至移动网络发送数据。
在第一方面的一些可实现方式中，采集终端进行多次数据采集操作：
第N次数据采集后，按照预先设置，所述采集终端筛选部分数据点位的数据直接经传输模块转发至远端的监控展示中心；
第N+1次数据采集后，所述采集终端提取设置的数据点位的数据，与第N次采集到的数据进行对比运算，若数据有变化，则将新的数据经传输模块转发至远端的监控展示中心，若数据无变化，则不发送新的数据，此时远端的监控展示中心的计算存储服务器默认维持原数据不变；
重复上述流程；设置预定频率对采集到的原始数据打包传输至远端的监控展示中心。
在第一方面的一些可实现方式中，掘进参数报表E1包括如下特征字段：刀盘推进扭矩i1、转速i2、推力i3、贯入度i4、总挤压力i5；物料消耗报表E2包括如下特征字段：同步注浆量i6、盾尾油脂注入量i7、主轴承密封润滑油脂i8；危险预警报表E3包括如下特征字段：施工工区监控视频自动识别与抓拍i9、盾构机台车监控视频自动识别与抓拍i10、隧道内部同步施工监控视频自动识别与抓拍i11、驾驶舱监控视频自动识别与抓拍i12、气垫仓监控视频自动识别与抓拍i13；施工质量报表E4包括如下特征字段：地表沉降i14、管片错台i15、管片上浮i16、盾尾间隙i17、盾构姿态导向i18；将所有特征字段构建成一个N维矢量，存储为数据点位表。
第二方面，提出一种盾构施工预警方法，基于第一方面所述的盾构施工预警系统以实现，步骤如下：
步骤1、盾构机开始掘进施工，操作人员现场或者远程控制预警交互终端，设置采集终端的采集参数，并下达指令，开始采集；
步骤2、采集终端采集盾构机产生的施工数据、传感器监控数据；
步骤3、采集终端存储采集到的施工数据和传感器监控数据，形成数据点位表，并利用边缘计算能力进行初步运算，得到处理后的数据，实时转发至数据转发终端，同时采集终端存储的原始数据定期传输至数据转发终端；
步骤4、监控视频网关终端将接入同一网关的视频监控统一编码，并发送至传输模块的数据转发终端；
步骤5、数据转发终端将传输的盾构机数据和视频监控打包，通过光纤固网传输至监控展示中心，当光纤固网不能使用时，自动切换至蜂窝移动网络传输；
步骤6、监控展示中心的通讯模块接收处理后的盾构施工数据和视频监控数据，在数据计算存储服务器进行分析处理，输出结果，并将接收到的原始盾构施工数据直接存储在服务器；
步骤7、数据计算存储服务器分析处理的结果实时传输至采集预警模块的预警交互终端展示，为盾构机驾驶人员提供辅助决策，当出现参数报警时，预警交互终端的声光报警功能启动，发出警报，同时盾构机操作人员与监控展示中心的技术人员可通过视频通话，远程沟通解决方案；
步骤8、消警后，声光报警自动关闭。
在第二方面进一步的实施例中，步骤3中采集终端进行多次数据采集操作：
第N次数据采集后，按照预先设置，所述采集终端筛选部分数据点位的数据直接经传输模块转发至远端的监控展示中心；
第N+1次数据采集后，所述采集终端提取设置的数据点位的数据，与第N次采集到的数据进行对比运算，若数据有变化，则将新的数据经传输模块转发至远端的监控展示中心，若数据无变化，则不发送新的数据，此时远端的监控展示中心的计算存储服务器默认维持原数据不变；
重复上述流程；设置预定频率对采集到的原始数据打包传输至远端的监控展示中心。
在第二方面进一步的实施例中，步骤6中数据计算存储服务器进行分析处理的过程进一步包括：
步骤6-1、计算盾构机掘进参数：获得预定时段内刀盘推进扭矩、转速、推力、贯入度、总挤压力数据的最值、均值、中位值、波动系数，并生成掘进参数报表；
计算盾构机物料消耗参数：获得预定时段内同步注浆量、盾尾油脂注入量、主轴承密封润滑油脂数据的均值、环值、与理论消耗量的差值，并生成物料消耗报表；
分析自动抓拍的监控视频：对各个施工工区、盾构机台车、隧道内部同步施工、驾驶舱、气垫仓监控视频自动识别、抓拍，生成危险预警报表；
自动测量施工质量数据：对地表沉降、管片错台、管片上浮、盾尾间隙、盾构姿态导向数据自动测量、记录，生成施工质量报表；
步骤6-2、数据计算存储服务器将生成的报表数据导入至权重模型中，由权重模型计算出评分，并与内建标准数据库比对，超出阈值即自动发出不同等级的预警，预警等级接入专家系统数据库，匹配解决方案。
在第二方面进一步的实施例中，权重模型的构建规则如下：
步骤6-2A、构建第一权重模型：定义盾构机掘进参数的总权重为WE1，其中权重WE1又进一步划分为如下子权重：刀盘推进扭矩、转速、推力、贯入度、总挤压力，分别对应权重为Wi1、Wi2、Wi3、Wi4、Wi5，Wi1+Wi2+Wi3+Wi4+Wi5＝WE1＝1；
此外，每个子权重Wi1下对应有预定时段内刀盘推进扭矩的最值、均值、中位值、波动系数，分别对应于权重Wi1max、Wi1average、Wi1median、Wi1fluctuation；其中Wi1max+Wi1average+Wi1median+Wi1fluctuation＝1；
每个子权重Wi2下对应有预定时段内转速的最值、均值、中位值、波动系数，分别 对应于权重Wi2max、Wi2average、Wi2median、Wi2fluctuation；其中Wi2max+Wi2average+Wi2median+Wi2fluctuation＝1；
每个子权重Wi3下对应有预定时段内推力的最值、均值、中位值、波动系数，分别对应于权重Wi3max、Wi3average、Wi3median、Wi3fluctuation；其中Wi3max+Wi3average+Wi3median+Wi3fluctuation＝1；
每个子权重Wi4下对应有预定时段内贯入度的最值、均值、中位值、波动系数，分别对应于权重Wi4max、Wi4average、Wi4median、Wi4fluctuation；其中Wi4max+Wi4average+Wi4median+Wi4fluctuation＝1；
每个子权重Wi5下对应有预定时段内总挤压力的最值、均值、中位值、波动系数，分别对应于权重Wi5max、Wi5average、Wi5median、Wi5fluctuation；其中Wi5max+Wi5average+Wi5median+Wi5fluctuation＝1；
步骤6-2B、构建第二权重模型：定义盾构机物料消耗参数的总权重为WE2，其中同步注浆量、盾尾油脂注入量、主轴承密封润滑油脂数据分别对应权重为Wi6、Wi7、Wi8，且Wi6+Wi7+Wi8＝1；
此外，每个子权重Wi6下对应有预定时段内同步注浆量的最值、均值、中位值、波动系数，分别对应于权重Wi6max、Wi6average、Wi6median、Wi6fluctuation；其中Wi6max+Wi6average+Wi6median+Wi6fluctuation＝1；
每个子权重Wi7下对应有预定时段内盾尾油脂注入量的最值、均值、中位值、波动系数分别对应于权重Wi7max、Wi7average、Wi7median、Wi7fluctuation；其中Wi7max+Wi7average+Wi7median+Wi7fluctuation＝1；
每个子权重Wi8下对应有预定时段内主轴承密封润滑油脂数据的最值、均值、中位值、波动系数，分别对应于权重Wi8max、Wi8average、Wi8median、Wi8fluctuation；其中Wi8max+Wi8average+Wi8median+Wi8fluctuation＝1；
步骤6-2C、构建第三权重模型：定义自动抓拍的监控视频的总权重为WE3；总权重为WE3下的各子数据的权重占比均分；
步骤6-2D、构建第四权重模型：定义测量得到的施工质量数据的总权重为WE4；总权重为WE4下的各子数据的权重占比均分；
步骤6-2E、数据计算存储服务器将生成的报表数据分别导入至第一权重模型、第二权重模型、第三权重模型、第四权重模型中，分别输出子评分A、子评分B、子评分C、 子评分D、总评分G；将上述评分与预设的标准数据库比对，标准数据库中分别对应有子告警极限W、子告警极限X、子告警极限Y、子告警极限Z、总告警极限S；
判定规则如下：
若输出子评分A＞子告警极限W，此时进一步判断所有总评分G是否到达总告警极限S，若总评分G≥总告警极限S，则直接发出一级预警；若总评分G＜总告警极限S，则直接发出二级预警；
若输出子评分A≤子告警极限W，此时进一步判断子评分B、子评分C、子评分D，若子评分B、子评分C、子评分D中任意一个评分超出其对应的子告警极限，则发出三级预警；
若子评分B、子评分C、子评分D中任意两个评分超出其对应的子告警极限，则发出二级预警；
若子评分B、子评分C、子评分D全部超出子告警极限X、子告警极限Y、子告警极限Z，则发出一级预警；
预警结果接入专家系统数据库，匹配解决方案，辅助技术人员驾驶盾构机。
本发明具有如下有益效果：
(1)本发明盾构施工数据采集与预警终端系统采用边缘计算架构，将部分数据计算下沉到采集终端，不但减轻了监控展示中心数据计算存储服务器的计算压力，同时采集终端计算后的数据量大大减少。经测算，掘进状态下盾构机有50％-80％的点位数据是实时变化的，而在停机和拼环状态下，盾构机只有20％-50％的点位数据是实时变化的，因此可以减轻约30％-50％的数据计算存储服务器数据吞吐量和网络传输时延。
(2)本发明盾构施工数据采集与预警终端系统的数据转发终端具备光纤固网和5G移动网络两种传输模式，并可以自动切换，保障了数据传输稳定。
(3)本发明盾构施工数据采集与预警方法创新性的将数据采集与参数预警融合，兼具施工数据采集、参数预警、辅助决策等多种功能，提升了施工效率，保障了施工安全。
附图说明
图1为本发明系统的原理框图。
图2为本发明终端设备的原理框图。
图3为本发明边缘计算系统架构图。
图4为本发明的边缘计算典型应用流程图。
图5为本发明数据转发终端数据转发流程图。
具体实施方式
在下文的描述中，给出了大量具体的细节以便提供对本发明更为彻底的理解。然而，对于本领域技术人员而言显而易见的是，本发明可以无需一个或多个这些细节而得以实施。在其他例子中，为了避免与本发明发生混淆，对于本领域公知的一些技术特征未进行描述。
实施例一：
本实施例提出一种采用边缘计算架构的盾构数据采集预警终端成套系统，如图1所示，该系统包括采集预警模块、传输模块、监控展示中心，采集预警模块包括采集终端、预警交互终端，传输模块包括数据转发终端、监控视频网关终端，监控展示中心包括数据计算存储服务器、通讯模块。
如图2所示，采集终端与盾构机PLC或控制主机、数据转发终端、预警交互终端相连，数据转发终端与采集终端、监控视频网关终端、通讯模块相连，数据计算存储服务器与通讯模块、预警交互终端相连，监控视频网关终端接入多个监控视频。
所述边缘计算架构如图3所示，设置监控展示中心的数据计算存储服务器为中心硬件，采集终端、监控视频网关终端为边缘硬件，具有一定计算能力，可参与盾构数据的预处理、数据分析、数据分发与策略执行。
所述采集终端部署在施工隧道的泥水平衡盾构机驾驶室内，通过网线直接与盾构机PLC连接，具体为采集终端使用基于OPC工业软件标准的自动化接口，遵循OPC数据范文规范，通过RSLinuxOPC服务器供应通讯通道，实现采集终端对PLC数据采集的自动采集。
进一步的，如图4所示，采集终端首次数据采集后，按照预先设置，筛选部分数据点位的数据直接经传输模块转发至远端的监控展示中心；第二次采集后，提取设置的数据点位的数据，与上一次采集到的数据进行差值运算，若运算结果不为零，则将新的数据经传输模块转发至远端的监控展示中心，若运算结果为零，则不发送新的数据，远端的监控展示中心的计算存储服务器默认维持原数据不变；后续采集重复上述流程；设置一定频率(如每分钟)对采集到的原始数据进行打包传输至远端的监控展示中心。
所述预警交互终端部署在施工隧道的泥水平衡盾构机驾驶室内，与采集终端通过电 缆直接相连。
进一步的，预警交互终端具有远程控制、采集设置、数据展示、声光报警、视频通话功能：监控展示中心可远程控制预警交互终端，对采集终端的采集参数(如采集频率、采集点位)进行查询和调整；监控展示中心对数据处理后的结果也会展示在预警交互终端，出现参数异常、穿越风险源等情况时，将会触发报警机制，进行声光报警；监控展示中心的技术人员可通过预警交互终端与盾构机驾驶员直接视频通话，实现“专家直达掌子面”。
所述数据转发终端部署在地面机房，通过路由器公网接入隧道内的采集终端。
进一步的，数据转发终端具有光纤固网与4G/5G移动网络多种数据转发模式，如图5所示，普通情况下数据转发终端接入地面机房公共网络，采用工业级千兆光纤直连路由器的方式发送加密过的盾构数据；当地面机房公共网络中断时，数据转发终端毫秒级自动切换至4G/5G移动网络，完成数据完整性校验，并继续发送数据。
所述监控视频网关终端部署在地面机房，与数据转发终端直接相连，与工区视频监控设备通过路由器网关相连。
进一步的，监控视频网关终端具备自动调制、解调视频编码的功能，可对接入同一网关的监控视频进行自动调制，统一为指定的H.264或H.265编码格式，并对视频进行加密压缩，通过数据转发终端发送至监控展示中心。
所述通讯模块部署在远端，与路由器连接，通过公共网络接收传输模块发送的数据，并转发至计算存储服务器。
所述计算存储服务器部署在远端异地，与通讯模块直接相连。
进一步的，数据计算存储服务器通过通讯模块接收盾构机原始施工数据进行自动存储，接收采集终端预处理的点位数据进行智能分析计算，并生成报警信息：如实时监测并统计盾构机刀盘推力、贯入度、总挤压力、推进速度、转速、扭矩，形成掘进参数报表；实时监测并统计盾构机物料消耗同步注浆量、盾尾油脂注入量、主轴承密封油脂(HBW)、主轴承润滑油脂(EP2)，形成物料消耗报表；实时监测并统计盾构姿态水平偏移量、盾构姿态垂直偏移量、管片上浮量、管片错台量、地表沉降监测值、盾尾间隙量，形成施工质量报表；实时监测并抓拍施工监控视频包括各个施工工区、盾构机台车、隧道内部同步施工、驾驶舱、气垫仓等重要场所的监控视频，形成危险预警报表。将报表中的数值与根据地质勘察、施工工况理论计算值进行差值运算，并将前述参数的差值 按照在不同工况中的重要性，乘以不同权重，累加得到非线性的波动值，根据波动值大小进行不同等级的预警，并自动调取专家库的数据，提出解决方案，辅助盾构技术人员决策驾驶。
权重的分配和评分方式具体如下：
首先，构建四个权重模型，分别为第一权重模型、第二权重模型、第三权重模型、第四权重模型。
第一权重模型：定义盾构机掘进参数的总权重为WE1，WE1占比为0.4。其中权重WE1又进一步划分为如下子权重：刀盘推进扭矩、转速、推力、贯入度、总挤压力，分别对应权重为0.2、0.2、0.2、0.2。此外，每个子权重Wi1下对应有预定时段内刀盘推进扭矩的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi2下对应有预定时段内转速的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi3下对应有预定时段内推力的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi4下对应有预定时段内贯入度的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi5下对应有预定时段内总挤压力的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。
第二权重模型：定义盾构机物料消耗参数的总权重为WE2，其中同步注浆量、盾尾油脂注入量、主轴承密封润滑油脂数据分别对应权重为0.3、0.3、0.4。此外，每个子权重Wi6下对应有预定时段内同步注浆量的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi7下对应有预定时段内盾尾油脂注入量的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。每个子权重Wi8下对应有预定时段内主轴承密封润滑油脂数据的最值、均值、中位值、波动系数，分别对应于权重0.25、0.25、0.25、0.25。
第三权重模型：定义自动抓拍的监控视频的总权重为WE3；总权重WE3又进一步划分为如下子权重：施工工区监控视频占比0.2、盾构机台车监控视频占比0.2、隧道内部同步施工监控视频占比0.2、驾驶舱监控视频占比0.2、气垫仓监控视频占比0.2。
第四权重模型：定义测量得到的施工质量数据的总权重为WE4；总权重WE4又进一步划分为如下子权重：地表沉降数据占比0.2、管片错台数据占比0.2、管片上浮数据占比0.2、盾尾间隙数据占比0.2、盾构姿态导向数据占比0.2。
数据计算存储服务器将生成的报表数据分别导入至第一权重模型、第二权重模型、第三权重模型、第四权重模型中，分别输出子评分A、子评分B、子评分C、子评分D、总评分G；将上述评分与预设的标准数据库比对，标准数据库中分别对应有子告警极限W、子告警极限X、子告警极限Y、子告警极限Z、总告警极限S；
判定规则如下：
若输出子评分A＞子告警极限W，此时进一步判断所有总评分G是否到达总告警极限S，若总评分G≥总告警极限S，则直接发出一级预警；若总评分G＜总告警极限S，则直接发出二级预警；
若输出子评分A≤子告警极限W，此时进一步判断子评分B、子评分C、子评分D，若子评分B、子评分C、子评分D中任意一个评分超出其对应的子告警极限，则发出三级预警；
若子评分B、子评分C、子评分D中任意两个评分超出其对应的子告警极限，则发出二级预警；
若子评分B、子评分C、子评分D全部超出子告警极限X、子告警极限Y、子告警极限Z，则发出一级预警；
预警结果接入专家系统数据库，匹配解决方案，辅助技术人员驾驶盾构机。
实施例二：
本实施例基于实施例一中提及的盾构施工预警系统，提出一种采用边缘计算架构的盾构施工数据采集与预警方法，步骤如下：
步骤1：盾构机开始掘进施工，监控展示中心的技术人员远程控制预警交互终端或者盾构机驾驶室技术人员现场控制预警交互终端，设置数据采集终端的采集频率、采集数据点位等参数，并下达指令，开始采集。
步骤2：采集终端自动对盾构机刀盘掘进、泥水循环、导向系统、油脂注浆、水土压力传感器等数据进行采集。
步骤3：采集终端对采集到的原始数据进行存储，并利用边缘计算能力进行初步运算：首先设置需求的数据点位表，如表1所示，所述数据点位表中的数据来自于采集终端，采集终端对安装在所述盾构机上的各类型传感器的实时数据进行采集，安装在所述盾构机上的传感器包括压力传感器、液位传感器、温度传感器、位移传感器、光学传感器等，这些传感器主要是测量盾构机掘进时的水土压力、推进行程、推进速度、刀盘转 速、密封油脂压力、盾尾油脂注入量、泥浆管路泵机压力、转速、流量等需要人为关注的重要数据。首次数据采集后，按照预先设置，采用数据挖掘技术中的粗糙集理论去掉数据中对预警决策不起作用的冗余信息，筛选对预警起决策作用的部分数据点位的数据直接经传输模块转发至远端的监控展示中心；第二次采集后，提取首先设置需求的数据点位的数据，与首次采集到的数据进行对比运算，若数据有变化，则将新的数据经传输模块转发至远端的监控展示中心，若数据无变化，则不发送新的数据，远端的监控展示中心的计算存储服务器默认维持原数据不变；后续采集重复上述流程；设置每分钟对采集到的原始数据进行打包传输至远端的监控展示中心。
表1：盾构机相关参数的实时监测
步骤4：监控视频网关终端对接入同一网关的监控视频进行自动调制，统一为指定的H.264或H.265编码格式，并对视频进行加密压缩，通过数据转发终端发送至监控展示中心。
步骤5：数据转发终端接入地面机房公共网络，将传输的盾构机数据和视频监控加密打包，采用工业级千兆光纤直连路由器的方式传输至监控展示中心，当地面机房公共网络中断时，数据转发终端毫秒级自动切换至5G移动网络，完成数据完整性校验，并继续发送数据。
步骤6：监控展示中心的通讯模块接收处理后的盾构施工数据和视频监控数据，在数据计算存储服务器进行存储的同时，开展智能分析。以盾构机刀盘结泥饼工况预警为例，所述盾构机在地层无突变的前提下，刀盘推进速度连续降低，刀盘扭矩连续增大，刀盘转速提高，贯入度下降，刀盘中心面板温度异常升高(一般超过35摄氏度)，且这 种参数持续变化超过一定里程(如3环)，数据计算存储服务器的智能监控程序监测以上关联耦合的参数，并对数据进行挖掘，提取特征变化，匹配机器学习大数据所建立的专家库，判定结泥饼工况，划定一到三级结泥饼预警，发出警报。
步骤7：数据计算存储服务器分析处理的结果实时传输至采集预警模块的预警交互终端展示，为盾构机驾驶人员提供辅助决策，当出现盾构机掘进参数异常时，预警交互终端的声光报警功能自动启动报警，并逐级向管理人员预警，监控展示中心的技术专家一键呼叫盾构机驾驶室的驾驶人员，远程视频通话沟通，及时提出解决方案。
步骤8：风险及参数异常问题排除后，预警消除，声光报警自动关闭。
步骤9：对监控视频进行机器识别，自动抓拍施工人员、设备危险动作，进行作业安全预警。
如上所述，尽管参照特定的优选实施例已经表示和表述了本发明，但其不得解释为对本发明自身的限制。在不脱离所附权利要求定义的本发明的精神和范围前提下，可对其在形式上和细节上做出各种变化。

Claims (10)

  1. 基于边缘计算架构的盾构施工预警系统，其特征在于，包括：
    采集预警模块，包括直连的采集终端和预警交互终端；所述采集终端直接与盾构机可编程逻辑控制器或盾构机操作主机连接；
    传输模块，与所述采集预警模块建立通信连接；所述传输模块包括直连的数据转发终端和监控视频网关；所述数据转发终端通过路由器公网与所述采集终端连接；所述监控视频网关终端与工区视频监控设备通过路由器网关相连；
    监控展示中心，与所述传输模块建立通信连接；所述监控展示中心包括直连的通讯模块和数据计算存储服务器；所述通讯模块与路由器连接，路由器通过公共网络接收传输模块的数据转发终端发送的数据；
    其中，所述监控展示中心设定为中心角色，所述采集预警模块和传输模块设定为边缘角色；所述中心角色和边缘角色共同构成系统架构层面的边缘计算架构；
    所述数据计算存储服务器及通讯模块为中心硬件，所述采集终端、预警交互终端、数据转发终端、监控视频网关终端为边缘硬件；所述中心硬件和边缘硬件共同构成硬件终端层面的边缘计算架构。
  2. 根据权利要求1所述的盾构施工预警系统，其特征在于：所述采集终端部署在施工隧道的泥水平衡盾构机驾驶室内，通过网线直接与盾构机可编程逻辑控制器连接；
    所述预警交互终端部署在施工隧道的泥水平衡盾构机驾驶室内，与采集终端通过电缆直接相连；
    所述数据转发终端部署在地面机房，通过路由器公网接入隧道内的采集终端；
    所述监控视频网关终端部署在地面机房，与数据转发终端直接相连，与工区视频监控设备通过路由器网关相连；
    所述通讯模块部署在远端，与路由器连接，通过公共网络接收传输模块发送的数据，并转发至计算存储服务器；
    所述计算存储服务器部署在远端异地，与通讯模块直接相连。
  3. 根据权利要求1所述的盾构施工预警系统，其特征在于：所述采集终端作为边缘硬件，参与盾构机采集数据的初步运算处理，并将结果转发至所述监控展示中心；
    所述监控视频网关终端作为边缘硬件，对接入同一网关的监控视频进行自动调制编码。
  4. 根据权利要求1所述的盾构施工预警系统，其特征在于：所述通讯模块接收所述 数据转发终端发送的数据，并转发至所述数据计算存储服务器；
    所述数据计算存储服务器自动存储接收到的数据，分别生成掘进参数报表、物料消耗报表、危险预警报表、施工质量报表，并将生成的报表数据与理论计算的数值进行加权对比，根据数值波动和偏差自动发出不同等级的预警，并且接入专家系统数据库，匹配解决方案，辅助技术人员驾驶盾构机。
  5. 根据权利要求1所述的盾构施工预警系统，其特征在于：所述数据转发终端具有光纤固网与移动网络两种数据转发模式；
    普通情况下采用光纤直连路由器固网的方式发送数据；当固网中断或网速波动范围大于预定值时，自动切换至移动网络发送数据。
  6. 根据权利要求1所述的盾构施工预警系统，其特征在于，所述采集终端进行多次数据采集操作：
    第N次数据采集后，按照预先设置，所述采集终端筛选部分数据点位的数据直接经传输模块转发至远端的监控展示中心；
    第N+1次数据采集后，所述采集终端提取设置的数据点位的数据，与第N次采集到的数据进行对比运算，若数据有变化，则将新的数据经传输模块转发至远端的监控展示中心，若数据无变化，则不发送新的数据，此时远端的监控展示中心的计算存储服务器默认维持原数据不变；
    设置预定频率对采集到的原始数据打包传输至远端的监控展示中心。
  7. 根据权利要求4所述的盾构施工预警系统，其特征在于，所述掘进参数报表E1包括如下特征字段：刀盘推进扭矩i1、转速i2、推力i3、贯入度i4、总挤压力i5；
    所述物料消耗报表E2包括如下特征字段：同步注浆量i6、盾尾油脂注入量i7、主轴承密封润滑油脂i8；
    所述危险预警报表E3包括如下特征字段：施工工区监控视频自动识别与抓拍i9、盾构机台车监控视频自动识别与抓拍i10、隧道内部同步施工监控视频自动识别与抓拍i11、驾驶舱监控视频自动识别与抓拍i12、气垫仓监控视频自动识别与抓拍i13；
    所述施工质量报表E4包括如下特征字段：地表沉降i14、管片错台i15、管片上浮i16、盾尾间隙i17、盾构姿态导向i18；
    将所有特征字段构建成一个N维矢量，存储为数据点位表。
  8. 一种盾构施工预警方法，基于如权利要求1至7中任一项所述的盾构施工预警系 统以实现，其特征在于包括如下步骤：
    步骤1、盾构机开始掘进施工，操作人员现场或者远程控制预警交互终端，设置采集终端的采集参数，并下达指令，开始采集；
    步骤2、采集终端采集盾构机产生的施工数据、传感器监控数据；
    步骤3、采集终端存储采集到的施工数据和传感器监控数据，形成数据点位表，并利用边缘计算能力进行初步运算，得到处理后的数据，实时转发至数据转发终端，同时采集终端存储的原始数据定期传输至数据转发终端；
    步骤4、监控视频网关终端将接入同一网关的视频监控统一编码，并发送至传输模块的数据转发终端；
    步骤5、数据转发终端将传输的盾构机数据和视频监控打包，通过光纤固网传输至监控展示中心，当光纤固网不能使用时，自动切换至蜂窝移动网络传输；
    步骤6、监控展示中心的通讯模块接收处理后的盾构施工数据和视频监控数据，在数据计算存储服务器进行分析处理，输出结果，并将接收到的原始盾构施工数据直接存储在服务器；
    步骤7、数据计算存储服务器分析处理的结果实时传输至采集预警模块的预警交互终端展示，为盾构机驾驶人员提供辅助决策，当出现参数报警时，预警交互终端的声光报警功能启动，发出警报，同时盾构机操作人员与监控展示中心的技术人员可通过视频通话，远程沟通解决方案；
    步骤8、消警后，声光报警自动关闭。
  9. 根据权利要求8所述的盾构施工预警方法，其特征在于，步骤6中数据计算存储服务器进行分析处理的过程进一步包括：
    步骤6-1、计算盾构机掘进参数：获得预定时段内刀盘推进扭矩、转速、推力、贯入度、总挤压力数据的最值、均值、中位值、波动系数，并生成掘进参数报表；
    计算盾构机物料消耗参数：获得预定时段内同步注浆量、盾尾油脂注入量、主轴承密封润滑油脂数据的均值、环值、与理论消耗量的差值，并生成物料消耗报表；
    分析自动抓拍的监控视频：对各个施工工区、盾构机台车、隧道内部同步施工、驾驶舱、气垫仓监控视频自动识别、抓拍，生成危险预警报表；
    自动测量施工质量数据：对地表沉降、管片错台、管片上浮、盾尾间隙、盾构姿态导向数据自动测量、记录，生成施工质量报表；
    步骤6-2、数据计算存储服务器将生成的报表数据导入至权重模型中，由权重模型计算出评分，并与内建标准数据库比对，超出阈值即自动发出不同等级的预警，预警等级接入专家系统数据库，匹配解决方案。
  10. 根据权利要求9所述的盾构施工预警方法，其特征在于，所述权重模型的构建规则如下：
    步骤6-2A、构建第一权重模型：定义盾构机掘进参数的总权重为WE1，其中权重WE1又进一步划分为如下子权重：刀盘推进扭矩、转速、推力、贯入度、总挤压力，分别对应权重为Wi1、Wi2、Wi3、Wi4、Wi5，Wi1+Wi2+Wi3+Wi4+Wi5＝WE1＝1；
    此外，每个子权重Wi1下对应有预定时段内刀盘推进扭矩的最值、均值、中位值、波动系数，分别对应于权重Wi1max、Wi1average、Wi1median、Wi1fluctuation；其中Wi1max+Wi1average+Wi1median+Wi1fluctuation＝1；
    每个子权重Wi2下对应有预定时段内转速的最值、均值、中位值、波动系数，分别对应于权重Wi2max、Wi2average、Wi2median、Wi2fluctuation；其中Wi2max+Wi2average+Wi2median+Wi2fluctuation＝1；
    每个子权重Wi3下对应有预定时段内推力的最值、均值、中位值、波动系数，分别对应于权重Wi3max、Wi3average、Wi3median、Wi3fluctuation；其中Wi3max+Wi3average+Wi3median+Wi3fluctuation＝1；
    每个子权重Wi4下对应有预定时段内贯入度的最值、均值、中位值、波动系数，分别对应于权重Wi4max、Wi4average、Wi4median、Wi4fluctuation；其中Wi4max+Wi4average+Wi4median+Wi4fluctuation＝1；
    每个子权重Wi5下对应有预定时段内总挤压力的最值、均值、中位值、波动系数，分别对应于权重Wi5max、Wi5average、Wi5median、Wi5fluctuation；其中Wi5max+Wi5average+Wi5median+Wi5fluctuation＝1；
    步骤6-2B、构建第二权重模型：定义盾构机物料消耗参数的总权重为WE2，其中同步注浆量、盾尾油脂注入量、主轴承密封润滑油脂数据分别对应权重为Wi6、Wi7、Wi8，且Wi6+Wi7+Wi8＝1；
    此外，每个子权重Wi6下对应有预定时段内同步注浆量的最值、均值、中位值、波动系数，分别对应于权重Wi6max、Wi6average、Wi6median、Wi6fluctuation；其中Wi6max+Wi6average+Wi6median+Wi6fluctuation＝1；
    每个子权重Wi7下对应有预定时段内盾尾油脂注入量的最值、均值、中位值、波动系数，分别对应于权重Wi7max、Wi7average、Wi7median、Wi7fluctuation；其中Wi7max+Wi7average+Wi7median+Wi7fluctuation＝1；
    每个子权重Wi8下对应有预定时段内主轴承密封润滑油脂数据的最值、均值、中位值、波动系数，分别对应于权重Wi8max、Wi8average、Wi8median、Wi8fluctuation；其中Wi8max+Wi8average+Wi8median+Wi8fluctuation＝1；
    步骤6-2C、构建第三权重模型：定义自动抓拍的监控视频的总权重为WE3；总权重为WE3下的各子数据的权重占比均分；
    步骤6-2D、构建第四权重模型：定义测量得到的施工质量数据的总权重为WE4；总权重为WE4下的各子数据的权重占比均分；
    步骤6-2E、数据计算存储服务器将生成的报表数据分别导入至第一权重模型、第二权重模型、第三权重模型、第四权重模型中，分别输出子评分A、子评分B、子评分C、子评分D、总评分G；将上述评分与预设的标准数据库比对，标准数据库中分别对应有子告警极限W、子告警极限X、子告警极限Y、子告警极限Z、总告警极限S；
    判定规则如下：
    若输出子评分A＞子告警极限W，此时进一步判断所有总评分G是否到达总告警极限S，若总评分G≥总告警极限S，则直接发出一级预警；若总评分G＜总告警极限S，则直接发出二级预警；
    若输出子评分A≤子告警极限W，此时进一步判断子评分B、子评分C、子评分D，若子评分B、子评分C、子评分D中任意一个评分超出其对应的子告警极限，则发出三级预警；
    若子评分B、子评分C、子评分D中任意两个评分超出其对应的子告警极限，则发出二级预警；
    若子评分B、子评分C、子评分D全部超出子告警极限X、子告警极限Y、子告警极限Z，则发出一级预警；
    预警结果接入专家系统数据库，匹配解决方案，辅助技术人员驾驶盾构机。
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