Urban underground pipe gallery system and big data analysis method

Abstract

The present invention provides an urban underground pipeline corridor system and a big data analysis method, which relate to the field of integrated pipeline corridor technology. The urban underground pipeline corridor system described in the present invention includes sensors, intelligent robots, Internet of Things cloud and intelligent devices; the pipeline corridor environment data is used to establish an urban underground pipeline corridor system model, so as to perform big data analysis on the urban underground pipeline corridor system model, determine the corresponding relationship between the data and the environmental trend, and predict the environmental trend based on the data. The technical solution described in the present invention establishes an IoT data model through the pipeline corridor environment data, establishes an urban underground pipeline corridor system model with the BIM model and the GIS layer, performs big data analysis on the urban underground pipeline corridor system model, determines the corresponding relationship between the data and the environmental trend, and predicts the environmental trend based on the data, so as to achieve effective management of the urban underground pipeline corridor.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated pipe gallery, and in particular to an urban underground pipe gallery system and a big data analysis method. Background Art

[0002] Underground utility corridors play an important role in improving the comprehensive carrying capacity of cities and meeting the basic needs of the people. The management of utility corridors is a systematic and complex project. Because they are buried underground, they have the narrow and long linear characteristics of the macro level of the city in terms of spatial form, and contain a variety of structures inside. It is difficult to express information uniformly during the design, construction and operation management process. How to realize data collection and management of urban underground utility corridors has become the focus of the current utility corridor technology upgrade. Summary of the invention

[0003] The problem solved by the present invention is how to realize data collection and management of urban underground pipe corridors.

[0004] To solve the above problems, the present invention provides an urban underground pipe gallery system, including a sensor, an intelligent robot, an Internet of Things cloud and an intelligent device; the sensor is used to monitor the pipe gallery environment data, and send the pipe gallery environment data to the intelligent robot through a serial communication network, wherein the sensor is suitable for sending the pipe gallery environment data to a relay node, the relay node is suitable for transmitting or forwarding the pipe gallery environment data to a gateway node according to an address, and the gateway node is suitable for connecting to the intelligent robot through the Internet or a serial port to send the pipe gallery environment data to the intelligent robot; wherein the sensor is suitable for collecting the pipe gallery environment data through the following processing method: interferometric processing of the monitoring image to obtain three-dimensional data of the underground pipe gallery to determine the static environment data of the underground pipe gallery; according to the monitoring phase difference between two adjacent time points, the position and speed of the moving body are determined to determine the dynamic environment data of the underground pipe gallery; the pipe gallery environment data is determined jointly according to the static environment data and the dynamic environment data; the intelligent robot is used to obtain the pipe gallery environment data collected by the sensor, and through The vehicle-mounted wireless base station is sent to the Internet of Things cloud, wherein the intelligent robot is suitable for sending the corridor environment data to the Internet of Things cloud, or processing the corridor environment data through the ROS node of the intelligent robot to generate control instructions, so as to control the intelligent robot and the intelligent device according to the control instructions; the Internet of Things cloud is used to receive the corridor environment data through a data receiving base station, generate linkage instructions according to the corridor environment data through a data processing module, and send the linkage instructions to the intelligent robot and the intelligent device through a data sending base station, so as to control the intelligent robot and the intelligent device according to the linkage instructions; wherein the linkage instructions are generated by an adaptive neural fuzzy inference system, and the adaptive neural fuzzy inference system is used to train the fuzzy system to adjust the weight of the model prediction control cost function to reduce the error in the path tracking process; wherein the corridor environment data is also used to establish an urban underground corridor system model, so as to perform big data analysis on the urban underground corridor system model, determine the corresponding relationship between data and environmental trends, and predict environmental trends based on the data.

[0005] The urban underground pipeline corridor system described in the present invention collects pipeline corridor environmental data through sensors, and sends the data to the Internet of Things cloud through an intelligent robot connected to the sensor in serial communication, so as to generate linkage instructions to control the intelligent robot and intelligent devices; an IoT data model is established through the pipeline corridor environmental data, and an urban underground pipeline corridor system model is established with the BIM model and the GIS layer, and big data analysis is performed on the urban underground pipeline corridor system model to determine the corresponding relationship between the data and the environmental trend, so as to predict the environmental trend based on the data and realize effective management of the urban underground pipeline corridor.

[0006] Optionally, the urban underground pipe gallery system also includes a controller, which includes a hardware driver module, a functional module and a main control logic module; the hardware driver module is used to drive the sensor to collect the pipe gallery environment data; the functional module is used to adopt a message publish and subscribe mode to enable the distributed ROS nodes to process the pipe gallery environment data to generate the control instructions; the main control logic module is used to call the corresponding functional control service provided in the functional module according to the pipe gallery environment data to control the intelligent robot.

[0007] The urban underground pipe gallery system described in the present invention controls the operation of sensors and intelligent robots through a controller composed of a hardware drive module, a functional module and a main control logic module, thereby achieving effective management of the urban underground pipe gallery.

[0008] Optionally, the sensor is also used to monitor audio signals and send them to the intelligent robot; the intelligent robot is suitable for dividing the audio signal to be detected into multiple audio segments; extracting audio features in each of the audio segments, wherein the audio features at least include time domain features and frequency domain features of the audio segment; and detecting a target speech segment from the audio segment based on the audio features of the audio segment.

[0009] The urban underground pipe gallery system of the present invention is conducive to realizing effective management of the urban underground pipe gallery by arranging sensors to monitor audio signals.

[0010] Optionally, the IoT cloud also includes a task planning module connected to the data processing module, the task planning module is used to determine the inspection task of the intelligent robot according to the pipeline corridor environment data, and send the inspection task to the data processing module to generate the linkage instruction, wherein the task planning module is suitable for determining the inspection task by the following processing method: the nth processing node obtains the nth task processing message from the data processing module, and performs the nth predetermined operation according to the nth task processing message, and sends a feedback message to the data processing module according to the execution result of the nth predetermined operation; wherein the nth predetermined operation is one or more predetermined sub-operations performed by the nth processing node; when the nth predetermined operation is executed correctly, the n+1th processing node obtains the nth task processing message from the data processing module, and performs the nth predetermined operation according to the execution result of the nth predetermined operation. The processing module obtains the n+1th task processing message formed based on the feedback message, and performs the n+1th scheduled operation according to the n+1th task processing message; wherein the n+1th scheduled operation is one or more scheduled sub-operations performed by the n+1th processing node; wherein the n+1th processing node is the next operation node of the nth processing node; the nth scheduled operation and the n+1th scheduled operation are response operations in response to the same task request; n is a positive integer less than N; N is the total number of processing nodes that respond to the task request; when the nth scheduled operation is executed incorrectly, the exception processing node obtains the abnormal task processing message formed based on the feedback message from the data processing module, and performs the abnormal processing operation according to the abnormal task processing message.

[0011] The urban underground pipe gallery system described in the present invention plans the inspection tasks and routes of the intelligent robot according to the pipe gallery environment data through the task planning module, so as to clarify the inspection plan of the intelligent robot for the pipe gallery, thereby realizing the effective management of the urban underground pipe gallery.

[0012] The present invention also provides a big data analysis method for an urban underground pipeline corridor, which is applied to the urban underground pipeline corridor system described above, and includes: establishing an IoT data model, a BIM model and a GIS layer of the urban underground pipeline corridor; establishing an urban underground pipeline corridor system model based on the IoT data model, the BIM model and the GIS layer; performing big data analysis on the urban underground pipeline corridor system model to determine the corresponding relationship between the data and the environmental trend, so as to predict the environmental trend based on the data.

[0013] The big data analysis method for urban underground pipeline corridors described in the present invention establishes an IoT data model through pipeline corridor environmental data, establishes an urban underground pipeline corridor system model with a BIM model and a GIS layer, performs big data analysis on the urban underground pipeline corridor system model, determines the corresponding relationship between the data and the environmental trend, predicts the environmental trend based on the data, and realizes effective management of the urban underground pipeline corridor.

[0014] Optionally, establishing the IoT data model of the urban underground pipeline corridor includes: obtaining the IoT data, processing the IoT data and applying the IoT data; wherein, obtaining the IoT data includes: collecting the IoT data of the urban underground pipeline corridor through intelligent robots and sensors; processing the IoT data includes: uploading the IoT data to the IoT cloud, and the IoT cloud generates linkage instructions based on the IoT data; applying the IoT data includes: controlling the operation of smart devices and the intelligent robots through the linkage instructions.

[0015] The big data analysis method for urban underground utility corridors described in the present invention collects utility corridor environment data through sensors, and sends the data to the Internet of Things cloud through an intelligent robot connected to the sensors in serial communication, so as to generate linkage instructions to control the intelligent robot and intelligent devices, thereby realizing effective management of urban underground utility corridors.

[0016] Optionally, establishing the BIM model of the urban underground utility corridor includes: establishing an initial BIM model based on the drawing information of the urban underground utility corridor; reading the attribute information of the initial BIM model, using the unique code of each BIM component of the initial BIM model as the primary key, and importing the attribute information into a relational database to establish the BIM model.

[0017] The big data analysis method of the urban underground utility gallery described in the present invention establishes an initial BIM model according to the drawing information of the urban underground utility gallery, and establishes a BIM model according to the attribute information of the initial BIM model, so that the urban underground utility gallery system model can be established according to the drawing information, so as to perform big data analysis on the urban underground utility gallery system model, determine the corresponding relationship between the data and the environmental trend, predict the environmental trend according to the data, and realize the effective management of the urban underground utility gallery.

[0018] Optionally, establishing the GIS layer of the urban underground pipeline corridor includes: generating a layer file of the urban underground pipeline corridor based on the drawing information of the urban underground pipeline corridor and the urban geographic information system; storing the layer file into the relational database to establish the GIS layer.

[0019] The big data analysis method of the urban underground pipeline corridor described in the present invention establishes an urban underground pipeline corridor system model by combining the drawing information of the urban underground pipeline corridor with the urban geographic information system, so as to perform big data analysis on the urban underground pipeline corridor system model, determine the corresponding relationship between the data and the environmental trend, predict the environmental trend according to the data, and realize the effective management of the urban underground pipeline corridor.

[0020] Optionally, establishing an urban underground utility corridor system according to the IoT data model, the BIM model and the GIS layer includes: using WebGL technology and WebGIS technology to determine a two-dimensional GIS model and a three-dimensional BIM model of the urban underground utility corridor according to the BIM model and the GIS layer: based on the two-dimensional GIS model and the three-dimensional BIM model, querying, retrieving, changing and locating component information according to the unique code of the component in the relational database; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship between the unique code of the BIM component and the shapefile to realize the linkage query of BIM data and GIS data; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship between the unique code of the BIM component and the IoT data to realize the linkage query of the BIM data and the IoT data.

[0021] The big data analysis method of the urban underground pipeline gallery described in the present invention establishes an urban underground pipeline gallery system through the IoT data model, the BIM model and the GIS layer, so as to perform big data analysis on the urban underground pipeline gallery system model, determine the corresponding relationship between the data and the environmental trend, predict the environmental trend according to the data, and realize the effective management of the urban underground pipeline gallery.

[0022] Optionally, the big data analysis of the urban underground utility corridor system model to determine the correspondence between the data and the environmental trend includes: judging whether the intelligent robot and the sensor are connected to the Internet of Things cloud by a data analysis unit; when the intelligent robot and the sensor are connected to the Internet of Things cloud, the data analysis unit sends the data collected by the intelligent robot and the sensor to the Internet of Things cloud; and the big data system stored in the Internet of Things cloud performs calculations and analysis on the received data to determine the correspondence between the data and the environmental trend.

[0023] The big data analysis method for urban underground utility corridors described in the present invention performs big data analysis on the urban underground utility corridor system model to predict environmental trends based on the data, thereby achieving effective management of urban underground utility corridors. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is an urban underground pipe gallery system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] like Figure 1 As shown, an embodiment of the present invention provides an urban underground pipe gallery system, including a sensor, an intelligent robot, an Internet of Things cloud and an intelligent device; the sensor is used to monitor the pipe gallery environment data, and send the pipe gallery environment data to the intelligent robot through a serial communication network, wherein the sensor is suitable for sending the pipe gallery environment data to a relay node, the relay node is suitable for transmitting or forwarding the pipe gallery environment data to a gateway node according to an address, and the gateway node is suitable for connecting to the intelligent robot through the Internet or a serial port to send the pipe gallery environment data to the intelligent robot; wherein the sensor is suitable for collecting the pipe gallery environment data by the following processing method: interferometric processing of the monitoring image to obtain three-dimensional data of the underground pipe gallery to determine the static environment data of the underground pipe gallery; determining the position and speed of the moving body according to the monitoring phase difference between two adjacent time points to determine the dynamic environment data of the underground pipe gallery; determining the pipe gallery environment data based on the static environment data and the dynamic environment data; the intelligent robot is used to obtain the pipe gallery environment data collected by the sensor, and transmit the data to the intelligent robot through a vehicle. The wireless base station is sent to the Internet of Things cloud, wherein the intelligent robot is suitable for sending the corridor environment data to the Internet of Things cloud, or processing the corridor environment data through the ROS node of the intelligent robot to generate a control instruction, so as to control the intelligent robot and the intelligent device according to the control instruction; the Internet of Things cloud is used to receive the corridor environment data through a data receiving base station, generate a linkage instruction according to the corridor environment data through a data processing module, and send the linkage instruction to the intelligent robot and the intelligent device through a data sending base station, so as to control the intelligent robot and the intelligent device according to the linkage instruction; wherein the linkage instruction is generated by an adaptive neural fuzzy inference system, and the adaptive neural fuzzy inference system is used to train the fuzzy system to adjust the weight of the model prediction control cost function to reduce the error in the path tracking process; wherein the corridor environment data is also used to establish an urban underground corridor system model, so as to perform big data analysis on the urban underground corridor system model, determine the corresponding relationship between the data and the environmental trend, and predict the environmental trend according to the data.

[0027] Specifically, in this embodiment, the urban underground pipeline corridor system includes sensors, intelligent robots, Internet of Things cloud and intelligent devices.

[0028] The sensor is used to monitor the environment data of the pipeline corridor and send the environment data of the pipeline corridor to the intelligent robot through the serial communication network, wherein the sensor is suitable for sending the environment data of the pipeline corridor to the relay node, the relay node is suitable for transmitting or forwarding the environment data of the pipeline corridor to the gateway node according to the address, and the gateway node is suitable for connecting to the intelligent robot through the Internet or the serial port to send the environment data of the pipeline corridor to the intelligent robot.

[0029] Among them, the sensor is suitable for collecting the environment data of the underground corridor through the following processing methods: interferometric processing of the monitoring image to obtain the three-dimensional data of the underground corridor to determine the static environment data of the underground corridor; according to the monitoring phase difference between two adjacent time points, the position and speed of the moving object are determined to determine the dynamic environment data of the underground corridor; the environment data of the corridor is determined jointly based on the static environment data and the dynamic environment data.

[0030] Among them, the sensors include environmental parameter sensors, ultrasonic sensors and noise sensors, which are used to collect and monitor the corridor environment data, and send the corridor environment data to the intelligent robot through a serial communication network.

[0031] The intelligent robot is used to obtain the pipeline corridor environment data collected by sensors and send it to the Internet of Things cloud through the vehicle-mounted wireless base station. The intelligent robot is suitable for sending the pipeline corridor environment data to the Internet of Things cloud, or processing the pipeline corridor environment data through the ROS node of the intelligent robot to generate control instructions, so as to control the intelligent robot and intelligent devices according to the control instructions.

[0032] The intelligent robot is an on-site work platform, responsible for functions such as monitoring the corridor environment information, fault risk analysis and instrument information collection, and sends the corridor environment data to the Internet of Things cloud through the vehicle-mounted wireless base station.

[0033] The IoT cloud includes a data communication unit, a data processing unit, a task planning unit and a human-computer interaction unit, wherein the data communication unit includes a data receiving base station and a data sending base station, and the data processing unit includes a data processing module. The IoT cloud receives the tunnel environment data through the data receiving base station, generates linkage instructions according to the tunnel environment data through the data processing module, and sends the linkage instructions to the intelligent robots and intelligent devices through the data sending base station to realize the control of the intelligent robots and intelligent devices, thereby realizing the effective management of the urban underground tunnel environment.

[0034] Among them, the linkage instructions are generated by an adaptive neuro-fuzzy inference system, and the adaptive neuro-fuzzy inference system is used to train the fuzzy system to adjust the weight of the model predictive control cost function to reduce the error in the path tracking process.

[0035] Among them, the intelligent robot cruises inside the tunnel for inspection, recognizes the readings of the instruments through vision, and uploads them to the IoT cloud. Since the intelligent robot is constantly moving and it is difficult to receive GPS satellite signals in the tunnel, the intelligent robot does not send geographic location information.

[0036] The sensors are responsible for monitoring the environment in the tunnel. Once an unexpected situation occurs (fire, excessive concentration of harmful gases, etc.), timely alarms are required. Therefore, the monitoring data can be uploaded every five seconds, so it can be viewed in real time on the human-computer interaction interface of the IoT cloud. Since the position of the sensor will not change after installation, the GPS data only needs to be uploaded once after the sensor is installed.

[0037] Among them, smart devices such as smart fans will turn on the fan to supply air when the concentration of harmful gases is greater than the threshold. The frequency of receiving commands is the same as that of sensors, which is downloaded once every five seconds. Smart devices do not need to send GPS location information.

[0038] Among them, the corridor environment data collected by sensors can be used as IoT (Internet of Things) data to establish an IoT data model, and an urban underground corridor system model can be established with the BIM model and GIS layer. Big data analysis can be performed on the urban underground corridor system model to determine the correspondence between the data and environmental trends, so as to predict environmental trends based on the data and achieve effective management of urban underground corridors.

[0039] In this embodiment, the pipeline corridor environmental data is collected by sensors and sent to the Internet of Things cloud through an intelligent robot connected to the sensor in serial communication to generate linkage instructions to control the intelligent robot and intelligent devices; an IoT data model is established through the pipeline corridor environmental data, and an urban underground pipeline corridor system model is established with the BIM model and GIS layer. Big data analysis is performed on the urban underground pipeline corridor system model to determine the correspondence between the data and the environmental trend, so as to predict the environmental trend based on the data and realize effective management of the urban underground pipeline corridor.

[0040] Optionally, the urban underground pipe gallery system also includes a controller, which includes a hardware driver module, a functional module and a main control logic module; the hardware driver module is used to drive the sensor to collect the pipe gallery environment data; the functional module is used to adopt a message publish and subscribe mode to enable the distributed ROS nodes to process the pipe gallery environment data to generate the control instructions; the main control logic module is used to call the corresponding functional control service provided in the functional module according to the pipe gallery environment data to control the intelligent robot.

[0041] Specifically, in this embodiment, the urban underground pipe gallery system also includes a controller, which includes a hardware driver module, a functional module and a main control logic module; the hardware driver module is used to drive the sensor to collect pipe gallery environment data; the functional module is used to use the message publishing and subscription mode to enable the distributed ROS nodes to process the pipe gallery environment data to generate control instructions; the main control logic module is used to call the corresponding functional control service provided in the functional module according to the pipe gallery environment data to control the intelligent robot. The controller composed of the hardware driver module, the functional module and the main control logic module controls the operation of the sensor and the intelligent robot, thereby realizing the effective management of the urban underground pipe gallery.

[0042] In this embodiment, the controller composed of a hardware driver module, a functional module and a main control logic module controls the operation of sensors and intelligent robots, thereby achieving effective management of urban underground pipe corridors.

[0043] Optionally, the sensor is also used to monitor audio signals and send them to the intelligent robot; the intelligent robot is suitable for dividing the audio signal to be detected into multiple audio segments; extracting audio features in each of the audio segments, wherein the audio features at least include time domain features and frequency domain features of the audio segment; and detecting a target speech segment from the audio segment based on the audio features of the audio segment.

[0044] Specifically, in this embodiment, the sensor is also used to monitor the audio signal and send it to the intelligent robot; the intelligent robot is suitable for dividing the audio signal to be detected into multiple audio segments; extracting the audio features in each audio segment, wherein the audio features at least include the time domain features and frequency domain features of the audio segment; and detecting the target speech segment from the audio segment according to the audio features of the audio segment. By setting up sensors to monitor audio signals, it is conducive to achieving effective management of urban underground pipe corridors.

[0045] In this embodiment, by setting up sensors to monitor audio signals, it is beneficial to achieve effective management of urban underground pipe galleries.

[0046] Optionally, the IoT cloud also includes a task planning module connected to the data processing module, the task planning module is used to determine the inspection task of the intelligent robot according to the pipeline corridor environment data, and send the inspection task to the data processing module to generate the linkage instruction, wherein the task planning module is suitable for determining the inspection task by the following processing method: the nth processing node obtains the nth task processing message from the data processing module, and performs the nth predetermined operation according to the nth task processing message, and sends a feedback message to the data processing module according to the execution result of the nth predetermined operation; wherein the nth predetermined operation is one or more predetermined sub-operations performed by the nth processing node; when the nth predetermined operation is executed correctly, the n+1th processing node obtains the nth task processing message from the data processing module, and performs the nth predetermined operation according to the execution result of the nth predetermined operation. The processing module obtains the n+1th task processing message formed based on the feedback message, and performs the n+1th scheduled operation according to the n+1th task processing message; wherein the n+1th scheduled operation is one or more scheduled sub-operations performed by the n+1th processing node; wherein the n+1th processing node is the next operation node of the nth processing node; the nth scheduled operation and the n+1th scheduled operation are response operations in response to the same task request; n is a positive integer less than N; N is the total number of processing nodes that respond to the task request; when the nth scheduled operation is executed incorrectly, the exception processing node obtains the abnormal task processing message formed based on the feedback message from the data processing module, and performs the abnormal processing operation according to the abnormal task processing message.

[0047] Specifically, in this embodiment, the IoT cloud also includes a task planning module connected to the data processing module, which is used to determine the inspection task of the intelligent robot according to the pipeline corridor environment data, and send the inspection task to the data processing module to generate a linkage instruction. That is, the task planning module can plan the inspection task and route of the intelligent robot according to the pipeline corridor environment data, so as to clarify the inspection plan of the intelligent robot for the pipeline corridor, thereby realizing the effective management of the urban underground pipeline corridor.

[0048] The task planning module is suitable for determining the inspection task by adopting the following processing method: the nth processing node obtains the nth task processing message from the data processing module, and performs the nth predetermined operation according to the nth task processing message, and sends a feedback message to the data processing module according to the execution result of the nth predetermined operation; wherein the nth predetermined operation is one or more predetermined sub-operations performed by the nth processing node; when the nth predetermined operation is performed correctly, the n+1th processing node obtains the n+1th task processing message formed based on the feedback message from the data processing module, and performs the n+1th predetermined operation according to the n+1th task processing message; wherein the n+1th predetermined operation is one or more predetermined sub-operations performed by the n+1th processing node; wherein the n+1th processing node is the next operation node of the nth processing node; the nth predetermined operation and the n+1th predetermined operation are response operations in response to the same task request; n is a positive integer less than N; N is the total number of processing nodes that respond to the task request; when the nth predetermined operation is performed incorrectly, the exception processing node obtains the abnormal task processing message formed based on the feedback message from the data processing module, and performs the abnormal processing operation according to the abnormal task processing message.

[0049] In this embodiment, the inspection tasks and routes of the intelligent robot are planned according to the pipeline corridor environment data through the task planning module to clarify the inspection plan of the intelligent robot for the pipeline corridor, thereby realizing effective management of the urban underground pipeline corridor.

[0050] Another embodiment of the present invention provides a big data analysis method for an urban underground utility corridor, which is applied to the urban underground utility corridor system described above, including: establishing an IoT data model, a BIM model and a GIS layer of the urban underground utility corridor; establishing an urban underground utility corridor system model based on the IoT data model, the BIM model and the GIS layer; performing big data analysis on the urban underground utility corridor system model to determine the correspondence between the data and the environmental trend, so as to predict the environmental trend based on the data.

[0051] Specifically, in this embodiment, the big data analysis method of the urban underground pipe gallery includes: establishing an IoT data model, a BIM model and a GIS layer of the urban underground pipe gallery; establishing an urban underground pipe gallery system model based on the IoT data model, the BIM model and the GIS layer; performing big data analysis on the urban underground pipe gallery system model to determine the corresponding relationship between the data and the environmental trend, so as to predict the environmental trend based on the data. The pipe gallery environmental data collected by the sensor can be used as IoT data to establish an IoT data model, and an urban underground pipe gallery system model is established with the BIM model and the GIS layer. Big data analysis is performed on the urban underground pipe gallery system model to determine the corresponding relationship between the data and the environmental trend, so as to predict the environmental trend based on the data, and realize the effective management of the urban underground pipe gallery.

[0052] In this embodiment, an IoT data model is established through the corridor environment data, and an urban underground corridor system model is established with the BIM model and the GIS layer. Big data analysis is performed on the urban underground corridor system model to determine the correspondence between the data and the environmental trend, so as to predict the environmental trend based on the data and realize the effective management of the urban underground corridor.

[0053] Optionally, establishing the IoT data model of the urban underground pipeline corridor includes: obtaining the IoT data, processing the IoT data and applying the IoT data; wherein, obtaining the IoT data includes: collecting the IoT data of the urban underground pipeline corridor through intelligent robots and sensors; processing the IoT data includes: uploading the IoT data to the IoT cloud, and the IoT cloud generates linkage instructions based on the IoT data; applying the IoT data includes: controlling the operation of smart devices and the intelligent robots through the linkage instructions.

[0054] Specifically, in this embodiment, establishing an IoT data model for an urban underground pipe gallery includes: obtaining IoT data, processing IoT data, and applying IoT data; wherein obtaining IoT data includes: collecting IoT data for an urban underground pipe gallery through intelligent robots and sensors; processing IoT data includes: uploading IoT data to the IoT cloud, and the IoT cloud generates linkage instructions based on the IoT data; applying IoT data includes: controlling the operation of intelligent devices and intelligent robots through linkage instructions. Pipe gallery environment data is collected through sensors, and sent to the IoT cloud through an intelligent robot connected to the sensor in serial communication, so as to generate linkage instructions to control the intelligent robot and intelligent devices.

[0055] In this embodiment, the pipeline corridor environment data is collected by sensors and sent to the Internet of Things cloud through an intelligent robot connected to the sensor in serial communication, so as to generate linkage instructions to control the intelligent robot and intelligent devices, thereby realizing effective management of the city's underground pipeline corridors.

[0056] Optionally, establishing the BIM model of the urban underground utility corridor includes: establishing an initial BIM model based on the drawing information of the urban underground utility corridor; reading the attribute information of the initial BIM model, using the unique code of each BIM component of the initial BIM model as the primary key, and importing the attribute information into a relational database to establish the BIM model.

[0057] Specifically, in this embodiment, establishing a BIM model of an urban underground utility corridor includes: establishing an initial BIM model based on the drawing information of the urban underground utility corridor (such as a comprehensive utility corridor plan network diagram); reading the attribute information of the initial BIM model, using the unique code of each BIM component of the initial BIM model as the primary key, and importing the attribute information into a relational database to establish the BIM model.

[0058] In this embodiment, an initial BIM model is established based on the drawing information of the urban underground utility corridor, and a BIM model is established based on the attribute information of the initial BIM model, so that an urban underground utility corridor system model can be established based on the drawing information, so as to perform big data analysis on the urban underground utility corridor system model, determine the correspondence between the data and the environmental trends, predict the environmental trends based on the data, and realize effective management of the urban underground utility corridor.

[0059] Optionally, establishing the GIS layer of the urban underground pipeline corridor includes: generating a layer file of the urban underground pipeline corridor based on the drawing information of the urban underground pipeline corridor and the urban geographic information system; storing the layer file into the relational database to establish the GIS layer.

[0060] Specifically, in this embodiment, establishing the GIS layer of the urban underground pipe gallery includes: generating a layer file of the urban underground pipe gallery according to the drawing information of the urban underground pipe gallery and the urban geographic information system; storing the layer file in a relational database to establish a GIS layer. That is, combining the drawing information of the urban underground pipe gallery (such as the integrated pipe gallery plane network diagram) with the urban geographic information system (such as the urban road network diagram, three-dimensional street view diagram) to establish an urban underground pipe gallery system model, so as to perform big data analysis on the urban underground pipe gallery system model, determine the corresponding relationship between the data and the environmental trend, and predict the environmental trend according to the data to achieve effective management of the urban underground pipe gallery.

[0061] In this embodiment, an urban underground pipeline corridor system model is established by combining the drawing information of the urban underground pipeline corridor with the urban geographic information system, so as to perform big data analysis on the urban underground pipeline corridor system model, determine the correspondence between the data and the environmental trend, and predict the environmental trend based on the data to achieve effective management of the urban underground pipeline corridor.

[0062] Optionally, establishing an urban underground utility corridor system according to the IoT data model, the BIM model and the GIS layer includes: using WebGL technology and WebGIS technology to determine a two-dimensional GIS model and a three-dimensional BIM model of the urban underground utility corridor according to the BIM model and the GIS layer: based on the two-dimensional GIS model and the three-dimensional BIM model, querying, retrieving, changing and locating component information according to the unique code of the component in the relational database; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship between the unique code of the BIM component and the shapefile to realize the linkage query of BIM data and GIS data; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship between the unique code of the BIM component and the IoT data to realize the linkage query of the BIM data and the IoT data.

[0063] Specifically, in the present embodiment, establishing an urban underground utility corridor system according to the IoT data model, the BIM model and the GIS layer includes: using WebGL technology and WebGIS technology to determine the two-dimensional GIS model and the three-dimensional BIM model of the urban underground utility corridor according to the BIM model and the GIS layer; based on the two-dimensional GIS model and the three-dimensional BIM model, querying, retrieving, changing and locating component information according to the unique coding of the component in the relational database; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship with the shapefile according to the unique coding of the BIM component, so as to realize the linkage query of BIM data and GIS data; based on the two-dimensional GIS model and the three-dimensional BIM model, establishing a one-to-one or many-to-one mapping relationship with the IoT data according to the unique coding of the BIM component, so as to realize the linkage query of BIM data and IoT data.

[0064] Among them, WebGL is a 3D drawing standard. This drawing technology standard allows JavaScript and OpenGLES 2.0 to be combined together. By adding a JavaScript binding to OpenGL ES 2.0, WebGL can provide hardware 3D accelerated rendering for HTML5Canvas, so that Web developers can use the system graphics card to display 3D scenes and models more smoothly in the browser, and create complex navigation and data visualization.

[0065] Among them, WebGIS (Web Geographic Information System) refers to GIS working on the Web. It is an extension and development of traditional GIS on the Internet. It has the characteristics of traditional GIS and can realize basic GIS functions such as spatial data retrieval, query, mapping output, and editing. It is also the basis for geographic information publishing, sharing, communication and collaboration on the Internet.

[0066] In this embodiment, an urban underground pipeline corridor system is established through the IoT data model, BIM model and GIS layer to perform big data analysis on the urban underground pipeline corridor system model, determine the correspondence between the data and environmental trends, predict environmental trends based on the data, and achieve effective management of the urban underground pipeline corridor.

[0067] Optionally, the big data analysis of the urban underground utility corridor system model to determine the correspondence between the data and the environmental trend includes: judging whether the intelligent robot and the sensor are connected to the Internet of Things cloud by a data analysis unit; when the intelligent robot and the sensor are connected to the Internet of Things cloud, the data analysis unit sends the data collected by the intelligent robot and the sensor to the Internet of Things cloud; and the big data system stored in the Internet of Things cloud performs calculations and analysis on the received data to determine the correspondence between the data and the environmental trend.

[0068] Specifically, in this embodiment, a big data analysis is performed on the urban underground pipeline corridor system model to determine the correspondence between the data and the environmental trends, including: judging whether the intelligent robot and the sensor are connected to the Internet of Things cloud through a data analysis unit; when the intelligent robot and the sensor are connected to the Internet of Things cloud, the data analysis unit sends the data collected by the intelligent robot and the sensor to the Internet of Things cloud; and the big data system stored in the Internet of Things cloud performs calculations and analyses on the received data to determine the correspondence between the data and the environmental trends.

[0069] Among them, after the corridor information system has been running smoothly for a period of time, a lot of sensor data and equipment operation and maintenance data will be accumulated. If this part of the data is not analyzed, it will be a huge waste. The existing solutions lack the analysis and utilization of big data. Therefore, in this embodiment, big data analysis is performed on the urban underground corridor system model to predict environmental trends based on the data, thereby realizing effective management of the urban underground corridor.

[0070] In this embodiment, big data analysis is performed on the urban underground utility corridor system model to predict environmental trends based on the data, thereby achieving effective management of the urban underground utility corridor.

[0071] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. An urban underground pipe gallery system, characterized in that: Including sensors, smart robots, IoT cloud and smart devices; The sensor is used to monitor the pipe gallery environment data and send the pipe gallery environment data to the intelligent robot through a serial communication network, wherein the sensor is suitable for sending the pipe gallery environment data to a relay node, the relay node is suitable for transmitting or forwarding the pipe gallery environment data to a gateway node according to an address, and the gateway node is suitable for connecting to the intelligent robot through the Internet or a serial port to send the pipe gallery environment data to the intelligent robot; The sensor is suitable for collecting the pipe gallery environment data through the following processing method: Interference processing is performed on the monitoring image to obtain three-dimensional data of the underground pipeline gallery to determine static environmental data of the underground pipeline gallery; Determine the position and speed of the moving object according to the monitoring phase difference between two adjacent time points to determine the dynamic environmental data of the underground pipe gallery; Determine the pipe gallery environment data jointly according to the static environment data and the dynamic environment data; The intelligent robot is used to obtain the pipe gallery environment data collected by the sensor and send it to the Internet of Things cloud through the vehicle-mounted wireless base station, wherein the intelligent robot is suitable for sending the pipe gallery environment data to the Internet of Things cloud, or processing the pipe gallery environment data through the ROS node of the intelligent robot to generate a control instruction, so as to control the intelligent robot and the intelligent device according to the control instruction; The IoT cloud is used to receive the tunnel environment data through a data receiving base station, generate a linkage instruction according to the tunnel environment data through a data processing module, and send the linkage instruction to the intelligent robot and the intelligent device through a data sending base station, so as to control the intelligent robot and the intelligent device according to the linkage instruction; wherein the linkage instruction is generated by an adaptive neural fuzzy inference system, and the adaptive neural fuzzy inference system is used to train the fuzzy system to adjust the weight of the model predictive control cost function to reduce the error in the path tracking process; Among them, the pipeline corridor environmental data is also used to establish an urban underground pipeline corridor system model to perform big data analysis on the urban underground pipeline corridor system model, determine the correspondence between the data and environmental trends, and predict environmental trends based on the data.

2. The urban underground pipe gallery system according to claim 1, characterized in that: It also includes a controller, which includes a hardware driver module, a function module and a main control logic module; The hardware driver module is used to drive the sensor to collect the pipe gallery environment data; The functional module is used to use a message publishing and subscription mode to enable the distributed ROS nodes to process the pipe gallery environment data to generate the control instructions; The main control logic module is used to call the corresponding function control service provided in the function module according to the pipeline corridor environment data to control the intelligent robot.

3. The urban underground pipe gallery system according to claim 1, characterized in that: The sensor is also used to monitor audio signals and send them to the intelligent robot; The intelligent robot is suitable for dividing the audio signal to be detected into a plurality of audio segments; Extracting audio features from each of the audio segments, wherein the audio features at least include time domain features and frequency domain features of the audio segment; A target speech segment is detected from the audio segment according to the audio feature of the audio segment.

4. The urban underground pipe gallery system according to claim 1, characterized in that: The IoT cloud also includes a task planning module connected to the data processing module, the task planning module is used to determine the inspection task of the intelligent robot according to the pipe gallery environment data, and send the inspection task to the data processing module to generate the linkage instruction, wherein the task planning module is suitable for determining the inspection task by the following processing method: The nth processing node obtains the nth task processing message from the data processing module, performs the nth predetermined operation according to the nth task processing message, and sends a feedback message to the data processing module according to the execution result of the nth predetermined operation; wherein the nth predetermined operation is one or more predetermined sub-operations performed by the nth processing node; When the nth predetermined operation is executed correctly, the n+1th processing node obtains the n+1th task processing message formed based on the feedback message from the data processing module, and executes the n+1th predetermined operation according to the n+1th task processing message; wherein the n+1th predetermined operation is one or more predetermined sub-operations executed by the n+1th processing node; Wherein, the n+1th processing node is the next operation node of the nth processing node; the nth scheduled operation and the n+1th scheduled operation are response operations in response to the same task request; n is a positive integer less than N; N is the total number of processing nodes that respond to the task request; When the nth predetermined operation is executed incorrectly, the exception processing node obtains an exception task processing message formed based on the feedback message from the data processing module, and performs an exception processing operation according to the exception task processing message.

5. A big data analysis method for an urban underground utility gallery, applied to the urban underground utility gallery system according to any one of claims 1 to 4, characterized in that: include: Establish IoT data model, BIM model and GIS layer of urban underground pipeline corridor; Establishing an urban underground pipe gallery system model according to the IoT data model, the BIM model and the GIS layer; A big data analysis is performed on the urban underground pipe gallery system model to determine the corresponding relationship between the data and the environmental trend so as to predict the environmental trend based on the data.

6. The big data analysis method for urban underground pipe gallery according to claim 5 is characterized in that: The establishment of the IoT data model of the urban underground pipe gallery includes: acquiring the IoT data, processing the IoT data, and applying the IoT data; Wherein, the obtaining of the IoT data includes: collecting the IoT data of the urban underground pipeline gallery through intelligent robots and sensors; The processing of the IoT data includes: uploading the IoT data to an IoT cloud, wherein the IoT cloud generates a linkage instruction according to the IoT data; The applying of the IoT data includes: controlling the operation of the smart device and the smart robot through the linkage instruction.

7. The big data analysis method for urban underground pipe gallery according to claim 6 is characterized in that: The BIM model of establishing an urban underground pipe gallery includes: Establish the initial BIM model based on the drawing information of the urban underground pipeline gallery; The attribute information of the initial BIM model is read, and the unique code of each BIM component of the initial BIM model is used as a primary key, and the attribute information is imported into a relational database to establish a BIM model.

8. The big data analysis method for urban underground pipe gallery according to claim 7 is characterized in that: The GIS layer for establishing the urban underground pipe gallery includes: Generate a layer file of the urban underground pipe gallery according to the drawing information of the urban underground pipe gallery and the urban geographic information system; The layer file is stored in the relational database to establish a GIS layer.

9. The big data analysis method for urban underground pipe gallery according to claim 7 is characterized in that: The establishing of the urban underground pipe gallery system according to the IoT data model, the BIM model and the GIS layer includes: Using WebGL technology and WebGIS technology, the two-dimensional GIS model and the three-dimensional BIM model of the urban underground pipeline corridor are determined according to the BIM model and the GIS layer: Based on the two-dimensional GIS model and the three-dimensional BIM model, query, retrieve, modify and locate component information according to the unique component code in the relational database; Based on the two-dimensional GIS model and the three-dimensional BIM model, a one-to-one or many-to-one mapping relationship is established between the unique code of the BIM component and the shapefile to realize the linkage query of BIM data and GIS data; Based on the two-dimensional GIS model and the three-dimensional BIM model, a one-to-one or many-to-one mapping relationship is established between the unique code of the BIM component and the IoT data to realize the linkage query of the BIM data and the IoT data.

10. The big data analysis method for urban underground pipe gallery according to claim 7, characterized in that: The performing of big data analysis on the urban underground pipe gallery system model to determine the corresponding relationship between the data and the environmental trend includes: Determine, by a data analysis unit, whether the intelligent robot and the sensor are connected to the Internet of Things cloud; When the intelligent robot and the sensor are connected to the Internet of Things cloud, the data analysis unit sends the data collected by the intelligent robot and the sensor to the Internet of Things cloud; The big data system stored in the cloud of the Internet of Things performs calculations and analyses on the received data to determine the corresponding relationship between the data and the environmental trend.

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