CN116866723B - Pipeline safety real-time monitoring and early warning system - Google Patents
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Abstract
The application is applicable to pipeline control technical field, provides a pipeline safety real-time supervision early warning system, include: the acquisition module is used for acquiring the pipeline panoramic image; the construction module is used for constructing a pipeline three-dimensional virtual model according to the pipeline panoramic image; the acquisition module is used for acquiring real-time monitoring data corresponding to each pipeline section; wherein; the determining module is used for determining the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model; an adding module for adding the real-time monitoring data to the pipeline three-dimensional virtual model based on the virtual position; the generation module is used for generating a three-dimensional monitoring early warning picture based on the pipeline three-dimensional virtual model added with the real-time monitoring data and pushing the three-dimensional monitoring early warning picture to a user. Therefore, the method and the device can be based on three-dimensional monitoring and early warning picture display of three dimensions, and a user can conveniently and intuitively check the real-time monitoring condition of the pipeline.
Description
Technical Field
The application belongs to the technical field of pipeline monitoring, and particularly relates to a pipeline safety real-time monitoring and early warning system.
Background
Natural gas pipelines and oil and gas pipelines are energy materials which are high-pressure, flammable and explosive, and once leakage, explosion or other safety accidents occur, serious casualties, property loss and environmental damage can be caused. At present, a video monitoring technology and other modes are adopted to monitor the pipeline in real time, and monitoring early warning pictures are pushed to workers for viewing, but only two-dimensional plane display is realized, and a pipeline safety real-time monitoring early warning system based on three-dimensional is lacked, so that a user can more intuitively view the monitoring condition.
Disclosure of Invention
The embodiment of the application provides a pipeline safety real-time monitoring and early warning system, which can solve the problem that a monitoring and early warning picture in the prior art can only be displayed on a two-dimensional plane, so that a user can not view the picture intuitively.
The embodiment of the application provides a pipeline safety real-time monitoring and early warning system, which comprises:
the acquisition module is used for acquiring the pipeline panoramic image; the pipeline comprises a plurality of pipeline sections which are sequentially connected end to end, and the pipeline panoramic image is a panoramic image corresponding to each pipeline section;
the construction module is used for constructing a pipeline three-dimensional virtual model according to the pipeline panoramic image;
the acquisition module is used for acquiring real-time monitoring data corresponding to each pipeline section; the real-time monitoring data comprise video data, pressure data and sound wave data;
the determining module is used for determining the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
an adding module for adding the real-time monitoring data to the pipeline three-dimensional virtual model based on the virtual position;
the generation module is used for generating a three-dimensional monitoring early warning picture based on the pipeline three-dimensional virtual model added with the real-time monitoring data and pushing the three-dimensional monitoring early warning picture to a user.
In one possible implementation, the building module includes:
the depth recognition sub-module is used for recognizing depth information corresponding to the pipeline panoramic image according to a preset depth recognition network architecture;
the point cloud generation sub-module is used for generating point cloud according to the pipeline panoramic image and the depth information;
and the point cloud reconstruction sub-module is used for reconstructing a pipeline three-dimensional virtual model according to the point cloud.
In one possible implementation, the preset depth recognition network architecture includes a preset feature extraction network and a preset depth prediction network;
the preset depth recognition network architecture is obtained by training according to sample data and the following loss functions:
wherein->Representing a loss function for measuring the difference between the depth prediction value and the true depth value,/>Representing the number of samples->Indicate->Depth prediction value corresponding to each sample, +.>Indicate->True depth values corresponding to the samples;
the depth recognition sub-module includes:
the feature processing unit is used for carrying out feature processing on the pipeline panoramic image according to a preset feature extraction network to obtain a feature image;
and the depth prediction unit is used for carrying out depth prediction on the characteristic image according to a preset depth prediction network to obtain depth information corresponding to the pipeline panoramic image.
In one possible implementation manner, the point cloud reconstruction sub-module includes:
the preprocessing unit is used for preprocessing the point cloud;
the registration processing unit is used for carrying out registration processing on the preprocessed point cloud according to a preset point Yun Peizhun algorithm;
and the gridding processing unit is used for carrying out gridding processing on the point clouds after registration according to a preset three-dimensional reconstruction algorithm to form a pipeline three-dimensional virtual model.
In one possible implementation, the adding module includes:
a first adding sub-module for adding the video data to the pipeline three-dimensional virtual model;
a second adding sub-module for adding the pressure data to the three-dimensional virtual model of the pipeline;
and the third adding sub-module is used for adding the sound wave data to the pipeline three-dimensional virtual model.
In one possible implementation manner, the video data are obtained by shooting by cameras arranged at corresponding positions of each pipeline section;
the first adding sub-module includes:
the first registration unit is used for registering the virtual camera corresponding to the camera in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
the first construction unit is used for constructing a video data projection area of the virtual camera in the pipeline three-dimensional virtual model;
and the first mapping unit is used for mapping the video data to a video data projection area in the pipeline three-dimensional virtual model.
In one possible implementation manner, the pressure data are acquired by pressure sensors arranged at corresponding positions of each pipeline section;
the second adding sub-module includes:
the second registration unit is used for registering the virtual pressure sensor corresponding to the pressure sensor in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
a second construction unit for constructing a pressure data adding position of the virtual pressure sensor in the pipeline three-dimensional virtual model;
and the second mapping unit is used for mapping the pressure data to the pressure data adding position in the pipeline three-dimensional virtual model.
In a possible implementation manner, the acoustic wave data are acquired by acoustic wave sensors arranged at corresponding positions of each pipeline section;
the third adding sub-module includes:
the third registration unit is used for registering the virtual acoustic wave sensor corresponding to the acoustic wave sensor in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
the third construction unit is used for constructing an acoustic wave data adding position of the virtual acoustic wave sensor in the pipeline three-dimensional virtual model;
and the third mapping unit is used for mapping the acoustic data to the acoustic data adding position in the pipeline three-dimensional virtual model.
In one possible implementation, the system further includes:
the early warning module is used for carrying out early warning analysis according to the real-time monitoring data to obtain an early warning analysis result;
and the pushing module is used for determining a target pipeline section if the early warning analysis result is abnormal, marking a target virtual position corresponding to the target pipeline section in the pipeline three-dimensional virtual model added with the real-time monitoring data, regenerating a three-dimensional monitoring early warning picture according to the target virtual position corresponding to the target pipeline section, and pushing the three-dimensional monitoring early warning picture to a user.
In one possible implementation, the early warning module includes:
the early warning calculation sub-module is used for calculating an early warning value according to the following formula based on the real-time monitoring data:
,
wherein,representing the early warning value->First weight value representing video data correspondence, < ->Representing a first difference between a similarity value between the video data and the preset abnormal video data and a preset similarity threshold value,/v>A second weight value representing the correspondence of the pressure data, < ->Representing a second difference between the pressure data and a preset pressure threshold value,/or->Third weight value representing acoustic wave data, < ->Representing a third difference between the acoustic data and a preset acoustic threshold;
and the early warning determination submodule is used for determining that an early warning analysis result is abnormal when the early warning value is larger than a preset early warning threshold value.
Compared with the prior art, the embodiment of the application has the beneficial effects that: the pipeline safety real-time monitoring and early warning system of this application embodiment includes: the acquisition module is used for acquiring the pipeline panoramic image; the pipeline comprises a plurality of pipeline sections which are sequentially connected end to end, and the pipeline panoramic image is a panoramic image corresponding to each pipeline section; the construction module is used for constructing a pipeline three-dimensional virtual model according to the pipeline panoramic image; the acquisition module is used for acquiring real-time monitoring data corresponding to each pipeline section; the real-time monitoring data comprise video data, pressure data and sound wave data; the determining module is used for determining the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model; an adding module for adding the real-time monitoring data to the pipeline three-dimensional virtual model based on the virtual position; the generation module is used for generating a three-dimensional monitoring early warning picture based on the pipeline three-dimensional virtual model added with the real-time monitoring data and pushing the three-dimensional monitoring early warning picture to a user. Therefore, the embodiment of the application can display the three-dimensional picture based on the three-dimensional of the pipeline, and the pipeline data acquired by the sensor in real time is added into the pipeline three-dimensional virtual model by constructing the pipeline three-dimensional virtual model, so that a user (such as a pipeline monitoring person) can intuitively check the monitoring condition. In addition, whether the pipeline section leaks or not can be early-warning analyzed based on the sensor data, and the pipeline section is marked in the three-dimensional virtual model, so that a user can timely arrive at the site to repair according to the target pipeline section marked in the three-dimensional virtual model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a pipeline safety real-time monitoring and early warning system provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of a construction module provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a depth recognition sub-module according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a point cloud reconstruction sub-module provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of an adding module provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a first adding sub-module provided in an embodiment of the present application;
FIG. 7 is a schematic structural diagram of a second add-on sub-module provided in an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a third add-on sub-module provided in an embodiment of the present application;
fig. 9 is another schematic structural diagram of a pipeline safety real-time monitoring and early warning system provided in an embodiment of the present application;
fig. 10 is a schematic structural diagram of an early warning module provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The following describes the technical solutions of the embodiments of the present application.
Referring to fig. 1, a schematic structural diagram of a pipeline safety real-time monitoring and early warning system provided in an embodiment of the present application may include an acquisition module 11, a construction module 12, an acquisition module 13, a determination module 14, an addition module 15, and a generation module 16.
An acquisition module 11, configured to acquire a panoramic image of a pipeline.
The pipeline comprises a plurality of pipeline sections which are sequentially connected end to end, and the pipeline panoramic image is a panoramic image corresponding to each pipeline section.
According to the embodiment of the application, the depth camera (for example, a monocular depth camera) can be used for shooting the pipeline panoramic image inside or outside each pipeline section, so that the panoramic image corresponding to each pipeline section is obtained, and the three-dimensional reconstruction can be carried out according to the pipeline panoramic image to obtain the pipeline three-dimensional virtual model.
A construction module 12 is configured to construct a three-dimensional virtual model of the pipeline from the pipeline panoramic image.
Illustratively, as shown in FIG. 2, the build module includes:
the depth recognition sub-module 21 is configured to recognize depth information corresponding to the pipeline panoramic image according to a preset depth recognition network architecture.
The preset depth recognition network architecture comprises a preset feature extraction network and a preset depth prediction network, wherein the preset feature extraction network can be formed by combining a plurality of convolution blocks and a plurality of residual blocks, and the preset depth prediction network can be formed by combining an up-sampling layer, a feature fusion layer, a fusion convolution layer, a channel attention layer and an output layer.
The preset depth recognition network architecture is obtained by training according to the sample data and the following loss functions:
wherein->Representing a loss function for measuring the difference between the depth prediction value and the true depth value,/>Representing the number of samples->Indicate->Depth prediction value corresponding to each sample, +.>Indicate->True depth values corresponding to the individual samples.
It is understood that the preset depth recognition network architecture can be trained by using sample data and a loss function to infer depth information of a single panoramic image.
In an alternative embodiment, as shown in fig. 3, the depth identification sub-module includes:
the feature processing unit 31 is configured to perform feature processing on the pipeline panoramic image according to a preset feature extraction network, so as to obtain a feature image.
Illustratively, the preset feature extraction network gradually extracts features of the pipeline panoramic image through a plurality of convolution blocks and a plurality of residual blocks to form a feature image.
The depth prediction unit 32 is configured to perform depth prediction on the feature image according to a preset depth prediction network, so as to obtain depth information corresponding to the pipeline panoramic image. The depth information corresponding to the pipeline panoramic image refers to depth values corresponding to feature points (i.e., pixel points with feature properties) in the pipeline panoramic image (i.e., distances between the feature points and the photographed object)
Illustratively, the preset depth prediction network amplifies a low-resolution feature map by an interpolation method through an up-sampling layer; then, a jump stage is introduced through a feature fusion layer to connect the shallower layer features extracted from the feature map with the advanced features in the decoder, and the feature fusion provides multi-scale feature information, thereby being beneficial to better recovering the detail and context relation; then, the channel weight is learned by the channel attention layer through an attention mechanism so as to adaptively adjust the contribution of each channel in the feature map, which is helpful for the network to pay more attention to the feature channels which are helpful for depth identification, and the accuracy of the depth identification is improved; finally, mapping the feature map to the depth recognition result by using a specific activation function (e.g. a Sigmmid activation function) through the output layer, and obtaining depth information corresponding to the pipeline panoramic image.
The point cloud generating sub-module 22 is configured to generate a point cloud according to the pipeline panoramic image and the depth information.
Illustratively, the three-dimensional coordinates of the point cloud are obtained according to the following equation:
，
wherein, (u, v) is the pixel coordinate of each feature point in the pipeline panoramic image, d is the depth value of each feature point in the pipeline panoramic image, K is the internal reference of the depth camera, and (X, Y, Z) is the three-dimensional coordinate of the point cloud. It should be noted that the internal reference of the depth camera may be calibrated in advance according to the Zhang Dingyou calibration method.
The point cloud reconstruction submodule 23 is used for reconstructing the three-dimensional virtual model of the pipeline according to the point cloud.
Illustratively, as shown in fig. 4, the point cloud reconstruction sub-module includes:
a preprocessing unit 41, configured to perform preprocessing on the point cloud.
In a specific application, the preprocessing includes removing outliers (outliers) and noise points (noise), filtering the point cloud, and sampling data, so that the point cloud data is tidier and more uniform.
The registration processing unit 42 is configured to perform registration processing on the preprocessed point cloud according to a preset point Yun Peizhun algorithm.
The preset point cloud registration algorithm may be an ICP iterative closest point algorithm.
The gridding processing unit 43 is configured to perform gridding processing on the registered point cloud according to a preset three-dimensional reconstruction algorithm, so as to form a three-dimensional virtual model of the pipeline.
The preset three-dimensional reconstruction algorithm may be a Marching cube algorithm.
It will be appreciated that the goal of the surface reconstruction is to recover the geometry of the object by connecting internal and external points in the cloud, once the point cloud data registration is complete, the surface reconstruction can be performed to generate a three-dimensional model.
And the acquisition module 13 is used for acquiring real-time monitoring data corresponding to each pipeline section, wherein the real-time monitoring data comprises video data, pressure data and sound wave data.
It can be appreciated that, in the embodiment of the present application, corresponding sensors are provided on each pipe section, including a camera that shoots corresponding video data of the pipe section in real time, a pressure sensor that collects corresponding pressure data of the pipe section, and an acoustic sensor that collects corresponding acoustic data of the pipe section.
A determining module 14 is configured to determine a corresponding virtual position of each pipe segment in the three-dimensional virtual model of the pipe.
In specific application, point clouds corresponding to the same pipeline section in the pipeline three-dimensional virtual model are identified, the point clouds corresponding to the same pipeline section are combined, and the combined point cloud coordinates are used as corresponding virtual positions of each pipeline section in the pipeline three-dimensional virtual model.
Illustratively, identifying a point cloud in the three-dimensional virtual model of the pipeline that corresponds to the same pipeline segment includes: extracting feature descriptors (such as local coordinate descriptors, normal histograms or surface shape descriptors) from each point cloud in the pipeline three-dimensional virtual model, carrying out nearest neighbor search on geometric features and preset feature descriptors in a preset K-d tree index structure, determining preset feature descriptors matched with the feature descriptors, taking the point cloud type corresponding to the preset feature descriptors matched with the feature descriptors as the type of each point cloud, and merging the point clouds of the same type corresponding to the same pipeline section.
Taking the combined point cloud coordinates as corresponding virtual positions of each pipeline section in the pipeline three-dimensional virtual model, wherein the method comprises the following steps: before merging, the method adopts a global coordinate system to represent, selects a main point cloud or a self-defined reference coordinate system as the origin and direction of the global coordinate system, and then transforms the coordinates of points in all the point clouds into the global coordinate system through translation and rotation, so that the origin and direction of the global coordinate system where the point clouds corresponding to the same pipeline section are located can be used as the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model.
An adding module 15 is configured to add real-time monitoring data to the three-dimensional virtual model of the pipeline based on the virtual position.
Illustratively, as shown in FIG. 5, the add-on module includes:
a first adding sub-module 51 for adding video data to the three-dimensional virtual model of the pipeline.
The video data are obtained by shooting cameras arranged at corresponding positions of each pipeline section. It can be understood that the corresponding positions of the pipe sections in the embodiment of the application are provided with cameras so as to be shot in real time.
As shown in fig. 6, the first adding sub-module includes:
the first registration unit 61 is configured to register, in the three-dimensional pipeline virtual model, a virtual camera corresponding to the camera according to a corresponding virtual position of each pipeline segment in the three-dimensional pipeline virtual model.
The first construction unit 62 is configured to construct a video data projection area of the virtual camera in the three-dimensional virtual model of the pipeline.
The video data projection area is an area corresponding to the video data mapped to the pipeline three-dimensional virtual model, and the virtual position of the virtual camera in the video data projection area in the pipeline three-dimensional virtual model is set according to the relative position of the camera in the real world and the corresponding pipeline section.
The first mapping unit 63 is configured to map video data to a video data projection area in the three-dimensional virtual model of the pipeline.
In a specific application, pixel coordinates in video data are converted into points in a coordinate system of a pipeline three-dimensional virtual model through projection transformation, so that the video data are displayed in a video data projection area.
A second addition sub-module 52 for adding pressure data to the three-dimensional virtual model of the pipeline.
The pressure data are acquired by pressure sensors arranged at corresponding positions of each pipeline section. It can be understood that the corresponding positions of the pipe sections in the embodiment of the application are provided with pressure sensors, so that real-time data acquisition can be performed.
As shown in fig. 7, the second adding sub-module includes:
the second registration unit 71 is configured to register a virtual pressure sensor corresponding to the pressure sensor in the three-dimensional pipeline virtual model according to the corresponding virtual position of each pipeline segment in the three-dimensional pipeline virtual model.
A second construction unit 72 for constructing a pressure data addition position of the virtual pressure sensor in the three-dimensional virtual model of the pipe.
Wherein the pressure data adding position of the virtual pressure sensor in the pipeline three-dimensional virtual model is set according to the relative position of the pressure sensor between the real world and the corresponding pipeline section.
A second mapping unit 73 for mapping the pressure data to a pressure data addition location in the three-dimensional virtual model of the pipeline.
In a specific application, the pressure data is linearly transformed from an original coordinate system to a coordinate system of a three-dimensional virtual model of the pipeline according to a transformation matrix, so that the pressure data is displayed at a pressure data adding position.
A third adding sub-module 53 for adding acoustic data to the three-dimensional virtual model of the pipe.
The sound wave data are acquired by sound wave sensors arranged at corresponding positions of each pipeline section. It can be understood that the corresponding positions of the pipe sections in the embodiment of the application are provided with acoustic wave sensors, so that real-time data acquisition can be performed.
As shown in fig. 8, the third adding sub-module includes:
and a third registration unit 81, configured to register a virtual acoustic wave sensor corresponding to the acoustic wave sensor in the three-dimensional pipeline virtual model according to the corresponding virtual position of each pipeline segment in the three-dimensional pipeline virtual model.
And a third construction unit 82 for constructing the acoustic wave data adding position of the virtual acoustic wave sensor in the three-dimensional virtual model of the pipeline.
The sound wave data adding position of the virtual sound wave sensor in the pipeline three-dimensional virtual model is set according to the relative position of the sound wave sensor between the real world and the corresponding pipeline section.
And a third mapping unit 83 for mapping the acoustic data to an acoustic data addition position in the three-dimensional virtual model of the pipeline.
In the specific application, the acoustic wave data is linearly transformed from an original coordinate system to a coordinate system of a three-dimensional virtual model of the pipeline according to the transformation matrix, so that the acoustic wave data is displayed at the acoustic wave data adding position.
The generating module 16 is configured to generate a three-dimensional monitoring and early warning picture based on the three-dimensional virtual model of the pipeline added with the real-time monitoring data, and push the three-dimensional monitoring and early warning picture to the user.
In an alternative implementation, as shown in fig. 9, the system further includes:
the early warning module 17 is used for carrying out early warning analysis according to the real-time monitoring data to obtain an early warning analysis result;
and the pushing module 18 is configured to determine a target pipeline section if the early warning analysis result indicates that an abnormality occurs, mark a target virtual position corresponding to the target pipeline section in the pipeline three-dimensional virtual model added with the real-time monitoring data, regenerate a three-dimensional monitoring early warning picture according to the target virtual position corresponding to the target pipeline section, and push the three-dimensional monitoring early warning picture to a user.
Illustratively, as shown in fig. 10, the early warning module includes:
the early warning calculation sub-module 101 is configured to calculate an early warning value according to the following formula based on the real-time monitoring data:
,
wherein,representing the early warning value->First weight value representing video data correspondence, < ->Representing similarity value and preset similarity between video data and preset abnormal video dataFirst difference between threshold values, ++>A second weight value representing the correspondence of the pressure data, < ->Representing a second difference between the pressure data and a preset pressure threshold value,/or->Third weight value representing acoustic wave data, < ->Representing a third difference between the acoustic data and a preset acoustic threshold. In practical application, the first weight value is far smaller than the second weight value and the third weight value, for example, the first weight value is 0.2, the second weight value is 0.4, and the third weight value is 0.4, and then the first weight value and the third weight value can be manually adjusted according to the practical early warning analysis result.
Preferably, the calculation mode of the similarity value between the video data and the preset abnormal video data may be: and calculating pixel-level similarity between the video data and preset abnormal video data by using a mean square error method, so as to obtain the similarity.
It can be appreciated that, in the embodiment of the present application, the first difference value (the larger the first difference value is, the smaller the possibility of judging that the pipeline leaks through the video data) between the similarity value between the video data and the preset abnormal video data (the video data obtained by shooting when the previous pipeline section leaks) and the preset similarity threshold value, the second difference value (the larger the second difference value is, the smaller the possibility of judging that the pipeline leaks through the pressure data is, the smaller the second difference value is) between the pressure data and the preset pressure threshold value, and the third difference value (the larger the third difference value is, the smaller the possibility of judging that the pipeline leaks through the sound wave data is) between the sound wave data and the preset sound wave threshold value are comprehensively calculated to be used as the early warning indicator for measuring whether the pipeline section leaks or not.
The early warning determination submodule 102 is configured to, when the early warning value is greater than a preset early warning threshold, cause an abnormality in an early warning analysis result.
In this application embodiment, pipeline safety real-time monitoring early warning system includes: the acquisition module is used for acquiring the pipeline panoramic image; the pipeline comprises a plurality of pipeline sections which are sequentially connected end to end, and the pipeline panoramic image is a panoramic image corresponding to each pipeline section; the construction module is used for constructing a pipeline three-dimensional virtual model according to the pipeline panoramic image; the acquisition module is used for acquiring real-time monitoring data corresponding to each pipeline section; the real-time monitoring data comprise video data, pressure data and sound wave data; the determining module is used for determining the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model; an adding module for adding the real-time monitoring data to the pipeline three-dimensional virtual model based on the virtual position; the generation module is used for generating a three-dimensional monitoring early warning picture based on the pipeline three-dimensional virtual model added with the real-time monitoring data and pushing the three-dimensional monitoring early warning picture to a user. Therefore, the embodiment of the application can display the three-dimensional picture based on the three-dimensional of the pipeline, and the pipeline data acquired by the sensor in real time is added into the pipeline three-dimensional virtual model by constructing the pipeline three-dimensional virtual model, so that a user (such as a pipeline monitoring person) can intuitively check the monitoring condition. In addition, whether the pipeline section leaks or not can be early-warning analyzed based on the sensor data, and the pipeline section is marked in the three-dimensional virtual model, so that a user can timely arrive at the site to repair according to the target pipeline section marked in the three-dimensional virtual model.
Fig. 11 is a schematic structural diagram of a server according to an embodiment of the present application. As shown in fig. 11, the server 11 of this embodiment includes: the system comprises at least one processor 110, a memory 111 and a computer program 112 stored in the memory 111 and capable of running on the at least one processor 110, wherein the processor 110 executes the computer program 112 to realize the corresponding application method steps of the pipeline safety real-time monitoring and early warning system in any system embodiment.
The server 11 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The server may include, but is not limited to, a processor 110, a memory 111. It will be appreciated by those skilled in the art that fig. 11 is merely an example of the server 11 and is not meant to be limiting as the server 11, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The processor 110 may be a central processing unit (Central Processing Unit, CPU), the processor 110 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 111 may in some embodiments be an internal storage unit of the server 11, such as a hard disk or a memory of the server 11. The memory 111 may in other embodiments also be an external storage device of the server 11, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the server 11. Further, the memory 111 may also include both an internal storage unit and an external storage device of the server 11. The memory 111 is used to store an operating system, application programs, boot loader (BootLoader), data, and other programs, etc., such as program codes of the computer program. The memory 111 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the corresponding application method steps of the pipeline safety real-time monitoring and early warning system in any system embodiment when being executed by a processor.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a server, a recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (4)
1. The utility model provides a pipeline safety real-time monitoring early warning system which characterized in that includes:
the acquisition module is used for acquiring the pipeline panoramic image; the pipeline comprises a plurality of pipeline sections which are sequentially connected end to end, and the pipeline panoramic image is a panoramic image corresponding to each pipeline section;
the construction module is used for constructing a pipeline three-dimensional virtual model according to the pipeline panoramic image;
the acquisition module is used for acquiring real-time monitoring data corresponding to each pipeline section; the real-time monitoring data comprise video data, pressure data and sound wave data;
the determining module is used for determining the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
an adding module for adding the real-time monitoring data to the pipeline three-dimensional virtual model based on the virtual position;
the generation module is used for generating a three-dimensional monitoring early warning picture based on the pipeline three-dimensional virtual model added with the real-time monitoring data and pushing the three-dimensional monitoring early warning picture to a user;
wherein, add the module, include: the system comprises a first adding sub-module, a second adding sub-module and a third adding sub-module;
the first adding sub-module is used for adding the video data to the pipeline three-dimensional virtual model, wherein the video data are obtained by shooting cameras arranged at corresponding positions of each pipeline section;
the first adding sub-module includes: the device comprises a first registration unit, a first construction unit and a first mapping unit;
the first registration unit is used for registering a virtual camera corresponding to the camera in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
the first construction unit is used for constructing a video data projection area of the virtual camera in the pipeline three-dimensional virtual model;
the first mapping unit is used for mapping the video data to a video data projection area in the pipeline three-dimensional virtual model; the first mapping unit is specifically used for converting pixel coordinates in video data into points under a coordinate system of the pipeline three-dimensional virtual model through projection transformation;
the second adding submodule is used for adding the pressure data to the pipeline three-dimensional virtual model, and the pressure data are acquired by pressure sensors arranged at corresponding positions of each pipeline section;
the second adding sub-module includes: a second registration unit, a second construction unit, and a second mapping unit;
the second registration unit is used for registering the virtual pressure sensor corresponding to the pressure sensor in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
the second construction unit is used for constructing a pressure data adding position of the virtual pressure sensor in the pipeline three-dimensional virtual model;
the second mapping unit is used for mapping the pressure data to a pressure data adding position in the pipeline three-dimensional virtual model; the second mapping unit is specifically used for linearly transforming the pressure data from the original coordinate system to the coordinate system of the pipeline three-dimensional virtual model according to the transformation matrix;
the third adding sub-module is used for adding the sound wave data to the three-dimensional virtual model of the pipeline, and the sound wave data are acquired by sound wave sensors arranged at corresponding positions of each pipeline section;
the third adding sub-module includes: a third registration unit, a third construction unit, and a third mapping unit;
the third registration unit is used for registering the virtual acoustic wave sensor corresponding to the acoustic wave sensor in the pipeline three-dimensional virtual model according to the corresponding virtual position of each pipeline section in the pipeline three-dimensional virtual model;
the third construction unit is used for constructing an acoustic wave data adding position of the virtual acoustic wave sensor in the pipeline three-dimensional virtual model;
the third mapping unit is used for mapping the acoustic wave data to an acoustic wave data adding position in the pipeline three-dimensional virtual model; the third mapping unit is specifically used for linearly transforming the acoustic wave data from the original coordinate system to the coordinate system of the pipeline three-dimensional virtual model according to the transformation matrix;
the construction module comprises:
the depth recognition sub-module is used for recognizing depth information corresponding to the pipeline panoramic image according to a preset depth recognition network architecture;
the point cloud generation sub-module is used for generating point cloud according to the pipeline panoramic image and the depth information;
the point cloud reconstruction sub-module is used for reconstructing a pipeline three-dimensional virtual model according to the point cloud;
the preset depth recognition network architecture comprises a preset feature extraction network and a preset depth prediction network;
the preset depth recognition network architecture is obtained by training according to sample data and the following loss functions:
wherein MAE represents a loss function for measuring the difference between the depth prediction value and the true depth value, N represents the number of samples, ">Represents the depth prediction value corresponding to the ith sample, d i Representing a true depth value corresponding to the i-th sample;
the depth recognition sub-module includes:
the feature processing unit is used for carrying out feature processing on the pipeline panoramic image according to a preset feature extraction network to obtain a feature image;
and the depth prediction unit is used for carrying out depth prediction on the characteristic image according to a preset depth prediction network to obtain depth information corresponding to the pipeline panoramic image.
2. The pipeline security real-time monitoring and early warning system of claim 1, wherein the point cloud reconstruction sub-module comprises:
the preprocessing unit is used for preprocessing the point cloud;
the registration processing unit is used for carrying out registration processing on the preprocessed point cloud according to a preset point Yun Peizhun algorithm;
and the gridding processing unit is used for carrying out gridding processing on the point clouds after registration according to a preset three-dimensional reconstruction algorithm to form a pipeline three-dimensional virtual model.
3. The pipeline security real-time monitoring and early warning system according to claim 1 or 2, characterized in that the system further comprises:
the early warning module is used for carrying out early warning analysis according to the real-time monitoring data to obtain an early warning analysis result;
and the pushing module is used for determining a target pipeline section if the early warning analysis result is abnormal, marking a target virtual position corresponding to the target pipeline section in the pipeline three-dimensional virtual model added with the real-time monitoring data, regenerating a three-dimensional monitoring early warning picture according to the target virtual position corresponding to the target pipeline section, and pushing the three-dimensional monitoring early warning picture to a user.
4. The pipeline security real-time monitoring and early warning system of claim 3, wherein the early warning module comprises:
the early warning calculation sub-module is used for calculating an early warning value according to the following formula based on the real-time monitoring data:
wherein Q represents an early warning value, W 1 A first weight value corresponding to the video data is represented, f (V) represents a first difference value between a similarity value between the video data and preset abnormal video data and a preset similarity threshold value, W 2 Represents a second weight value corresponding to the pressure data, g (P) represents a second difference value between the pressure data and a preset pressure threshold value, W 3 The third weight value corresponding to the sound wave data is represented, and h (S) represents a third difference value between the sound wave data and a preset sound wave threshold value;
and the early warning determination submodule is used for determining that an early warning analysis result is abnormal when the early warning value is larger than a preset early warning threshold value.
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