CN116045833B - Bridge construction deformation monitoring system based on big data - Google Patents
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Abstract
The invention discloses a bridge construction deformation monitoring system based on big data, which comprises a sensor module, a first scheduling module, an edge computing module and a distributed server module, wherein the sensor module is in communication connection with the edge computing module through the first scheduling module, and the first scheduling module is in communication connection with the distributed server through a public network. In the bridge construction deformation monitoring system based on big data, the first scheduling module executes the relevant steps for monitoring the bridge construction deformation through the control sensor module, the edge computing module and the distributed server module, so that the information is timely and synchronously shared, the reasonable configuration of computing resources is realized, and the efficient monitoring of the deformation condition in the bridge construction process is facilitated.
Description
Technical Field
The invention relates to the technical field of buildings, in particular to a bridge construction deformation monitoring system based on big data.
Background
Patent CN112985287B discloses a bridge construction deformation monitoring device, a plurality of reference surfaces are selected, the change of a detection device to which each reference surface belongs is detected through a laser alignment mode, the reference surface with the smallest change range is selected, the reference surface is divided into a plurality of areas, a liquid level control mechanism is embedded in each area, the liquid level control mechanism pushes monitoring equipment to correspondingly adjust the position when the plurality of areas change through a liquid communication principle, the monitoring equipment is kept in a state with the smallest change, the monitoring equipment is automatically straightened through a gravity straightening mode and is easy to install through the laser alignment mode, and the error is avoided.
Patent CN111580098B discloses a bridge deformation monitoring method, which comprises the following steps: acquiring a radar image sequence of a target bridge, and acquiring a deformation data sequence of the target bridge in a preset time period according to the radar image sequence; determining a preset deformation class library corresponding to the target bridge, and matching the deformation data sequence with a sample deformation data sequence in the deformation class library to obtain a target deformation class corresponding to the deformation data sequence; and determining whether to send out deformation warning according to the target deformation category.
However, the monitoring device in the technical scheme cannot synchronize related information in time and in multiple ways, which is not beneficial to information synchronization among departments in the bridge construction process.
Therefore, how to design a bridge construction deformation monitoring system which is favorable for timely and synchronous information sharing is a technical problem to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a bridge construction deformation monitoring system based on big data, which is beneficial to timely and synchronously sharing information.
In order to solve the technical problems, the invention discloses a bridge construction deformation monitoring system based on big data, which comprises a sensor module, a first scheduling module, an edge computing module and a distributed server module,
the sensor module is in communication connection with the edge computing module through the first scheduling module, the first scheduling module is also in communication connection with the distributed server through a public network,
the first scheduling module executes the steps of:
the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge computing module;
the first scheduling module sends an information processing request to the edge computing module, so that the edge computing module executes information verification operation on the real-time structural deformation parameter information;
the first scheduling module acquires feedback information of the edge computing module about the information verification operation;
and the first scheduling module judges whether the feedback information contains warning information or not, if so, the first scheduling module sends an abnormality investigation request about the warning information to the distributed server module, so that the distributed server module executes an abnormality investigation operation on the warning information.
Therefore, in the bridge construction deformation monitoring system based on big data, the first scheduling module executes relevant steps for monitoring bridge construction deformation through the control sensor module, the edge computing module and the distributed server module, so that timely synchronous sharing of information is realized, reasonable configuration of computing resources is realized, and efficient monitoring of deformation conditions in the bridge construction process is facilitated.
In an alternative embodiment, the edge calculation module comprises a data transmission unit and a micro server of a bridge construction site, the sensor module comprises a laser sensor, a wind speed transmitter and an infrared thermometer, and the distributed service module comprises a big data server cluster and a blockchain server cluster.
In an alternative embodiment, after receiving the real-time structural deformation parameter information synchronized by the sensor module, the edge calculation module generates a structural deformation parameter time course curve of the bridge,
after receiving the information processing request sent by the first scheduling module, the edge computing module executes information verification operation on the real-time structural deformation parameter information, and specifically includes:
the edge calculation module performs feature extraction operation on the target image of the structural deformation parameter time course curve based on the trained convolutional neural network;
the edge calculation module judges whether the target image is an abnormal image according to the extracted target image characteristics,
if so, marking an abnormal region of a structural deformation parameter time course curve in the target image, generating first feedback information which indicates that the target image verification result fails, wherein the first feedback information comprises attribute information of the abnormal region,
if not, generating second feedback information which indicates that the target image verification result passes.
In an optional implementation manner, in the process of determining whether the target image is an abnormal image according to the extracted target image features, the edge calculation module takes the target image features as input, performs classification processing through a Softmax classifier, and outputs a result indicating that the target image is an abnormal image or that the target image is not an abnormal image.
In an alternative embodiment, in the present invention, the distributed server and the mobile terminal implement a communication connection based on a public network,
and before the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge computing module, the first scheduling module executing steps further include:
the first scheduling module acquires a construction plan of a target bridge and a foundation structure model of the target bridge from the distributed server;
the first scheduling module controls the edge computing module to generate a structure deformation point cloud data set corresponding to the basic structure model according to the construction plan;
the first scheduling module controls the edge computing module to construct a three-dimensional dynamic model of the target bridge about the construction planning according to the structural deformation point cloud data set;
the first scheduling module controls the edge computing module to mark displacement parameters of the three-dimensional dynamic model;
and the first scheduling module controls the edge calculation module to judge whether the displacement parameter is larger than or equal to a predetermined displacement parameter threshold value, and if so, the first scheduling module sends abnormal information about the construction plan to the distributed server module, so that the distributed server module forwards the abnormal information to the mobile terminal.
In an optional implementation manner, in the process that the first scheduling module controls the edge calculating module to label the displacement parameter of the three-dimensional dynamic model, the executing steps of the edge calculating module include:
the edge calculation module selects a plurality of space anchor points from the three-dimensional dynamic model;
the edge calculation module takes the space anchor points as the center to construct a space grid, so that the three-dimensional dynamic model is divided by a plurality of space grids;
the edge calculation module calculates the space grid displacement parameter of each space grid in the whole construction planning process;
and the edge calculation module determines the displacement parameters of the three-dimensional dynamic model according to the distribution of the space anchors and the space grid displacement parameters.
In an alternative embodiment, the structural deformation point cloud data set includes detection data corresponding to a position point set of a laser sensor and detection data corresponding to a position point set of a laser projection point of the laser sensor on a target bridge,
when the first scheduling module controls the edge computing module to judge whether the displacement parameter is greater than or equal to a predetermined displacement parameter threshold, the edge computing module executes the steps of: the coordinate difference between the position point set of the laser sensor and the position point set of the laser projection point of the laser sensor on the target bridge is obtained as follows:
q i ＝p i -p
q i ′ ＝p i ′ -p ′
wherein P is the position point set P of the laser sensor i Center of gravity, p ′ Set of position points Q for the laser sensor at the laser projection point of the target bridge i Center of gravity, q i Is p i The difference in coordinates of the point and the center of gravity p,
wherein the position point set of the laser sensor is { P ] i I=1, 2, 3 … M }, the set of position points of the laser sensor at the laser projection point of the target bridge is { Q } i ，i＝1、2、3…M}，
p is expressed as:
p ′ expressed as:
for p (x, y, z), p ′ (x ′ ,y ′ ,z ′ ) The correlation matrix A, B is constructed as follows:
the displacement parameter correlation matrix N reflecting the structural deformation of the target bridge is obtained as follows:
wherein N is i ＝A*B；
The edge calculation module judges whether the modulus of the displacement parameter correlation matrix N is larger than or equal to the modulus of a predetermined displacement parameter correlation matrix.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and 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 bridge construction deformation monitoring system based on big data according to an embodiment of the present invention;
FIG. 2 is a flow chart of a portion of the control steps performed by the first scheduling module according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating another portion of the control steps performed by the first scheduling module according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a part of control steps of the edge calculation module according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a bridge construction deformation monitoring system based on big data, which comprises a sensor module, a first scheduling module, an edge computing module and a distributed server module, wherein the sensor module is in communication connection with the edge computing module through the first scheduling module, and the first scheduling module is in communication connection with the distributed server through a public network.
The large data mainly has the characteristics of huge data volume, various data types, low unit data value and high required data processing efficiency, and the bridge construction deformation monitoring system is based on the large data technology.
The first scheduling module executes the steps of:
s101, the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge calculation module.
S102, the first scheduling module sends an information processing request to the edge computing module, so that the edge computing module performs information verification operation on the real-time structural deformation parameter information.
S103, the first scheduling module acquires feedback information of the edge computing module about information verification operation.
S104, the first scheduling module judges whether the feedback information contains warning information, if so, step S105 is executed.
S105, the first scheduling module sends an abnormality troubleshooting request about the warning information to the distributed server module, so that the distributed server module performs an abnormality troubleshooting operation on the warning information.
Therefore, in the bridge construction deformation monitoring system based on big data, the first scheduling module executes relevant steps for monitoring bridge construction deformation through the control sensor module, the edge computing module and the distributed server module, so that timely synchronous sharing of information is realized, reasonable configuration of computing resources is realized, and efficient monitoring of deformation conditions in the bridge construction process is facilitated.
In some application scenarios, optionally, the edge computing module includes a data transmission unit and a mini-server of the bridge construction site, the sensor module includes a laser sensor, a wind speed transmitter and an infrared thermometer, and the distributed service module includes a big data server cluster and a blockchain server cluster.
Further optionally, after receiving the real-time structural deformation parameter information synchronized by the sensor module, the edge calculation module generates a structural deformation parameter time course curve of the bridge. Specifically, the structural deformation parameter time course curve may be a curve reflecting the change of the physical quantity (for example, the displacement detected by the laser sensor, the ambient wind speed detected by the wind speed transmitter, and the infrared temperature and the detected ambient temperature) detected by the sensor module with time as an abscissa.
After receiving the information processing request sent by the first scheduling module, the edge computing module executes information verification operation on the real-time structure deformation parameter information, and specifically comprises the following steps:
and the edge calculation module performs characteristic extraction operation on the target image of the structural deformation parameter time course curve based on the trained convolutional neural network. Still further alternatively, the convolutional neural network may refer to the network structure of Fast R-CNN.
The edge calculation module judges whether the target image is an abnormal image according to the extracted target image characteristics. Still further alternatively, a Softmax classifier may be selected for classification, and a result indicating that the target image is an abnormal image or that the target image is not an abnormal image is output. The Softmax classifier can output classification results and the credibility of the classification results, and the results with lower credibility can be eliminated as soon as possible.
If yes, marking an abnormal region of the structural deformation parameter time course curve in the target image, and generating first feedback information which indicates that the verification result of the target image fails. Wherein the first feedback information includes attribute information of the abnormal region. Still further alternatively, the attribute information of the anomaly region may include image location information (e.g., a corresponding curve segment) representing the anomaly region of the structural deformation parameter time course curve.
If not, generating second feedback information which indicates that the target image verification result passes.
Therefore, the edge calculation module performs feature extraction operation on the target image of the structural deformation parameter time course curve, judges whether the image is an abnormal image according to the extracted and mentioned image features, marks the abnormal region of the abnormal image, and is beneficial to timely checking the hidden danger of the structural deformation of the bridge in the bridge construction process.
In other application scenarios, the distributed server and the mobile terminal may be in communication connection based on a public network. Further, before the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge computing module, the first scheduling module performs the steps further including:
s1011, the first scheduling module acquires a construction plan of the target bridge and a foundation structure model of the target bridge from the distributed server. Specifically, the construction plan may include construction content and construction time, and the base structure model may be a construction pattern determined before the target bridge is constructed.
S1012, the first scheduling module controls the edge calculation module to generate a structure deformation point cloud data set corresponding to the basic structure model according to the construction plan.
S1013, a first scheduling module controls an edge calculation module to construct a three-dimensional dynamic model of the target bridge about construction planning according to the structural deformation point cloud data set.
S1014, the first scheduling module controls the edge computing module to label the displacement parameters of the three-dimensional dynamic model.
S1015, the first scheduling module controls the edge computing module to judge whether the displacement parameter is larger than or equal to a predetermined displacement parameter threshold, if yes, the step S1016 is executed.
S1016, the first scheduling module sends the abnormal information about the construction plan to the distributed server module, so that the distributed server module forwards the abnormal information to the mobile terminal.
Before real-time structural deformation monitoring for bridge construction process, an edge calculation module generates a structural deformation point cloud data set corresponding to a basic structural model according to a construction plan, constructs a three-dimensional dynamic model related to the construction plan according to the point cloud data set, and determines whether the construction plan is abnormal according to whether displacement parameters of the three-dimensional dynamic model are larger than or equal to displacement parameter threshold values, so that the abnormality of the construction plan is favorably checked before construction operation, and the rigor of bridge deformation monitoring is favorably realized.
Further optionally, the structural deformation point cloud data set includes detection data corresponding to a position point set of the laser sensor and detection data corresponding to a position point set of a laser projection point of the laser sensor on the target bridge,
and the displacement parameter is a module of a displacement parameter correlation matrix determined by the edge calculation module according to the structural deformation point cloud data set, and the predetermined displacement parameter threshold is a module of a predetermined displacement parameter correlation matrix.
The situation in the above application scenario will be described in connection with a specific implementation procedure.
Let the position point set of the laser sensor be { P ] i I=1, 2, 3 … M }, assuming that the set of position points of the laser sensor at the laser projection point of the target bridge is { Q } i Specifically, the point cloud data set may include detection data corresponding to the position point set of the laser sensor and detection data corresponding to the position point set of the laser projection point of the laser sensor on the target bridge.
The coordinate difference between the two point sets is obtained as follows:
q i ＝p i -p
q i ′ ＝p i ′ -p ′
wherein P is the position point set P of the laser sensor i Center of gravity, p ′ Set of position points Q for the laser sensor at the laser projection point of the target bridge i Center of gravity, q i Is p i The coordinates of the point and the center of gravity p are different.
Wherein p can be expressed as:
p ′ can be expressed as:
let p be (x, y, z), p ′ Is (x) ′ ,y ′ ,z ′ ) The correlation matrix A, B can be constructed as follows:
the displacement parameter correlation matrix N reflecting the structural deformation of the target bridge is obtained as follows:
wherein N is i ＝A*B。
Then, in the step of determining whether the displacement parameter is greater than or equal to the predetermined displacement parameter threshold by the edge calculation module, the displacement parameter correlation matrix N may be modulo, and whether the modulus of the displacement parameter correlation matrix is greater than or equal to the modulus of the predetermined displacement parameter correlation matrix may be compared.
Further optionally, the edge calculation module may first select an anchor point of the three-dimensional dynamic model, determine a displacement parameter based on the anchor point, and specifically, in a process that the first scheduling module controls the edge calculation module to label the displacement parameter of the three-dimensional dynamic model, the edge calculation module performs the steps including:
s10141, selecting a plurality of space anchor points in the three-dimensional dynamic model by the edge calculation module.
S10142, the edge calculation module takes a space anchor point as a center to construct a space grid, so that the three-dimensional dynamic model is divided by a plurality of space grids.
S10143, calculating the space grid displacement parameter of each space grid in the whole construction planning process by the edge calculation module.
S10144, determining displacement parameters of the three-dimensional dynamic model by the edge calculation module according to the distribution of the space anchors and the space grid displacement parameters.
The three-dimensional dynamic model is divided by a space grid by taking the space anchor point as the center, and the displacement parameters of the three-dimensional dynamic model are determined according to the space grid displacement parameters and the distribution of the space anchor point, so that the risk of data redundancy and excessive loss of calculation resources caused by overlarge calculation amount is reduced, and the high efficiency of determining the displacement parameters of the three-dimensional dynamic model is improved.
Further optionally, based on the idea of calculus, each space grid is used as a differential unit, and is subjected to mechanical analysis according to the stress condition of each space grid in the construction planning process, so as to construct a corresponding mechanical model, and the displacement parameters of the three-dimensional dynamic model are finally obtained by integrating along long lines of the space anchor points according to the outline size of the three-dimensional dynamic model as a boundary condition.
Finally, it should be noted that: in the bridge construction deformation monitoring system based on big data disclosed in the embodiment of the invention, the disclosed embodiment is only a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (1)
1. A bridge construction deformation monitoring system based on big data is characterized in that the monitoring system comprises a sensor module, a first scheduling module, an edge calculation module and a distributed server module,
the sensor module is in communication connection with the edge computing module through the first scheduling module, the first scheduling module is also in communication connection with the distributed server through a public network,
the first scheduling module executes the steps of:
the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge computing module;
the first scheduling module sends an information processing request to the edge computing module, so that the edge computing module executes information verification operation on the real-time structural deformation parameter information;
the first scheduling module acquires feedback information of the edge computing module about the information verification operation;
the first scheduling module judges whether the feedback information contains warning information or not, if yes, the first scheduling module sends an abnormality troubleshooting request about the warning information to the distributed server module, so that the distributed server module executes an abnormality troubleshooting operation on the warning information;
the edge calculation module comprises a data transmission unit and a miniature server of a bridge construction site, the sensor module comprises a laser sensor, a wind speed transmitter and an infrared thermometer, and the distributed server module comprises a big data server cluster and a block chain server cluster;
after receiving the real-time structure deformation parameter information synchronized by the sensor module, the edge calculation module generates a structure deformation parameter time course curve of the bridge,
after receiving the information processing request sent by the first scheduling module, the edge computing module executes information verification operation on the real-time structural deformation parameter information, and specifically includes:
the edge calculation module performs feature extraction operation on the target image of the structural deformation parameter time course curve based on the trained convolutional neural network;
the edge calculation module judges whether the target image is an abnormal image according to the extracted target image characteristics,
if so, marking an abnormal region of a structural deformation parameter time course curve in the target image, generating first feedback information which indicates that the target image verification result fails, wherein the first feedback information comprises attribute information of the abnormal region,
if not, generating second feedback information which indicates that the target image verification result passes;
in the process that the edge calculation module judges whether the target image is an abnormal image according to the extracted target image characteristics, the target image characteristics are used as input, and are subjected to classification processing by a Softmax classifier, and a result which indicates that the target image is the abnormal image or the target image is not the abnormal image is output;
the distributed server and the mobile terminal realize communication connection based on a public network,
and before the first scheduling module synchronizes the real-time structural deformation parameter information of the target bridge detected by the sensor module to the edge computing module, the first scheduling module executing steps further include:
the first scheduling module acquires a construction plan of a target bridge and a foundation structure model of the target bridge from the distributed server;
the first scheduling module controls the edge computing module to generate a structure deformation point cloud data set corresponding to the basic structure model according to the construction plan;
the first scheduling module controls the edge computing module to construct a three-dimensional dynamic model of the target bridge about the construction planning according to the structural deformation point cloud data set;
the first scheduling module controls the edge computing module to mark displacement parameters of the three-dimensional dynamic model;
the first scheduling module controls the edge computing module to judge whether the displacement parameter is larger than or equal to a predetermined displacement parameter threshold value, if yes, the first scheduling module sends abnormal information about the construction plan to the distributed server module, so that the distributed server module forwards the abnormal information to the mobile terminal;
in the process that the first scheduling module controls the edge computing module to mark the displacement parameter of the three-dimensional dynamic model, the executing steps of the edge computing module include:
the edge calculation module selects a plurality of space anchor points from the three-dimensional dynamic model;
the edge calculation module takes the space anchor points as the center to construct a space grid, so that the three-dimensional dynamic model is divided by a plurality of space grids;
the edge calculation module calculates the space grid displacement parameter of each space grid in the whole construction planning process;
the edge calculation module determines displacement parameters of the three-dimensional dynamic model according to the distribution of the space anchors and the space grid displacement parameters;
the structural deformation point cloud data set comprises detection data corresponding to a position point set of the laser sensor and detection data corresponding to a position point set of a laser projection point of the laser sensor on a target bridge,
when the first scheduling module controls the edge computing module to judge whether the displacement parameter is greater than or equal to a predetermined displacement parameter threshold, the edge computing module executes the steps of: the coordinate difference between the position point set of the laser sensor and the position point set of the laser projection point of the laser sensor on the target bridge is obtained as follows:
q i ＝p i -p
q′ i ＝p′ i -p′
wherein P is the position point set P of the laser sensor i P' is the set of points Q of the laser sensor at the laser projection point of the target bridge i Center of gravity, q i Is p i The difference in coordinates of the point and the center of gravity p,
wherein the position point set of the laser sensor is { P ] i I=1, 2, 3 … M }, the set of position points of the laser sensor at the laser projection point of the target bridge is { Q } i ，i＝1、2、3…M}，
p is expressed as:
p ′ expressed as:
for q i (x,y,z)，q′ i (x ', y ', z ') to construct a correlation matrix A, B as follows:
the displacement parameter correlation matrix N reflecting the structural deformation of the target bridge is obtained as follows:
wherein N is i ＝A*B；
The edge calculation module judges whether the modulus of the displacement parameter correlation matrix N is larger than or equal to the modulus of a predetermined displacement parameter correlation matrix.
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