CN112084684A - Bridge health visual monitoring system based on Internet of things - Google Patents
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Abstract
The invention discloses a bridge health visual monitoring system based on the Internet of things, which comprises a plurality of sensors arranged on a bridge site, an Internet of things module, a gateway and a cloud server, wherein the sensors form a group, or a plurality of similar sensors form a group, the Internet of things module is distributed near each group of sensors, each group of sensors are connected to the Internet of things module nearby to transmit collected data, the Internet of things module is connected with the gateway through a LoRa wireless communication module of the Internet of things module, the collected sensor data are sent to the gateway and then forwarded to the cloud server through a 4G or 5G mobile communication network, and the collection and processing of the sensor data are completed on the cloud server in a centralized manner, so that a visual monitoring interface is formed. The system of the invention has distributed acquisition of sensor data, avoids a large amount of cable arrangement work, gets rid of region limitation in equipment installation, and has simple system arrangement and convenient later maintenance.
Description
Technical Field
The invention relates to the field of bridge management, in particular to a bridge health visual monitoring system based on the Internet of things.
Background
With the development of economic society and the continuous progress of civil engineering technology, the construction of bridges is rapidly developed to meet the increasingly expanding social demands caused by social development, population mobility and material transportation. The bridge is used as the throat of urban and highway traffic, is related to national economy and life safety of the masses, and is increasingly concerned and valued by people. The bridge is affected by various loads, natural environments and material degradation during construction and operation, so that the stability and durability of the bridge are reduced, and the service life of the bridge may be reached before the design life is reached. The painful accidents have led to the profound recognition that the safety of bridges is not only reflected in the quality control during construction, but also in the aspects of safety evaluation, maintenance, management, etc. during operation for decades thereafter. The traditional management method relying on manual detection and maintenance cannot meet the requirements, and a bridge health monitoring system is developed in order to know the implementation condition of the bridge in time.
The traditional bridge health monitoring system is generally divided into a sensor layer, a data centralized acquisition layer (acquisition instrument), a data collection layer (sub-workstation), a communication network layer and a system software layer (server). The system is characterized in that data of the sensors are collected in a centralized mode, data transmission is carried out in the whole process through a wired network (cables or optical cables), data collection and data preprocessing of different types of collecting instruments are carried out, a sub workstation (generally an industrial personal computer) placed on a bridge site is used for carrying out the data collection and the data preprocessing, health monitoring system software is generally installed on a physical server, and the server is generally placed on the bridge site.
According to the design scheme of the traditional bridge health monitoring system, one bridge needs one set of independent monitoring system, and the following restrictions exist in the setting of hardware equipment:
(a) the sensor data adopts a centralized acquisition mode, one field acquisition instrument needs to acquire the sensor data of a plurality of positions, a cable used for acquiring the data must be arranged to the acquisition instrument from each sensor position, and the construction difficulty is very high. In addition, once the acquisition instrument fails in the later operation and maintenance process, the data of a plurality of sensors connected with the acquisition instrument cannot be acquired continuously;
(b) the equipment communication is carried out by a wired mode, a large amount of cables are arranged in the system deployment process, the construction amount and the construction difficulty are large, in addition, the risk that the cables are aged, accidentally broken or even stolen can be met in the later-stage operation maintenance, and the later-stage operation and maintenance difficulty is serious. Moreover, the arrangement of a large number of cables also makes the surge protection work of a hardware system difficult;
(c) by means of the mode that data are collected to the work substations for centralized processing, each bridge needs to compile different data collection processing programs into the industrial personal computer according to the actual hardware environment. Therefore, when a large number of bridges need to deploy the health monitoring system, not only the workload of software development, testing and deployment is huge, but also the remote maintenance of the software cannot be realized in the later operation and maintenance, so that the great operation and maintenance cost is brought;
(d) because the system software is deployed on the entity server, the data receiving, processing, analyzing and subsequent application all depend on the entity server, which brings huge workload for the deployment, operation and maintenance of the software. In addition, a plurality of servers of the bridge health monitoring system are directly placed on a bridge site, and the server is accelerated in aging and unstable in performance due to the severe working environment.
Besides the above mentioned problems, the existing bridge health monitoring system has the following problems in monitoring data: the bridge health monitoring is mainly aimed at large bridges and extra-large bridges, the large-scale measuring points of the bridges are large in quantity, and the monitoring frequency is high. Monitoring systems generate large amounts of data during operation. Currently, these data are directly stored, and are not effectively utilized, and effective decision information cannot be provided for users.
Disclosure of Invention
The existing bridge health monitoring system has the restrictive conditions that the centralized acquisition of sensor data, the equipment communication are carried out in a wired mode, different processing programs are required to be compiled based on a hardware environment by a field sub-workstation, a software system depends on an entity server and the like, and the development of the bridge health monitoring system is greatly limited. The invention aims to provide a bridge health monitoring system which is simple to deploy and convenient to maintain at a later stage.
The invention aims to be realized by the following technical scheme: the utility model provides a visual monitoring system of bridge health based on thing networking, is including installing a plurality of sensors at the bridge scene, a sensor is from becoming a set of or a plurality of close sensor constitution is a set of, still includes thing networking module, gateway and cloud ware, thing networking module distributes near each group sensor, every group the sensor links to each other to the thing networking module nearby to the transmission data collection, thing networking module through its loRa wireless communication module with the gateway links to each other, arrives the sensor data of gathering the gateway, again by the gateway forwards to through 4G or 5G mobile communication network cloud ware, the collection and the processing of sensor data are in the completion of concentrating on the cloud ware to form visual monitoring interface.
The field environment of the bridge is complex, and the sensors are distributed. The invention changes the idea of data centralized collection of the traditional sensor, adopts a data distributed collection mode that one sensor is self-grouped or is composed of a plurality of similar sensors and is respectively connected to the Internet of things module, avoids the occurrence of the condition that the data collection of a large sensor is influenced by the problem of one collection device, and avoids the wiring work of seven-turn and eight-turn from each sensor to the collection instrument in the data centralized collection mode. The internet of things module with simple function and low price and the characteristic of transmitting sensor data through the LoRa wireless communication network also well support the distributed acquisition mode provided by the invention. Because the long-distance transmission of data is supported to LoRa wireless communication module, and data transmission stability is good moreover. In addition, the system wiring is further simplified in a mode that the module of the Internet of things sends data through a wireless network.
From the whole system framework of the monitoring system, the data of the invention is directly sent to the cloud server by the internet of things module through the gateway, so that the regional limitation of equipment installation is eliminated in equipment management and data processing, a large amount of bridge management can be realized by only one set of software, and the workload of system maintenance, system upgrading and the like is greatly reduced.
In the bridge health monitoring system, a great variety and number of sensors are applied to the bridge health monitoring system, so that massive monitoring data is generated, and the application rate of historical data is extremely low due to data flooding. Aiming at the problem, the invention adopts the following improvement scheme:
the processing method of the invention for the collected sensor data is as follows:
the invention carries out visual display on the collected sensor data by a control chart analysis method, and specifically comprises the following steps:
step 1) dividing data into different data block intervals according to a time period;
step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving relevant data forming the control chart;
the control chart includes the following information: measuring points, cycle time, maximum value and minimum value, a frequency histogram based on a set confidence interval, and the number of intermediate ranges in a maximum probability interval.
The stored data also comprises the mean value and the standard deviation of the data in the set confidence interval, and the information displayed by the control chart also comprises bridge load overrun probability and failure probability deduced according to the mean value and the standard deviation.
The bridge load overrun probability and damage probability calculation mode is as follows:
the mathematical expression of a normal probability density function is:
in the formula (I), the compound is shown in the specification,
x is a random variable;
p (x) is the probability density of a particular value;
the standard deviation is shown as a value of sigma,
according to the data of each calculation period, a corresponding normal probability density function is obtained through the formula, and the load overrun probability and the damage probability are calculated through the integration of the following formula:
when t is a design value, the value obtained by P is the load overrun probability;
when t is equal to a limit value, the value obtained by P is the damage probability;
design values and limits are calculated from design specifications and finite element simulations.
Or, the collected sensor data is displayed visually by forming the monitoring data in the periodic time into a radar map.
The display effect and the realization mode of the radar map are as follows:
early warning values are set on the radar map, wherein the early warning values comprise a radar map primary early warning value and a radar map secondary early warning value, the radar map secondary early warning value is used as an outer ring of the radar map, and the radar map primary early warning value is used as an inner ring of the radar map;
the first-level early warning value represents a design value of a parameter at a certain position, and the second-level early warning value represents a material damage limit value of the parameter at a certain position;
and taking the '1' after the normalization processing of the secondary early warning values of each position of the parameter as the secondary early warning value of the radar map, and taking the maximum early warning value in the primary early warning values of each position of the parameter after the synchronization normalization processing as the primary early warning value of the radar map.
AijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
1a)Aijno data over two-stage early warning value
2a)AijNo data exceeds the first-class early warning value
The treatment method comprises the following steps:
step 11a) performing dimensionality reduction on the acquired data by a principal component analysis method;
the raw data matrix of a certain type of sensor is denoted as [ A ]ij]n*mM represents the number of measuring points of the sensor, n represents the acquisition times, and each column of the matrix is a dimension which is m dimensions;
forming a new matrix [ A 'by the matrix and the primary early warning value of each position sensor'ij](n+1)*mAnd reducing the new matrix to one dimension as [ A ] "ij](n+1)*1(ii) a The results after dimensionality reduction are presented as: a number of (n +1) points on the new feature dimension space;
step 12a) calculation of the composite index
Taking the minimum value of the dimensionality reduction space as a reference point, and recording the distance between the early warning value of the later dimensionality reduction stage and the reference point as x1By the analogy, the method can be used,the distances from other dimensionality-reduced points are respectively recorded as x2，x3，…，xn；
The calculation formula of the comprehensive index is as follows:
wherein r is0A first-level early warning value of the radar map is obtained;
Δ X represents the distance between the maximum value and the minimum value after dimensionality reduction, and Δ X ═ X1。
AijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
1b)Aijno data over two-stage early warning value
2b)AijThe data exceeds the first-class early warning value
The treatment method comprises the following steps:
step 11b), firstly, carrying out principal component analysis and dimension reduction processing on the acquired data;
the raw data matrix of a certain type of sensor is denoted as [ A ]ij]n*mM represents the number of measuring points of the sensor, n represents the acquisition times, and each column of the matrix is a dimension which is m dimensions;
forming a new matrix [ A 'by the matrix and the primary early warning value of each position sensor'ij](n+1)*mAnd reducing the new matrix to one dimension as [ A ] "ij](n+1)*1(ii) a The results after dimensionality reduction are presented as: a number of (n +1) points on the new feature dimension space;
step 12b) calculation of the composite index
Dividing the distance between the inner ring position and the outer ring into n +1 equal parts according to the sampling frequency n in the period by taking the inner ring position as a reference; the calculation formula of the comprehensive index value is as follows:
wherein r is0A first-level early warning value bit of the radar map;
k is the number of values exceeding the first-level early warning value after dimension reduction.
AijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
Aijif the data exceeds the second-level early warning value;
the treatment method comprises the following steps:
and one sensor exceeds a secondary early warning value, the comprehensive index value is positioned at the outer ring position, and the representing state risk is extremely high.
The method comprises the following specific dimensionality reduction steps:
first, calculate matrix [ A'ij](n+1)*mSubtracting the mean value of each dimension to obtain new data A after centralizationnew；
Second, finding the characteristic covariance matrix of the new matrix
Thirdly, solving corresponding eigenvalue and eigenvector according to the covariance matrix;
fourthly, arranging the eigenvalues according to a descending order, correspondingly giving out eigenvectors, and selecting the largest principal component to obtain a transformation matrix P;
fifthly, solving the data Y after dimensionality reduction as PA according to the transformation matrixnew。
Compared with the prior art, the invention has the following effective effects:
1) the sensor data distributed acquisition avoids a large amount of cable arrangement work, and reduces the influence range when a single data acquisition instrument (the Internet of things module in the invention) breaks down. In addition, the data of the sensors are collected in a distributed mode, the output signal of each sensor can be connected to a data collection instrument nearby the sensor through a short cable and then sent to the cloud server through the gateway, and the system transmission from the data collection instrument to the cloud server is digital signals, so that the stability and the precision of the signals can be improved.
2) The Internet of things architecture determines that the management of the equipment and the collection and processing of the sensor data are all completed at the cloud end, so that the regional limitation of equipment installation is eliminated in the management of the equipment and the processing of the data, no matter where the equipment is deployed, the Internet of things platform at the cloud end can uniformly perform equipment identification and data processing as long as the equipment can be connected into a network, so that the management and processing of a large number of bridges can be realized only by one set of software, and the subsequent data maintenance and the increase of monitoring points can be easily realized (in a distributed layout mode, the increase of the monitoring points does not need to be changed in lines and wires and only needs to increase the information of the sensor on the system) due to the cloud of the equipment identification and the data processing, so that the workload of maintenance and system upgrading is greatly reduced.
3) The control graph and the radar map algorithm change various data into valuable information, and the valuable information is displayed visually in real time so as to provide necessary information for decision making and early warning for bridge management departments.
Drawings
Fig. 1 is a structural diagram of a bridge health visualization monitoring system based on the internet of things according to a preferred embodiment of the present invention;
fig. 2 is a general network topology architecture of a bridge health visualization monitoring system based on the internet of things according to a preferred embodiment of the present invention;
FIG. 3 is an example of a daily control graph formed;
FIG. 4 is a daily control chart for a day of FIG. 3;
FIG. 5 is a diagram showing the effect of radar chart;
FIG. 6 is a software design flow of a bridge health monitoring visualization system.
Detailed Description
In this embodiment, the bridge health visual monitoring system based on the internet of things is powered by the commercial power and/or the solar cell panel, as shown in fig. 1, and is divided into 4 parts from the aspect of the logic architecture:
the data acquisition layer is mainly used for periodically acquiring bridge data in operation through various sensors;
the wireless transmission layer transmits data to a field Internet of things gateway through a wireless communication network based on an LoRa technology, and the gateway sends the data to the cloud server through a 4G/5G mobile communication network;
the data analysis layer is used for preprocessing, analyzing and storing the acquired data through a virtual server deployed at the cloud end;
and the data display layer is mainly used for visually displaying the bridge data by using an algorithm, so that the remote real-time monitoring and early warning of the bridge are realized.
(1) Data acquisition layer
The data acquisition layer is responsible for uniformly acquiring original analog signals such as voltage, current, frequency and optical signals induced by the sensor system. The data acquisition layer mainly comprises various sensors on site. Each sensor has own coding information, and the sensors with different codes are distributed according to the measuring point positions and recorded in the background during installation.
(2) Wireless transmission layer
The wireless transmission layer mainly comprises an Internet of things module and a gateway. The internet of things module in this embodiment collects data acquisition and wireless communication in an organic whole, including embedded microprocessor, data acquisition interface and loRa wireless communication module based on Modbus bus agreement, its data acquisition interface includes RS485 communication interface and RS232 communication interface, its embedded microprocessor with RS485 communication interface, RS232 communication interface and loRa wireless communication module links to each other respectively. And the RS485 communication interface and the RS232 communication interface are monitoring interfaces.
The internet of things module is the core of data acquisition of the bridge health visual monitoring system, and data are transmitted by adopting a LoRa wireless communication network, so that the system can be well adapted to the common application environment of the monitoring system (the monitoring system is usually applied to a bridge or a grand bridge). The overall network topology architecture of the monitoring system of the embodiment is shown in fig. 2, and comprises a plurality of sensors installed on a bridge site, such as static levels 01 and 02, inclinometers 01 and 02, strain sensors 01 and 02, magnetoelastic instruments and cable force sensors 01 and 02, wherein the sensors form a group or a plurality of similar sensors form a group, each group of sensors is connected to an internet of things module nearby the sensors to transmit collected data, the internet of things module is connected with a gateway through a LoRa wireless communication network, the gateway receives data sent by the internet of things module, distinguishes data of different internet of things modules through identification of numbers of internet of things modules EUIs, and then sends the data to a cloud server through a 4G or 5G mobile communication network to complete data collection, processing, distribution and other work, and form a visual monitoring interface.
The LoRa adopted by the internet of things module in this embodiment is a low-power consumption long-distance wireless communication technology using a spread spectrum modulation mechanism, and combines digital spread spectrum, digital signal processing and forward error correction coding technologies. The method uses a linear frequency modulation spread spectrum modulation technology, not only maintains the characteristic of low power consumption, but also obviously increases the communication distance, simultaneously improves the network efficiency and eliminates interference, namely, terminals of different spread spectrum sequences can not mutually interfere even if the terminals use the same frequency to simultaneously transmit. LoRaWAN (low-power wide area network) adopting LoRa technology really realizes the Internet of things with low power consumption, mobility, standardization, simplicity and safety. LoRa technical characteristics: the networking capability is strong, the cost is low, star, tree and MESH networks are supported, and the laying period is short; not fully dependent on the network operator; low power consumption, standby current 5 uA; the receiving circuit is less than 14 mA; emission current 100 mA; the transmission distance is long and can be as long as 20 kilometers at most; the signal penetration capability is strong; the anti-interference is strong, the transmission is stable, the spread spectrum technology is used, the signal-to-noise ratio is high, and the immunity to electronic noise and multipath distortion is realized; and (4) security and confidentiality are realized, and signals are disguised in noise after being added with pseudo-random code spread spectrum.
The field environment of the bridge is complex, and the sensors are distributed. The invention changes the idea of data centralized collection of the traditional sensor, adopts a data distributed collection mode that one sensor is self-grouped or is composed of a plurality of similar sensors and is respectively connected to the Internet of things module, avoids the occurrence of the condition that the data collection of a large sensor is influenced by the problem of one collection device, and avoids the wiring work of seven-turn and eight-turn from each sensor to the collection instrument in the data centralized collection mode. The internet of things module with simple function and low price and the characteristic of transmitting sensor data through the LoRa wireless communication network also well support the distributed acquisition mode provided by the invention. In addition, the system wiring is further simplified in a mode that the module of the Internet of things sends data through a wireless network. In addition, the data of the sensors are collected in a distributed mode, the output signal of each sensor can be connected to a data collection instrument nearby the sensor through a short cable and then sent to the cloud server through the gateway, and the system transmission from the data collection instrument to the cloud server is digital signals, so that the stability and the precision of the signals can be improved.
From the whole system framework of the monitoring system, the data of the invention is directly sent to the cloud server by the internet of things module through the gateway, so that the regional limitation of equipment installation is eliminated in equipment management and data processing, and the equipment identification and data processing can be uniformly carried out by the cloud internet of things platform as long as the equipment can be connected to the network no matter where the equipment is deployed, so that not only can the management and processing of a large number of bridges be realized by only one set of software, but also the subsequent data maintenance and the increase of monitoring points can be easily realized (in a distributed layout mode, the increase of the monitoring points does not need to be changed and wired, and only the information of the sensor needs to be increased on the system) due to the cloud of the equipment identification and data processing.
(3) Data analysis layer
The cloud server first preprocesses the data. The data preprocessing is to carry out digital filtering processing according to the output characteristics, sampling frequency and signal transmission length of different sensors: for example, a digital filter is used for carrying out partial or full low-pass, band-pass and band-stop processing on the signal, and identifying and evaluating the reliability of the data, including data conversion and abnormal data elimination.
The data analysis mainly refers to the analysis and processing of data and corresponding structural mathematical models through correlation, convolution, FFT, statistical analysis, integral differentiation and other mathematical processing methods, and the analyzed data can be stored. The data processing analysis algorithm is directly nested in the system, and a user can freely combine and call the data processing analysis algorithm.
The cloud server can also distribute data through an MQTT (Message Queuing Telemetry Transport) topic subscription mode.
(4) Display layer
The display layer realizes the visual display of the health monitoring system data. The visualization system platform acquires data through data interaction with the cloud server, performs classification processing on the data and realizes graphical interface data display, and the display interface of the display layer can be divided into an APP version and a web page. The display layer can enter the display interface of the sensor signal by directly clicking the icon of a certain sensor according to the principle of what you see is what you get, observe the response curve, conveniently give the statistical data of all sensor data every day, every week, every month or every year, and utilize the structural analysis toolbox to carry out on-line parameter or structural analysis.
The display layer interface can carry out graphical early warning on abnormal acquired data, users with different authorities log in the platform and then check real-time data of health monitoring of each part of the bridge through the platform according to role authorities, and relevant schemes are processed on abnormal early warning.
Aiming at the problems of bridge monitoring data flooding and low utilization rate, the invention also respectively establishes a control chart and a radar chart visual analysis method, changes various data into valuable information, and visually displays the valuable information in real time to provide necessary information for decision making and early warning for bridge management departments.
The control chart analysis method mainly comprises two steps, namely, step 1) dividing data into different data block intervals according to time periods such as days, weeks, months and years;
step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving relevant data forming the control chart; the control chart here includes the following information: measuring points, cycle time, maximum value and minimum value, a frequency histogram based on a set confidence interval, and the number of intermediate ranges in a maximum probability interval. The condition of the bridge changing with the time (day, week, month and year) cycle can be judged from the form of the control chart.
Taking a daily control chart as an example, the histogram is a frequency histogram which is obtained by screening 2880 data of daily data with a sampling frequency of 30 s/time to 95% confidence intervals, counting the number according to the intervals where the data are located and then converting the data.
FIG. 3 is an example of a daily control graph formed. As shown in the figure, the daily control graphs of different dates are arranged in a coordinate system, the abscissa is the date, and the ordinate is the magnitude of the monitoring value. The daily control graph can play a statistical role, and the daily bridge range of the measuring points, whether the bridge is in a normal interval, whether the state is stable and the like can be conveniently checked. FIG. 4 is a daily control chart for a day of FIG. 3. As shown in fig. 4, the names and representations of the respective locations have the following meanings:
1-the data maximum value for the day dmaxWeight;
2-the day data minimum value dminWeight;
3-95% confidence interval upper limit fmaxWeight;
4-95% lower confidence interval limit fminWeight;
5-maximum probability interval upper bound sectionMax;
6-lower limit of maximum probability interval section Min;
7-number of intermediate maximum probability intervals (sectionMax + sectionMin)/2.
They are all identified in the same coordinate system.
Taking the daily control chart as an example, after the monitoring of a certain type of sensor is finished at 24 o' clock in the evening, the daily data x stored in the database is processed1，x2……xnCarrying out statistical analysis, recording the maximum value dmaxWeight of the data of the day and the minimum value dminWeight of the data of the day;
the confidence interval is colloquially the range spanned by the confidence levels required to be achieved, and the confidence interval of a sample can be estimated as an interval of the overall mean. The calculation method of the confidence interval comprises the following steps:
the first step is as follows: averaging the data
And standard error
The second step is that: determining a confidence interval, taking the confidence as an example, wherein when the confidence is equal to 95%, Z is 1.96; determining confidence intervals
According to the configured equal part Y, the confidence intervals are divided into Y groups, and the group distance s is (fmaxWeight-fminWeight)/Y. Reasonable configuration equal parts can be made for the data quantity according to the sampling frequency. The more monitoring data, the more the group distance can be continuously reduced. The smaller the group distance of the histogram is, the more the group number is, and the more obvious the effect of data distribution is. The height of the histogram indicates the frequency count, i.e. the number of times the variable value occurs within the interval.
The third step: counting the number of data falling into each cell according to the group distance, calculating the probability falling into the cell according to the data quantity divided by the total data quantity of the current day, taking out the maximum value of the probability after sorting as section delivery, and taking the upper limit and the lower limit of the interval as section Max and section Min respectively, wherein (section Max + section Min)/2 is the middle range number of the maximum probability interval. Therefore, the control chart is formed, and the frequency data of the histogram is also retained.
The control chart not only reserves the most key three data in the bridge monitoring data: maximum, minimum, and median maximum probability intervals, and frequency histogram data forming the confidence interval. The distribution state of the data can be visually judged according to the frequency histogram, the data is used for carrying out state evaluation on the bridge, and the confidence interval is based on the purpose of improving the reliability and the precision of the data. The data is really important data in the bridge health monitoring data, and the development trend of the bridge state can be qualitatively judged if the intermediate range number in the maximum probability interval is. The data extracted by the invention has small occupied storage space, is convenient to be kept for a long time, forms a control chart, is visual and convenient to check, and can fully show the vitality and the value of the historical data.
In addition, the overrun probability and the damage probability of the monitoring parameters are analyzed by a probability statistical method and are added into the control chart for the reference of bridge management personnel.
Trend prediction (overrun probability, damage probability)
According to the results of scientific and engineering experiments, the data almost follow a normal distribution. Assuming that the data of the sensors per day accord with normal distribution, taking interval data with a confidence interval of 95%, and analyzing the load overrun probability and the damage probability by adopting a normal probability density function. The mathematical expression of a normal probability density function is:
in the formula (I), the compound is shown in the specification,
x-random variable
P (x) -probability density of a particular value
X is the mean value of the number of the particles,
sigma-the standard deviation of the signal from the signal,
according to the data of each calculation period, obtaining a corresponding normal probability density function through the above formula, and calculating the load overrun probability and the damage probability through the following integral:
when t is a design value, the value obtained by P is the load overrun probability.
When t is a limit value, the value obtained by P is the destruction probability.
Design values and limits are calculated from design specifications and finite element simulations.
The index model can be used for evaluating the development trend of the position state of the bridge measuring point and is suitable for various monitoring parameters of different types.
The control graph database of the invention only needs to record the following information: measuring point, cycle time, maximum value, minimum value, maximum probability interval intermediate range number, mean value, standard deviation and histogram frequency. After corresponding weekly control chart, monthly control chart and one-year period control chart are formed based on the original data (the same-day control chart in the forming process), if the bridge operation condition is good, the original data in the database can be abandoned in time. Therefore, the invention can greatly reduce the data volume and solve various problems caused by the flooding of historical data. The control chart of the invention also presents the load overrun probability and the failure probability. The invention efficiently retains important data in bridge operation for a long time, facilitates the management and the checking of the data, and provides corresponding information for bridge management personnel to know the change trend of the bridge.
The structural parameters of the bridge such as stress, strain, deflection, wind speed, displacement, inclination angle and the like can be described by using the control chart analysis method, and the method has excellent containment.
The radar map analysis method comprises the following specific steps:
in the bridge health monitoring process, a plurality of parameters such as deflection, stress, strain and the like of a bridge need to be monitored, different types of sensors need to be adopted, and the number of each type of sensors is different. The sensors of the same type (aiming at a single parameter) are arranged at different positions (namely different measuring points) of the bridge, although the sampling frequency of each measuring point is consistent, the primary early warning value and the secondary early warning value of different measuring points are generally different. The first-level early warning value represents the design value of the parameter at the position, namely the upper limit value under the normal use condition, and the second-level early warning value represents the material damage limit of the parameter at the position.
Within the period time, in order to reflect the monitoring data condition of a plurality of monitoring points of the single parameter sensor, if the monitoring data condition exceeds a first-level early warning value or a second-level early warning value, or the monitoring data condition is a safety state. The radar chart of the data analysis model constructed by the invention follows the following principle:
1) the parameter has a sensor data exceeding the first-level early warning, and the comprehensive index is reflected;
2) the parameter has a sensor data exceeding the secondary early warning, and the comprehensive index is reflected;
3) all sensors of the parameter are normal, and the comprehensive index is reflected.
FIG. 5 is a radar map defined as follows:
1) each corner of the radar map represents a dimension representing a composite index of single parameter sensor data.
2) The outer circle is the normalized secondary early warning value, the inner circle is the normalized primary early warning value, and the point located on the dimension represents the comprehensive index of the parameter.
3) And in the period time, if the value of one sensor of the parameter exceeds the secondary early warning value, the index position is positioned at the outermost circle position.
4) And in the period time, if the value of no sensor exceeds the secondary early warning value, one sensor exceeds the primary early warning value, and the index position is between the inner ring and the outermost ring.
5) And in the time period, if no sensor exceeds a first-level early warning value, the index is positioned in the inner ring.
6) The position of the composite index does not exceed the position of the outermost circle.
The above definitions are in accordance with the principles of the model. In order to ensure the uniformity of the radar map, the size of the outermost circle needs to be uniform, the data of all different types of sensors needs to be normalized, and finally the value of the outermost circle is 1. The primary early warning value is different even if the sensors of the same type are in different positions, and the normalized maximum early warning value is finally adopted.
Data classification
In the period time, after the data acquisition of a certain type of sensor is completed, AijRepresenting the data of the type A sensor at the position of the bridge j in the ith acquisition, the data can be divided into the following three cases:
the first condition is as follows:
Aijdata exceeding second-level early warning value
Case two:
1)Aijno data over two-stage early warning value
2)AijThe data exceeds the first-class early warning value
Case three:
1)Aijno data over two-stage early warning value
2)AijNo data exceeds the first-class early warning value
For the first situation, one sensor exceeds a second-level early warning value, the comprehensive index value is located at the outer ring position, and the representing state risk is extremely high.
For the second case and the third case, the collected data is subjected to Principal Component Analysis (PCA) dimensionality reduction processing and then is analyzed.
Data dimension reduction
In the period time, each position (m positions, n times of acquisition) where the sensor is located acquires a column of data as a dimension which is characterized by a matrix [ A ]ij]n*mI.e. each column of the matrix is one dimension, for a total of m dimensions (features).
The original data matrix [ A ]ij]n*mAnd the primary early warning value of each position form a new matrix [ A'ij](n+1)*mAnd adding the position of the first-level early warning value to the last row, wherein the specific adding position has no influence on the result in the actual calculation.
Then reducing the new matrix to one dimension, namely [ A ] "ij](n+1)*1。
The PCA principal component analysis method is mainly used for identifying main features from high-dimensional data and reducing the dimensionality of a data set, so that some redundant information of the data is removed, the data is simpler and more efficient, and the features with the largest data contribution are kept. And finally, a plurality of groups of data of the same type of sensors are finally converted into a comprehensive index, so that the complexity of analyzing problems is reduced.
The PCA dimensionality reduction steps are as follows:
first, calculate matrix [ A'ij](n+1)*m(writing A) average value of each dimension, and subtracting the average value of each dimension to obtain new centralized data Anew；
Second, finding the characteristic covariance matrix of the new matrix
Thirdly, solving corresponding eigenvalue and eigenvector according to the covariance matrix;
fourthly, arranging the eigenvalues according to a descending order, correspondingly giving out eigenvectors, and selecting the largest principal component (the previous row) to obtain a transformation matrix P;
fifthly, solving the data Y after dimensionality reduction as PA according to the transformation matrixnew。
The result of dimension reduction into one dimension is presented as: a number (n +1) points on the new feature dimension (one-dimensional) space.
Calculation of synthetic index
And for the third case, namely, the case that the first-level early warning value is not exceeded.
Taking the minimum value of the dimensionality reduction space as a reference point, and taking the distance between the early warning value of the later dimensionality reduction stage and the reference point as x1By analogy, the distance from other dimensionality reduction points is x2，x3，…，xn，x1>x2>...>xnAssuming no value repetition.
The calculation formula of the comprehensive index is as follows:
wherein r is0Normalizing primary early warning value positions for radar maps
the distance between the maximum value and the minimum value after dimension reduction is delta X ═ X1。
For the second condition, i.e. the first-level early warning value is exceeded but the second-level early warning value is not exceeded
And taking the inner ring position (the normalized first-level early warning value) as a reference, and dividing the distance between the inner ring position and the outer ring into n +1 equal parts according to the sampling frequency n in the period. After PCA dimensionality reduction, counting the number k of the early warning values exceeding the first level through the relative position in a new space, wherein the calculation formula of the comprehensive index value is as follows:
wherein r is0Normalizing the position of the primary early warning value for the radar map.
Data examples
Taking a certain parameter as an example, a certain type of sensor such as an A type sensor in a period acquires data for n times, and an original data matrix is multidimensional data as follows:
TABLE 1
Aij represents the measured value of a class A sensor, wherein i represents the ith sample, and j represents the position (measuring point) of the class sensor.
The following are test data:
TABLE 2
It can be seen that it corresponds to case two above.
The first step of the normalization of the early warning values is shown in table 3:
TABLE 3
And the outer ring (normalized secondary early warning value) is 1, and Max (Aj/Aj) is taken as a relative position drawn by the inner ring.
The results of the early warning value normalization processing in the first step, which correspond to the specific test data in table 2, are shown in table 4.
TABLE 4
| Normalizing first-level early warning values | 0.49 | 0.48 | 0.47 | 0.50 |
| Normalized secondary early warning value | 1.00 | 1.00 | 1.00 | 1.00 |
Find r0＝Max(aj/Aj)＝0.5
Second, data classification
A series of data collected by each period at each position where the sensor is located is one dimension, namely a matrix [ Aij ] n × m is provided with one dimension in each column, m represents the position, n represents the number of times, and the total number of the dimensions (characteristics) is m. After the dimensionality reduction is carried out by adopting the PCA algorithm, new features can be generated in the data, and the new features are not in the original dimensional space.
And forming a new matrix by the original data matrix [ Aij ] n × m and the primary early warning value, and carrying out PCA (principal component analysis) dimension reduction processing on the new matrix until the new matrix is reduced to be one-dimensional.
The PCA dimensionality reduction steps are as follows:
TABLE 5
The results after dimensionality reduction are presented as: a number of points on a new axis (new feature dimension space) including the raw data and the results after the early warning values are reduced in dimension, see table 6.
TABLE 6
After the dimension reduction of the test data, the method comprises the following steps:
TABLE 7
The data are obtained by performing dimensionality reduction on the data in table 2.
Taking the inner ring position (normalized primary early warning value) 0.79 as a reference, and equally dividing the distance between the inner ring position and the outer ring into 15 equal parts according to the sampling frequency in the period of 14 times. After PCA dimensionality reduction, counting the number of the early warning values (more than 0.79) exceeding the first level to be 4 through the relative position on the new space.
The calculation formula of the comprehensive index value is as follows:
from the above, the invention can not only simultaneously show the comprehensive index states of a plurality of parameters on one graph, but also qualitatively show the amount of the early warning data according to the positions of the early warning data in the radar graph.
The framework and the flow of the software system of the bridge health visualization monitoring system are as follows:
as shown in fig. 6, the internet of things cloud platform (i.e., cloud server) sends data to MQTT data receiving service and HTTP data receiving service of the monitoring system through MQTT or HTTP, and the data receiving service performs preliminary verification to determine whether the data meet the specification, and discards the data that do not meet the specification. Data meeting the specification enters a message queue middleware RabbitMQ, and the purpose of adding the data into the message queue is to ensure that data receiving can still be normally carried out after the data processing service of the following sensor exits under the abnormal condition.
The data in the sensor data processing service message queue is processed according to the configuration parameters of the sensor, and the sensor data processing service is mainly responsible for the following work: firstly, calculating the real engineering physical quantity of the bridge, secondly, carrying out single-value early warning judgment, thirdly, storing the calculation result and the original data into a MongoDB database, and fourthly, storing the calculation result and the original data into a Redis database stream. The sensor data processing service starts a plurality of service multithreads to calculate data according to different sensor types, so that the data processing efficiency is guaranteed.
The data alarm service extracts data from the streams of the Redis database to perform early warning judgment, performs real-time early warning message sending processing after finding early warning, and mainly creates a RabbitMQ message and a Redis stream and stores the early warning data into a mysql database.
The data statistical service mainly obtains data of past hour from Redis stream and data of past day from MongoDB, and performs statistical analysis to extract characteristic value.
And the data analysis service acquires data from the MongoDB database, performs algorithm analysis and calculation and stores the calculation result into the mysql database.
The page background service mainly provides an interface for the visualization of front-end data, reads data from databases such as Redis, MongoDB, mysql and the like, displays the data in a front-end web page, and displays an early warning message in the page.
The monitoring system adopts a micro-service architecture, each module of the monitoring system is designed into an independent service, data interaction is carried out between micro-services by using a middleware RabbitMQ message queue, and the micro-services mainly comprise page background service, MQTT data receiving service, HTTP data receiving service, sensor data processing service, data analysis service, data alarm service and data statistics service.
The front end of the system uses a vue.js frame, the background adopts C # programming language, a net core frame is developed and realized, the database adopts MySQL and MongoDB, and the middleware adopts Redis and RabbitMQ. The application software is deployed and operated on a cloud server, and the Nginx server, the mysql, the MongoDB and the net core application are packaged into a container mirror image and operated on a docker container engine.
The invention applies the monitoring technology in the field of Internet of things to the deployment of the bridge health monitoring system, and has the main advantages that: (1) the sensors are used for collecting in a distributed mode, so that the constraint of a communication cable is eliminated; (2) performing wireless transmission on data by adopting an LoRa technology; (3) the system comprises a Internet of things architecture and a set of software, wherein the set of software is used for realizing the management of a plurality of bridges; (4) by introducing the cloud technology, system software is not limited on a physical server. In addition, the invention introduces a data visualization technology, which is a scientific method for analyzing and refining the internal rules and characteristics of data and can be used for solving the problem that data information in the monitoring field is difficult to analyze and display in time.
Claims (10)
1. The utility model provides a visual monitoring system of bridge health based on thing networking, its characterized in that, is including installing a plurality of sensors at the bridge scene, a sensor is one by oneself a set of or a plurality of close sensor constitution is a set of, still includes thing networking module, gateway and cloud ware, thing networking module distributes near each group sensor, every group the sensor links to near thing networking module to transmit data collection, thing networking module through its LoRa wireless communication module with the gateway links to each other, sends the sensor data of gathering to the gateway, again by the gateway passes through 4G or 5G mobile communication network and forwards to cloud ware, the collection and the processing of sensor data are in the completion of concentrating on the cloud ware to form visual monitoring interface.
2. The monitoring system according to claim 1, wherein the collected sensor data is visually displayed by a control chart analysis method, comprising the following steps:
step 1) dividing data into different data block intervals according to a time period;
step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving relevant data forming the control chart;
the control chart includes the following information: measuring points, cycle time, maximum value and minimum value, a frequency histogram based on a set confidence interval, and the number of intermediate ranges in a maximum probability interval.
3. The monitoring system of claim 2, wherein the stored data further includes a mean and a standard deviation of the data within the set confidence interval, and the information displayed by the control chart further includes a bridge load overrun probability and a failure probability inferred from the mean and the standard deviation.
4. The monitoring system of claim 3, wherein the bridge load overrun probability and failure probability are calculated as follows:
the mathematical expression of a normal probability density function is:
in the formula (I), the compound is shown in the specification,
x is a random variable;
p (x) is the probability density of a particular value;
the standard deviation is shown as a value of sigma,
according to the data of each calculation period, a corresponding normal probability density function is obtained through the formula, and the load overrun probability and the damage probability are calculated through the integration of the following formula:
when t is a design value, the value obtained by P is the load overrun probability;
when t is equal to a limit value, the value obtained by P is the damage probability;
design values and limits are calculated from design specifications and finite element simulations.
5. The monitoring system of claim 1, wherein the aggregated sensor data is visualized by forming the monitoring data over a period of time into a radar map.
6. The monitoring system of claim 2, wherein the radar map is displayed and implemented as follows:
early warning values are set on the radar map, wherein the early warning values comprise a radar map primary early warning value and a radar map secondary early warning value, the radar map secondary early warning value is used as an outer ring of the radar map, and the radar map primary early warning value is used as an inner ring of the radar map;
the first-level early warning value represents a design value of a parameter at a certain position, and the second-level early warning value represents a material damage limit value of the parameter at a certain position;
and taking the '1' after the normalization processing of the secondary early warning values of each position of the parameter as the secondary early warning value of the radar map, and taking the maximum early warning value in the primary early warning values of each position of the parameter after the synchronization normalization processing as the primary early warning value of the radar map.
7. The monitoring system of claim 6, wherein A isijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
1a)Aijno data over two-stage early warning value
2a)AijNo data exceeds the first-class early warning value
The treatment method comprises the following steps:
step 11a) performing dimensionality reduction on the acquired data by a principal component analysis method;
the raw data matrix of a certain type of sensor is denoted as [ A ]ij]n*mM represents the number of measuring points of the sensor, n represents the acquisition times, and each column of the matrix is a dimension which is m dimensions;
forming a new matrix [ A 'by the matrix and the primary early warning value of each position sensor'ij](n+1)*mAnd reducing the new matrix to one dimension as [ A ] "ij](n+1)*1(ii) a The results after dimensionality reduction are presented as: a number of (n +1) points on the new feature dimension space;
step 12a) calculation of the composite index
Taking the minimum value of the dimensionality reduction space as a reference point, and recording the distance between the early warning value of the later dimensionality reduction stage and the reference point as x1By analogy, the distances from other dimensionality reduction points are respectively marked as x2，x3，…，xn；
The calculation formula of the comprehensive index is as follows:
wherein r is0A first-level early warning value of the radar map is obtained;
Δ X represents the distance between the maximum value and the minimum value after dimensionality reduction, and Δ X ═ X1。
8. The monitoring system of claim 7, wherein A isijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
1b)Aijno data over two-stage early warning value
2b)AijThe data exceeds the first-class early warning value
The treatment method comprises the following steps:
step 11b), firstly, carrying out principal component analysis and dimension reduction processing on the acquired data;
the raw data matrix of a certain type of sensor is denoted as [ A ]ij]n*mM represents the number of measuring points of the sensor, n represents the acquisition times, and each column of the matrix is a dimension which is m dimensions;
forming a new matrix [ A 'by the matrix and the primary early warning value of each position sensor'ij](n+1)*mAnd dropping the new matrixTo one dimension is denoted as [ A ] "ij](n+1)*1(ii) a The results after dimensionality reduction are presented as: a number of (n +1) points on the new feature dimension space;
step 12b) calculation of the composite index
Dividing the distance between the inner ring position and the outer ring into n +1 equal parts according to the sampling frequency n in the period by taking the inner ring position as a reference; the calculation formula of the comprehensive index value is as follows:
wherein r is0A first-level early warning value bit of the radar map;
k is the number of values exceeding the first-level early warning value after dimension reduction.
9. The monitoring system of claim 8, wherein aijData representing the type a sensor at the location of bridge j in the ith acquisition for the following cases:
Aijif the data exceeds the second-level early warning value;
the treatment method comprises the following steps:
and one sensor exceeds a secondary early warning value, the comprehensive index value is positioned at the outer ring position, and the representing state risk is extremely high.
10. The monitoring system of claim 9, wherein the step of reducing the dimensions is as follows:
first, calculate matrix [ A'ij](n+1)*mSubtracting the mean value of each dimension to obtain new data A after centralizationnew；
Second, finding the characteristic covariance matrix of the new matrix
Thirdly, solving corresponding eigenvalue and eigenvector according to the covariance matrix;
fourthly, arranging the eigenvalues according to a descending order, correspondingly giving out eigenvectors, and selecting the largest principal component to obtain a transformation matrix P;
fifthly, solving the data Y after dimensionality reduction as PA according to the transformation matrixnew。
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