CN112084684B - Bridge health visual monitoring system based on Internet of things - Google Patents
Bridge health visual monitoring system based on Internet of things Download PDFInfo
- Publication number
- CN112084684B CN112084684B CN202010724543.3A CN202010724543A CN112084684B CN 112084684 B CN112084684 B CN 112084684B CN 202010724543 A CN202010724543 A CN 202010724543A CN 112084684 B CN112084684 B CN 112084684B
- Authority
- CN
- China
- Prior art keywords
- data
- early warning
- value
- sensor
- warning value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 77
- 230000036541 health Effects 0.000 title claims abstract description 36
- 230000000007 visual effect Effects 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 39
- 238000004891 communication Methods 0.000 claims abstract description 24
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 claims abstract description 15
- 238000010295 mobile communication Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 48
- 230000009467 reduction Effects 0.000 claims description 41
- 238000004364 calculation method Methods 0.000 claims description 21
- 230000006378 damage Effects 0.000 claims description 16
- 238000013461 design Methods 0.000 claims description 14
- 238000000034 method Methods 0.000 claims description 14
- 238000000513 principal component analysis Methods 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 239000000463 material Substances 0.000 claims description 5
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 abstract description 16
- 238000009434 installation Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 15
- 238000005516 engineering process Methods 0.000 description 11
- 238000007726 management method Methods 0.000 description 11
- 230000005540 biological transmission Effects 0.000 description 10
- 230000002354 daily effect Effects 0.000 description 10
- 238000013480 data collection Methods 0.000 description 7
- 238000010276 construction Methods 0.000 description 6
- 238000007405 data analysis Methods 0.000 description 6
- 230000018109 developmental process Effects 0.000 description 6
- 238000005070 sampling Methods 0.000 description 6
- 238000001228 spectrum Methods 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000007619 statistical method Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 230000032683 aging Effects 0.000 description 2
- 238000013079 data visualisation Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 238000012916 structural analysis Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
-
- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Computer Hardware Design (AREA)
- Pure & Applied Mathematics (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- Human Computer Interaction (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Structural Engineering (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Emergency Management (AREA)
- Algebra (AREA)
- Civil Engineering (AREA)
- Architecture (AREA)
- Databases & Information Systems (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a bridge health visual monitoring system based on the Internet of things, which comprises a plurality of sensors installed on a bridge site, wherein one sensor forms a group from one group or a plurality of similar sensors, the bridge health visual monitoring system also comprises an Internet of things module, a gateway and a cloud server, the Internet of things module is distributed near each group of sensors, each group of sensors is connected to the Internet of things module nearby to transmit acquired data, the Internet of things module is connected with the gateway through a LoRa wireless communication module thereof, the acquired sensor data is transmitted to the gateway, and then the gateway forwards the acquired sensor data 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 to form a visual monitoring interface. The system sensor data are acquired in a distributed manner, a large amount of cable laying work is avoided, the equipment installation is free from regional limitation, the system deployment is simple, and the later maintenance is convenient.
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 the economic society and the continuous progress of the civil engineering technology, the construction of bridges is rapidly developed to meet the increasingly expanding social demands caused by social development, population flow and material transportation. Bridges are increasingly attracting attention as throats for urban and highway traffic, in relation to national economy and mass life safety. The stability and durability of the bridge are reduced and the service life may be reached before the design life is reached due to the influence of various loads, natural environment and degradation of the material itself during the construction and operation of the bridge. The pain accident makes it deeply recognized that the safety of bridges is not only manifested in quality control during construction, but also in safety evaluation, maintenance, management, etc. during operation for decades thereafter. The traditional management method relying on manual detection and maintenance can not meet the requirements, and in order to know the implementation condition of the bridge in time, a bridge health monitoring system is generated.
Conventional bridge health monitoring systems are generally divided into a sensor layer, a data collection layer (collector), a data collection layer (sub-workstation), a communication network layer and a system software layer (server). The system is characterized in that the data of the sensors are collected in a centralized way, the whole data transmission process depends on a wired network (cable or optical cable), the data collection of different types of collectors is preprocessed, a sub-workstation (generally an industrial personal computer) placed on a bridge site is responsible, and health monitoring system software is generally installed on an entity server, and the server is usually placed on the bridge site.
According to the design scheme of the traditional bridge health monitoring system, one bridge needs an independent monitoring system, and the following restrictions exist on the setting of hardware equipment:
(a) The sensor data adopts a centralized acquisition mode, one acquisition instrument on site generally needs to acquire sensor data of a plurality of positions, cables for acquiring the data are required to be arranged at the acquisition instrument from the positions of the sensors, and construction difficulty is high. In addition, once the failure of the acquisition instrument occurs in the operation and maintenance process of the later period of centralized acquisition, the data of a plurality of sensors connected with the failure cannot be continuously acquired;
(b) The equipment communication is carried out in a wired mode, a large number of cables are laid in the system deployment process, the construction amount and the construction difficulty are large, and in addition, the risks of cable aging, accidental breakage and even theft can be faced to later operation and maintenance, so that the later operation and maintenance are difficult and heavy. Furthermore, the arrangement of a large number of cables also makes the surge protection of the hardware system more difficult;
(c) By means of centralized processing of data collection to the workstation, each bridge needs to write 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 be deployed with 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 by the later operation and maintenance, and the great operation and maintenance cost is brought;
(d) Because the system software is deployed on the entity server, the receiving, processing and analyzing of the data and the subsequent application depend on the entity server, and huge workload is brought to the deployment and operation of the software. In addition, many servers of the bridge health monitoring system are directly placed on the bridge site, and the severe working environment also accelerates the ageing of the servers and has unstable performance.
In addition to the above-mentioned problems, the existing bridge health monitoring system has the following problems in monitoring data: bridge health monitoring is mainly aimed at large bridges and oversized bridges, the large-scale and large-measuring points of the bridges are large in number, and monitoring frequency is high. The monitoring system may generate a large amount of data during operation. Currently, these data are stored directly, are not effectively utilized, and cannot provide effective decision information to the user.
Disclosure of Invention
The existing bridge health monitoring system is characterized in that sensor data are collected in a centralized mode, equipment communication is carried out in a wired mode, different processing programs are required to be compiled on the basis of a hardware environment by a field sub-workstation, a software system depends on constraint conditions such as an entity server, and the development of the bridge health monitoring system is greatly limited. The invention aims to provide a bridge health monitoring system which is easy to deploy and convenient to maintain in the later period.
The invention aims at realizing the following technical scheme: the bridge health visual monitoring system based on the Internet of things comprises a plurality of sensors installed on a bridge site, wherein one sensor forms a group by itself or a plurality of similar sensors form a group, the bridge health visual monitoring system further comprises an Internet of things module, a gateway and a cloud server, 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 thereof, the collected sensor data is sent to the gateway, and then the gateway forwards the collected sensor data 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 concentrated mode to form a visual monitoring interface.
The bridge field environment is complex, and the sensor is distributed more dispersedly. The invention changes the idea of the traditional sensor data centralized collection, adopts a data distributed collection mode that one sensor is self-formed into a group or a plurality of similar sensors are respectively connected to the internet of things module, avoids the condition that one collection device problem affects the data collection of a large sensor, and avoids the wiring work from each sensor to the seven-bend eight-turn of the collection instrument in the data centralized collection mode. The distributed acquisition mode provided by the invention is well supported by the characteristics of the Internet of things module which is simple in function and low in price and the sensor data transmission through the LoRa wireless communication network. Because the LoRa wireless communication module supports long-distance data transmission, the data transmission stability is good. In addition, the data are transmitted by the Internet of things module through the wireless network, so that system wiring is further simplified.
From the whole system framework of the monitoring system, the data of the invention are directly sent to the cloud server by the Internet of things module through the gateway, so that the regional limit of equipment installation is eliminated in the aspects of equipment management and data processing, a large number of bridges can be managed by only one set of software, and the workload of system maintenance, system upgrading and the like is greatly reduced.
In bridge health monitoring systems, a large number of sensors are used, so that massive monitoring data are generated, and the historical data application rate is extremely low due to data flooding. Aiming at the problem, the invention adopts the following improvement scheme:
for collected sensor data, the processing mode of the invention 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 time periods;
Step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving related data forming the control chart;
The control map includes the following information: the measuring point, the cycle time, the maximum value, the minimum value, and the number of the strokes in the maximum probability interval are based on the frequency histogram of the set confidence interval.
The stored data also comprises a mean value and a 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 damage probability inferred according to the mean value and the standard deviation.
The bridge load overrun probability and the damage probability are calculated as follows:
The mathematical expression of the normal probability density function is:
In the method, in the process of the invention,
X is a random variable;
p (x) is a probability density of a particular value;
Is mean value/>
Sigma is the standard deviation of the sum of the squares,
According to the data of each calculation period, a corresponding normal probability density function is obtained through the above formula, and the load overrun probability and the damage probability are calculated specifically through the following integral:
When t=design value, the value obtained by P is load overrun probability;
When t=limit value, the value obtained by P is the destruction probability;
design values and limit values are calculated through design specifications and finite element simulation.
Or the collected sensor data is visually displayed by forming a radar map of the monitored data over the period of time.
The radar chart has the following display effect and implementation mode:
The radar chart is provided with an early warning value, which comprises a first-level early warning value and a second-level early warning value of the radar chart, wherein the second-level early warning value of the radar chart is used as an outer ring of the radar chart, and the first-level early warning value of the radar chart is used as an inner ring of the radar chart;
the first-level early warning value represents a design value of the 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 a1 after 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 synchronous normalization processing as the primary early warning value of the radar map.
A ij represents data for a type A sensor at the bridge j position in the ith acquisition for the following cases:
1a) A ij has no data exceeding the secondary early warning value
2A) A ij has no data exceeding the primary early warning value
The treatment mode is as follows:
step 11 a), performing dimension reduction processing on the acquired data through a principal component analysis method;
The original data matrix of a certain type of sensor is represented as [ A ij]n*m, m represents the number of measuring points of the sensor of the certain type, n represents the acquisition times, and each column of the matrix is one dimension, and m dimensions are all adopted;
Forming a new matrix [ A 'ij](n+1)*m by the matrix and the first-level early warning value of each position sensor, and reducing the new matrix to one-dimensional mark [ A' ij](n+1)*1; the results after dimension reduction are presented as: a number of (n+1) points on the new feature dimension space;
Step 12 a) comprehensive index calculation
Taking the minimum value of the dimension reduction space as a reference point, marking the distance between the dimension reduction post-stage early warning value and the reference point as x 1, and similarly, marking the distance between the dimension reduction post-stage early warning value and other dimension reduction points as x 2,x3,…,xn respectively;
The calculation formula of the comprehensive index is as follows:
Wherein r 0 is a first-level early warning value of the radar chart;
for the average value of the data after dimension reduction,/>
Δx represents the distance between the maximum value and the minimum value after dimension reduction, and Δx=x 1.
A ij represents data for a type A sensor at the bridge j position in the ith acquisition for the following cases:
1b) A ij has no data exceeding the secondary early warning value
2B) A ij has data exceeding the first-level early warning value
The treatment mode is as follows:
step 11 b), carrying out principal component analysis and dimension reduction treatment on the acquired data;
The original data matrix of a certain type of sensor is represented as [ A ij]n*m, m represents the number of measuring points of the sensor of the certain type, n represents the acquisition times, and each column of the matrix is one dimension, and m dimensions are all adopted;
Forming a new matrix [ A 'ij](n+1)*m by the matrix and the first-level early warning value of each position sensor, and reducing the new matrix to one-dimensional mark [ A' ij](n+1)*1; the results after dimension reduction are presented as: a number of (n+1) points on the new feature dimension space;
Step 12 b) comprehensive index calculation
Taking the position of the inner ring as a reference, and dividing the space between the position of the inner ring and the outer ring into n+1 equal parts according to the frequency n sampled in the period; the calculation formula of the comprehensive index value is as follows:
Wherein r 0 is the first-level early warning value bit of the radar chart;
k is the number of values exceeding the primary early warning value after dimension reduction.
A ij represents data for a type A sensor at the bridge j position in the ith acquisition for the following cases:
A ij has data exceeding a secondary early warning value;
The treatment mode is as follows:
the sensor exceeds the secondary early warning value, the comprehensive index value is positioned at the outer ring position, and the characteristic state risk is extremely high.
The specific dimension reduction steps adopted by the invention are as follows:
Firstly, calculating the average value of each dimension of the matrix [ A' ij](n+1)*m ], and subtracting the average value of the dimension from each dimension to obtain new data A new after centralization;
second, solving the characteristic covariance matrix of the new matrix
Thirdly, according to the covariance matrix, obtaining corresponding eigenvalues and eigenvectors;
Fourthly, arranging the characteristic values according to descending order, correspondingly giving out characteristic vectors, and selecting the largest principal component to obtain a transformation matrix P;
And fifthly, obtaining the dimension-reduced data y=pa new according to the transformation matrix.
Compared with the prior art, the invention has the following effective effects:
1) The distributed acquisition of the sensor data avoids a large amount of cable laying work, and reduces the influence range of a single data acquisition instrument (the Internet of things module in the invention) during faults. In addition, the sensor data are acquired in a distributed mode, output signals of each sensor can be connected to a nearby data acquisition instrument through a short cable and then sent to a cloud server through a gateway, and the data acquisition instrument is transmitted to the cloud server through a system to form digital signals, so that the stability and the accuracy of the signals can be improved.
2) The Internet of things architecture determines that the management of equipment and the collection and processing of sensor data are completed in the cloud, so that the regional limitation of equipment installation is eliminated in the management of equipment and the processing of data, no matter where the equipment is deployed, the Internet of things platform in the cloud can uniformly perform the equipment identification and the processing of data as long as the equipment can be connected into a network, the management and the processing of a large number of bridges can be realized only by a set of software, and the follow-up data maintenance and the increase of monitoring points can be realized easily (a distributed layout mode, the increase of the monitoring points does not need to change lines and wires, and only the information of the sensors is needed to be added on a system), so that the workload of maintenance and system upgrading is greatly reduced.
3) The control diagram and radar diagram algorithm of the invention changes various data into valuable information, and the valuable information is intuitively displayed in real time, thus providing necessary information for decision and early warning for bridge management departments.
Drawings
FIG. 1 is a block diagram of a visual monitoring system for bridge health based on the Internet of things in accordance with 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 in accordance with 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 of the day of FIG. 3;
FIG. 5 is a diagram showing the effect of a radar chart;
FIG. 6 is a software design flow of the bridge health monitoring visualization system.
Detailed Description
The bridge health visual monitoring system based on the internet of things is powered by a mains supply and/or a solar panel, and as shown in fig. 1, the bridge health visual monitoring system is divided into 4 parts from a logic architecture:
The data acquisition layer is used for periodically acquiring bridge data in operation mainly through various sensors;
the wireless transmission layer is used for transmitting the data to an on-site Internet of things gateway through a wireless communication network based on the LoRa technology, and the gateway is used for transmitting the data to a 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;
the data display layer is used for visually displaying bridge data by mainly utilizing an algorithm, so that remote real-time monitoring and early warning of the bridge are realized.
(1) Data acquisition layer
The data acquisition layer is responsible for carrying out unified acquisition on original analog signals such as voltage, current, frequency, optical signals and the like 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 positions of the measuring points during installation and recorded in the background.
(2) Wireless transmission layer
The wireless transmission layer mainly comprises an Internet of things module and a gateway. The internet of things module in the embodiment integrates data acquisition and wireless communication, and comprises an embedded microprocessor, a data acquisition interface based on a Modbus bus protocol and a LoRa wireless communication module, wherein the data acquisition interface comprises an RS485 communication interface and an RS232 communication interface, and the embedded microprocessor is respectively connected with the RS485 communication interface, the RS232 communication interface and the LoRa wireless communication module. And the RS485 communication interface and the RS232 communication interface are monitoring interfaces.
The internet of things module is a core for data acquisition of the bridge health visual monitoring system, adopts a LoRa wireless communication network to send data, and can be well adapted to the common application environment of the monitoring system (the monitoring system is commonly applied to a bridge or an extra-large bridge). The overall network topology architecture of the monitoring system of the embodiment is shown in fig. 2, and comprises a plurality of sensors, such as static level 01 and 02, inclinometers 01 and 02, strain sensors 01 and 02, and magneto-elastic instruments and cable sensors 01 and 02, wherein one sensor is self-formed into a group or a plurality of similar sensors, each group of sensors is closely connected to an internet of things module nearby 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, the data sent by the internet of things module are distinguished by identifying EUI numbers of the internet of things module, and then the data are sent to a cloud server through a 4G or 5G mobile communication network to finish the work of collecting, processing, distributing and the like of the data, and a visual monitoring interface is formed.
The LoRa adopted by the internet of things module in the embodiment is a low-power-consumption long-distance wireless communication technology using a spread spectrum modulation mechanism, and integrates digital spread spectrum, digital signal processing and forward error correction coding technologies. The method uses the linear frequency modulation spread spectrum modulation technology, thereby not only maintaining the low power consumption characteristic, but also obviously increasing the communication distance, simultaneously improving the network efficiency and eliminating the interference, namely terminals with different spread spectrum sequences can not interfere with each other even if simultaneously transmitting with the same frequency. LoRaWAN (Low Power consumption Wide area network) adopting LoRa technology truly realizes low power consumption, mobile, standardized, simple and safe Internet of things. LoRa technical characteristics: the networking capability is strong, the cost is low, star-shaped, tree-shaped and MESH networks are supported, and the laying period is short; not completely dependent on the network operator; low power consumption, standby current 5uA; the receiving circuit is smaller than 14mA; the emission current is 100mA; the transmission distance is far and can reach 20 km at maximum; 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; the signal is camouflaged in noise after being added with pseudo-random code spread spectrum.
The bridge field environment is complex, and the sensor is distributed more dispersedly. The invention changes the idea of the traditional sensor data centralized collection, adopts a data distributed collection mode that one sensor is self-formed into a group or a plurality of similar sensors are respectively connected to the internet of things module, avoids the condition that one collection device problem affects the data collection of a large sensor, and avoids the wiring work from each sensor to the seven-bend eight-turn of the collection instrument in the data centralized collection mode. The distributed acquisition mode provided by the invention is well supported by the characteristics of the Internet of things module which is simple in function and low in price and the sensor data transmission through the LoRa wireless communication network. In addition, the data are transmitted by the Internet of things module through the wireless network, so that system wiring is further simplified. In addition, the sensor data are acquired in a distributed mode, output signals of each sensor can be connected to a nearby data acquisition instrument through a short cable and then sent to a cloud server through a gateway, and the data acquisition instrument is transmitted to the cloud server through a system to form digital signals, so that the stability and the accuracy of the signals can be improved.
From the whole system framework of the monitoring system, the data of the invention are 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 the management and data processing of equipment, no matter where the equipment is arranged, the Internet of things platform of the cloud can uniformly perform equipment identification and data processing as long as the equipment is connected to a network, a large number of bridges can be managed and processed only by a set of software, and the follow-up data maintenance and the increase of monitoring points can be easily realized (the distributed layout mode, the increase of the monitoring points does not need to change lines and wires, and only the information of the sensor is needed to be added on the system), thereby greatly reducing the workload of maintenance and system upgrading.
(3) Data analysis layer
The cloud server first pre-processes the data. The data preprocessing is to perform digital filtering processing on the data 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 whole-course low-pass, band-pass and band-stop processing on the signals, and the reliability of the data is identified and evaluated, including data conversion and abnormal data rejection.
The data analysis mainly refers to analyzing and processing the data and the corresponding structural mathematical model through mathematical processing methods such as correlation, convolution, FFT, statistical analysis, integral derivative and the like, and can store the analyzed data. The data processing analysis algorithm is directly nested into the system, and the user can freely combine and call.
The cloud server may also distribute data via MQTT (Message Queuing Telemetry Transport, message queue telemetry transport protocol) topic subscription mode.
(4) Display layer
The display layer realizes the visual display of the health monitoring system data. The visual system platform obtains data through data interaction with the cloud server to conduct classification processing and achieve graphic interface data display, and a display interface of a display layer can be divided into an APP version and a web page. The display layer can directly click on an icon of a certain sensor according to the principle of what you see is what you get, so as to enter a display interface of the sensor signal, observe a response curve of the sensor signal, conveniently give out statistics of all sensor data daily, weekly, monthly or yearly, and utilize a structural analysis tool box to carry out online parameter or structural analysis.
The display layer interface can graphically early warn the abnormal collected data, and users with different authorities can check the real-time data of health monitoring of each part of the bridge through the platform according to the role authorities after logging in the platform, and perform relevant scheme processing on the abnormal early warning.
Aiming at the problems of excessive monitoring data and low utilization rate of the bridge, the invention also respectively establishes a control diagram and a radar diagram visual analysis method, changes various data into valuable information, intuitively displays the valuable information in real time and provides necessary information for decision making and early warning for bridge management departments.
The control diagram analysis method mainly comprises two steps, namely, step 1) dividing data into different data block intervals according to time periods such as day, week, month, year and the like;
step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving related data forming the control chart; the control diagram here includes the following information: the measuring point, the cycle time, the maximum value, the minimum value, and the number of the strokes in the maximum probability interval are based on the frequency histogram of the set confidence interval. The bridge period change with time (day, week, month, year) can be judged from the form of the control chart.
Taking a daily control chart as an example, the histogram is a frequency histogram obtained by screening daily data to a 95% confidence interval with a sampling frequency of 30 s/time as an example and then carrying out statistics on the number according to the interval in which the data are located and converting the data.
Fig. 3 is an example of a daily control graph formed. As shown in the figure, the daily control charts 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 chart can play a statistical role, and is convenient to check whether the range of the bridge where the measuring point is located every day is in a normal interval, whether the state is stable and the like. Fig. 4 is a day control diagram of a day of fig. 3. As shown in fig. 4, the names and representative meanings of the respective positions are as follows:
1—data max dmaxWeight for the same day;
2-data min DMINWEIGHT for the same day;
3-95% confidence interval upper limit fmaxWeight;
4-95% confidence interval lower limit FMINWEIGHT;
5—maximum probability interval upper limit sectionMax;
6-maximum probability interval lower limit sectionMin;
7-number of runs in the maximum probability interval (sectionMax + sectionMin)/2.
They are all identified in the same coordinate system.
Taking a daily control chart as an example, after 24 points in the evening are monitored by a certain sensor, carrying out statistical analysis on the data x 1,x2……xn of the day stored in the database, and recording the maximum value dmaxWeight of the data of the day and the minimum value DMINWEIGHT of the data of the day;
Confidence interval colloquially, i.e., the range spanned by the required degree of confidence, the confidence interval of the sample can be used as an interval estimate of the overall mean. The confidence interval calculating method comprises the following steps:
the first step: average value of data Standard error/>
And a second step of: determining a confidence interval, taking a confidence equal to 95% as an example, when the confidence is equal to 95%, z=1.96; determining confidence intervals
The confidence intervals are divided into Y groups according to the configured equal Y, and the group distance s= (fmaxWeight-FMINWEIGHT)/Y. The data volume can be reasonably configured and equal according to the sampling frequency. The more data is monitored, the smaller the group spacing can be. The smaller the group distance of the histogram, the more groups, and the more significant the effect of the data distribution. The height of the histogram represents the frequency, i.e. the number of times the variable value occurs within the interval.
And a third step of: counting the number of data falling into each cell according to the group distance, dividing the data amount by the total data amount of the same day, calculating the probability of falling into the cells, sorting, and then taking out the maximum probability, wherein the upper limit and the lower limit of the region are respectively used as sectionMax, sectionMin, (sectionMax + sectionMin)/2 are the number of strokes in the maximum probability region. Therefore, the control chart is formed, and frequency data of the histogram is also retained.
The control chart not only reserves three most critical data in bridge monitoring data: maximum, minimum, and maximum probability interval, and also retains the frequency histogram data forming confidence interval-based. The distribution state of the data can be intuitively 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 confidence interval so as to improve the credibility and the accuracy of the data. The data are truly important data in bridge health monitoring data, such as the number of the strokes in the maximum probability interval can qualitatively judge the development trend of the bridge state. The data provided by the invention has small occupied storage space, is convenient to reserve for a long time, forms a control chart, is visual and convenient to check, and can fully display the vitality and the value of the historical data.
In addition, the invention analyzes the overrun probability and the damage probability of the monitoring parameter through a probability statistical method and is arranged in the control chart for the reference of bridge maintenance personnel.
Trend prediction (overrun probability, destruction probability)
According to the results of the scientific and engineering experiments, the data almost always follow a normal distribution. And assuming that the data of the daily sensor accords 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 the normal probability density function is:
In the method, in the process of the invention,
X-random variable
Probability density of P (x) -a particular value
X is the average value of the two values,
Sigma-the standard deviation of the two-dimensional curve,
According to the data of each calculation period, a corresponding normal probability density function is obtained through the above formula, and the load overrun probability and the damage probability are calculated through the following integral:
when t=design value, the value obtained by P is load overrun probability.
When t=limit value, the value obtained by P is the destruction probability.
Design values and limit values are calculated through design specifications and finite element simulation.
The index model can be used for evaluating the development trend of the position state of the bridge measuring point, and can be suitable for various monitoring parameters.
The control diagram database only needs to record the following information: measuring points, cycle time, maximum value, minimum value, number of runs in the maximum probability interval, mean value, standard deviation and histogram frequency. After forming corresponding cycle control diagram, month control diagram and year cycle control diagram (forming process same day control diagram) based on the original data, if the bridge operation condition is good, the original data in the database can be abandoned at proper time. Therefore, the invention can greatly reduce the data volume and solve various problems caused by historical data flooding. The control diagram also presents load overrun probability and damage probability. The invention is convenient for data management and checking while efficiently preserving important data in bridge operation for a long time, and can provide corresponding information for bridge maintenance personnel to know the change trend of the bridge.
Structural parameters such as stress, strain, deflection, wind speed, displacement, inclination angle and the like of the bridge can be described by using the control chart analysis method, and the method has excellent inclusion.
The radar chart analysis method in the invention comprises the following specific steps:
in the bridge health monitoring process, a plurality of parameters such as deflection, stress, strain and the like of the bridge are required to be monitored, different types of sensors are required to be adopted, and the number of each type of sensor 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, the sampling frequency of each measuring point is consistent, but the primary early warning value and the secondary early warning value of the 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.
In the period time, in order to reflect the condition of monitoring data of a plurality of monitoring points of the single parameter sensor, if the condition exceeds a primary early warning value or a secondary early warning value or is in a safety state. The radar chart of the data analysis model constructed by the invention follows the following principles:
1) The parameter has a sensor data exceeding the first-level early warning, and the comprehensive index is to be embodied;
2) The parameter has a sensor data exceeding the second-level early warning, and the comprehensive index is to be embodied;
3) All sensors of the parameter are normal, and the comprehensive index is embodied.
Fig. 5 is a radar chart, defined as follows:
1) Each corner of the radar chart represents a dimension, representing a composite index of single parameter sensor data.
2) The outermost circle is a normalized secondary early warning value, the inner circle is a 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 second-level early warning value, the index position is positioned at the outermost ring position.
4) In the period time, if the value of the no-sensor exceeds the second-level early warning value, one sensor exceeds the first-level 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 the primary early warning value, the index is positioned in the inner ring.
6) The position of the comprehensive index does not exceed the position of the outermost ring.
The above definition conforms to the principles of the model. In order to ensure the uniformity of the radar map, the outermost ring needs to be uniform in size, all different types of sensor data need to be normalized, and finally the value of the outermost ring is 1. The first-level early warning value is different even if the same type of sensor is used, 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, A ij represents the data of the type A sensor at the j position of the bridge in the ith acquisition, and the data can be divided into the following three cases:
Case one:
A ij has data exceeding the secondary early warning value
And a second case:
1) A ij has no data exceeding the secondary early warning value
2) A ij has data exceeding the first-level early warning value
And a third case:
1) A ij has no data exceeding the secondary early warning value
2) A ij has no data exceeding the primary early warning value
For the first case, one sensor exceeds a secondary early warning value, the comprehensive index value is located at the outer ring position, and the representing state is extremely high in risk.
And for the second case and the third case, performing Principal Component Analysis (PCA) dimension reduction treatment on the acquired data, and then performing analysis.
Data dimension reduction
In the cycle time, each position (m positions, n acquisitions) where the sensor is located acquires a column of data as one dimension, which is characterized by a matrix [ a ij]n*m ], that is, each column of the matrix is one dimension, and m dimensions (features) are taken as a whole.
And forming a new matrix [ A' ij](n+1)*m ] by the original data matrix [ A ij]n*m and the primary early warning value of each position, wherein the position of the primary early warning value is added to the last row, and the specific added position has no influence on the result in actual calculation.
The new matrix is then reduced to one dimension, i.e., [ 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 dimension 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 greatest data contribution are maintained. Finally, the multiple groups of data of the same type of sensors are finally converted into a comprehensive index, so that the complexity of analysis problems is reduced.
The PCA dimension reduction steps are as follows:
Firstly, calculating the average value of each dimension of a matrix [ A' ij](n+1)*m (written A), and subtracting the average value of the dimension from each dimension to obtain new data A new after centering;
second, solving the characteristic covariance matrix of the new matrix
Thirdly, according to the covariance matrix, obtaining corresponding eigenvalues and eigenvectors;
Fourthly, arranging the characteristic values according to descending order, correspondingly giving out characteristic vectors, and selecting the largest principal component (the previous row) to obtain a transformation matrix P;
And fifthly, obtaining the dimension-reduced data y=pa new according to the transformation matrix.
The result of dimension reduction into one dimension is presented as: the new feature dimension (one-dimensional) is a number of (n+1) points in space.
Comprehensive index calculation
And for the third situation, namely, the situation that the first-level early warning value is not exceeded.
Taking the minimum value of the dimension reduction space as a reference point, the distance between the post-dimension reduction early warning value and the reference point is x 1, and the like, and the distance between the post-dimension reduction early warning value and other post-dimension reduction points is x 2,x3,…,xn,x1>x2>...>xn, and under the condition that no value is repeated.
The calculation formula of the comprehensive index is as follows:
Wherein r 0 is the position of the normalized primary early warning value of the radar chart
Mean value of data after dimension reduction,/>
Δx is the distance between the maximum and minimum values after dimension reduction, and Δx=x 1.
For the second case, that is, the first level early warning value is exceeded but the second level early warning value is not exceeded
Taking the position of the inner ring (normalized primary early warning value) as a reference, and dividing the space between the position of the inner ring and the outer ring into n+1 equal parts according to the sampling frequency n in the period. After the PCA is subjected to dimension reduction, counting the number k exceeding the first-level early warning value through the relative position on the new space, wherein the calculation formula of the comprehensive index value is as follows:
wherein r 0 is the position of the first-level early warning value normalized by the radar chart.
Data instance
Taking a certain parameter as an example, a certain sensor such as a type A sensor in a period collects n times of data, and an original data matrix is as follows, which is multidimensional data:
TABLE 1
Aij represents the value measured by the class A sensor, wherein i represents the ith sample and j represents the position (measuring point) at which the class A sensor is located.
The following are test data:
TABLE 2
It can be seen that it corresponds to case two above.
The first step of early warning value normalization treatment is shown in table 3:
TABLE 3 Table 3
The outer circle (normalized secondary early warning value) is 1, and Max (Aj/Aj) is used as the relative position drawn by the inner circle.
The processing results of the early warning value normalization processing corresponding to the specific test data in table 2 are shown in table 4.
TABLE 4 Table 4
| Normalized primary warning value | 0.49 | 0.48 | 0.47 | 0.50 |
| Normalized secondary early warning value | 1.00 | 1.00 | 1.00 | 1.00 |
Find r 0＝Max(aj/Aj) =0.5
Second, data classification
The series of data collected by each period of each position where the sensor is located is one dimension, namely, each column of the matrix [ Aij ] n ] m is one dimension, m represents the position, n represents the times, and m dimensions (features) are all taken as a whole. After the PCA algorithm is adopted for reducing the dimension, new features are generated in the data, and the new features are not in the original dimension space.
The original data matrix [ Aij ] n x m and the primary early warning value form a new matrix, and PCA dimension reduction processing is carried out on the new matrix to one dimension.
The PCA dimension reduction steps are as follows:
TABLE 5
/>
The results after dimension reduction are presented as: several points on a new number axis (new feature dimension space) including the original data and the results after the early warning value is reduced in dimension are shown in table 6.
TABLE 6
The dimension reduction of the test data is as follows:
TABLE 7
The data are obtained by performing dimension reduction treatment on the data in table 2.
And taking the position of the inner ring (normalized primary early warning value) as a reference, and equally dividing the space between the position of the inner ring and the outer ring into 15 equal parts according to 14 sampling frequencies in a period. After the PCA is subjected to dimension reduction, the number of the first-level early warning values (more than 0.79) is counted to be 4 through the relative positions in the new space.
The calculation formula of the comprehensive index value is as follows:
from the above, the invention can not only simultaneously display the comprehensive index states of a plurality of parameters on one graph, but also qualitatively see the amount of early warning data at the position of the radar graph.
The framework and the flow of the software system of the bridge health visual monitoring system of the embodiment 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 meets the specification, and discards the data which does not meet the specification. And the 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 the data receiving can still be normally performed after the abnormal condition of the data processing service of the following sensor exits.
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 multithreading to perform data calculation according to different sensor types, so that the data processing efficiency is ensured.
The data alarm service extracts data from the stream of the Redis database to perform early warning judgment, discovers that early warning is performed to perform real-time early warning message sending processing, mainly creates RabbitMQ message and Redis stream, and stores early warning data into the mysql database.
The data statistics service mainly acquires data of one hour in the past from the Redis stream and data of one day in the past from the MongoDB, and performs statistical analysis to extract characteristic values.
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 front-end data visualization, reads data from a Redis, mongoDB, mysql database and the like, displays the data into a front-end web page, and displays early warning information in the page.
The monitoring system adopts a micro-service architecture, each module of the monitoring system is designed into independent services, and middleware RabbitMQ message queues are used among the micro-services for data interaction, wherein the micro-services mainly comprise page background services, MQTT data receiving services, HTTP data receiving services, sensor data processing services, data analysis services, data alarm services and data statistics services.
The front end of the system uses a vue.js framework, the background adopts a C# programming language, the net core framework is developed and realized, a database adopts MySQL, mongoDB, and middleware adopts Redis and RabbitMQ. The application software deployment runs on the cloud server, packages the nmginx server, mysql, mongoDB, netcore applications into container images, and runs on the docker container engine.
The invention applies the monitoring technology in the field of the Internet of things to the deployment of the bridge health monitoring system, and has the main advantages that: (1) The distributed acquisition of the sensors gets rid of the constraint of the communication cable; (2) wirelessly transmitting the data by using LoRa technology; (3) The system comprises an Internet of things architecture, wherein one set of software realizes the management of a plurality of bridges; (4) Cloud technology is introduced, and system software is not limited to 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 features of the data, and can be used for solving the problem that the data information in the monitoring field is difficult to analyze and display in time.
Claims (9)
1. The bridge health visual monitoring system based on the Internet of things is characterized by comprising a plurality of sensors installed on a bridge site, wherein one sensor forms a group from one group or a plurality of similar sensors, the bridge health visual monitoring system also comprises an Internet of things module, a gateway and a cloud server, the Internet of things module is distributed near each group of sensors, each group of sensors is connected to the Internet of things module nearby to transmit acquired data, the Internet of things module is connected with the gateway through a LoRa wireless communication module thereof, the acquired sensor data is transmitted to the gateway, and then the gateway forwards the acquired sensor data to the cloud server through a 4G or 5G mobile communication network, and the collection and the processing of the sensor data are completed on the cloud server in a concentrated manner to form a visual monitoring interface;
the monitoring system visually displays the collected sensor data through a control chart analysis method, and specifically comprises the following steps:
step 1) dividing data into different data block intervals according to time periods;
Step 2) drawing a control chart based on the data block interval, and saving bridge health monitoring historical data by saving related data forming the control chart;
The control map includes the following information: the measuring point, the cycle time, the maximum value, the minimum value, and the number of the strokes in the maximum probability interval are based on the frequency histogram of the set confidence interval.
2. The monitoring system of claim 1, wherein the stored data further comprises a mean and standard deviation of the data within the set confidence interval, and the information presented by the control map further comprises bridge load overrun probabilities and destruction probabilities inferred from the mean and standard deviation.
3. The monitoring system according to claim 2, wherein the bridge load overrun probability and the failure probability are calculated as follows:
The mathematical expression of the normal probability density function is:
In the method, in the process of the invention,
X is a random variable;
p (x) is a probability density of a particular value;
Is mean value/>
Sigma is the standard deviation of the sum of the squares,
According to the data of each calculation period, a corresponding normal probability density function is obtained through the above formula, and the load overrun probability and the damage probability are calculated specifically through the following integral:
When t=design value, the value obtained by P is load overrun probability;
When t=limit value, the value obtained by P is the destruction probability;
design values and limit values are calculated through design specifications and finite element simulation.
4. The monitoring system of claim 1, wherein the sensor data collected is visually displayed by forming the monitored data over a period of time into a radar map.
5. The monitoring system of claim 4, wherein the radar map is displayed as follows:
The radar chart is provided with an early warning value, which comprises a first-level early warning value and a second-level early warning value of the radar chart, wherein the second-level early warning value of the radar chart is used as an outer ring of the radar chart, and the first-level early warning value of the radar chart is used as an inner ring of the radar chart;
the first-level early warning value represents a design value of the 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 a1 after 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 synchronous normalization processing as the primary early warning value of the radar map.
6. The monitoring system of claim 5, wherein a ij represents data from a type a sensor at a bridge j position in an i-th acquisition for the following cases:
1a) A ij has no data exceeding the secondary early warning value
2A) A ij has no data exceeding the primary early warning value
The treatment mode is as follows:
step 11 a), performing dimension reduction processing on the acquired data through a principal component analysis method;
The original data matrix of a certain type of sensor is represented as [ A ij]n*m, m represents the number of measuring points of the sensor of the certain type, n represents the acquisition times, and each column of the matrix is one dimension, and m dimensions are all adopted;
Forming a new matrix [ A 'ij](n+1)*m by the matrix and the first-level early warning value of each position sensor, and reducing the new matrix to one-dimensional mark [ A' ij](n+1)*1; the results after dimension reduction are presented as: a number of (n+1) points on the new feature dimension space;
Step 12 a) comprehensive index calculation
Taking the minimum value of the dimension reduction space as a reference point, marking the distance between the dimension reduction post-stage early warning value and the reference point as x 1, and similarly, marking the distance between the dimension reduction post-stage early warning value and other dimension reduction points as x 2,x3,…,xn respectively;
The calculation formula of the comprehensive index is as follows:
Wherein r 0 is a first-level early warning value of the radar chart;
for the average value of the data after dimension reduction,/>
Δx represents the distance between the maximum value and the minimum value after dimension reduction, and Δx=x 1.
7. The monitoring system of claim 6, wherein a ij represents data from a type a sensor at the bridge j position in the ith acquisition for the following cases:
1b) A ij has no data exceeding the secondary early warning value
2B) A ij has data exceeding the first-level early warning value
The treatment mode is as follows:
step 11 b), carrying out principal component analysis and dimension reduction treatment on the acquired data;
The original data matrix of a certain type of sensor is represented as [ A ij]n*m, m represents the number of measuring points of the sensor of the certain type, n represents the acquisition times, and each column of the matrix is one dimension, and m dimensions are all adopted;
Forming a new matrix [ A 'ij](n+1)*m by the matrix and the first-level early warning value of each position sensor, and reducing the new matrix to one-dimensional mark [ A' ij](n+1)*1; the results after dimension reduction are presented as: a number of (n+1) points on the new feature dimension space;
Step 12 b) comprehensive index calculation
Taking the position of the inner ring as a reference, and dividing the space between the position of the inner ring and the outer ring into n+1 equal parts according to the frequency n sampled in the period; the calculation formula of the comprehensive index value is as follows:
Wherein r 0 is the first-level early warning value bit of the radar chart;
k is the number of values exceeding the primary early warning value after dimension reduction.
8. The monitoring system of claim 7, wherein a ij represents data from a type a sensor at the bridge j position in the ith acquisition for the following cases:
A ij has data exceeding a secondary early warning value;
The treatment mode is as follows:
the sensor exceeds the secondary early warning value, the comprehensive index value is positioned at the outer ring position, and the characteristic state risk is extremely high.
9. The monitoring system of claim 8, wherein the step of dimension reduction operation is as follows:
Firstly, calculating the average value of each dimension of the matrix [ A' ij](n+1)*m ], and subtracting the average value of the dimension from each dimension to obtain new data A new after centralization;
second, solving the characteristic covariance matrix of the new matrix
Thirdly, according to the covariance matrix, obtaining corresponding eigenvalues and eigenvectors;
Fourthly, arranging the characteristic values according to descending order, correspondingly giving out characteristic vectors, and selecting the largest principal component to obtain a transformation matrix P;
And fifthly, obtaining the dimension-reduced data y=pa new according to the transformation matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010724543.3A CN112084684B (en) | 2020-07-24 | 2020-07-24 | Bridge health visual monitoring system based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010724543.3A CN112084684B (en) | 2020-07-24 | 2020-07-24 | Bridge health visual monitoring system based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112084684A CN112084684A (en) | 2020-12-15 |
| CN112084684B true CN112084684B (en) | 2024-05-31 |
Family
ID=73735612
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010724543.3A Active CN112084684B (en) | 2020-07-24 | 2020-07-24 | Bridge health visual monitoring system based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112084684B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113194554B (en) * | 2020-12-16 | 2022-06-24 | 上海欣芙信息科技有限公司 | Multi-protocol water and electricity meter intelligent acquisition gateway system and use method thereof |
| CN113091825A (en) * | 2021-04-16 | 2021-07-09 | 上海城建信息科技有限公司 | Edge side processing method of bridge safety monitoring data |
| CN113254929B (en) * | 2021-05-21 | 2023-11-07 | 昆山翦统智能科技有限公司 | Immune calculation and decision-making method and system for enterprise remote intelligent service |
| CN113587977A (en) * | 2021-06-23 | 2021-11-02 | 浙江瑞邦科特检测有限公司 | Old dangerous house collapse dynamic monitoring method based on multi-element sensing data |
| CN114446020B (en) * | 2022-01-12 | 2024-01-30 | 江西飞尚科技有限公司 | Linkage early warning management method, system, storage medium and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106017960A (en) * | 2016-06-01 | 2016-10-12 | 清华大学合肥公共安全研究院 | MIS and GIS combination-based bridge safety monitoring system |
| CN108460231A (en) * | 2018-03-23 | 2018-08-28 | 中交公路长大桥建设国家工程研究中心有限公司 | A kind of bridge builds foster overall process intellectual monitoring assessment early warning decision system and method |
| WO2019071238A2 (en) * | 2017-10-06 | 2019-04-11 | Johnson Controls Technology Company | Building management system with cloud-based data platform |
| CN209148032U (en) * | 2018-11-16 | 2019-07-23 | 武汉理工光科股份有限公司 | Bridge health monitoring system based on Internet of Things |
| KR102065435B1 (en) * | 2019-08-14 | 2020-01-13 | 주식회사 심플비트 | Infrastructure health monitoring system and method |
-
2020
- 2020-07-24 CN CN202010724543.3A patent/CN112084684B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106017960A (en) * | 2016-06-01 | 2016-10-12 | 清华大学合肥公共安全研究院 | MIS and GIS combination-based bridge safety monitoring system |
| WO2019071238A2 (en) * | 2017-10-06 | 2019-04-11 | Johnson Controls Technology Company | Building management system with cloud-based data platform |
| CN108460231A (en) * | 2018-03-23 | 2018-08-28 | 中交公路长大桥建设国家工程研究中心有限公司 | A kind of bridge builds foster overall process intellectual monitoring assessment early warning decision system and method |
| CN209148032U (en) * | 2018-11-16 | 2019-07-23 | 武汉理工光科股份有限公司 | Bridge health monitoring system based on Internet of Things |
| KR102065435B1 (en) * | 2019-08-14 | 2020-01-13 | 주식회사 심플비트 | Infrastructure health monitoring system and method |
Non-Patent Citations (3)
| Title |
|---|
| 基于大数据架构的桥梁健康监测云平台;梁柱;中国交通信息化;第115-117页 * |
| 基于物联网技术的桥梁健康监测与安全预警技术研究;隋莉颖;刘浩;陈智宏;黄建玲;王立勋;;公路交通科技(应用技术版)(02);全文 * |
| 基于物联网技术的桥梁监测系统;李政等;物联网学报;第104-110页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112084684A (en) | 2020-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112084684B (en) | Bridge health visual monitoring system based on Internet of things | |
| CN110097297A (en) | A kind of various dimensions stealing situation Intellisense method, system, equipment and medium | |
| CN111367241A (en) | Enterprise comprehensive energy management and control system and method | |
| CN112541028B (en) | Water environment big data monitoring system and method | |
| CN111275948B (en) | Electric fire fighting early warning system | |
| CN112307003B (en) | Power grid data multidimensional auxiliary analysis method, system, terminal and readable storage medium | |
| CN116937575A (en) | Energy monitoring management system for grid system | |
| CN116742799A (en) | Auxiliary power distribution monitoring and early warning system based on Internet of things technology | |
| CN110837532A (en) | Method for detecting electricity stealing behavior of charging pile based on big data platform | |
| CN114862278B (en) | Power transmission line lightning stroke risk assessment method and system based on distribution network lightning stroke data | |
| CN106980055A (en) | A kind of student dormitory based on data mining electrical equipment violating the regulations uses monitoring system | |
| CN115375202A (en) | Railway tunnel engineering safety detection method and system | |
| CN117955245B (en) | Method and device for determining running state of power grid, storage medium and electronic equipment | |
| CN115798155A (en) | Natural gas metering and analyzing system | |
| CN116050599A (en) | Line icing fault prediction method, system, storage medium and equipment | |
| CN117335411A (en) | Medium-and-long-term power generation capacity prediction method for photovoltaic power station group | |
| CN108540093A (en) | Photovoltaic power station monitoring and warning system based on the acquisition of Fluctuation Method big data | |
| CN109886538B (en) | Railway signal equipment quality evaluation method and device based on dynamic monitoring data | |
| CN116450625A (en) | Metering abnormal data screening device based on electricity consumption information acquisition system | |
| CN115829208A (en) | Assessment method and device for gridding atmospheric pollutant emission | |
| CN105024379B (en) | A kind of maintenance of fan management scheduling system | |
| CN114238490A (en) | Thunder and lightning parameter statistical system | |
| CN113466598A (en) | Mountain area distribution line lightning monitoring method based on edge calculation | |
| CN116910681B (en) | Electric power data analysis method and system based on Internet of things | |
| CN115711206B (en) | Wind driven generator blade icing state monitoring system based on clustering weight |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210325 Address after: 528437 room 409-17, 4 / F, building C, innovation center building, 34 Xiangshan Avenue, Cuiheng New District, Zhongshan City, Guangdong Province Applicant after: Guangdong Jianke Innovation Technology Research Institute Co.,Ltd. Address before: 8 / F, new office building, Jianke academy, 121 Xianlie East Road, Tianhe District, Guangzhou City, Guangdong Province, 510500 Applicant before: GUANGDONG PROVINCIAL ACADEMY OF BUILDING RESEARCH GROUP Co.,Ltd. |
|
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |