Bridge icing early warning system based on Internet of Things control

Abstract

The invention relates to a bridge icing early warning system based on Internet of Things control, which comprises a central processing module, a data processing module, a sensing detection module, an early warning output module and an electric energy supply module, and is characterized in that the data processing module, the sensing detection module, the electric energy supply module and the early warning output module are electrically connected with the central processing module; according to the bridge icing early warning system based on control of the Internet of Things, temperature measurement of multiple temperature sensing modes is carried out by adopting a bridge floor arrangement and in-bridge pre-embedding mode, and a piezoelectric effect measurement mode combining energy collection and intelligent sensing is adopted for cooperative implementation, so that the reliability and sustainability of the system are improved; and the accuracy of data measurement is improved.

Description

Technical Field

[0001] The present invention relates to a bridge icing warning system based on Internet of Things control. Background Art

[0002] The existing bridge icing warning systems generally use simple temperature and humidity sensors to collect, feedback, and process the temperature and humidity data of the bridge deck, and finally obtain the warning information of bridge deck icing according to corresponding algorithms. However, the results obtained by this data collection and processing method have a large error from the actual situation and are not very practical. Summary of the Invention

[0003] The present invention provides a bridge icing warning system based on Internet of Things control.

[0004] The technical solution adopted by the present invention to solve its technical problems is: a bridge icing warning system based on Internet of Things control, including a central processing module, a data processing module, a sensing detection module, a warning output module, and a power supply module. The data processing module, the sensing detection module, the power supply module, and the warning output module are all electrically connected to the central processing module;

[0005] The central processing module includes a data analysis server, an icing prediction algorithm model, a database management system, and a user management interface;

[0006] The data processing module includes a data acquisition terminal, a wireless communication module, a standby wired communication interface, and a local data storage unit;

[0007] The warning output module includes an LED variable message board, a wireless broadcast system, a mobile terminal push unit, and a traffic management center linkage unit;

[0008] The sensing detection module includes a temperature sensing unit, a humidity sensing unit, a road surface state sensing unit, and a meteorological detection unit;

[0009] The temperature sensing unit includes a contact temperature sensor and a non-contact infrared thermometer;

[0010] The contact temperature sensor includes quantum dot temperature sensors. Among them, there are several quantum dot temperature sensors, and each quantum dot temperature sensor is distributed in a matrix;

[0011] The power supply module includes several embedded piezoelectric sensors, and the power supply module is electrically connected to the quantum dot temperature sensors.

[0012] Preferably, the detection frequency of the embedded piezoelectric sensors is set to 50 - 200 kHz.

[0013] Preferably, the piezoelectric composite material of the embedded piezoelectric sensor is a 1-3 type piezoelectric fiber composite material.

[0014] Preferably, the algorithm module of the icing prediction algorithm model includes a signal preprocessing layer, a feature engineering layer, an intelligent analysis layer, and a decision output layer.

[0015] Preferably, the processing flow of the algorithm module includes:

[0016] a. Acquisition of the original signal;

[0017] b. Preprocessing of the original signal;

[0018] c. Feature extraction of the original signal;

[0019] d. State recognition of the original signal;

[0020] e. Decision output.

[0021] Preferably, the signal preprocessing adopts an adaptive noise reduction algorithm and a correlation-based alignment algorithm. The formula of the correlation-based alignment algorithm is: τmax = argmaxτ(r(t) * s(t + τ)), where s(t) is the signal to be calibrated and r(t) is the reference signal.

[0022] Preferably, the feature extraction includes a time-domain feature set, a frequency-domain feature set, and a time-frequency joint feature set.

[0023] Preferably, the state recognition algorithms are a physical model method and a machine learning method. In the physical model method, the formula of the attenuation coefficient inversion model is where A0 is the amplitude spectrum at the excitation end and A d is the amplitude spectrum at the receiving end.

[0024] Preferably, the decision output includes a decision algorithm, and the decision algorithm is an improved D-S evidence theory algorithm. The formula is: where K = ∑ B∩C＝A m1(B)m2(C) is the conflict factor.

[0025] Preferably, the principle formula of the quantum dot temperature sensor is the relationship between the peak wavelength of quantum dot fluorescence and temperature: λpeak(T) = λ0 + α·T + β·T 2 , where λ0 is the peak wavelength at zero temperature, α and β are material characteristic coefficients, and T is the temperature; the formula for the quenching effect of fluorescence intensity with temperature is: I0 is the reference intensity at room temperature, E a is the activation energy related to the quantum dot material, and k B is the Boltzmann constant; the temperature field reconstruction includes distributed sensing network data fusion and heat conduction assisted correction.

[0026] The beneficial effects of the present invention are as follows. The bridge icing warning system based on Internet of Things control measures the temperature through multiple temperature sensing methods by setting on the bridge deck and embedding inside the bridge, and adopts the piezoelectric effect measurement method combining energy harvesting and intelligent sensing to cooperate and achieve, which improves the reliability and sustainability of the system and the accuracy of data measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be further described below in conjunction with the drawings and embodiments.

[0028] Figure 1 It is the system schematic diagram of the bridge icing warning system based on Internet of Things control of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The present invention will be further described in detail below in conjunction with the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0030] As Figure 1 shown, a bridge icing warning system based on Internet of Things control includes a central processing module, a data processing module, a sensing detection module, a warning output module, and a power supply module. The data processing module, the sensing detection module, the power supply module, and the warning output module are all electrically connected to the central processing module;

[0031] The central processing module includes a data analysis server, an icing prediction algorithm model, a database management system, and a user management interface;

[0032] The data processing module includes a data acquisition terminal, a wireless communication module, a standby wired communication interface, and a local data storage unit;

[0033] The warning output module includes an LED variable message board, a wireless broadcast system, a mobile terminal push unit, and a traffic management center linkage unit;

[0034] The sensing detection module includes a temperature sensing unit, a humidity sensing unit, a road surface state sensing unit, and a meteorological detection unit;

[0035] The temperature sensing unit includes a contact temperature sensor and a non-contact infrared thermometer;

[0036] The contact temperature sensor includes quantum dot temperature sensors. Among them, there are several quantum dot temperature sensors, and each quantum dot temperature sensor is distributed in a matrix;

[0037] The power supply module includes several embedded piezoelectric sensors, and the power supply module is electrically connected to the quantum dot temperature sensors.

[0038] Here, the measurement and acquisition of bridge deck temperature and humidity data are realized through the sensing and detection module. The temperature sensing unit includes two types: contact temperature detection and non-contact temperature detection. Among them, the contact temperature detection is realized by using a matrix quantum dot temperature sensor. In fact, the nano-level quantum dot temperature sensor is adopted here, which can realize the mapping of the micro-meter level temperature field of the bridge deck, with a resolution of 0.01 °C. It can detect the tiny temperature difference of the local bridge deck, and cooperate with the embedded piezoelectric sensor for self-power supply, improving the stability and sustainability of temperature detection. Here, the energy of bridge vibration is collected through the piezoelectric effect and converted into electrical energy supply. The infrared non-contact temperature measurement is an auxiliary verification for the bridge deck temperature detection. Although the detection accuracy of this method is low, it can cooperate with the algorithm model to verify the conclusion and improve the accuracy of early warning information.

[0039] The humidity sensing unit is used to measure the air humidity of the bridge deck.

[0040] The road surface condition sensing unit is an intelligent device for real-time monitoring of road surface conditions, which is widely used in intelligent transportation systems, autonomous driving, road maintenance and other fields. It detects the key parameters of the road surface through multi-sensor fusion technology, providing data support for driving safety, road maintenance and traffic management. It has an optical sensor, a thermometer, an accelerometer, a gyroscope and a millimeter wave radar. Its core functions include road surface humidity detection, temperature detection, friction coefficient evaluation, snow recognition, ice recognition and pothole crack detection. In fact, the road surface condition sensing unit is also an auxiliary conclusion verification test unit for cooperating with separately set temperature sensors, humidity sensors, etc. Multiple conclusions are obtained through multiple detection methods, and then auxiliary demonstration is carried out in combination with the test conclusions of the high-precision test unit, so as to improve the accuracy of data.

[0041] The optical sensor here is mainly used to detect the difference in reflection wavelengths to distinguish between dry-wet and ice-water states;

[0042] The thermometer of the road surface condition sensing unit is actually also a non-contact road surface temperature detection device;

[0043] Both the accelerometer and the gyroscope are used to monitor vehicle vibrations, so as to infer the flatness of the road surface and then infer the ice condition of the road surface;

[0044] The millimeter wave radar can penetrate rain and snow to detect the characteristics of surface substances.

[0045] The meteorological detection unit is an intelligent device for real-time monitoring of environmental meteorological parameters, which is widely used in smart cities, traffic management, agriculture, disaster early warning and other fields. It collects key meteorological data through multi-sensor integration technology, providing a scientific basis for weather prediction, environmental monitoring and decision-making support.

[0046] The meteorological detection unit mainly includes a temperature and humidity sensor, an anemometer and wind vane, a barometric pressure sensor, and an optical sensor. The temperature and humidity sensor is mainly used to measure the temperature and humidity of the air environment. The anemometer and wind vane are mainly used to measure the wind speed and direction. The barometric pressure sensor is mainly used to measure the atmospheric pressure. The optical sensor is mainly used to sense rain, snow, fog, etc. Through these sensors, it is possible to identify the temperature, humidity, precipitation, and snowfall in the climate, and measure the intensity and cumulative amount of precipitation and snowfall. The meteorological detection unit is used to collect the status data of the surrounding environment. Combining with the existing climate data model, it is also possible to draw corresponding conclusions about bridge icing information, cooperate with and verify other units, so as to improve the accuracy of early warning information.

[0047] The data analysis server is mainly used to realize the analysis and processing of data and belongs to the central processing unit.

[0048] The icing prediction algorithm model is an algorithm architecture formed by existing icing data and is used for data matching and deduction.

[0049] The database management system is a core software system for efficiently organizing, storing, managing, and retrieving data. Its function runs through the entire life cycle of data and provides underlying support for modern information applications.

[0050] The user management interface mainly realizes the human-computer interaction between the system and the operator.

[0051] The data acquisition terminal is a special device for real-time collecting, processing, and transmitting on-site data.

[0052] The wireless communication module is mainly used to realize the wireless transmission of data, such as the Internet, Bluetooth, etc.

[0053] The standby wired communication interface is an emergency wired communication set up to prevent problems with wireless communication.

[0054] The local data storage unit is mainly used for data storage functions.

[0055] The LED variable message sign is actually an LED display screen. The content on the display screen is controlled by the system to display, that is, to display relevant early warning information.

[0056] The wireless broadcast system is a voice system that realizes early warning reminders through voice.

[0057] The mobile terminal push unit is mainly a service module used to send messages to mobile devices such as smart phones and tablets in real time for real-time remote reminders.

[0058] The traffic management center linkage unit is mainly used to transmit real-time data to the background of the traffic management department.

[0059] Preferably, the detection frequency of the embedded piezoelectric sensor is set to 50 - 200 kHz.

[0060] Preferably, the piezoelectric composite material of the embedded piezoelectric sensor is a 1 - 3 type piezoelectric fiber composite material.

[0061] Preferably, the algorithm module of the icing prediction algorithm model includes a signal pre - processing layer, a feature engineering layer, an intelligent analysis layer, and a decision - making output layer.

[0062] Preferably, the processing flow of the algorithm module includes:

[0063] a. Acquisition of the original signal;

[0064] b. Pre - processing of the original signal;

[0065] c. Feature extraction of the original signal;

[0066] d. State recognition of the original signal;

[0067] e. Decision - making output.

[0068] Preferably, the signal pre - processing uses an adaptive noise reduction algorithm and a cross - correlation - based alignment algorithm. The formula of the cross - correlation - based alignment algorithm is: τmax=argmaxτ(r(t)*s(t + τ)), where s(t) is the signal to be calibrated and r(t) is the reference signal.

[0069] Preferably, the feature extraction includes a time - domain feature set, a frequency - domain feature set, and a time - frequency joint feature set.

[0070] Preferably, the state recognition algorithms are the physical model method and the machine learning method. In the physical model method, the formula of the attenuation coefficient inversion model is where A0 is the amplitude spectrum at the excitation end, and A d is the amplitude spectrum at the receiving end.

[0071] Preferably, the decision - making output includes a decision - making algorithm, and the decision - making algorithm is an improved D - S evidence theory algorithm. The formula is: where K=∑ B∩C＝A m1(B)m2(C) is the conflict factor.

[0072] Preferably, the principle formula of the quantum dot temperature sensor is the relationship between the peak fluorescence wavelength of the quantum dot and temperature: λpeak(T)=λ0+α·T+β·T 2 , where λ0 is the peak wavelength at zero temperature, α and β are material characteristic coefficients, and T is the temperature; the formula for the quenching effect of fluorescence intensity with temperature: I0 is the reference intensity at room temperature, E a is the activation energy related to the quantum dot material, kB is the Boltzmann constant; the temperature field reconstruction includes distributed sensor network data fusion and heat conduction assisted correction.

[0073] Compared with the prior art, the bridge icing warning system based on Internet of Things control measures the temperature in a multi-temperature sensing manner by means of bridge deck installation and in-bridge embedding, and is realized by cooperating with the piezoelectric effect measurement method combining energy harvesting and intelligent sensing, which improves the reliability and sustainability of the system and the accuracy of data measurement.

[0074] Inspired by the ideal embodiments of the present invention described above, through the above description, relevant staff can make various changes and modifications completely within the scope not deviating from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A bridge icing warning system based on Internet of Things control, characterized in that: It includes a central processing module, a data processing module, a sensor detection module, an early warning output module and an electric energy supply module, wherein the data processing module, the sensor detection module, the electric energy supply module and the early warning output module are all electrically connected to the central processing module; The central processing module includes a data analysis server, an icing prediction algorithm model, a database management system and a user management interface; The data processing module includes a data acquisition terminal, a wireless communication module, a backup wired communication interface and a local data storage unit; The warning output module includes an LED variable information board, a wireless broadcasting system, a mobile terminal push unit and a traffic management center linkage unit; The sensing detection module includes a temperature sensing unit, a humidity sensing unit, a road surface state sensing unit and a weather detection unit; The temperature sensing unit includes a contact temperature sensor and a non-contact infrared thermometer; The contact temperature sensor includes a quantum dot temperature sensor, wherein there are a plurality of quantum dot temperature sensors, and each quantum dot temperature sensor is distributed in a matrix; The power supply module includes a plurality of embedded piezoelectric sensors, and the power supply module is electrically connected to the quantum dot temperature sensor.

2. The bridge icing warning system based on Internet of Things control as claimed in claim 1 is characterized in that: The detection frequency of the embedded piezoelectric sensor is set to 50-200kHz.

3. The bridge icing warning system based on Internet of Things control as claimed in claim 1 is characterized in that: The piezoelectric composite material of the embedded piezoelectric sensor is a 1-3 type piezoelectric fiber composite material.

4. The bridge icing warning system based on Internet of Things control as claimed in claim 1 is characterized in that: The algorithm modules of the icing prediction algorithm model include signal preprocessing layer, feature engineering layer, intelligent analysis layer and decision output layer.

5. The bridge icing warning system based on Internet of Things control as claimed in claim 4 is characterized in that: The processing flow of the algorithm module includes: a. Collection of original signals; b. Preprocessing of original signal; c. Feature extraction of original signal; d. State recognition of the original signal; e. Decision output.

6. The bridge icing warning system based on Internet of Things control as claimed in claim 5 is characterized in that: Signal preprocessing adopts an adaptive noise reduction algorithm and an alignment algorithm based on cross-correlation. The formula of the alignment algorithm based on cross-correlation is: τmax=argmaxτ(r(t)*s(t+τ)), where s(t) is the signal to be calibrated and r(t) is the reference signal.

7. The bridge icing warning system based on Internet of Things control as claimed in claim 5 is characterized in that: Feature extraction includes time domain feature set, frequency domain feature set and time-frequency joint feature set.

8. The bridge icing warning system based on Internet of Things control as claimed in claim 5, characterized in that: The state recognition algorithm is the physical model method and the machine learning method. In the physical model method, the formula of the attenuation coefficient inversion model is: Among them, A0 is the amplitude spectrum of the excitation end, A d is the amplitude spectrum at the receiving end.

9. The bridge icing warning system based on Internet of Things control as claimed in claim 5, characterized in that: The decision output includes the decision algorithm, which is an improved algorithm of DS evidence theory, and the formula is: Where K = ∑ B∩C＝A m1(B)m2(C) are conflict factors.

10. The bridge icing warning system based on Internet of Things control as claimed in claim 1, characterized in that: The principle formula of quantum dot temperature sensor is the relationship between quantum dot fluorescence peak wavelength and temperature: λpeak(T)=λ0+α·T+β·T 2 , where λ0 is the peak wavelength at zero temperature, α and β are material property coefficients, and T is temperature; the formula for the quenching effect of fluorescence intensity with temperature is: I0 is the reference intensity at room temperature, E a is the activation energy associated with the quantum dot material, k B is the Boltzmann constant; temperature field reconstruction includes distributed sensor network data fusion and heat conduction auxiliary correction.

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