Heterogeneous historic building intelligent real-time monitoring and early warning device based on multiple sensors

Abstract

The invention discloses a multi-sensor-based heterogeneous historic building intelligent real-time monitoring and early warning device, which comprises an acquisition terminal, a transmission unit and a controller, and is characterized in that the acquisition terminal is installed at a corresponding monitoring position of a historic building and comprises an acceleration sensor, an inclination sensor, a displacement sensor, a crack sensor and a multi-path time sequence control module; the acceleration sensor, the inclination sensor, the displacement sensor and the crack sensor form a multi-mode sensor group to measure and monitor an ancient building, and the multi-channel time sequence control module ensures that all electric pulse signals collected by the multi-mode sensor group are synchronous and transmits all the collected electric pulse signals to the controller through the transmission unit. According to the method, the multi-mode sensor group is used for measuring and monitoring the ancient building, the sensing accuracy is improved through the excellent performance of the sensor in the aspects of time sequence data processing and mode recognition, and support can be provided for health monitoring of the ancient building more efficiently and intelligently.

Description

Technical Field

[0001] The present invention relates to the field of measurement and monitoring, and in particular to an intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors. Background Art

[0002] The value of ancient buildings is multi-dimensional, not only reflected in the inheritance of history and culture, but also has a far-reaching impact on art, science, society, economy, environment and spirit. The protection and rational use of ancient buildings are of great significance for the inheritance of human civilization, the promotion of social development and the enhancement of cultural confidence. Using modern scientific and technological means to monitor ancient buildings accordingly can detect structural problems in time and prevent small problems from evolving into major damages. At the same time, more scientific maintenance and management of ancient buildings can be carried out accordingly.

[0003] In the field of ancient building monitoring, most of the current monitoring is relatively macroscopic or the monitoring method is single, which makes it difficult to achieve comprehensive and in-depth monitoring. In addition, there are the following problems: the health risk problems presented by ancient buildings are often multi-dimensional and complex. The current monitoring methods are often based on independent analysis of single parameter indicators, which makes it difficult to obtain accurate assessment and early warning results; in the process of ancient building monitoring, due to the small changes in the results themselves, the attenuation of the signal transmission process, and external environmental factors (wind influence, temperature changes, etc.), the monitored signals are often weak, which increases the difficulty of subsequent signal analysis and processing. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors to solve the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A multi-sensor based heterogeneous ancient building intelligent real-time monitoring and early warning device, comprising a collection terminal, a transmission unit and a controller, wherein the collection terminal is installed at the corresponding monitoring position of the ancient building, and comprises an acceleration sensor, a tilt sensor, a displacement sensor, a crack sensor and a multi-channel timing control module, wherein the acceleration sensor is used to detect the vibration of the ancient building, the displacement sensor is used to detect the displacement of the ancient building, the tilt sensor is used to detect the tilt change of the ancient building, and the crack sensor is used to detect the change in the width of the crack in the ancient building, wherein the acceleration sensor, the tilt sensor, the displacement sensor and the crack sensor constitute a multi-mode sensor group to realize the measurement and monitoring of the ancient building, wherein the input end of the multi-channel timing control module is connected to the signal output end of the multi-mode sensor group, and ensures that the collected electrical pulse signals of the multi-mode sensor group are synchronized, wherein the multi-channel timing control module transmits the collected electrical pulse signals to the controller through the transmission unit, wherein the controller comprises a signal, a time calibrator, a time amplitude converter, a signal conversion module and a data analysis module, wherein the time calibrator is used to record the electrical pulse signals ... The instantaneous value and time interval of the pulse signal, the signal conversion module converts the electric pulse signal into a set of discrete pulse time series based on the instantaneous value and time interval of the electric pulse signal, the time amplitude converter is used to convert the time interval of the electric pulse signal into a pulse amplitude, and outputs an analog pulse proportional to the time, and then generates a set of time amplitude pulse sequences with time information through the signal conversion module, the signal conversion module sends the pulse time sequence and the time amplitude pulse sequence to the data analysis module, and performs health status analysis and evaluation of the ancient building, the data analysis module includes a multi-mode sensor and a pulse neural network system, the multi-mode sensor is used to perform binary fitting on the pulse time sequence, the multi-mode sensor is a plurality of sensors connected together to perform XOR Boolean operation, the pulse neural network system includes a building state feature extraction layer and a building state detection layer, the building state feature extraction layer obtains the building state feature pulse sequence based on the pulse time sequence after binary fitting, the building state detection layer performs building state detection based on the building state feature pulse sequence, and obtains the building state evaluation result.

[0006] Preferably, the multi-channel timing control module adopts a data acquisition unit card, and the data acquisition unit card adopts a NI PCI-6229 multi-function data acquisition unit card. The data acquisition unit card is integrated with LabVIEW software, and the LabVIEW software is used to perform synchronous configuration of the signal acquisition unit to ensure that the signal acquisition unit actions of each sensor start at the same time.

[0007] Preferably, the transmission unit includes an electro-optical modulator, a wavelength division multiplexer, a wavelength splitter and a photoelectric converter. The electro-optical modulator, wavelength division multiplexer and wavelength splitter are connected in series on the same optical path through optical fibers. The input end of the electro-optical modulator is connected to the output end of the multi-channel timing control module, and is used to convert multiple electrical pulse signals output by the multi-channel timing control module into multiple optical pulse signals. The wavelength division multiplexer is used to combine multiple optical pulse signals and transmit them through optical fibers. The wavelength splitter is used to separate the combined optical pulse signal to obtain multiple optical pulse signals. The photoelectric converter is used to convert multiple optical pulse signals into multiple electrical pulse signals.

[0008] Preferably, the controller further comprises a signal fusion module, and the signal fusion module is used to sum the pulse time series and the time amplitude pulse series as the input of the multi-mode sensor.

[0009] Preferably, the acquisition terminal further comprises a configuration module, and the configuration module is used to configure the acquisition period and the range of the acquisition signal value.

[0010] Preferably, the binarization fitting of the multi-mode perceptron is implemented by the following formula:

[0011] in, is the output value of n inputs, represents the input vector, represents the number of input vectors, Represents the on-duty weight, represents the adjustment factor, For the i The output value of all inputs at the moment, As the input of the activation function, the output of the activation function is the result of the binary fitting.

[0012] Preferably, the activation function adopts a step function, which is expressed as:

[0013] in, The result of binarization fitting is As the input of the activation function, is the output threshold.

[0014] Preferably, the network structure of the spiking neural network includes an input layer, a hidden layer, and an output layer, each layer is composed of a group of LIF neuron models, and the input layer and the hidden layer constitute a memory generation module; the pulse time series after binarization fitting of the input layer is introduced into the spiking neural network, so that the corresponding input neurons generate pulses, and the pulses are transmitted to the hidden layer for information processing and conversion.

[0015] Preferably, the building state characteristic pulse sequence is a pulse sequence representing a building risk factor, and the building state assessment result is a result representing that the ancient building may have risks; The building status detection layer performs building status detection based on the building status characteristic pulse sequence, which is realized by the Pearson correlation analysis model. The Pearson correlation analysis model is:

[0016] in, and are the observed values ​​of the two variables, which are determined based on the characteristic pulse sequence of the building state, and are the observed values ​​of two variables, k is the sample size.

[0017] After adopting the above technical solution, the present invention has the following beneficial effects compared with the background technology: 1. The present invention uses acceleration sensors, tilt sensors, displacement sensors, and crack sensors to perform multi-dimensional measurement and monitoring of ancient buildings, and utilizes the excellent performance of sensors in processing time series data and pattern recognition to improve the perception accuracy. By setting up a multi-channel timing control module, it can ensure that the signal acquisition units of each sensor start at the same time, which facilitates the multi-mode sensor to monitor events at the same time.

[0018] 2. The present invention obtains a pulse time sequence and a time-amplitude pulse sequence with time information by setting a time calibrator and a time-amplitude converter, so that multiple sensors can obtain accurate time intervals and the order of perceived events. At the same time, based on the time-amplitude pulse sequence, weak pulses can be better perceived by the sensors.

[0019] 3. The present invention sets a signal fusion module to sum the pulse time series and the time amplitude pulse series, and then uses them as the input of the multi-mode sensor, further ensuring that an effective and stable perception data basis is provided for subsequent operations.

[0020] 4. The intelligent real-time monitoring and early warning device for ancient buildings of the present invention has high energy efficiency, accurate time processing, and strong fault tolerance, and can provide support for the health monitoring of ancient buildings in a more efficient and intelligent manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Example

[0023] The present invention discloses an intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors, comprising a collection terminal 100, a transmission unit 200 and a controller 300, wherein: The acquisition terminal 100 is installed at the corresponding monitoring position of the ancient building, and includes an acceleration sensor 110, a tilt sensor 120, a displacement sensor 130, a crack sensor 140 and a multi-channel timing control module 150. The acceleration sensor 110 is used to detect the vibration of the ancient building, the displacement sensor 130 is used to detect the displacement of the ancient building, the tilt sensor 120 is used to detect the tilt change of the ancient building, and the crack sensor 140 is used to detect the change in the width of the cracks in the ancient building. The acceleration sensor 110, the displacement sensor 130, the tilt sensor 120, and the crack sensor 140 constitute a multi-mode sensor group to achieve measurement and monitoring of the ancient building. The input end of the multi-channel timing control module 150 is connected to the signal output end of the multi-mode sensor group, and ensures that the various electrical pulse signals collected by the multi-mode sensor group are synchronized. The multi-channel timing control module 150 transmits the collected various electrical pulse signals to the controller 300 through the transmission unit 200. In this embodiment, the multi-channel timing control module 150 uses a data acquisition unit card, and the data acquisition unit card uses a NI PCI-6229 multi-function data acquisition unit card. The data acquisition unit card is integrated with LabVIEW software, and the LabVIEW software is used to perform synchronous configuration of the signal acquisition unit to ensure that the signal acquisition unit actions of each sensor start at the same time. NI PCI-6229 is a multi-function data acquisition unit card produced by National Instruments (NI), which has 16 or 32 analog input channels, 4 analog output channels, 48 ​​I / O channels, and 2 32-bit timers, and supports LabVIEW software. The present invention realizes "the signal acquisition unit actions of each sensor start at the same time" by utilizing the real-time signal acquisition unit function of LabVIEW software.

[0024] The acquisition terminal 100 also includes a configuration module 160, which is used to configure the acquisition period and the range of the acquisition signal value. The acquisition period can be a continuous time period or an interval segmented period. The range of the acquisition signal value can be set according to the experience of the forerunner, and the range of the acquisition signal value should also match the acquisition unit range of the sensor.

[0025] The transmission unit 200 includes an electro-optical modulator 210, a wavelength division multiplexer 220, a wavelength splitter 230 and a photoelectric converter 240. The electro-optical modulator 210, the wavelength division multiplexer 220 and the wavelength splitter 230 are connected in series in the same optical path through optical fibers. The input end of the electro-optical modulator 210 is connected to the output end of the multi-channel timing control module 150, and is used to convert multiple electrical pulse signals output by the multi-channel timing control module 150 into multiple optical pulse signals. The wavelength division multiplexer 220 is used to combine multiple optical pulse signals and transmit them through optical fibers. The wavelength splitter 230 is used to separate the combined optical pulse signal to obtain multiple optical pulse signals. The photoelectric converter 240 is used to convert multiple optical pulse signals into multiple electrical pulse signals.

[0026] The controller 300 includes a signal controller 300 including a time calibrator 310, a time amplitude converter 320, a signal conversion module 330 and a data analysis module, wherein: The time calibrator 310 is used to record the instantaneous value and time interval of the electric pulse signal. The signal conversion module 330 converts the electric pulse signal into a set of discrete pulse time series based on the instantaneous value and time interval of the electric pulse signal. The time amplitude converter 320 is used to convert the time interval of the electric pulse signal into a pulse amplitude and output an analog pulse proportional to time. Then, a set of time amplitude pulse sequences with time information is generated through the signal conversion module 330. The signal conversion module 330 sends the pulse time series and the time amplitude pulse sequence to the data analysis module to analyze and evaluate the health status of the ancient building.

[0027] The data analysis module includes a multi-mode sensor 350 and a pulse neural network system 360. The multi-mode sensor 350 is used to perform binary fitting on the pulse time series. The multi-mode sensor 350 is a plurality of sensors connected together to perform XOR Boolean operations.

[0028] A perceptron can have one input and one output, or multiple inputs and one output, that is, multiple perceptions. This not only collects more data, but also serves as a review and verification. Multi-mode perception can be said to be a very complex electrochemical reaction collection unit. The entire working process is dynamic, so we need to select effective, that is, the most stable perception data as the basic value for calculation. One output can be used as the input of other units. The perceptron has a certain fitting ability and can classify the input into two categories, that is, divide the input data into two categories, that is, given an input, the output is 0 (belonging to category 0) or 1 (belonging to category 1). The perceptron can use Boolean operations to simulate such operations. For more than two weight parameters, that is, M0..., we can adjust the parameter value so that the perceptron can simulate Boolean operations. For example, let M0=0.3, M1=0.3,...M n =0.5, then the perceptron can simulate Boolean operations.

[0029] The binarization fitting of the multi-mode perceptron 350 is implemented by the following formula:

[0030] in, is the output value of n inputs, represents the input vector, represents the number of input vectors, Represents the on-duty weight, represents the adjustment factor, For the i The output value of all inputs at the moment, As the input of the activation function, the output of the activation function is the result of the binary fitting.

[0031] The activation function uses a step function, which is expressed as:

[0032] in, The result of binarization fitting is As the input of the activation function, is the output threshold.

[0033] The pulse is an instantaneous stimulus and also a perceptual stimulus. It may succeed or fail. That is, the sensor has a threshold. If it is received, it is successful, and if it is not received, it is a failure. If it fails, no disturbance value will be generated. If it succeeds, the disturbance will form a certain standard form and be transmitted along the link and defined as a mathematical value. Now we change the pulse to computer binary, and when it fails (no pulse), it is represented by a value "0", and when it succeeds (with pulse), it is represented by a value "1". In theory, a sensor can have multi-mode computing capabilities, but in fact, it is difficult for a sensor to imitate complex multi-mode logic operations. At this time, it is necessary to connect multiple sensors together to make multiple sensors become multi-mode sensors 350 with the ability to imitate complex logic operations. Multi-mode sensor 350 is a perception system, which can be regarded as the most basic monitoring neural network. As long as the micro-energy activates the sensor multi-mode sensor 350, it has a more powerful ability to simulate neurons, so it can handle more complex problems.

[0034] The pulse neural network system 360 includes a building state feature extraction layer 361 and a building state detection layer 362. The building state feature extraction layer 361 obtains a building state feature pulse sequence based on the pulse time sequence after binarization fitting. The building state detection layer 362 performs building state detection based on the building state feature pulse sequence and obtains a building state assessment result. The building state feature pulse sequence is a pulse sequence representing a building risk factor, and the building state assessment result is a result representing that an ancient building may have risks. The above-mentioned building state detection layer 362 performs building state detection based on the building state feature pulse sequence, which is realized by a Pearson correlation analysis model. The Pearson correlation analysis model is:

[0035] in, and are the observed values ​​of the two variables, which are determined based on the characteristic pulse sequence of the building state, and are the observed values ​​of two variables, k is the sample size.

[0036] The network structure of the spiking neural network includes an input layer, a hidden layer, and an output layer. Each layer is composed of a group of LIF neuron models. The input layer and the hidden layer form a memory generation module. The pulse time series after binarization fitting of the input layer is introduced into the spiking neural network to make the corresponding input neurons generate pulses and pass the pulses to the hidden layer for information processing and conversion.

[0037] The controller 300 further includes a signal fusion module 340, which is used to sum the pulse time sequence and the time amplitude pulse sequence as the input of the multi-mode sensor 350. Before summing the pulse time sequence and the time amplitude pulse sequence, in order to ensure the uniform processing of the subsequent multi-mode sensor 350, it is necessary to use the signal fusion module 340 to first convert the pulse time sequence and the time amplitude pulse sequence into a unified period, that is, a unified pulse frequency. The "summing the pulse time sequence and the time amplitude pulse sequence" involved here refers to the pulse sequence derived from the pulse signal of the same sensor acquisition unit, especially the processing for the weak sensor pulse information.

[0038] It can be seen from the above description that the present invention uses acceleration sensor 110, displacement sensor 130, tilt sensor 120, and crack sensor 140 to perform multi-dimensional measurement and monitoring of ancient buildings, and utilizes the excellent performance of the sensor in processing time series data and pattern recognition to improve the perception accuracy. By setting a multi-channel timing control module 150, it can ensure that the signal acquisition unit of each sensor starts at the same time, which is convenient for realizing the event monitoring of the multi-mode sensor 350 at the same time. The present invention obtains a pulse time sequence and a time amplitude pulse sequence with time information by setting a time calibrator 310 and a time amplitude converter 320, which enables multiple sensors to obtain accurate time intervals and the order of perceived events, and at the same time, based on the time amplitude pulse sequence, weak pulses can be better perceived by the sensor. The present invention sets a signal fusion module 340 to sum the pulse time sequence and the time amplitude pulse sequence, and then uses them as the input of the multi-mode sensor 350, further ensuring that an effective and stable perception data basis is provided for subsequent operations. The intelligent real-time monitoring and early warning device for ancient buildings of the present invention has high energy efficiency, accurate time processing, and strong fault tolerance, and can provide support for the health monitoring of ancient buildings in a more efficient and intelligent manner.

[0039] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors, characterized by: The invention comprises a collection terminal, a transmission unit and a controller. The collection terminal is installed at a corresponding monitoring position of the ancient building. The collection terminal comprises an acceleration sensor, a tilt sensor, a displacement sensor, a crack sensor and a multi-channel timing control module. The acceleration sensor is used to detect the vibration of the ancient building, the displacement sensor is used to detect the displacement of the ancient building, the tilt sensor is used to detect the tilt change of the ancient building, and the crack sensor is used to detect the change in the width of the crack in the ancient building. The acceleration sensor, the tilt sensor, the displacement sensor and the crack sensor constitute a multi-mode sensor group to realize the measurement and monitoring of the ancient building. The input end of the multi-channel timing control module is connected to the signal output end of the multi-mode sensor group, and ensures that the collected electric pulse signals of the multi-mode sensor group are synchronized. The multi-channel timing control module transmits the collected electric pulse signals to the controller through the transmission unit. The controller comprises a signal. The controller comprises a time calibrator, a time amplitude converter, a signal conversion module and a data analysis module. The time calibrator is used to record the instantaneous value and time of the electric pulse signal. Interval, the signal conversion module converts the electric pulse signal into a set of discrete pulse time series based on the instantaneous value and time interval of the electric pulse signal, the time amplitude converter is used to convert the time interval of the electric pulse signal into a pulse amplitude, and output an analog pulse proportional to the time, and then a set of time amplitude pulse sequences with time information is generated through the signal conversion module, the signal conversion module sends the pulse time sequence and the time amplitude pulse sequence to the data analysis module to perform health status analysis and evaluation of the ancient building, the data analysis module includes a multi-mode sensor and a pulse neural network system, the multi-mode sensor is used to perform binary fitting on the pulse time sequence, the multi-mode sensor is a plurality of sensors connected together to perform XOR Boolean operation, the pulse neural network system includes a building state feature extraction layer and a building state detection layer, the building state feature extraction layer obtains a building state feature pulse sequence based on the pulse time sequence after binary fitting, the building state detection layer performs building state detection based on the building state feature pulse sequence, and obtains a building state evaluation result.

2. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 1 is characterized in that: The multi-channel timing control module adopts a data acquisition unit card, and the data acquisition unit card adopts a NI PCI-6229 multi-function data acquisition unit card. The data acquisition unit card is integrated with LabVIEW software, and the LabVIEW software is used to perform synchronous configuration of the signal acquisition unit to ensure that the signal acquisition unit actions of each sensor start at the same time.

3. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 1 is characterized in that: The transmission unit includes an electro-optical modulator, a wavelength division multiplexer, a wave splitter and a photoelectric converter. The electro-optical modulator, wavelength division multiplexer and wave splitter are connected in series in the same optical path through optical fibers. The input end of the electro-optical modulator is connected to the output end of the multi-channel timing control module, and is used to convert multiple electrical pulse signals output by the multi-channel timing control module into multiple optical pulse signals. The wavelength division multiplexer is used to combine multiple optical pulse signals and transmit them through optical fibers. The wave splitter is used to separate the combined optical pulse signal to obtain multiple optical pulse signals. The photoelectric converter is used to convert multiple optical pulse signals into multiple electrical pulse signals.

4. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as described in any one of claims 1 to 3, characterized in that: The controller further comprises a signal fusion module, which is used to sum the pulse time sequence and the time amplitude pulse sequence as an input of the multi-mode sensor.

5. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 4 is characterized in that: The acquisition terminal also includes a configuration module, and the configuration module is used to configure the acquisition period and the range of the acquisition signal value.

6. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 4 is characterized in that: The binary fitting of the multi-mode perceptron is achieved by the following formula: in, is the output value of n inputs, represents the input vector, represents the number of input vectors, Represents the on-duty weight, represents the adjustment factor, For the i The output value of all inputs at the moment, As the input of the activation function, the output of the activation function is the result of the binary fitting.

7. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 6 is characterized in that: The activation function adopts a step function, which is expressed as: in, The result of binarization fitting is As the input of the activation function, is the output threshold.

8. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 1 is characterized in that: The network structure of the spiking neural network includes an input layer, a hidden layer, and an output layer, each layer is composed of a group of LIF neuron models, and the input layer and the hidden layer constitute a memory generation module; the pulse time series after binarization fitting of the input layer is introduced into the spiking neural network, so that the corresponding input neurons generate pulses, and the pulses are transmitted to the hidden layer for information processing and conversion.

9. The intelligent real-time monitoring and early warning device for heterogeneous ancient buildings based on multiple sensors as claimed in claim 1 is characterized in that: The building state characteristic pulse sequence is a pulse sequence representing a building risk factor, and the building state assessment result is a result representing a possible risk of the ancient building; The building status detection layer performs building status detection based on the building status characteristic pulse sequence, which is realized by the Pearson correlation analysis model. The Pearson correlation analysis model is: in, and are the observed values ​​of the two variables, which are determined based on the characteristic pulse sequence of the building state, and are the observed values ​​of two variables, k is the sample size.

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