An online wavelet analysis edge computing sensor and its application method

Abstract

The present invention discloses an online wavelet analysis edge computing sensor and an application method thereof. The sensor includes an upper cover, a main control board, an adapter, a battery, and a base. The present invention collects vibration information of several structures through an acceleration sensor arranged on the main control board, adopts edge computing ideas, and utilizes a wavelet packet decomposition algorithm programmed in an MCU on the main control board to process acceleration time-history signals in real time, thereby realizing online analysis of the structural data signals of the monitoring points. At the same time, by realizing the identification and characterization of transient phenomena in the time and frequency domains of the structural signals, and further correlating them with structural fault characteristics, an important basis for timely evaluating the degree of structural faults can be provided.

Description

Technical Field

[0001] The present invention relates to the technical field of structural safety detection, and in particular to an online wavelet analysis edge computing sensor and an application method thereof. Background Art

[0002] Over time and under the long-term influence of the external environment, many existing engineering structures often experience aging and even cracking. Timely monitoring and assessment of the overall structural condition has become a crucial research direction for ensuring project safety, timely maintenance, and extending the service life of structures. During operation, vibration generates accelerations. The acceleration signals contain information about the structural service state. By analyzing the acceleration signals, a wide range of physical and external excitation information can be inferred. Numerous factors influence the acceleration response, including structural stiffness, component connections, external constraints, and excitation. When a structure fails, its stiffness and / or connection changes, leading to changes in its vibration characteristics, such as frequency and mode, and ultimately, changes in the energy of its wavelet packet components. Online monitoring of the maximum energy change of a structure's wavelet packet can provide an important reference for assessing structural failure conditions. Laboratory experiments on valve vibration condition monitoring have demonstrated a high correlation between the energy change of the structural wavelet packet components and the severity of the failure or damage.

[0003] Based on the measured acceleration time-history signal, wavelet packet decomposition can be used to identify and characterize transient phenomena in the time and frequency domains of structural signals, and correlate them with structural fault characteristics, providing a solution for structural fault identification and diagnosis. Generally, for the measured acceleration signal, the acceleration time-history signal is first transmitted and stored, and then a wavelet packet transform is performed on the computer through software or a program to finally obtain the required component energy information. This process often requires a certain period of time, making it difficult to achieve the purpose of real-time online analysis. This design proposes an online wavelet analysis edge computing intelligent sensor to realize online analysis of the component energy of the monitoring point, so as to provide an important basis for timely assessment of the degree of structural failure. Summary of the Invention

[0004] The purpose of the present invention is to provide an online wavelet analysis edge computing sensor and its application method to solve the defect that the existing technology is difficult to quickly analyze structural faults. The proposed online wavelet analysis edge computing sensor helps to shorten the data processing cycle and timely evaluate the fault status of the structure.

[0005] In order to realize the above functions, the present invention designs an online wavelet analysis edge computing sensor, including an upper cover 1, a main control board 2, an adapter 3, a battery 4, and a base 5;

[0006] Among them, the adapter seat 3 is provided with a control cavity 31 with a groove structure, and the inner wall edge of the control cavity 31 of the adapter seat 3 is provided with a preset number of convex fitting openings. A fitting plate 12 is provided on one side of the upper cover 1 for aligning and fixing the upper cover 1 and the adapter seat 3. The edge shape of the fitting plate 12 is adapted to the edge shape of the control cavity 31 of the adapter seat 3, and the edge of the fitting plate 12 is provided with concave fitting openings corresponding to the position, number and size of the fitting openings on the inner wall edge of the control cavity 31. The fitting plate 12 is provided with a through hole passing through the fitting plate 12 and the upper cover 1. The other side of the upper cover 1 is provided with a wired interface 11 connected to the main control board 2 for transmitting the stored data of the main control board 2 to an external receiving device;

[0007] The main control board 2 is placed in the control cavity 31 of the adapter 3. The main control board 2 includes an upper connecting plate 24 and a lower connecting plate 26 of the same shape and size. Four sockets are provided on the edges of the upper connecting plate 24 and the lower connecting plate 26 to accommodate the welding rods 25. The upper connecting plate 24 and the lower connecting plate 26 are fixed by welding rods 25 corresponding in number and size to the sockets, and are kept at a preset distance.

[0008] The edges of the upper connecting plate 24 and the lower connecting plate 26 of the main control board 2 are provided with concave fittings corresponding in position, number, and size to the convex fittings on the inner wall edge of the control cavity 31 of the adapter 3, for securing the main control board 2 in the control cavity 31 of the adapter 3. Furthermore, the outer wall of the adapter 3 is provided with an edge groove 32 communicating with the control cavity 31 for accommodating a display screen connected to the main control board 2.

[0009] The upper connecting plate 24 of the main control board 2 is provided with a sensor button switch 23, a wireless transmission antenna 22, and an LED buzzer 21. The LED buzzer 21 is cylindrical and fits into the through hole of the mating plate 12.

[0010] The lower connecting board 26 of the main control board 2 is provided with an acceleration sensor 29, an MCU 27, a local memory 28, and a LoRa wireless communication interface 210;

[0011] A battery cavity 33 is provided at the bottom of the adapter 3. The battery cavity 33 is hollow and cylindrical, and its outer wall has external threads. The inner cavity is connected to the control cavity 31 and is used to place the battery 4. The battery 4 is connected to the main control board 2.

[0012] The base 5 is hollow and cylindrical, and its inner wall has internal threads that match the external threads on the outer wall of the battery cavity 33. The adapter 3 and the base 5 are screw-fixed by the internal and external threads engaging with each other.

[0013] As a preferred technical solution of the present invention: the button switch 23, wireless transmission antenna 22, LED buzzer 21, acceleration sensor 29, MCU 27, local memory 28, LoRa wireless communication interface 210, and display screen are respectively connected to the battery 4;

[0014] The acceleration sensor 29 , the local memory 28 , the LoRa wireless communication interface 210 , the LED buzzer 21 , the wired interface 11 , and the display screen are all connected to the MCU 27 , and the LoRa wireless communication interface 210 is connected to the wireless transmission antenna 22 .

[0015] The present invention also designs an online wavelet analysis edge computing method. Based on the online wavelet analysis edge computing sensor, the following steps S1 to S3 are performed for the collected acceleration time history signal to obtain the wavelet packet component energy corresponding to the acceleration time history signal:

[0016] Step S1: Decomposing the collected acceleration time history signal by a preset number of decomposition layers to obtain wavelet packet component signals corresponding to the preset number of decomposition layers;

[0017] Step S2: Decomposing the wavelet packet into components for each wavelet packet component signal, and decomposing the wavelet packet component signal into a linear combination of wavelet packet functions and wavelet packet coefficients;

[0018] Step S3: Based on the wavelet packet component signals and the signal energy of the preset decomposition layer, the wavelet packet component energy of each component of the wavelet packet is obtained according to the wavelet packet function and its orthogonal characteristics.

[0019] As a preferred technical solution of the present invention: the specific method of step S1 is as follows:

[0020] The collected acceleration time history signal is subjected to wavelet packet level decomposition, where the recursive relationship between the j-th order and j+1-th order wavelet packet level decomposition of the acceleration time history signal is as follows:

[0021]

[0022] Where, is the wavelet packet component signal, where i is the amplitude modulation of the wavelet packet function, j is the horizontal decomposition order of the wavelet packet, k is the translation parameter of the wavelet packet function, t is the variable time, H and G correspond to the filtering operators h(k) and g(k) respectively, and their expressions are as follows:

[0023]

[0024] Where h(k) and g(k) are the integral mirror filter coefficients.

[0025] As a preferred technical solution of the present invention: the specific method of step S2 is as follows:

[0026] After j-order wavelet packet decomposition, the acceleration time history signal f(t) is expressed as follows:

[0027]

[0028] Where, is the wavelet packet component signal, and the wavelet packet component signal Represented as wavelet packet function The linear combination of is as follows:

[0029]

[0030] Wavelet packet coefficients Obtained according to the following formula:

[0031]

[0032] The wavelet packet function has the orthogonal characteristics as follows:

[0033]

[0034] As a preferred technical solution of the present invention: the specific method of step S3 is as follows:

[0035] Based on wavelet packet component signal Define its signal energy at order j As follows:

[0036]

[0037] According to the orthogonal characteristics of the wavelet packet function, the signal energy Converted into the following formula:

[0038]

[0039] In the formula is the wavelet packet component energy, that is, the component signal stored in the wavelet packet The energy in.

[0040] The present invention also provides an application method for an online wavelet analysis edge computing sensor. Based on a system consisting of two or more online wavelet analysis edge computing sensors connected in parallel, the following application steps S1 to S3 are performed, using the energy of the wavelet packet components corresponding to the acceleration time history signal as the transmission data, which is wirelessly transmitted to a computer serving as an external receiving device:

[0041] Step S1: The computer sends a window opening instruction to allow the online wavelet analysis edge computing sensor to access the network;

[0042] Step S2: The online wavelet analysis edge computing sensor sends a network access request signal through the wireless transmission antenna 22. After receiving the network access request signal, the computer assigns a network address to the online wavelet analysis edge computing sensor.

[0043] Step S3: The computer obtains the transmission data sent by the online wavelet analysis edge computing sensor and numbers the online wavelet analysis edge computing sensor;

[0044] The specific steps of step S3 are as follows:

[0045] Step S31: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0046] Step S32: The computer sends an AD instruction to the online wavelet analysis edge computing sensor;

[0047] Step S33: The online wavelet analysis edge computing sensor receives the AD instruction and sends a response message;

[0048] Step S34: After receiving the response message, the computer receives the transmission data sent by the online wavelet analysis edge computing sensor through the wireless transmission antenna 22;

[0049] Step S35: The computer selects the next online wavelet analysis edge computing sensor in the system and repeats steps S31 to S34 until all online wavelet analysis edge computing sensors in the system complete sending the transmission data.

[0050] The present invention also provides an application method for an online wavelet analysis edge computing sensor. Based on the online wavelet analysis edge computing sensor, the following application steps S1 and S2 are performed, using the energy of the wavelet packet components corresponding to the acceleration time-history signal as the transmission data, which is transmitted by wire to a computer serving as an external receiving device:

[0051] Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0052] Step S2: Online wavelet analysis edge computing sensor sends the transmission data to the computer through serial communication based on the wired interface 11.

[0053] The present invention also designs an application method of an online wavelet analysis edge computing sensor. Based on the online wavelet analysis edge computing sensor, the following application steps S1-S2 are executed, and the energy of the wavelet packet component corresponding to the acceleration time history signal is used as the transmission data, which is transmitted to a display screen connected to the main control board 2 for display:

[0054] Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0055] Step S2: obtaining energy spectrum distribution data after wavelet packet component decomposition according to the wavelet packet component energy, and displaying it on a display screen connected to the main control board 2.

[0056] As a preferred technical solution of the present invention: an upper threshold value of the wavelet packet component energy corresponding to the acceleration time history signal is preset. If the wavelet packet component energy exceeds the upper threshold value, a mark is made at the corresponding node, and an audible and visual alarm is issued through the LED buzzer 21.

[0057] Beneficial effects: Compared with the prior art, the advantages of the present invention include:

[0058] Compared with existing technologies, the advantages of this invention are: 1. It eliminates the need for subsequent data processing by personnel and enables real-time online analysis of acceleration time-history signals, enabling timely understanding of structural changes and providing an important basis for assessing the severity of structural failures. 2. By analyzing mechanical data signals, this invention identifies and characterizes transient phenomena in the time and frequency domains of mechanical signals and correlates them with structural fault characteristics, providing a predictive approach for mechanical structural fault identification and diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 2. It is a schematic diagram of the overall structure of the online wavelet analysis edge computing sensor provided by an embodiment of the present invention;

[0060] Figure 2 is a structural schematic diagram of an upper cover provided according to an embodiment of the present invention;

[0061] Figure 3 This is a structural diagram of a main control board provided according to an embodiment of the present invention;

[0062] Figure 4 is a schematic structural diagram of an adapter provided according to an embodiment of the present invention;

[0063] Figure 5 is a structural schematic diagram of a base provided according to an embodiment of the present invention;

[0064] Figure 6 2. It is a schematic diagram of the functional division of the online wavelet analysis edge computing sensor provided by an embodiment of the present invention;

[0065] Figure 7 2. It is a schematic diagram of the functional module of the online wavelet analysis edge computing sensor provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0067] An embodiment of the present invention provides an online wavelet analysis edge computing sensor, referring to Figure 1 , including an upper cover 1, a main control board 2, an adapter 3, a battery 4, and a base 5; wherein the upper cover 1 is made of plastic material, and the adapter 3 is made of stainless steel material.

[0068] Among them, reference Figure 1 、 Figure 2 、 Figure 4 The adapter seat 3 is provided with a control cavity 31 with a groove structure, and a preset number of convex fitting openings are provided on the inner wall edge of the control cavity 31 of the adapter seat 3. A fitting plate 12 is provided on one side of the upper cover 1 for aligning and fixing the upper cover 1 and the adapter seat 3. The edge shape of the fitting plate 12 is adapted to the edge shape of the control cavity 31 of the adapter seat, and the edge of the fitting plate 12 is provided with a concave fitting opening corresponding to the position, number and size of the fitting opening on the inner wall edge of the control cavity 31. The fitting plate 12 is provided with a through hole passing through the fitting plate 12 and the upper cover 1. The other side of the upper cover 1 is provided with a wired interface 11 connected to the main control board 2 for transmitting the stored data of the main control board 2 to an external receiving device;

[0069] Reference Figure 3 、 Figure 4 The main control board 2 is placed in the control cavity 31 of the adapter 3. The main control board 2 includes an upper connecting plate 24 and a lower connecting plate 26 of the same shape and size and made of aluminum alloy. Four sockets are set on the edges of the upper connecting plate 24 and the lower connecting plate 26 to adapt to the welding rods 25. The upper connecting plate 24 and the lower connecting plate 26 are fixed by the welding rods 25 corresponding to the number and size of the sockets and maintain a preset spacing distance.

[0070] The edges of the upper connecting plate 24 and the lower connecting plate 26 of the main control board 2 are provided with concave fittings corresponding in position, number and size to the convex fittings on the inner wall edge of the control cavity 31 of the adapter 3, which are used to fix the main control board 2 in the control cavity 31 of the adapter 3. In addition, the outer wall of the adapter 3 is provided with an edge groove 32 connected to the control cavity 31, which is used to place the display screen connected to the main control board 2; the display screen adopts a liquid crystal touch display with a serial interface, such as Devin DWT48270T043, as a human-machine interface to realize control operation and display, and display the monitoring signal spectrum on site.

[0071] The upper connecting plate 24 of the main control board 2 is provided with a sensor button switch 23, a wireless transmission antenna 22, and an LED buzzer 21. The LED buzzer 21 is cylindrical and fits into the through hole of the mating plate 12.

[0072] The lower connecting board 26 of the main control board 2 is equipped with an acceleration sensor 29, an MCU 27, local memory 28, and a LoRa wireless communication interface 210. Acceleration sensor 29 is an MPU6500 model, which includes a 3-axis gyroscope and a 3-axis accelerometer. Using its own digital motion processor, the MPU6500 outputs complete 9-axis attitude fusion calculation data to the data processing terminal for monitoring structural vibration and converting the detection signal into an acceleration time-history electrical signal representing the current vibration intensity. The MCU 27, which has signal acquisition and analysis capabilities, receives the acceleration time-history signal from the sensor, decomposes the collected signal into its component energy using the wavelet packet principle, and transmits the processed results to the local memory 28. The MCU 27 controls the LoRa wireless communication interface 210 to transmit the stored data to an external receiving device via the wireless transmission antenna 22, thereby obtaining the decomposed signal spectrum data. Alternatively, the MCU 27 transmits the stored data to an external receiving device via the dual interface 11 via a serial interface.

[0073] The bottom of the adapter 3 is provided with a battery cavity 33. The battery cavity 33 is hollow and cylindrical, and its outer wall has an external thread. The external thread adopts the Metric Die standard M42*4.5 thread. The internal cavity is connected to the control cavity 31 and is used to accommodate the battery 4. The battery 4 is connected to the main control board 2.

[0074] Reference Figure 5 The base 5 is a hollow cylinder made of stainless steel, and its inner wall has an internal thread that matches the external thread of the outer wall of the battery cavity 33. The adapter 3 is threadedly fixed to the base 5 through the engagement of the internal and external threads.

[0075] The button switch 23, wireless transmission antenna 22, LED buzzer 21, acceleration sensor 29, MCU 27, local memory 28, LoRa wireless communication interface 210, and display screen are respectively connected to the battery 4;

[0076] The acceleration sensor 29 , the local memory 28 , the LoRa wireless communication interface 210 , the LED buzzer 21 , the wired interface 11 , and the display screen are all connected to the MCU 27 , and the LoRa wireless communication interface 210 is connected to the wireless transmission antenna 22 .

[0077] Reference Figure 6 、 Figure 7 ,The above-mentioned online wavelet analysis edge computing sensor is divided into five modules according to ,function: power management module, signal acquisition module, data processing module, data transmission module, and display module.

[0078] The battery management module is powered by battery 4 or an external DC power supply, such as a precision DC regulated power supply such as the MS-605D power supply from Maisheng, and powers the acceleration sensor, operational amplifier, wireless transmission, local transmission, and local display through a wired interface 11, where battery 4 is a lithium battery.

[0079] The signal acquisition module includes an MPU6500 acceleration sensor 29, which monitors the vibration of the structure and converts the detection signal into an acceleration time-history electrical signal representing the current vibration intensity.

[0080] The data processing module includes an MCU 27 and a data memory 28, which provide signal acquisition and analysis capabilities. MCU 27 acquires data from an MPU6500 accelerometer 29 and performs wavelet component decomposition on the acceleration time-history signal to obtain the energy distribution of the wavelet packet components. Experimental results show that using the db3 wavelet and a wavelet packet decomposition level of 3 yields excellent results in practical engineering applications.

[0081] Waves are composed of a family of basis functions that can describe the local characteristics of a signal in the time (space) and frequency (scale) domains. The greatest advantage of using wavelet analysis is that it can perform local analysis on a signal, and can analyze the signal in any time or space domain. The wavelet packet transform is an extension of the wavelet transform, which can provide complete decomposition results of the signal at different levels. Wavelet packets are usually a series of optional basis functions formed by linear combinations of wavelet functions. Therefore, the wavelet packet transform can extract signal features synthesized by steady-state and non-steady-state signals with arbitrary time-frequency resolution.

[0082] Wavelet packets are usually composed of wavelet functions through linear combination, so they have the orthogonality and time-frequency localization characteristics of wavelet functions. A wavelet packet function It has three indices, i, j, and k, which represent the amplitude modulation of the wavelet packet function, the scale of the wavelet packet function (i.e., the order of the wavelet packet horizontal decomposition), and the translation parameter of the wavelet packet function. The specific expressions are as follows:

[0083]

[0084] Wavelet packet function Ψ i This can be solved by the following two-scale equation:

[0085]

[0086] Where φ i (t) is the scaling function, the initial wavelet function Ψ 1 (t) is called the mother wavelet function:

[0087] Ψ 1 (t) = Ψ(t)

[0088] The discrete filter coefficients h(k) and g(k) are integral mirror filter coefficients associated with the scaling function and the mother wavelet function.

[0089] An embodiment of the present invention provides an online wavelet analysis edge computing method. Based on the online wavelet analysis edge computing sensor, the following steps S1 to S3 are performed for the collected acceleration time history signal to obtain the wavelet packet component energy corresponding to the acceleration time history signal:

[0090] Performing a wavelet transform on an acceleration time-history signal decomposes it layer by layer, performing high-frequency and low-frequency decomposition at each decomposition level, and decomposing only the low-frequency portion at the next level. The wavelet packet transform, on the other hand, decomposes high-frequency and low-frequency components at each decomposition level, and simultaneously decomposes both components at the next level. Therefore, the wavelet packet transform has a high-resolution characteristic for the high-frequency portion of the signal.

[0091] Step S1: Decomposing the collected acceleration time history signal by a preset number of decomposition layers to obtain wavelet packet component signals corresponding to the preset number of decomposition layers.

[0092] The specific method of step S1 is as follows:

[0093] The collected acceleration time history signal is subjected to wavelet packet level decomposition, where the recursive relationship between the j-th order and j+1-th order wavelet packet level decomposition of the acceleration time history signal is as follows:

[0094]

[0095] Where, is the wavelet packet component signal, where i is the amplitude modulation of the wavelet packet function, j is the horizontal decomposition order of the wavelet packet, k is the translation parameter of the wavelet packet function, t is the variable time, H and G correspond to the filtering operators h(k) and g(k) respectively, and their expressions are as follows:

[0096]

[0097] Where h(k) and g(k) are the integral mirror filter coefficients.

[0098] Step S2: Decompose the wavelet packet into components for each wavelet packet component signal, and decompose the wavelet packet component signal into a linear combination of wavelet packet functions and wavelet packet coefficients.

[0099] The specific method of step S2 is as follows:

[0100] After j-order wavelet packet decomposition, the acceleration time history signal f(t) is expressed as follows:

[0101]

[0102] In the formula, is the wavelet packet component signal. The wavelet packet component signal is expressed as a linear combination of the wavelet packet function as follows:

[0103]

[0104] The wavelet packet coefficient is obtained according to the following formula:

[0105]

[0106] where the wavelet packet function has the orthogonal property as follows:

[0107]

[0108] Step S3: Based on the wavelet packet component signal and the signal energy of the preset decomposition level, according to the wavelet packet function and its orthogonal property, obtain the wavelet packet component energy of each component of the wavelet packet.

[0109] The specific method of Step S3 is as follows:

[0110] Based on the wavelet packet component signal define its signal energy at the j-th order as follows:

[0111]

[0112] According to the orthogonal property of the wavelet packet function, transform the signal energy into the following formula:

[0113]

[0114] In the formula is the wavelet packet component energy, that is, the energy stored in the wavelet packet component signal :

[0115]

[0116] It can be seen that the wavelet packet component signal is composed of the superposition of the wavelet packet function under the time-domain -∞<k<∞ transformation at the same scale j. This means that the wavelet packet component energy is the signal energy within the frequency band determined by the wavelet packet function . The expression of the signal energy can be interpreted as that the total energy of the signal can be regarded as the sum of the wavelet packet component energies corresponding to different frequency bands.

[0117] The energy spectrum distribution data after the decomposition of the wavelet packet components of the acceleration sensor can be obtained through the data processing module, and the MCU27 stores the data in the data storage 28 and the serial interface liquid crystal touch display.

[0118] The data transmission module includes a wired interface 11, a LoRa wireless communication interface 210, a wireless transmission antenna 22, and an MCU 27. The MCU 27 controls the LoRa wireless communication interface 210 to transmit stored data to an external receiving device via the wireless transmission antenna 22, thereby acquiring data after wavelet packet component decomposition. Alternatively, the MCU 27 can transmit stored data to an external receiving device via the wired interface 11 via a serial interface.

[0119] Reference Figure 7 ,The transmission modes of the signal transmission module are wireless ,transmission mode, wired transmission mode, and local transmission mode.

[0120] The specific steps of wireless transmission mode are as follows:

[0121] Based on a system consisting of two of the above-mentioned online wavelet analysis edge computing sensors connected in parallel, the following application steps S1 to S3 are executed, using the wavelet packet component energy corresponding to the acceleration time history signal as the transmission data, which is wirelessly transmitted to a computer serving as an external receiving device:

[0122] Step S1: The computer sends a window opening instruction to allow the online wavelet analysis edge computing sensor to access the network;

[0123] Step S2: The online wavelet analysis edge computing sensor sends a network access request signal through the wireless transmission antenna 22. After receiving the network access request signal, the computer assigns a network address to the online wavelet analysis edge computing sensor.

[0124] Step S3: The computer obtains the transmission data sent by the online wavelet analysis edge computing sensor and numbers the online wavelet analysis edge computing sensor.

[0125] The specific steps of the online wavelet analysis edge computing sensor sending transmission data to the computer in step S3 are as follows:

[0126] Step S31: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0127] Step S32: The computer sends an AD instruction to the online wavelet analysis edge computing sensor;

[0128] Step S33: The online wavelet analysis edge computing sensor receives the AD instruction and sends a response message;

[0129] Step S34: After receiving the response message, the computer receives the transmission data sent by the online wavelet analysis edge computing sensor through the wireless transmission antenna 22;

[0130] Step S35: The computer selects the next online wavelet analysis edge computing sensor in the system and repeats steps S31 to S34 until all online wavelet analysis edge computing sensors in the system complete sending the transmission data.

[0131] The specific steps for wired transmission mode are as follows:

[0132] Based on the online wavelet analysis edge computing sensor, the following application steps S1-S2 are executed, and the energy of the wavelet packet component corresponding to the acceleration time history signal is used as the transmission data, which is transmitted by wire to a computer serving as an external receiving device:

[0133] Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0134] Step S2: Online wavelet analysis edge computing sensor sends the transmission data to the computer through serial communication based on the wired interface 11.

[0135] The display module includes a liquid crystal touch display with a serial interface, such as the DWT48270T043. MCU27 transmits the processed voltage signal to the liquid crystal touch display, and researchers can directly determine the structural status by consulting the component energy spectrum on the display.

[0136] The specific steps of local transmission mode are as follows:

[0137] Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components based on the online wavelet analysis edge computing method, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory 28;

[0138] Step S2: obtaining energy spectrum distribution data after wavelet packet component decomposition according to the wavelet packet component energy, and displaying it on a display screen connected to the main control board 2.

[0139] In one embodiment, an upper threshold value of the wavelet packet component energy corresponding to the acceleration time history signal is preset. If the wavelet packet component energy exceeds the upper threshold value, a mark is made at the corresponding node, and an audible and visual alarm is issued through the LED buzzer 21.

[0140] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in this field without departing from the spirit of the present invention.

Claims

1. An online wavelet analysis edge computing sensor, characterized in that: It includes an upper cover (1), a main control board (2), an adapter (3), a battery (4), and a base (5); The adapter seat (3) is provided with a control cavity (31) having a groove structure, and a preset number of convex fitting openings are provided on the inner wall edge of the control cavity (31) of the adapter seat (3). A fitting plate (12) is provided on one side of the upper cover (1) for centering and fixing the upper cover (1) and the adapter seat (3). The shape of the edge of the fitting plate (12) matches the shape of the edge of the control cavity (31) of the adapter seat (3), and the edge of the fitting plate (12) is provided with concave fitting openings corresponding to the position, number and size of the fitting openings on the inner wall edge of the control cavity (31). The fitting plate (12) is provided with a through hole penetrating the fitting plate (12) and the upper cover (1). The other side of the upper cover (1) is provided with a wired interface (11) connected to the main control board (2) for transmitting the stored data of the main control board (2) to an external receiving device. The main control board (2) is placed in the control cavity (31) of the adapter (3). The main control board (2) includes an upper connecting board (24) and a lower connecting board (26) of the same shape and size. Four sockets adapted to the welding rods (25) are provided at the edges of the upper connecting board (24) and the lower connecting board (26). The upper connecting board (24) and the lower connecting board (26) are fixed by the welding rods (25) corresponding to the number and size of the sockets, and a preset spacing is maintained. The edges of the upper connecting plate (24) and the lower connecting plate (26) of the main control board (2) are provided with concave fitting openings corresponding in position, number and size to the convex fitting openings on the inner wall edge of the control cavity (31) of the adapter seat (3), for fixing the main control board (2) in the control cavity (31) of the adapter seat (3), and the outer wall of the adapter seat (3) is provided with an edge groove (32) in communication with the control cavity (31) for accommodating a display screen connected to the main control board (2); A sensor button switch (23), a wireless transmission antenna (22), and an LED buzzer (21) are provided on the upper connecting plate (24) of the main control board (2), wherein the LED buzzer (21) is cylindrical and adapted to the through hole of the fitting plate (12), and is sleeved in the through hole of the fitting plate (12); The lower connecting plate (26) of the main control board (2) is provided with an acceleration sensor (29), an MCU (27), a local memory (28), and a LoRa wireless communication interface (210); A battery cavity (33) is provided at the bottom of the adapter (3). The battery cavity (33) is hollow and cylindrical, and its outer wall has an external thread. The inner cavity is connected to the control cavity (31) and is used to accommodate the battery (4). The battery (4) is connected to the main control board (2). The base (5) is hollow and cylindrical, and its inner wall has an internal thread that matches the external thread of the outer wall of the battery cavity (33). The adapter (3) and the base (5) are screw-fixed by the internal and external threads engaging with each other. Based on the online wavelet analysis edge computing sensor, the following steps S1 to S3 are performed for the collected acceleration time history signal to obtain the wavelet packet component energy corresponding to the acceleration time history signal: Step S1: Decomposing the collected acceleration time history signal by a preset number of decomposition layers to obtain wavelet packet component signals corresponding to the preset number of decomposition layers; The specific method of step S1 is as follows: The collected acceleration time history signal is subjected to wavelet packet level decomposition, where the recursive relationship between the j-th order and j+1-th order wavelet packet level decomposition of the acceleration time history signal is as follows: Where, is the wavelet packet component signal, where i is the amplitude modulation of the wavelet packet function, j is the horizontal decomposition order of the wavelet packet, k is the translation parameter of the wavelet packet function, t is the variable time, H and G correspond to the filtering operators h(k) and g(k) respectively, and their expressions are as follows: Where h(k) and g(k) are integral mirror filter coefficients; Step S2: Decomposing the wavelet packet into components for each wavelet packet component signal, and decomposing the wavelet packet component signal into a linear combination of wavelet packet functions and wavelet packet coefficients; Step S3: Based on the wavelet packet component signals and the signal energy of the preset decomposition layer, the wavelet packet component energy of each component of the wavelet packet is obtained according to the wavelet packet function and its orthogonal characteristics; The specific method of step S3 is as follows: Based on wavelet packet component signal Define its signal energy at order j As follows: According to the orthogonal characteristics of the wavelet packet function, the signal energy Converted into the following formula: In the formula is the wavelet packet component energy, that is, the component signal stored in the wavelet packet The energy in.

2. The online wavelet analysis edge computing sensor according to claim 1, characterized in that: The button switch (23), wireless transmission antenna (22), LED buzzer (21), acceleration sensor (29), MCU (27), local memory (28), LoRa wireless communication interface (210), and display screen are all connected to the battery (4); The acceleration sensor (29), the local memory (28), the LoRa wireless communication interface (210), the LED buzzer (21), the wired interface (11), and the display screen are respectively connected to the MCU (27), and the LoRa wireless communication interface (210) is connected to the wireless transmission antenna (22).

3. The online wavelet analysis edge computing sensor according to claim 1, characterized in that: The specific method of step S2 is as follows: After j-order wavelet packet decomposition, the acceleration time history signal f(t) is expressed as follows: Where, is the wavelet packet component signal, and the wavelet packet component signal Represented as wavelet packet function The linear combination of is as follows: Wavelet packet coefficients Obtained according to the following formula: The wavelet packet function has the orthogonal characteristics as follows:

4. An application method of online wavelet analysis edge computing sensor, characterized in that: Based on a system consisting of two or more online wavelet analysis edge computing sensors as described in claim 2 connected in parallel, the following application steps S1 to S3 are performed, using the wavelet packet component energy corresponding to the acceleration time history signal as the transmission data, and wirelessly transmitting it to a computer serving as an external receiving device: Step S1: The computer sends a window opening instruction to allow the online wavelet analysis edge computing sensor to access the network; Step S2: the online wavelet analysis edge computing sensor sends a network access request signal through the wireless transmission antenna (22), and the computer allocates a network address to the online wavelet analysis edge computing sensor after receiving the network access request signal; Step S3: The computer obtains the transmission data sent by the online wavelet analysis edge computing sensor and numbers the online wavelet analysis edge computing sensor; The specific steps of step S3 are as follows: Step S31: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory (28); Step S32: The computer sends an AD instruction to the online wavelet analysis edge computing sensor; Step S33: The online wavelet analysis edge computing sensor receives the AD instruction and sends a response message; Step S34: After receiving the response message, the computer receives the transmission data sent by the online wavelet analysis edge computing sensor through the wireless transmission antenna (22); Step S35: The computer selects the next online wavelet analysis edge computing sensor in the system and repeats steps S31 to S34 until all online wavelet analysis edge computing sensors in the system complete sending the transmission data.

5. An application method of online wavelet analysis edge computing sensor, characterized in that: Based on the online wavelet analysis edge computing sensor according to claim 2, the following application steps S1-S2 are performed, and the energy of the wavelet packet component corresponding to the acceleration time history signal is used as the transmission data, which is transmitted by wire to a computer serving as an external receiving device: Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory (28); Step S2: Online wavelet analysis edge computing sensor sends the transmission data to the computer through serial communication based on the wired interface (11).

6. An application method of online wavelet analysis edge computing sensor, characterized in that: Based on the online wavelet analysis edge computing sensor as claimed in claim 2, the following application steps S1-S2 are performed, and the wavelet packet component energy corresponding to the acceleration time history signal is used as the transmission data, which is transmitted to the display screen connected to the main control board (2) for display: Step S1: The online wavelet analysis edge computing sensor decomposes the acceleration time history signal into wavelet packet components, obtains the wavelet packet component energy corresponding to the acceleration time history signal, and stores it in the local memory (28); Step S2: According to the wavelet packet component energy, energy spectrum distribution data after wavelet packet component decomposition is obtained, and displayed on a display screen connected to the main control board (2).

7. The application method of an online wavelet analysis edge computing sensor according to claim 4, 5 or 6, characterized in that: An upper threshold value of the wavelet packet component energy corresponding to the acceleration time history signal is preset. If the wavelet packet component energy exceeds the upper threshold value, a mark is made at the corresponding node, and an audible and visual alarm is made through an LED buzzer (21).

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