A variable frequency immersion ultrasonic probe

Abstract

The present invention relates to the technical field of ultrasonic probes, and more specifically, to a variable frequency water immersion ultrasonic probe, comprising: a multi-frequency piezoelectric chip assembly, arranged at the front end of the probe; a spherical focusing backing structure, adjacent to the multi-frequency piezoelectric chip assembly; an adaptive matching network, electrically connected to the multi-frequency piezoelectric chip assembly; a signal generator, electrically connected to the adaptive matching network; a signal receiving and processing unit, electrically connected to the multi-frequency piezoelectric chip assembly; a frequency selection control unit, electrically connected to the adaptive matching network, the signal generator and the signal receiving and processing unit respectively; an intelligent cooling system, arranged around the inside of the probe; a data analysis and visualization module, electrically connected to the signal receiving and processing unit; capable of identifying defects as small as 0.5 mm; a penetration depth of 150 mm in a low-frequency mode; a signal-to-noise ratio of 42 dB to ensure clear and reliable detection results; and a detection efficiency of 1.5 m <supgt;2< / supgt; / min。

Description

Technical Field

[0001] The present invention relates to the technical field of ultrasonic probes, and more particularly to a variable frequency water immersion ultrasonic probe. Background Art

[0002] With the rapid development of marine resource development and marine engineering, underwater ultrasonic testing technology has received widespread attention in recent years. As a non-destructive testing method, underwater ultrasonic testing plays a key role in the fields of marine engineering structure safety assessment, underwater pipeline integrity inspection, and ship hull inspection. However, traditional underwater ultrasonic testing technology faces many challenges and is difficult to meet the increasingly complex underwater testing needs.

[0003] Most early underwater ultrasonic probes were designed with a single frequency, such as the immersion ultrasonic probe introduced by Quirce et al. in the journal Applied Sciences (Volume 10, 2020, page 8771). Although this type of probe performs well in specific applications, it is often difficult to balance detection accuracy and penetration when faced with detection objects of different depths and materials. For example, although high-frequency probes can provide higher resolution, their penetration depth is limited; while low-frequency probes have strong penetration ability, but their resolution is low and it is difficult to detect tiny defects.

[0004] In order to overcome the limitations of single-frequency probes, some researchers have tried to develop multi-frequency probes. However, these early multi-frequency designs often simply combined probes of different frequencies and lacked an effective collaborative working mechanism. This not only increased the size and weight of the probe, but also failed to achieve intelligent switching and optimization between frequencies, resulting in low detection efficiency.

[0005] Another problem that has long plagued underwater ultrasonic testing is environmental adaptability. The underwater environment is complex and changeable, and temperature, pressure and medium characteristics will have a significant impact on the propagation of ultrasonic waves. Traditional probes often have difficulty maintaining stable performance under different water depths and different temperature conditions. For example, in deep water environments, due to increased pressure and temperature changes, the working parameters of the probes tend to drift, affecting the accuracy and reliability of the detection results.

[0006] In addition, traditional underwater ultrasonic probes also have deficiencies in signal processing and data analysis. Most probes can only provide basic A-scan or B-scan images and lack the ability to intelligently identify and classify complex defects. This makes the interpretation of test results heavily dependent on the operator's experience, which is prone to misjudgment or omission.

[0007] In the face of these challenges, a new type of ultrasonic probe with high precision and high efficiency that can be flexibly applied in different underwater environments is urgently needed. The variable frequency immersion ultrasonic probe of the present invention is designed to address the above problems. The probe achieves efficient operation at different frequencies through innovative multi-frequency piezoelectric chip design, adaptive matching network and intelligent control system, and can automatically select the best working mode according to the detection object and environmental conditions. Summary of the invention

[0008] It is in this context that the present invention is proposed, and aims to address the limitations of the prior art and provide a variable-frequency immersion ultrasonic probe to achieve multi-frequency operation in a compact probe and intelligent switching between different frequencies; to improve the adaptability of the probe in complex underwater environments and maintain stable detection performance; to improve detection accuracy and efficiency and achieve accurate identification of tiny defects; and to enhance signal processing and data analysis capabilities to provide more intuitive and reliable detection results.

[0009] The present invention provides a variable frequency water immersion ultrasonic probe, comprising: a multi-frequency piezoelectric chip assembly, arranged at the front end of the probe; a spherical focusing backing structure, adjacent to the multi-frequency piezoelectric chip assembly;

[0010] An adaptive matching network is electrically connected to the multi-frequency piezoelectric chip assembly; a signal generator is electrically connected to the adaptive matching network; a signal receiving and processing unit is electrically connected to the multi-frequency piezoelectric chip assembly; a frequency selection control unit is electrically connected to the adaptive matching network, the signal generator and the signal receiving and processing unit respectively; an intelligent cooling system is arranged around the inside of the probe; a data analysis and visualization module is electrically connected to the signal receiving and processing unit; wherein the frequency selection control unit determines the optimal operating frequency based on the characteristics of the measured object and the initial scanning results, and controls the signal generator to generate an excitation signal of the corresponding frequency, the adaptive matching network adjusts the impedance matching parameters according to the current operating frequency, the signal receiving and processing unit obtains and processes the reflected signal, and the data analysis and visualization module realizes image reconstruction based on the processed signal.

[0011] Specifically, the multi-frequency piezoelectric chip assembly includes a three-layer concentric ring structure, with piezoelectric chips of frequencies of 1MHz, 3MHz and 5MHz from the inside to the outside, wherein: the diameter of the inner ring is 8mm, the diameter of the middle ring is 14mm, and the diameter of the outer ring is 20mm; the thickness of each layer is 0.5mm, and the total thickness is 1.5mm; the piezoelectric chip adopts a composite material of PZT-5H and epoxy resin, with a volume ratio of 60:40; gold-plated electrodes are formed on the upper and lower surfaces of each layer, and the thickness of the electrode is 0.1μm; the electrodes are led out by ultrafine gold wires, and the diameter of the gold wires is 25μm; the multi-frequency piezoelectric chip assembly is connected to the adaptive matching network through a micro coaxial cable, and the model of the micro coaxial cable is UT-034-SS.

[0012] Specifically, the frequency selection control unit adopts a fuzzy logic control algorithm, which includes the following steps: obtaining the depth, density and initial signal-to-noise ratio data of the measured object; dividing it into three levels of shallow, medium and deep based on the depth data; dividing it into three levels of low, medium and high based on the density data; dividing it into three levels of poor, medium and good based on the initial signal-to-noise ratio data; setting fuzzy rules, including rules such as selecting a high frequency if the depth is shallow, the density is low and the signal-to-noise ratio is good; calculating the trigger strength of each rule based on the depth, density and initial signal-to-noise ratio data; obtaining a frequency selection result based on the weighted average of all trigger rules; defuzzifying the frequency selection result to obtain a specific operating frequency value; and outputting the operating frequency value to the signal generator.

[0013] Specifically, the adaptive matching network adopts a particle swarm optimization algorithm, which includes the following steps: obtaining the current load impedance and source impedance; initializing a particle swarm, where each particle represents a set of possible inductance and capacitance values; calculating the fitness of each particle, based on the voltage standing wave ratio formula Wherein Γ is the reflection coefficient; updating the position and speed of each particle, wherein the position represents the inductance and capacitance values, and the speed represents the rate of change of these values; repeating the steps of calculating fitness and updating the position and speed until the maximum number of iterations is reached or the stop condition is met; obtaining the optimal inductance and capacitance values; and adjusting the parameters of the adaptive matching network according to the optimal inductance and capacitance values.

[0014] Specifically, the signal receiving and processing unit adopts an adaptive filtering algorithm, which includes the following steps: obtaining an input signal x(n) and a desired signal d(n); initializing the filter weight w to a zero vector; for each time step n: based on the current weight w and the input signal x(n), calculating the filter output y(n)=w T*x(n); calculate the error e(n) = d(n) - y(n); update the filter weight w = w + 2μe(n)x(n) according to the error e(n) and the step size parameter μ; repeat the above steps until convergence or the maximum number of iterations is reached; output the final filter weight w and the filtered signal y(n).

[0015] Specifically, the data analysis and visualization module adopts a synthetic aperture focusing algorithm, which includes the following steps: obtaining the original received signal s(t) and the transducer array element position information r i ; Define the grid points p(x,y,z) of the reconstructed image; For each grid point p(x,y,z): calculate the sound path d from the point to each array element i =|pr i |; Based on the sound path d i and the speed of sound c, calculate the time delay Interpolate the original signal s(t) to obtain the delayed signal s(t-τ i ); add the delayed signals of all array elements to obtain the image value of the point I(p) = Σs(t-τ i ); repeat the above steps for all grid points; output the reconstructed image I(p).

[0016] Specifically, the intelligent cooling system adopts a PID control algorithm, which includes the following steps: obtaining a target temperature T set and the current temperature T current ; Calculate the temperature error e(t) = T set -T current ; Calculate the proportional term P(t) = K p *e(t), where K p is the proportional coefficient; calculate the integral term I(t) = K i *∫e(t)dt, where K i is the integral coefficient; calculate the differential term Where K d is the differential coefficient; the proportional term, the integral term and the differential term are added to obtain the control output u(t)=P(t)+I(t)+D(t); based on the control output u(t), the cooling power of the intelligent cooling system is adjusted; wherein the intelligent cooling system includes a temperature sensor, a micro water pump and a radiator, the temperature sensor is a PT100 model with an accuracy of ±0.1°C, the micro water pump model is TCS M400S, and the flow range is 0.4-2L / min.

[0017] Specifically, it also includes a surface protection coating, which is arranged on the surface of the multi-frequency piezoelectric chip assembly, including: a bottom layer with a thickness of 2μm, mainly composed of epoxy resin Epon 828, for providing adhesion; a middle layer with a thickness of 6μm, composed of a mixture of polytetrafluoroethylene and silica nanoparticles with a diameter of 20nm, for providing hydrophobicity and wear resistance; a top layer with a thickness of 2μm, composed of pure PTFE, for maximizing the hydrophobic effect; wherein the preparation process of the surface protection coating includes: plasma cleaning the surface of the multi-frequency piezoelectric chip assembly, spin coating the bottom layer and UV curing, spin coating the middle layer and thermal curing, spin coating the top layer and finally thermal curing.

[0018] Specifically, the spherical focusing backing structure is made of a composite material of a polyurethane matrix and an alumina filler, wherein: the Shore hardness of the polyurethane matrix is ​​60A; the particle size of the alumina filler is 1-5μm, and the filling ratio is 30%; the spherical radius of the spherical focusing backing structure is 25mm, the center thickness is 8mm, the edge thickness is 3mm, and the total diameter is 22mm; the spherical focusing backing structure is made by a 3D printed photosensitive resin mold, and is completed by vacuum degassing, injection molding, pressurization and heat treatment processes.

[0019] Specifically, it also includes a probe shell, which is made of 3D printed titanium alloy Ti-6Al-4V, wherein: the total length of the probe shell is 80mm, the outer diameter is 25mm, and the wall thickness is 1mm; an acoustic transparent window is provided at the front end of the probe shell, which is made of polyvinylidene fluoride (PVDF) material with a thickness of 0.1mm; a sealed interface panel is provided at the rear of the probe shell, which includes a power interface, a data interface and a coolant interface; an isolation cavity is provided inside the probe shell for separating electronic components and coolant paths; the probe shell is sealed with an O-ring, the O-ring material is fluororubber, the inner diameter is 20mm, and the wire diameter is 1.5mm; all external interfaces of the probe shell adopt an IP68 waterproof design, and the cable outlet is double-sealed with a heat shrink tube and epoxy resin.

[0020] The present invention successfully solves the above problems by integrating multiple innovative technologies. Its core innovations include multi-frequency piezoelectric chip components, adaptive matching networks, intelligent frequency selection algorithms, advanced signal processing units, and intelligent cooling systems. These innovations not only enable the probe to operate in a wide frequency range of 0.5-5MHz, but also automatically select the optimal frequency according to the detection requirements. At the same time, the probe's adaptive matching network and intelligent cooling system ensure stable performance under different water depths and temperature conditions.

[0021] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0022] 1. High detection accuracy: It can identify defects as small as 0.5mm, which is 2-4 times higher than traditional probes;

[0023] 2. Strong penetration ability: the penetration depth can reach 150mm in low-frequency mode, meeting the needs of deep detection;

[0024] 3. High signal-to-noise ratio: 42dB signal-to-noise ratio ensures clear and reliable detection results;

[0025] 4. High efficiency: detection efficiency reaches 1.5m 2 / min, 2-3 times that of traditional probes;

[0026] 5. Strong environmental adaptability: can work stably in the water depth range of 0-300m, and the temperature drift is controlled within ±0.3℃;

[0027] 6. High degree of intelligence: It can automatically select the best operating frequency and perform real-time signal processing and defect identification.

[0028] In conclusion, the frequency-converting immersion ultrasonic probe of the present invention represents a major breakthrough in underwater ultrasonic detection technology. It not only solves many technical problems faced by traditional probes, but also greatly expands the application scope and possibilities of underwater ultrasonic detection. The development of this technology will provide strong technical support for the fields of marine engineering safety, underwater resource exploration, and marine environmental protection, and has broad application prospects and important practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is the overall structural block diagram of the present invention.

[0030] Figure 2 It is the overall logic block diagram of the system of the present invention.

[0031] Figure 3 It is a logic block diagram of the multi-frequency piezoelectric chip assembly of the present invention.

[0032] Figure 4 It is a logic block diagram of the adaptive matching network of the present invention.

[0033] Figure 5 It is the logic block diagram of the intelligent frequency selection algorithm of the present invention.

[0034] Figure 6 It is a logic block diagram of the signal processing unit of the present invention.

[0035] Figure 7 It is a logic block diagram of the intelligent cooling system of the present invention.

[0036] Figure 8 It is a performance bar chart comparison of the present invention and comparative examples 1-3.

[0037] Fig. 9 It is a multi-dimensional performance waveform diagram of the present invention and comparative examples 1-3. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects thereof are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0040] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0041] The present invention relates to the field of ultrasonic detection technology, and in particular to a variable frequency ultrasonic probe for underwater detection. The probe can work at different frequencies and is suitable for a variety of underwater detection scenarios, such as marine engineering structure detection, underwater pipeline detection, ship hull detection, etc.

[0042] like Figure 1-9 As shown, the frequency-converting water-immersion ultrasonic probe of the present invention is mainly composed of the following parts: a multi-frequency piezoelectric chip assembly 1, a spherical focusing backing structure 2, an adaptive matching network 3, a signal generator 4, a signal receiving and processing unit 5, a frequency selection control unit 6, an intelligent cooling system 7, a data analysis and visualization module 8, and a probe housing 9. These components are integrated together in a compact and efficient manner to form a complete ultrasonic detection system.

[0043] The overall structure of the probe adopts a cylindrical design, with a length of 80mm and a diameter of 25mm. From front to back, the layout and connection relationship of each component are as follows: the front end is the multi-frequency piezoelectric chip assembly 1, which is in direct contact with the medium to be measured. It is composed of three layers of concentric ring-shaped piezoelectric chips, corresponding to the operating frequencies of 1MHz, 3MHz and 5MHz respectively. Close to the back of the multi-frequency piezoelectric chip assembly 1 is the spherical focusing backing structure 2. This structure not only provides mechanical support, but also realizes the natural focusing of sound waves. About 5mm behind the spherical focusing backing structure 2, an adaptive matching network 3 is installed. This network achieves the best impedance matching at different frequencies by dynamically adjusting the capacitance and inductance values. Following closely are the signal generator 4 and the signal receiving and processing unit 5. These two units are connected by a high-speed digital bus to ensure the precise control of the transmitted signal and the real-time processing of the received signal. The frequency selection control unit 6 adopts a flexible PCB design, surrounds the middle of the probe, and is connected to all other electronic units to play a role in central control and coordination. The intelligent cooling system 7 is distributed throughout the internal space of the probe. It includes a micro water channel surrounding the inner wall, a micro water pump located at the tail of the probe, and temperature sensors distributed around each key component. The data analysis and visualization module 8 is located at the tail of the probe. It is connected to the signal receiving and processing unit 5 through a high-speed interface and is responsible for the final data processing and image reconstruction. All of the above components are encapsulated in the probe housing 9. An acoustically transparent window is provided at the front end of the housing, which is made of polyvinylidene fluoride (PVDF) material. A sealed interface panel is provided at the tail of the housing, including a power interface, a data interface, and a coolant interface.

[0044] This compact and fully functional design enables the probe to be used flexibly in various underwater environments. The various components inside the probe are tightly integrated through a carefully designed mechanical structure and electrical connections, achieving multi-frequency, adaptive, and intelligent ultrasonic detection.

[0045] Next, we will introduce in detail the specific structure, working principle and key technology of each component, and how they work together to achieve efficient and accurate underwater ultrasonic testing.

[0046] First of all, the multi-frequency piezoelectric chip assembly 1 is the core component of this probe. The assembly adopts a three-layer concentric ring structure, with piezoelectric chips of 1MHz, 3MHz and 5MHz frequencies from the inside to the outside. This design allows the probe to work at different frequencies to meet different detection needs. For example, when detecting thicker steel structures, a 1MHz frequency can be used to obtain better penetration; when detecting thin-walled pipes, a 5MHz frequency can be switched to obtain higher resolution.

[0047] Preferably, in one embodiment of the present invention, the inner ring diameter is 8 mm, the middle ring diameter is 14 mm, and the outer ring diameter is 20 mm. The thickness of each layer is 0.5 mm, and the total thickness is 1.5 mm. The piezoelectric chip adopts a composite material of PZT-5H and epoxy resin, with a volume ratio of 60:40. This material combination can improve the mechanical strength of the chip while ensuring good piezoelectric performance, making it more suitable for application in underwater environments.

[0048] Next, in order to protect the piezoelectric chip and improve its performance in water, we designed a special protective coating on the surface of the chip. The coating consists of three sublayers: bottom layer, middle layer and top layer. The bottom layer is 2μm thick and mainly consists of epoxy resin Epon 828 to provide good adhesion. The middle layer is 6μm thick and consists of polytetrafluoroethylene (PTFE) mixed with silica nanoparticles with a diameter of 20nm to provide hydrophobicity and wear resistance. The top layer is 2μm thick and consists of pure PTFE to maximize the hydrophobic effect. This multi-layer structure design can effectively prevent water penetration while ensuring good transmission of sound waves.

[0049] On the back of the piezoelectric chip assembly, we designed a spherical focusing backing structure 2. The structure is made of a composite material of a polyurethane matrix and an alumina filler. The shore hardness of the polyurethane matrix is ​​60A, the particle size of the alumina filler is 1-5μm, and the filling ratio is 30%. This material combination can ensure the mechanical strength of the structure while providing good acoustic attenuation performance. The spherical radius of the spherical focusing backing structure is 25mm, the center thickness is 8mm, the edge thickness is 3mm, and the total diameter is 22mm. This spherical design can achieve natural focusing of sound waves and improve the detection sensitivity of the probe.

[0050] In order to achieve multi-frequency operation, the present invention designs an adaptive matching network 3. The network uses a particle swarm optimization algorithm to dynamically adjust impedance matching parameters. The specific steps are as follows:

[0051] Step S1: obtaining the current load impedance and source impedance;

[0052] Step S2: Initialize a particle swarm, where each particle represents a set of possible inductance and capacitance values;

[0053] Step S3: Calculate the fitness of each particle based on the voltage standing wave ratio formula Where Γ is the reflection coefficient;

[0054] Step S4: Update the position and velocity of each particle;

[0055] Step S5: Repeat steps S3 and S4 until the maximum number of iterations is reached or the stop condition is met;

[0056] Step S6: Obtain optimal inductance and capacitance values, and adjust matching network parameters accordingly.

[0057] This adaptive matching method can maintain good impedance matching at different frequencies, thereby improving the energy conversion efficiency of the probe.

[0058] Signal generator 4 is responsible for generating the excitation signal. It uses direct digital synthesis (DDS) technology to generate high-precision sine wave signals. In practical applications, we usually use chirp signals to improve the signal-to-noise ratio of detection. The generation algorithm of chirp signals is as follows:

[0059] def chirp_signal(f0,f1,duration,sampling_rate):

[0060] t=np.linspace(0,duration,int(duration*sampling_rate),endpoint=False)

[0061] k=(f1-f0) / duration

[0062] phase=2*np.pi*(f0*t+0.5*k*t**2)

[0063] return np.sin(phase)

[0064] Among them, f0 is the starting frequency, f1 is the ending frequency, duration is the signal duration, and sampling_rate is the sampling rate. By adjusting these parameters, we can generate chirp signals with different characteristics to meet different detection requirements.

[0065] The signal receiving and processing unit 5 uses an adaptive filtering algorithm to improve the quality of the received signal. The specific steps of the algorithm are as follows:

[0066] Step S1: Obtain an input signal x(n) and a desired signal d(n);

[0067] Step S2: Initialize the filter weight w to a zero vector;

[0068] Step S3: For each time step n, perform the following operations:

[0069] a) Calculate the filter output y(n) = w T *x(n);

[0070] b) Calculate the error e(n) = d(n) - y(n);

[0071] c) Update the filter weight w = w + 2μe(n)x(n), where μ is the step size parameter;

[0072] Step S4: Repeat step S3 until convergence or the maximum number of iterations is reached;

[0073] Step S5: Output the final filter weight w and the filtered signal y(n).

[0074] In practical applications, the choice of the step size parameter μ is very important. Usually, we will choose a small value, such as 0.01, to ensure the stability of the algorithm. At the same time, the maximum number of iterations is usually set to 2-3 times the signal length to ensure sufficient convergence.

[0075] The frequency selection control unit 6 uses a fuzzy logic control algorithm to automatically select the optimal operating frequency. The algorithm takes into account factors such as the depth, density and initial signal-to-noise ratio of the measured object. The specific steps are as follows:

[0076] Step S1: obtaining the depth, density and initial signal-to-noise ratio data of the measurement object;

[0077] Step S2: Divide the depth data into three levels: shallow, medium, and deep;

[0078] Step S3: Divide the density data into three levels: low, medium and high;

[0079] Step S4: Divide the initial signal-to-noise ratio data into three levels: poor, medium, and good;

[0080] Step S5: setting fuzzy rules, for example, if the depth is shallow and the density is low and the signal-to-noise ratio is good, then select high frequency, etc.;

[0081] Step S6: Calculate the trigger strength of each rule according to the input data;

[0082] Step S7: Obtain a frequency selection result based on the weighted average of all trigger rules;

[0083] Step S8: Defuzzify the frequency selection result to obtain a specific operating frequency value.

[0084] In practical applications, we usually set 20-30 fuzzy rules to cover various possible situations. For example, for underwater pipeline detection, we may set the following rules:

[0085] If the depth is shallow (<5m) and the density is low (<1g / cm 3 ) and the signal-to-noise ratio is good (>20dB), then choose a high frequency (5MHz);

[0086] If the depth is medium (5-20m) and the density is medium (1-3g / cm 3) and the signal-to-noise ratio is medium (10-20dB), then choose the medium frequency (3MHz);

[0087] If the depth is deep (>20m) and the density is high (>3g / cm 3 ) and the signal-to-noise ratio is poor (<10dB), select a low frequency (1MHz).

[0088] This adaptive frequency selection mechanism can automatically select the optimal operating frequency according to the actual detection environment, greatly improving the adaptability and detection effect of the probe.

[0089] In order to ensure the stability of the probe during long-term operation, we designed an intelligent cooling system7. The system uses a PID (proportional-integral-differential) control algorithm to accurately adjust the operating temperature of the probe. The specific steps are as follows:

[0090] Step S1: Obtain target temperature T set and the current temperature T current ;

[0091] Step S2: Calculate temperature error e(t) = T set -T current ;

[0092] Step S3: Calculate the proportional term P(t)=K p ·e(t);

[0093] Step S4: Calculate the integral term I(t)=K i ·∫e(t)dt;

[0094] Step S5: Calculate the differential term

[0095] Step S6: Calculate the control output u(t)=P(t)+I(t)+D(t);

[0096] Step S7: Adjust the cooling power according to the control output u(t).

[0097] In practical applications, the selection of PID parameters is very important. Through a large number of experiments, we found that for this probe, K p =2.0,K i =0.1,K d = 0.05 provides the best temperature control effect. This set of parameters can ensure fast response while avoiding excessive temperature fluctuations.

[0098] The intelligent cooling system uses a PT100 temperature sensor with an accuracy of ±0.1°C, which can accurately monitor the working temperature of the probe. The micro water pump uses the TCS M400S model with a flow range of 0.4-2L / min, and can flexibly adjust the cooling water flow as needed. This design ensures that the probe can maintain the optimal working temperature in various working environments, thereby ensuring the stability and reliability of the test results.

[0099] The data analysis and visualization module 8 uses the synthetic aperture focusing algorithm (SAFT) to achieve high-resolution image reconstruction. The specific steps of the algorithm are as follows:

[0100] Step S1: Obtain the original received signal s(t) and the transducer array element position information r i ;

[0101] Step S2: define the grid points p(x, y, z) of the reconstructed image;

[0102] Step S3: For each grid point p(x,y,z), perform the following operations:

[0103] a) Calculate the acoustic path d from this point to each array element i =|pr i |;

[0104] b) Based on sound path d i and the speed of sound c, calculate the time delay τ i =d i / c;

[0105] c) Interpolate the original signal s(t) to obtain the delayed signal s(t-τ i );

[0106] d) Add the delayed signals of all array elements to obtain the image value of the point I(p) = ∑s(t-τ i ); Step S4: Repeat step S3 for all grid points;

[0107] Step S5: Output the reconstructed image I(p).

[0108] In practical applications, in order to improve the efficiency of image reconstruction, we usually use GPU acceleration technology. For example, using NVIDIA's CUDA platform, the running time of the SAFT algorithm can be shortened to about 1 / 10 of the original, thus achieving real-time image reconstruction and display.

[0109] Finally, the entire probe is encapsulated in a carefully designed housing. The probe housing is made of 3D printed titanium alloy Ti-6Al-4V, which has good strength and corrosion resistance. The total length of the housing is 80mm, the outer diameter is 25mm, and the wall thickness is 1mm. This compact design allows the probe to be easily installed on various underwater detection equipment, such as ROV (remotely operated vehicle) or AUV (autonomous underwater vehicle).

[0110] The front end of the probe housing is equipped with an acoustically transparent window, which is made of polyvinylidene fluoride (PVDF) material with a thickness of 0.1 mm. PVDF material has good acoustic transparency and water resistance, which can effectively protect the internal piezoelectric chip components while ensuring good transmission of sound waves.

[0111] In order to ensure the sealing performance of the probe in deep water environment, we adopt multiple sealing measures. First, a fluororubber O-ring is used for the main seal, the inner diameter of the O-ring is 20mm, and the wire diameter is 1.5mm. Secondly, all external interfaces adopt IP68 waterproof design. Finally, the cable outlet is double-sealed with heat shrink tubing and epoxy resin. This multiple sealing design ensures that the probe can work stably in an environment with a water depth of 300 meters.

[0112] In practical applications, the variable frequency immersion ultrasonic probe of the present invention has shown excellent performance. For example, in an underwater pipeline inspection project of an offshore oil field, the probe can perform all-round inspection of various pipelines with a diameter of 50-500 mm within a water depth range of 5-50 meters. Through the automatic frequency selection function, the probe can automatically select the optimal operating frequency according to the pipeline material and wall thickness, thereby realizing accurate detection of various defects such as cracks, corrosion, inclusions, etc. The detection accuracy can reach 0.1mm, which greatly improves the detection efficiency and reliability of underwater pipelines.

[0113] In summary, the frequency-converting immersion ultrasonic probe of the present invention realizes efficient and accurate detection in different underwater environments and detection objects through innovative multi-frequency piezoelectric chip design, adaptive matching network, intelligent frequency selection and advanced signal processing algorithm. The intelligent cooling system and carefully designed sealing structure ensure the stable operation of the probe in a long-term and deep-water environment. These characteristics make the probe have broad application prospects in many fields such as marine engineering, ship detection, and underwater archaeology.

[0114] Below, we will further explain in detail some key technical points of the present invention and their implementation methods.

[0115] First, let's talk about the manufacturing process of the multi-frequency piezoelectric chip assembly. In order to ensure the consistency and stability of the three-layer piezoelectric chip, we use precision cutting and lamination technology. The specific steps are as follows:

[0116] Step S1: Mix the PZT-5H piezoelectric ceramic sheet and epoxy resin in a volume ratio of 60:40 and stir them thoroughly in a vacuum environment;

[0117] Step S2: inject the mixture into a mold and cure at 80°C for 12 hours;

[0118] Step S3: using a precision cutting machine to cut the solidified material into a desired ring shape;

[0119] Step S4: evaporating 0.1 μm thick gold electrodes on the upper and lower surfaces of each ring layer;

[0120] Step S5: accurately aligning and bonding the three layers of rings;

[0121] Step S6: Polarizing at 120° C. for 24 hours to form the final multi-frequency piezoelectric chip assembly.

[0122] This manufacturing process can ensure the consistency and stability of the piezoelectric chip and is the key to achieving high-performance multi-frequency detection.

[0123] Secondly, regarding the specific implementation of the adaptive matching network, we use a programmable capacitor and inductor array to achieve dynamic adjustment of impedance. The specific structure is as follows:

[0124] 1. Variable capacitor array: It consists of 8 variable capacitors connected in parallel. The adjustment range of each capacitor is 1-100pF and is controlled by a digital potentiometer.

[0125] 2. Variable inductor array: It consists of 4 variable inductors connected in series. The adjustment range of each inductor is 0.1-250nH, which is also controlled by a digital potentiometer;

[0126] 3. Control unit: It uses ATmega328P microcontroller, which is responsible for executing the particle swarm optimization algorithm and controlling the digital potentiometer.

[0127] This design allows us to achieve precise impedance matching within the frequency range of 0.5-10MHz, with a matching accuracy of up to 0.1dB.

[0128] Again, regarding the specific implementation of the intelligent cooling system. In addition to the PID control algorithm mentioned above, we also adopted an adaptive control strategy to cope with different working environments. Specifically:

[0129] 1. In shallow water environment (<10m), the system mainly relies on natural convection of the surrounding water to dissipate heat. At this time, PID control mainly adjusts the flow rate of the micro water pump;

[0130] 2. At medium water depth (10-100m), the system will start the internal micro Peltier cooler, and PID control will adjust the water pump flow and Peltier cooler power;

[0131] 3. In deep water environment (>100m), due to the low ambient temperature, the system mainly works in heating mode, and PID control adjusts the power of the internal heater.

[0132] This adaptive cooling strategy maintains optimal operating temperature at all water depths while maximizing energy savings.

[0133] Finally, regarding the implementation of the data analysis and visualization module, in addition to the SAFT algorithm mentioned above, we have also integrated a variety of advanced signal processing and image enhancement algorithms, including:

[0134] 1. Adaptive Time Gain Control (ATGC): used to compensate for the attenuation of sound waves during propagation and improve the detection capability of deep defects;

[0135] 2. Piecewise linear super-resolution algorithm: used to improve image resolution, especially in pipeline inner wall detection, the resolution can be increased to twice the original;

[0136] 3. Deep learning-based defect recognition algorithm: Use pre-trained convolutional neural networks to automatically identify and classify various types of defects, such as cracks, corrosion, inclusions, etc.

[0137] The combined use of these algorithms has greatly improved the accuracy and efficiency of detection. For example, in a submarine pipeline inspection, we were able to complete a comprehensive inspection of a 100-meter-long pipeline within 30 minutes, with a defect detection rate of 98% and a false positive rate of less than 1%.

[0138] In practical applications, the variable frequency immersion ultrasonic probe of the present invention can also be customized according to specific needs. For example, for large ship hull detection, we can array the probes to form a large-area detection system composed of multiple probe units. Each probe unit can work independently, but can also work together through a central control system, thereby achieving fast and large-area hull integrity detection.

[0139] Another application example is in the field of underwater archaeology. By integrating this probe into a small AUV, archaeologists can perform high-precision three-dimensional imaging of underwater cultural relics without destroying the site. The multi-frequency characteristics of the probe enable it to simultaneously obtain the fine structure of the surface of the cultural relic and the general outline of the interior, providing valuable data support for underwater archaeological work.

[0140] In general, the frequency-converting immersion ultrasonic probe of the present invention realizes high efficiency, accuracy and flexibility of underwater ultrasonic detection through the organic combination of multiple innovative technologies. It is not only widely used in the traditional industrial detection field, but also can play an important role in emerging fields such as marine scientific research and environmental monitoring, providing strong technical support for humans to better understand and utilize marine resources.

[0141] In order to verify the performance of the variable frequency immersion ultrasonic probe of the present invention, we conducted a series of comprehensive tests. These tests are designed to evaluate the performance of the probe under different operating frequencies, different water depths and different detection objects, so as to fully demonstrate its advantages of multi-frequency, self-adaptation and intelligence.

[0142] First, we selected three typical underwater inspection scenarios: shallow water pipeline inspection, medium water depth ship hull inspection, and deep water oil and gas platform inspection. In each scenario, we tested the key indicators of the probe, such as resolution, penetration depth, signal-to-noise ratio, and inspection efficiency.

[0143] In shallow water pipeline inspection, we simulated a 5-meter water depth environment and inspected a carbon steel pipeline with a diameter of 200 mm and a wall thickness of 10 mm. In this case, the probe mainly operates at a frequency of 5 MHz to obtain the best surface resolution. The test results show that the probe can clearly identify a 0.5 mm surface crack, which is much better than the traditional single-frequency probe with a resolution of about 1 mm.

[0144] For medium-depth hull inspection, we conducted tests in a 30-meter water depth environment, with steel plates varying in thickness from 20mm to 50mm. In this case, the probe automatically selected a 3MHz operating frequency, which ensured sufficient penetration depth while still maintaining a high resolution. The test results showed that the probe could accurately detect internal defects up to 40mm deep while maintaining a resolution of 1mm, which is of great significance in hull inspection.

[0145] In the deepwater oil and gas platform inspection, we simulated an extreme environment of 200 meters deep water. The inspection object was a high-strength steel structure with a thickness of 100mm. In this case, the probe automatically switched to 1MHz frequency to obtain maximum penetration. Even in such a harsh environment, the probe can still detect internal defects of 80mm depth, which is crucial to ensure the structural safety of the deepwater platform.

[0146] In addition to these scenario tests, we also conducted various performance index tests on the probe. The test results are summarized in the following table:

[0147] Performance Indicators 1MHz 3MHz 5MHz Axial resolution 2mm 0.8mm 0.5mm Horizontal resolution 3mm 1.5mm 1mm Maximum penetration depth (steel) 200mm 80mm 40mm Signal-to-Noise Ratio 35dB 40dB 45dB Working depth 0-300m 0-300m 0-300m Temperature stability ±0.5℃ ±0.5℃ ±0.5℃ Detection efficiency <![CDATA[0.5m 2 / min]]> <![CDATA[1m 2 / min]]> <![CDATA[2m 2 / min]]>

[0148] These test results fully demonstrate the excellent performance of the probe of the present invention. First, in terms of resolution, the 0.5mm axial resolution at 5MHz frequency has reached the industry-leading level, thanks to our innovative multi-frequency piezoelectric chip design and advanced signal processing algorithms. This high resolution enables the probe to accurately identify tiny defects, greatly improving the accuracy of detection.

[0149] Secondly, the probe has excellent penetration capability at different frequencies. In particular, at 1MHz, the maximum penetration depth of 200mm provides strong support for deep-water structure detection. This deep penetration capability comes from our adaptive matching network and intelligent frequency selection algorithm, which can automatically adjust the working parameters according to the characteristics of the detection object to achieve optimal energy transmission.

[0150] Another thing worth noting is the signal-to-noise ratio of the probe. At all frequencies, the probe maintains a high signal-to-noise ratio of more than 35dB, which means it can provide clear and reliable detection results in complex underwater environments. This is mainly due to the adaptive filtering algorithm and high-performance signal processing unit we use.

[0151] The probe has an operating depth range of 0-300m, covering most underwater detection scenarios. In such a wide water depth range, the probe can still maintain a temperature stability of ±0.5℃, which fully demonstrates the effectiveness of our intelligent cooling system. The stable operating temperature not only ensures the consistency of the detection results, but also greatly extends the service life of the probe.

[0152] Finally, the probe also performs well in terms of detection efficiency. In 5MHz high-frequency mode, it can detect an area of ​​2 square meters per minute, which is nearly double the efficiency of traditional single-frequency probes. This high efficiency comes from our multi-frequency design and intelligent control system, which can quickly switch the best working mode according to different detection objects.

[0153] In general, these test results fully prove that the multiple innovations of the probe of the present invention, such as multi-frequency piezoelectric chip design, adaptive matching network, intelligent frequency selection algorithm, etc., have played a significant role in practical applications. It not only reaches the leading level in key indicators such as resolution, penetration ability and signal-to-noise ratio, but also shows excellent adaptability and efficiency. This enables the probe to provide high-quality detection results in various complex underwater environments, providing strong technical support for underwater structure safety, marine engineering and underwater archaeology.

[0154] In order to comprehensively evaluate the performance advantages of the variable frequency immersion ultrasonic probe of the present invention, we designed a set of comparative experiments, which includes one embodiment of the present invention and three comparative examples, aiming to verify the innovation and superiority of the probe from multiple angles.

[0155] Example 1 adopts all technical solutions of the present invention, including core innovations such as multi-frequency piezoelectric chip assembly, adaptive matching network, intelligent frequency selection algorithm, etc. Comparative Example 1 uses a traditional single-frequency ultrasonic probe, Comparative Example 2 adopts a multi-frequency design but without an adaptive matching network, and Comparative Example 3 is a commercial underwater ultrasonic probe.

[0156] We chose a comprehensive test environment that simulates actual application scenarios. The test object is a steel cylinder with a length of 5 meters and a diameter of 1 meter. Various types of defects are artificially created inside, including surface cracks, internal pores and inclusions. The test environment has a water depth of 50 meters and a water temperature of 20°C.

[0157] During the test, we focused on the following key indicators: detection accuracy, penetration depth, signal-to-noise ratio, detection efficiency, and environmental adaptability. Each probe was tested repeatedly to ensure the reliability of the results. The test results are shown in the following table:

[0158]

[0159]

[0160] It can be clearly seen from the test results that Example 1 of the present invention shows obvious advantages in all indicators.

[0161] For a more intuitive comparison, please refer to Figure 8 The ultrasonic probe performance comparison chart shows the comparison of Example 1 with three comparative examples on six key performance indicators. These indicators include minimum detectable defect, maximum penetration depth, signal-to-noise ratio, detection efficiency, temperature drift and frequency range. Example 1 performs well in all indicators, especially in detection accuracy, penetration depth and signal-to-noise ratio.

[0162] Please refer to Fig. 9 , the horizontal axis represents six key performance indicators: signal-to-noise ratio, detection efficiency, temperature drift, frequency range, minimum detectable defect, and maximum penetration depth. The vertical axis represents the performance level, with better performance at the top. Each probe is represented by a curve of a unique color. The peaks and troughs of the curve reflect the performance on each indicator. The width of the waveform indicates the importance or weight of the indicator. The curve of the present invention is located at the top as a whole, and the waveform is relatively smooth, indicating excellent performance in all indicators. The curve of comparative example 1 has a significant drop in frequency range and temperature drift. The curve of comparative example 2 is lower than that of the present invention as a whole, but higher than that of comparative example 1. The curve of comparative example 3 is close to the present invention in some indicators, but the overall performance is not as stable as that of the present invention.

[0163] First, in terms of detection accuracy, Example 1 can detect defects as small as 0.5 mm, which is at least twice as high as other comparative examples. This high accuracy is due to our innovative multi-frequency piezoelectric chip design and advanced signal processing algorithms. Especially in the high-frequency (5MHz) working mode, the probe can clearly identify sub-millimeter surface cracks, which is crucial for early defect detection.

[0164] Secondly, in terms of penetration depth, Example 1 reaches 150mm, far exceeding other comparative examples. This is mainly due to our adaptive matching network and intelligent frequency selection algorithm. When deep detection is required, the system automatically switches to low-frequency mode and optimizes matching parameters to achieve maximum energy transmission and reception. This adaptive capability enables the probe to maintain excellent performance in a variety of complex detection objects.

[0165] In terms of signal-to-noise ratio, the 42dB of Example 1 is also far ahead. A high signal-to-noise ratio means clearer images and more reliable detection results. This is mainly due to the adaptive filtering algorithm and high-performance signal processing unit we use. Even in a water depth of 50 meters, the probe can still maintain excellent signal quality, which is particularly important for detection in complex underwater environments.

[0166] Detection efficiency is another outstanding advantage. The detection efficiency of Example 1 reaches 1.5m 2 / min, which is three times that of traditional single-frequency probes. This high efficiency comes not only from the multi-frequency design, but more importantly from our intelligent control system. The system can adjust the working mode in real time according to the characteristics of the detection object, always maintaining the best detection state, thereby greatly improving work efficiency.

[0167] The test results of temperature stability are also impressive. The temperature drift of Example 1 is only ±0.3℃, which is much better than other comparative examples. This is due to our innovative intelligent cooling system, which can adjust the cooling strategy in real time according to the working environment and load. The stable working temperature not only ensures the consistency of the test results, but also greatly extends the service life of the probe.

[0168] Finally, the wide frequency range of 1-5MHz of Example 1 provides great flexibility for various inspection needs. Whether it is surface inspection requiring high resolution or internal inspection requiring deep penetration, this probe can easily handle it. This versatility greatly expands the application range of the probe, making it a truly universal underwater inspection tool.

[0169] In summary, this set of comparative experiments fully demonstrates the significant advantages of the present invention in multiple key indicators. Example 1 not only performs well in traditional indicators such as detection accuracy, penetration depth and signal-to-noise ratio, but more importantly, it demonstrates excellent environmental adaptability and operating efficiency. These advantages are directly derived from the core innovations of the present invention, such as multi-frequency piezoelectric chip design, adaptive matching network and intelligent control system.

[0170] Such comprehensive performance enables this probe to provide high-quality and efficient detection services in various complex underwater environments. Whether it is marine engineering, ship detection or underwater archaeology, this probe can provide unprecedented detection capabilities. We have reason to believe that this technology will bring revolutionary changes to the field of underwater detection and provide strong technical support for the development and protection of marine resources.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A variable frequency immersion ultrasonic probe, characterized in that: include: A multi-frequency piezoelectric chip assembly is arranged at the front end of the probe; a spherical focusing backing structure adjacent to the multi-frequency piezoelectric wafer assembly; an adaptive matching network electrically connected to the multi-frequency piezoelectric chip assembly; A signal generator is electrically connected to the adaptive matching network; a signal receiving and processing unit is electrically connected to the multi-frequency piezoelectric chip assembly; a frequency selection control unit is electrically connected to the adaptive matching network, the signal generator and the signal receiving and processing unit respectively; an intelligent cooling system is arranged around the inside of the probe; a data analysis and visualization module is electrically connected to the signal receiving and processing unit; wherein the frequency selection control unit determines the optimal operating frequency based on the characteristics of the measurement object and the initial scanning results, and controls the signal generator to generate an excitation signal of the corresponding frequency, the adaptive matching network adjusts the impedance matching parameters according to the current operating frequency, the signal receiving and processing unit obtains and processes the reflected signal, and the data analysis and visualization module realizes image reconstruction based on the processed signal; The multi-frequency piezoelectric chip assembly includes a three-layer concentric ring structure, and the piezoelectric chips with frequencies of 1MHz, 3MHz and 5MHz from the inside to the outside are respectively, wherein: the inner ring diameter is 8mm, the middle ring diameter is 14mm, and the outer ring diameter is 20mm; the thickness of each layer is 0.5mm, and the total thickness is 1.5mm; the piezoelectric chip is made of a composite material of PZT-5H and epoxy resin, with a volume ratio of 60:40; the upper and lower surfaces of each layer are gold-plated electrodes, and the thickness of the electrodes is 0.1μm; the electrodes are led out by ultra-fine gold wires, and the diameter of the gold wires is 25μm; the multi-frequency piezoelectric chip assembly is connected to the adaptive matching network through a micro coaxial cable, and the model of the micro coaxial cable is UT-034-SS; The frequency selection control unit adopts a fuzzy logic control algorithm, which includes the following steps: obtaining the depth, density and initial signal-to-noise ratio data of the measured object; dividing the depth data into three levels: shallow, medium and deep; dividing the density data into three levels: low, medium and high; dividing the initial signal-to-noise ratio data into three levels: poor, medium and good; setting fuzzy rules, including selecting a high frequency rule if the depth is shallow, the density is low and the signal-to-noise ratio is good; calculating the trigger strength of each rule based on the depth, density and initial signal-to-noise ratio data; obtaining a frequency selection result based on a weighted average of all trigger rules; defuzzifying the frequency selection result to obtain a specific operating frequency value; and outputting the operating frequency value to the signal generator.

2. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: The adaptive matching network adopts a particle swarm optimization algorithm, which includes the following steps: obtaining the current load impedance and source impedance; initializing the particle swarm, where each particle represents a set of possible inductance and capacitance values; calculating the fitness of each particle based on the voltage standing wave ratio formula Wherein Γ is the reflection coefficient; updating the position and speed of each particle, wherein the position represents the inductance and capacitance values, and the speed represents the rate of change of these values; repeating the steps of calculating fitness and updating the position and speed until the maximum number of iterations is reached or the stop condition is met; obtaining the optimal inductance and capacitance values; and adjusting the parameters of the adaptive matching network according to the optimal inductance and capacitance values.

3. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: The signal receiving and processing unit adopts an adaptive filtering algorithm, which includes the following steps: obtaining an input signal x(n) and a desired signal d(n); initializing the filter weight w to a zero vector; for each time step n: based on the current weight w and the input signal x(n), calculating the filter output y(n)=w T *x(n); calculate the error e(n) = d(n) - y(n); update the filter weight w = w + 2μe(n)x(n) according to the error e(n) and the step size parameter μ; repeat the above steps until convergence or the maximum number of iterations is reached; output the final filter weight w and the filtered signal y(n).

4. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: The data analysis and visualization module adopts a synthetic aperture focusing algorithm, which includes the following steps: obtaining the original received signal s(t) and the transducer array element position information r i ; Define the grid points p(x,y,z) of the reconstructed image; For each grid point p(s,y,z): calculate the sound path d from the point to each array element i =|pr i |; Based on the sound path d i and the speed of sound c, calculate the time delay Interpolate the original signal s(t) to obtain the delayed signal s(t-τ i ); add the delayed signals of all array elements to obtain the image value of the point I(p) = Σs(t-τ i ); repeat the above steps for all grid points; output the reconstructed image I(p).

5. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: The intelligent cooling system adopts a PID control algorithm, which includes the following steps: obtaining a target temperature T set and the current temperature T current ; Calculate the temperature error e(t) = T set -T current ; Calculate the proportional term P(t) = K p *e(t), where K p is the proportional coefficient; calculate the integral term I(t) = K i *∫e(t)dt, where K i is the integral coefficient; calculate the differential term Where K d is the differential coefficient; the proportional term, the integral term and the differential term are added to obtain the control output u(t)=P(t)+I(t)+D(t); based on the control output u(t), the cooling power of the intelligent cooling system is adjusted; wherein the intelligent cooling system includes a temperature sensor, a micro water pump and a radiator, the temperature sensor is a PT100 model with an accuracy of ±0.1°C, the micro water pump model is TCS M400S, and the flow range is 0.4-2L / min.

6. The variable frequency water immersion ultrasonic probe according to claim 1, characterized in that: It also includes a surface protection coating, which is arranged on the surface of the multi-frequency piezoelectric chip component, including: a bottom layer with a thickness of 2μm, mainly composed of epoxy resin Epon 828, for providing adhesion; a middle layer with a thickness of 6μm, composed of a mixture of polytetrafluoroethylene and silica nanoparticles with a diameter of 20nm, for providing hydrophobicity and wear resistance; a top layer with a thickness of 2μm, composed of pure PTFE, for maximizing the hydrophobic effect; wherein the preparation process of the surface protection coating includes: plasma cleaning the surface of the multi-frequency piezoelectric chip component, spin coating the bottom layer and UV curing, spin coating the middle layer and thermal curing, spin coating the top layer and finally thermal curing.

7. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: The spherical focusing backing structure is made of a composite material of a polyurethane matrix and an alumina filler, wherein: the shore hardness of the polyurethane matrix is ​​60A; the particle size of the alumina filler is 1-5μm, and the filling ratio is 30%; the spherical radius of the spherical focusing backing structure is 25mm, the center thickness is 8mm, the edge thickness is 3mm, and the total diameter is 22mm; the spherical focusing backing structure is made by a 3D printed photosensitive resin mold, and is completed by vacuum degassing, injection molding, pressurization and heat treatment processes.

8. The variable frequency immersion ultrasonic probe according to claim 1, characterized in that: It also includes a probe shell, which is made of 3D printed titanium alloy Ti-6Al-4V, wherein: the total length of the probe shell is 80mm, the outer diameter is 25mm, and the wall thickness is 1mm; an acoustic transparent window is provided at the front end of the probe shell, which is made of polyvinylidene fluoride (PVDF) material with a thickness of 0.1mm; a sealed interface panel is provided at the rear of the probe shell, which includes a power interface, a data interface and a coolant interface; an isolation cavity is provided inside the probe shell for separating electronic components and coolant paths; the probe shell is sealed with an O-ring, the O-ring material is fluororubber, the inner diameter is 20mm, and the wire diameter is 1.5mm; all external interfaces of the probe shell adopt an IP68 waterproof design, and the cable outlet is double-sealed with a heat shrink tube and epoxy resin.

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