Shockwave therapy equipment and method based on real-time monitoring and dynamic feedback

Abstract

This invention discloses a shockwave therapy device and method based on real-time monitoring and dynamic feedback, relating to the field of medical shockwave therapy technology. It includes: a device host for acquiring and analyzing treatment plans to obtain corresponding shockwave application paths and shockwave output commands; a shockwave generating system connected to the device host for outputting shockwave energy according to the shockwave output commands; and a robotic arm connected to both the device host and the shockwave generating system for controlling the movement of a controller in the shockwave generating system according to the shockwave application path. This invention enables quantitative monitoring and control of shockwave output-related parameters.

Description

Technical Field

[0001] This invention relates to the field of medical shockwave therapy technology, and more specifically to a shockwave therapy device and method based on real-time monitoring and dynamic feedback. Background Technology

[0002] Currently, Extracorporeal Shock Wave Therapy (ESWT) is a novel method that utilizes mechanical energy in the form of shock waves (SW) for precise disease treatment. It boasts advantages such as safety, high efficiency, precision, and patient acceptance. In recent years, shock wave therapy has developed rapidly, demonstrating promising application prospects in various disease treatment areas.

[0003] However, the key to successful shockwave therapy is to accurately apply appropriate and stable output energy to the target site. Due to equipment errors and the influence of factors such as air resistance, tissue characteristics, and texture, the actual output energy of existing shockwave therapy equipment is inconsistent with the theoretical output energy. Furthermore, it is impossible to provide timely feedback and adjustment of the actual output energy during treatment, thus making it impossible to achieve precise control of the shockwave dose. In addition, during existing shockwave therapy, staff need to hold the shockwave therapy handle throughout the entire process, constantly moving it and adjusting the direction and intensity, which can take anywhere from tens of minutes to 1 to 2 hours, greatly increasing the workload and burden.

[0004] Therefore, how to provide a shockwave therapy device that can solve the above problems is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] In view of this, the present invention provides a shockwave therapy equipment and method based on real-time monitoring and dynamic feedback, which can realize the quantitative monitoring and control of shockwave energy output parameters. By combining deep learning methods to obtain the actual output energy of the shockwave and feeding it back to the shockwave therapy equipment, the shockwave output dose can be adjusted in real time, thereby achieving precise energy output of the shockwave equipment. At the same time, it can also solve the problems of limited treatment range and heavy workload of staff in the existing shockwave therapy process, improve work efficiency, reduce the workload of staff, and make the shockwave therapy process more intelligent, precise and convenient.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A shockwave therapy device based on real-time monitoring and dynamic feedback includes:

[0008] The device host is used to acquire and parse the treatment plan to obtain the corresponding shock wave application path and shock wave output command.

[0009] A shock wave generating system, which is connected to the main unit of the equipment, is used to complete the energy output of the shock wave according to the shock wave output command;

[0010] The robotic arm is a flexible and safe robotic arm. The surface of the robotic arm is covered with electronic skin, and a torque sensor can be installed at the end of the robotic arm to prevent the controller of the shock wave generating system, such as the shock wave output handle, from applying excessive force to the human body and causing injury. The robotic arm is connected to the main unit of the device and the shock wave generating system and is used to control the movement of the controller in the shock wave generating system according to the shock wave application path.

[0011] A first detection system, connected to the shock wave generating system, is used to detect the output shock wave signal of the shock wave generating system in real time.

[0012] The second detection system is used to detect the effective shock wave signal in the treatment area in real time.

[0013] The processing system has its input end connected to the first detection system and the second detection system, and its output end connected to the device host. It is used to process the difference between the intensity of the output shock wave signal and the intensity of the effective shock wave signal, and to feed back the processing result to the device host to update the shock wave application path and the shock wave output command.

[0014] Preferably, the device host includes:

[0015] The planning system is used to acquire and analyze the treatment plan to obtain the corresponding shock wave application path and shock wave output command, wherein the shock wave output command includes the theoretical output energy of the shock wave and the theoretical number of impacts.

[0016] A control system, connected to the planning system and the robotic arm, is used to control the robotic arm to move according to the path of the shock wave application, and to receive and send the shock wave output command.

[0017] Preferably, the shock wave generating system includes:

[0018] A shock wave generating module, which is connected to the control system, is used to generate the required shock wave according to the shock wave output command.

[0019] A controller, connected to the shock wave generating module, is used to output shock wave energy according to the required shock wave until the theoretical number of impacts is reached.

[0020] Preferably, the first detection system includes:

[0021] An output shock wave signal detection module is provided, which is connected to the controller and is used to detect the output shock wave signal of the controller in real time.

[0022] A first preprocessing module, connected to the output shock wave signal detection module, is used to preprocess and convert the output shock wave signal and use it as a first preprocessing result, and send the first preprocessing result to the processing system.

[0023] Preferably, the second detection system includes:

[0024] An effective shock wave signal detection module is used to detect the effective shock wave signal in the treatment area in real time.

[0025] The second preprocessing module has its input end connected to the effective shock wave signal detection module and its output end connected to the processing system. It is used to preprocess and convert the effective shock wave signal and use it as the second preprocessing result, and then send the second preprocessing result to the processing system.

[0026] Preferably, the processing system includes:

[0027] A receiving module, which is connected to the first preprocessing module and the second preprocessing module, is used to receive the first preprocessing result and the second preprocessing result;

[0028] A feedback module, connected to the receiving module and the controller, is used to compare the first preprocessing result and the second preprocessing result and take the difference between the two as the first processing result, and to provide energy feedback to the controller based on the first processing result;

[0029] A fitting module is connected to the feedback module, and the fitting module pre-stores the correspondence between output shock wave signals and a preset difference threshold, which is used to update the output shock wave signal according to the correspondence when the first processing result exceeds the preset difference threshold.

[0030] The calibration module, connected to the fitting module and the control system, is used to construct and train a long short-term memory neural network, input the effective shock wave signal into the long short-term memory neural network for processing, calibrate the output shock wave signal based on the processing result of the long short-term memory neural network, and finally send the calibration result to the control system to obtain the latest shock wave output command and shock wave implementation path.

[0031] This invention also provides a method for implementing shockwave therapy equipment based on real-time monitoring and dynamic feedback, comprising the following steps:

[0032] S1: The treatment plan is obtained and analyzed by the planning system to obtain the corresponding shock wave application path and shock wave output command, wherein the shock wave output command includes the theoretical output energy of the shock wave and the theoretical number of impacts;

[0033] S2: The control system receives the shock wave application path and the shock wave output command, controls the robotic arm to move according to the shock wave application path, and controls the controller of the shock wave generating system to output the required shock wave according to the shock wave output command until the theoretical number of impacts is reached.

[0034] During the process of outputting the required shock wave, the output shock wave signal of the controller is detected in real time by the output shock wave signal detection module, and the effective shock wave signal of the treatment area is detected in real time by the effective shock wave signal detection module applied to the surface of the treatment area.

[0035] S3: The output shock wave signal and the effective shock wave signal are preprocessed and converted to obtain the corresponding first preprocessing result and second preprocessing result, and the first preprocessing result and second preprocessing result are sent to the processing system;

[0036] S4: The processing system processes the first preprocessing result and the second preprocessing result obtained in S3, updates the shock wave output command and the shock wave application path, and controls the output of the shock wave.

[0037] Preferably, the specific process of S4 also includes:

[0038] S41: Receive the first preprocessing result and the second preprocessing result through the receiving module;

[0039] S42: The first preprocessing result and the second preprocessing result are compared by the feedback module, and the difference between the two is taken as the first processing result. Energy feedback is then given to the controller based on the first processing result.

[0040] S43: The fitting module pre-stores the correspondence between changes in the output shock wave signal. When the first processing result exceeds the preset difference threshold, the output shock wave signal is updated according to the correspondence.

[0041] S44: Construct and train a long short-term memory neural network, input the effective shock wave signal into the long short-term memory neural network for processing, output the calibration result, and send the calibration result to the control system;

[0042] S45: The control system generates a new shock wave output command and a new shock wave application path based on the calibration results obtained in S44, and controls the energy output of the shock wave generating system and the movement of the robotic arm.

[0043] Preferably, the specific process of S44 includes:

[0044] S441: Construct a long short-term memory neural network, obtain a historical dataset, and divide the historical dataset into a training set and a test set. Use the training set to train the long short-term memory neural network and use the test set to test the long short-term memory neural network. Use the root mean square error method to verify the model's prediction accuracy. When the accuracy meets the requirements, the training is completed.

[0045] S442: The effective shock wave signal is normalized and input into the long short-term memory neural network obtained in S441 to obtain the corresponding calibration result.

[0046] Preferably, the preprocessing and transformation process in S3 includes: outlier removal, mean filtering, and analog-to-digital conversion.

[0047] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a shockwave therapy equipment and method based on real-time monitoring and dynamic feedback. On the basis of conventional medical shockwave equipment, a novel flexible membrane mechanical energy signal sensor (including but not limited to polyvinylidene difluoride (PVDF) membrane, single-sided multi-level sinusoidal magnetized thin film, etc.) is used to realize the quantitative monitoring and control of shockwave mechanical signals and energy density. Combined with deep learning algorithms, the shockwave pressure field and dynamic distribution characteristics are obtained and fed back to the shockwave therapy equipment to adjust the shockwave output dose in real time, thereby realizing the precise energy output of the shockwave equipment. At the same time, it can also solve the problems of limited treatment range and heavy workload of staff in the existing shockwave therapy process, improve work efficiency, reduce the workload of staff, and make the shockwave therapy process more intelligent, precise, convenient and simple.

[0048] This invention can also control the movement of the robotic arm to drive the movement of the shockwave therapy handle, freeing up the doctor's hands and allowing the doctor to directly adjust the shockwave therapy plan and output dose through the control panel. Compared with conventional medical shockwave therapy equipment, doctors do not need to hold the treatment handle of the external shockwave therapy device back and forth, greatly reducing the workload of medical staff. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 A structural principle block diagram of a shockwave therapy device based on real-time monitoring and dynamic feedback provided by the present invention;

[0051] Figure 2 This is a structural principle block diagram of the first detection system provided in an embodiment of the present invention;

[0052] Figure 3 This is a structural principle block diagram of the second detection system provided in an embodiment of the present invention;

[0053] Figure 4 The structural principle block diagram of the processing system provided by the present invention;

[0054] Figure 5 This is an overall flowchart of the method for implementing the shockwave therapy device provided by the present invention;

[0055] Figure 6 A flowchart of step S4 provided by the present invention;

[0056] Figure 7 The flowchart of step S44 provided by the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] See appendix Figure 1 As shown, this embodiment of the invention provides a shockwave therapy device based on real-time monitoring and dynamic feedback, comprising:

[0059] The host device 1 is used to acquire and parse the treatment plan to obtain the corresponding shock wave application path and shock wave output command. The specific process of acquiring and parsing the treatment plan can be obtained by acquiring and processing the patient's medical information and health records, or it can be realized by planning the treatment plan based on relevant experience summarized by machine learning.

[0060] Shock wave generating system 2 is connected to the main equipment 1 and is used to complete the energy output of the shock wave according to the shock wave output command.

[0061] Robotic arm 3 is a flexible robotic arm, or a multi-degree-of-freedom flexible collaborative robotic arm, composed of multiple joints with multiple degrees of freedom. Its surface is covered with electronic skin, enabling automatic obstacle avoidance and safe force detection. A torque sensor is installed at the end of the robotic arm, and a three-dimensional torque sensor can also be installed at the end of the robotic arm 3 for end-effector torque feedback, achieving higher control precision and better safety protection. Robotic arm 3 is connected to the controller 22 in the main equipment 1 and the shock wave generating system 2, used to control the movement of the shock wave generating system 2 according to the shock wave application path.

[0062] The first detection system 4 is connected to the shock wave generating system 2 and is used to detect the output shock wave signal of the shock wave generating system 2 in real time.

[0063] The second detection system 5 is used to detect the effective shock wave signal in the treatment area in real time.

[0064] The processing system 6 has its input end connected to the first detection system 4 and the second detection system 5, and its output end connected to the device host 1. It is used to process the difference between the output shock wave signal and the effective shock wave signal, and to feed back the processing result to the device host 1 to update the shock wave application path and the shock wave output command.

[0065] In one specific embodiment, the device host 1 includes:

[0066] Planning system 11 is used to acquire and analyze treatment plans to obtain the corresponding shock wave application path and shock wave output command, wherein the shock wave output command includes the theoretical output energy of the shock wave and the theoretical number of impacts;

[0067] The control system 12 is connected to the planning system 11 and the robotic arm 3. It is used to control the robotic arm 3 to move according to the path of the shock wave application, and at the same time to receive and send shock wave output commands.

[0068] In one specific embodiment, the shock wave generating system 2 includes:

[0069] Shock wave generating module 21 is connected to control system 12 and is used to generate the required shock wave according to shock wave output command;

[0070] The controller 22 includes, but is not limited to, various forms of shock wave output devices such as an output handle. The controller 22 is connected to the shock wave generating module 21 and is used to complete the shock wave energy output according to the required shock wave until the theoretical number of impacts is reached.

[0071] In one specific embodiment, the robotic arm 3 is mounted on a support or directly on a base;

[0072] The support frame can house the main unit 1 and the shock wave generating system 2; or the robotic arm can be directly mounted on the base, with the main unit 1 and the shock wave generating system 2 being external devices that are electrically connected to the controller on the robotic arm.

[0073] See appendix Figure 2 As shown, in a specific embodiment, the first detection system 4 includes:

[0074] The output shock wave signal detection module 41 is connected to the controller 22 and is used to detect the output shock wave signal of the controller 22 in real time.

[0075] The first preprocessing module 42 is connected to the output shock wave signal detection module 41. It is used to preprocess and convert the output shock wave signal and use it as the first preprocessing result, and send the first preprocessing result to the processing system 6.

[0076] Specifically, the output shock wave signal can include the actual output energy and the actual number of impacts. That is, the output shock wave signal detection module 41 is used to detect the actual output energy and the actual number of impacts. This can be achieved using a laser sensor. Since laser sensors usually output corresponding analog quantities and the data acquisition volume is large, the preprocessing and conversion process can sequentially perform outlier removal, mean filtering, and analog-to-digital conversion.

[0077] See appendix Figure 3 As shown, in one specific embodiment, the second detection system 5 includes:

[0078] The effective shock wave signal detection module 51 is used to detect the effective shock wave signal in the treatment area in real time.

[0079] The second preprocessing module 52 has its input end connected to the effective shock wave signal detection module 51 and its output end connected to the processing system 6. It is used to preprocess and convert the effective shock wave signal and use it as the second preprocessing result, and then send the second preprocessing result to the processing system 6.

[0080] Specifically, the effective shock wave signal includes the local mechanical parameters, position parameters, and penetration depth of the treatment area. The treatment area may include related human tissues such as skin, muscles, and bones within the area. The effective shock wave signal detection module 51 includes a mechanical signal detection unit 511, a position detection unit 512, and a penetration depth detection unit 513, which are used to detect the local mechanical parameters, position parameters, and penetration depth in real time.

[0081] In a specific embodiment, both the mechanical signal detection unit 511 and the penetration depth detection unit 513 can be detected using any one or more of the PVDF charge pressure sensor and the capacitive flexible electronic pressure sensor. The preprocessing and conversion can also be performed sequentially by outlier removal, mean filtering and analog-to-digital conversion.

[0082] In one specific embodiment, the mechanical parameters of a local tissue may include any one or more of the following: hardness, elasticity, and thickness.

[0083] See appendix Figure 4 As shown, in one specific embodiment, the processing system 6 includes:

[0084] The receiving module 61 is connected to the first preprocessing module 52 and the second preprocessing module 42, and is used to receive the first preprocessing result and the second preprocessing result.

[0085] Feedback module 62 is connected to receiving module 61 and controller 22. It is used to compare the first preprocessing result and the second preprocessing result and take the difference between the two as the first processing result. It also provides energy feedback to controller 22 based on the first processing result.

[0086] Fitting module 63 is connected to feedback module 62, and the fitting module 63 pre-stores the correspondence between output shock wave signals and preset difference threshold, which is used to update the output shock wave signal according to the correspondence when the first processing result exceeds the preset difference threshold.

[0087] The calibration module 64 is connected to the fitting module 63 and the control system 12. It is used to construct and train the long short-term memory neural network, input the effective shock wave signal into the long short-term memory neural network for processing, calibrate the output shock wave signal according to the processing result of the long short-term memory neural network, and finally send the calibration result to the control system 12 to obtain the latest shock wave output command and shock wave implementation path.

[0088] Specifically, the feedback module 62 can provide energy feedback based on the comparison results. If the energy is less than the specified value, it will apply pressure; if the energy is equal to the specified value, it will not apply pressure; and if the energy is greater than the specified value, it will apply pressure.

[0089] See appendix Figure 5 As shown, this embodiment of the invention also provides a method for implementing shockwave therapy equipment based on real-time monitoring and dynamic feedback, including the following steps:

[0090] S1: The treatment plan is obtained and analyzed by the planning system 11 to obtain the corresponding shock wave application path and shock wave output command, wherein the shock wave output command includes the theoretical output energy of the shock wave and the theoretical number of impacts.

[0091] S2: The control system 12 receives the shock wave application path and shock wave output command, controls the robotic arm 3 to move according to the shock wave application path, and controls the controller 22 of the shock wave generating system 2 to output the required shock wave according to the shock wave output command until the theoretical number of impacts is reached.

[0092] During the process of outputting the required shock wave, the output shock wave signal of the controller 22 is detected in real time by the output shock wave signal detection module 41, and the effective shock wave signal of the treatment area is detected in real time by the effective shock wave signal detection module 51 applied to the surface of the treatment area.

[0093] S3: The output shock wave signal and the effective shock wave signal are preprocessed and converted to obtain the corresponding first preprocessing result and second preprocessing result, and the first preprocessing result and the second preprocessing result are sent to the processing system 6;

[0094] S4: The first and second preprocessing results obtained from S3 are processed by the processing system 6 to update the shock wave output command and the shock wave application path, and control the output of the shock wave.

[0095] Specifically, the specific expression used to implement step S2 can be:

[0096]

[0097]

[0098] Formula (1) describes the pressure distribution within a tissue under a shock wave pressure load of a certain frequency, where p ω () represents the pressure distribution at different depths within the tissue, r p Let u be the radius. ω ω is the displacement value of the shock wave output piston, ρ is the frequency of the shock wave output piston, and c is the tissue density. s Let ω be the propagation speed of the shock wave in the tissue, k = ω / c be the wave number, r be the tissue depth, θ be the normal angle between the shock wave output piston and the shock wave output piston, J1() be the Bessel function, and the corresponding shock wave output pressure value P is obtained by formula (1).

[0099] Formula (2) describes the energy flux density (EFD) within the tissue, where P is the pressure value at different depths within the tissue. For a given location, EFD describes the energy flux density (J / mm²) at that location. 2 The value of is the sum of the pressure value P at that location, which is a square integral term.

[0100] See appendix Figure 6As shown, in a specific embodiment, the specific process of S4 further includes:

[0101] S41: Receive the first preprocessing result and the second preprocessing result through the receiving module 61;

[0102] S42: The first preprocessing result and the second preprocessing result are compared by the feedback module 62 and the difference between the two is taken as the first processing result. Energy feedback is given to the controller 22 based on the first processing result.

[0103] S43: The calibration module 64 has a pre-stored correspondence between changes in the output shock wave signal. When the first processing result exceeds the preset difference threshold, the output shock wave signal is updated according to the correspondence.

[0104] S44: Construct and train a long short-term memory neural network, input the effective shock wave signal into the long short-term memory neural network for processing, output the calibration result, and send the calibration result to the control system 12;

[0105] S45: The control system 12 generates a new shock wave output command and a new shock wave application path based on the calibration results obtained in S44, and controls the energy output of the shock wave generating system 2 and the movement of the robotic arm 3.

[0106] See appendix Figure 7 As shown, in a specific embodiment, the specific process of S44 includes:

[0107] S441: Construct a long short-term memory neural network, obtain a historical dataset, and divide the historical dataset into a training set and a test set. Use the training set to train the long short-term memory neural network and use the test set to test the long short-term memory neural network. Use the root mean square error method to verify the model's prediction accuracy. When the accuracy meets the requirements, the training is complete.

[0108] S442: Normalize the effective shock wave signal and input it into the long short-term memory neural network obtained in S441 to obtain the corresponding calibration result.

[0109] Specifically, the basic idea of ​​a Long Short-Term Memory Neural Network (LSTM Neural Network) is to design a neuron (i.e., a memory module) controlled by multiple control gates, thereby overcoming the gradient vanishing phenomenon in recurrent neural networks. It consists of three layers: an input layer, a hidden layer, and an output layer.

[0110] In a specific embodiment, the preprocessing and transformation process in S3 includes: outlier removal, mean filtering, and analog-to-digital conversion.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0112] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A shockwave therapy device based on real-time monitoring and dynamic feedback, characterized in that, include: The device host (1) is used to acquire and parse the treatment plan to obtain the corresponding shock wave application path and shock wave output command; Shock wave generating system (2), which is connected to the main unit of the equipment (1) and is used to complete the energy output of the shock wave according to the shock wave output command; The robotic arm (3) is a flexible robotic arm, and the robotic arm (3) is connected to the main equipment (1) and the shock wave generating system (2) for controlling the movement of the shock wave generating system (2) according to the shock wave application path; The first detection system (4) is connected to the shock wave generating system (2) and is used to detect the output shock wave signal of the shock wave generating system (2) in real time. The output shock wave signal includes the actual output energy and the actual number of impacts. The second detection system (5) is used to detect the effective shock wave signal of the treatment area in real time. The effective shock wave signal includes the local mechanical parameters, positional parameters and penetration depth of the treatment area. The processing system (6), whose input is connected to the first detection system (4) and the second detection system (5) and whose output is connected to the device host (1), is used to process the difference between the output shock wave signal and the effective shock wave signal, and to feed the processing result back to the device host (1) to update the shock wave application path and the shock wave output command, including: The receiving module (61) is used to receive the first preprocessing result obtained by preprocessing and converting the output shock wave signal, and the second preprocessing result obtained by preprocessing and converting the effective shock wave signal. Feedback module (62) is used to compare the first preprocessing result and the second preprocessing result and take the difference between the two as the first processing result, and to perform energy feedback based on the first processing result; The fitting module (63) is connected to the feedback module (62), and the fitting module (63) stores the output shock wave signal correspondence and the preset difference threshold in advance, which is used to update the output shock wave signal according to the correspondence when the first processing result exceeds the preset difference threshold. The calibration module (64) is connected to the fitting module (63) and the control system (12) to construct and train the long short-term memory neural network, input the effective shock wave signal into the long short-term memory neural network for processing, calibrate the output shock wave signal according to the processing result of the long short-term memory neural network, and finally send the calibration result to the control system (12) to obtain the latest shock wave output command and shock wave implementation path.

2. The shockwave therapy equipment based on real-time monitoring and dynamic feedback according to claim 1, characterized in that, The device host (1) includes: Planning system (11) is used to acquire and analyze treatment plan, obtain corresponding shock wave application path and shock wave output command, wherein the shock wave output command includes theoretical shock wave output energy and theoretical number of impacts; The control system (12) is connected to the planning system (11) and the robotic arm (3) and is used to control the robotic arm (3) to reach the designated position according to the shock wave application path, and at the same time receive and send the shock wave output command.

3. The shockwave therapy equipment based on real-time monitoring and dynamic feedback according to claim 2, characterized in that, The shock wave generating system (2) includes: Shock wave generating module (21), which is connected to the control system (12), is used to generate the required shock wave according to the shock wave output command; The controller (22) is connected to the shock wave generating module (21) and is used to complete the shock wave energy output according to the required shock wave until the theoretical number of shocks is reached.

4. The shockwave therapy equipment based on real-time monitoring and dynamic feedback according to claim 3, characterized in that, The first detection system (4) includes: Output shock wave signal detection module (41), which is connected to the controller (22) and is used to detect the output shock wave signal of the controller (22) in real time; The first preprocessing module (42) is connected to the output shock wave signal detection module (41) and is used to preprocess and convert the output shock wave signal and use it as the first preprocessing result, and send the first preprocessing result to the processing system (6).

5. The shockwave therapy equipment based on real-time monitoring and dynamic feedback according to claim 4, characterized in that, The second detection system (5) includes: Effective shock wave signal detection module (51), which is applied non-invasively to the surface of the treatment area to detect the effective shock wave signal of the treatment area in real time; The second preprocessing module (52) has its input end connected to the effective shock wave signal detection module (51) and its output end connected to the processing system (6). It is used to preprocess and convert the effective shock wave signal and use it as the second preprocessing result, and send the second preprocessing result to the processing system (6).

6. The shockwave therapy equipment based on real-time monitoring and dynamic feedback according to claim 5, characterized in that, The processing system (6) includes: A receiving module (61) is connected to the second preprocessing module (52) and the first preprocessing module (42); Feedback module (62), which is connected to the receiving module (61) and the controller (22), is used to compare the first preprocessing result and the second preprocessing result and take the difference between the two as the first processing result, and to provide energy feedback to the controller (22) based on the first processing result.

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