Self-correction method, system and device for predicting remaining stimulable times and storage medium

Abstract

The invention discloses a self-correction method, system and device for predicting the number of remaining stimulatable times, and a storage medium, and the method comprises the steps: obtaining the operation data of the device after a standard scheme is applied in advance, receiving a user scheme, calculating the actual stimulation data of the device, obtaining the predicted number of remaining stimulatable times through a prediction algorithm, and carrying out the calculation of the predicted number of remaining stimulatable times. If the number of times is not enough to complete the user scheme, calculating a correction coefficient by adopting a correction algorithm and displaying the correction coefficient in an image, and according to the correction coefficient, requiring the user to adjust the scheme and re-executing the actual stimulation data of the calculation equipment and subsequent steps until the user scheme can be continuously completed, so that the user experience is improved in the actual use process of the user. And the prediction error is reduced by self-correcting the predicted residual stimulable times in real time.

Description

Technical Field

[0001] The present invention relates to the field of correction technology, and in particular to a self-correction method, system, device and storage medium for predicting the remaining number of stimulations. Background Art

[0002] In the field of medical devices, especially in neuromodulation and treatment, transcranial magnetic stimulation (TMS) is an important technical means. TMS devices usually include a coil that generates a high-intensity magnetic field that can penetrate the skull and act on the cerebral cortex. In the field of biomedical engineering, in order to ensure the normal operation and therapeutic effect of TMS devices, the temperature of the coil must be strictly controlled. Prolonged use or frequent stimulation may cause the coil to overheat, thus affecting the performance and safety of the device. Therefore, predicting the remaining number of stimulations of the coil is an important issue.

[0003] The existing technology is not accurate enough in predicting the remaining stimulation times of the coil and in real-time predicting the actual remaining stimulation times of the coil, which affects the accuracy of the prediction results. Summary of the invention

[0004] The purpose of the present invention is to address the technical problems existing in the background technology and to propose a self-correction method, system, device and storage medium for predicting the remaining number of stimulations.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0006] A first implementation of the first aspect of the present invention provides a self-correction method for predicting the remaining number of stimulations, comprising:

[0007] S101, configuring a standard solution, and obtaining operation data of the pre-set equipment after applying the standard solution;

[0008] S102, receiving a user plan, applying the user plan to the device, processing the operation data, and calculating actual stimulation data of the user plan;

[0009] S103, calculating the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted remaining number of stimulations;

[0010] S104, determining whether the predicted remaining number of stimulations can continuously complete the user plan;

[0011] S105. If not, based on a preset correction algorithm, the actual stimulation data and the predicted remaining number of stimulations are processed, a correction coefficient is calculated and output in the form of an image, the adjusted user plan is received, and step S102 is re-executed.

[0012] Optionally, in a second implementation of the first aspect of the present invention, the standard scheme includes a determined room temperature condition, a fixed heating value, a preset device initial temperature, a device continuous stimulation time, and periodic recording of device temperature changes.

[0013] Optionally, in a third implementation of the first aspect of the present invention, the operating data includes heat dissipation of the device, and the heat dissipation of the device after applying the standard solution is calculated as:

[0014] H o =H i -ΔT×c×V;

[0015] Among them, H o is the heat dissipation of the equipment, H i is a fixed heating value, ΔT is the temperature change of the equipment recorded every 30 seconds, c is the specific heat capacity of the refrigerant used by the equipment to dissipate heat, and V is the volume of the refrigerant.

[0016] Optionally, in a fourth implementation of the first aspect of the present invention, the actual stimulation data includes heat generation, and the actual stimulation data of the user scheme is calculated as follows:

[0017] H i1 =U2×tu×f / R;

[0018] Among them, H i1 is the calorific value of the user's plan, U is the pulse voltage set in the user's plan, tu is the pulse width set in the user's plan, f is the plan frequency set in the user's plan, and R is the impedance of the device.

[0019] Optionally, in a fifth implementation of the first aspect of the present invention, the prediction algorithm is:

[0020] N=K×(H i1 -H o ) / (T max -T0)×c×V;

[0021] Where N is the predicted number of remaining stimulations, K is the initial correction coefficient, and T max is the maximum operating temperature allowed by the equipment, and T0 is the initial temperature of the equipment.

[0022] Optionally, in a sixth implementation of the first aspect of the present invention, the correction coefficient is calculated as follows:

[0023] K i =K×(N+N r ) / 2N

[0024] Among them, K i is the correction factor, N r The number of stimulations actually output by the device.

[0025] A first implementation of the second aspect of the present invention provides a self-correction system for predicting the number of remaining stimulation times, comprising:

[0026] The acquisition module is used to configure the standard solution and obtain the operation data of the preset equipment after applying the standard solution;

[0027] A calculation module, used for receiving a user plan, applying the user plan to the device, processing the operation data, and calculating the actual stimulation data of the user plan;

[0028] A prediction module is used to calculate the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted number of remaining stimulation times;

[0029] A judgment module, used to judge whether the predicted remaining number of stimulations can continuously complete the user plan;

[0030] The correction module is used to process the actual stimulation data and the predicted remaining number of stimulations based on a preset correction algorithm, calculate the correction coefficient and output it in the form of an image, receive the adjusted user plan, and re-execute the calculation module.

[0031] A first implementation of the third aspect of the present invention provides a self-correction device for predicting the number of remaining stimulation times, the self-correction device for predicting the number of remaining stimulation times comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a line;

[0032] The at least one processor calls the instructions in the memory to enable the self-calibration device for predicting the remaining number of possible stimulations to perform the self-calibration method for predicting the remaining number of possible stimulations as described in any one of the first aspects of the present invention.

[0033] A first implementation method of the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the self-correction method for predicting the remaining number of stimulations as described in any one of the first aspects of the present invention.

[0034] Compared with the prior art, the present invention has the following beneficial technical effects: obtaining in advance the operating data of the device after applying the standard plan, receiving the user plan and calculating the actual stimulation data of the device, using the prediction algorithm to obtain the predicted remaining number of stimulations, if the number is not enough to complete the user plan, using the correction algorithm to calculate the correction coefficient and display it in an image, requiring the user to adjust the plan according to the correction coefficient and re-execute the above-mentioned calculation of the actual stimulation data of the device and subsequent steps until the user plan can be completed continuously, thereby realizing real-time self-correction of the predicted remaining number of stimulations by the user during actual use, thereby reducing the prediction error. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of a first embodiment of a self-correction method for predicting the remaining number of stimulations in an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of an embodiment of a self-correction system for predicting the remaining number of stimulations in an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of an embodiment of a self-correction device for predicting the remaining number of stimulations in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0040] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or a specific connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0041] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Figure 1 , the self-correction method for predicting the remaining number of stimulations in an embodiment of the present invention comprises:

[0042] S101, configuring a standard solution, and obtaining operation data of the pre-set equipment after applying the standard solution;

[0043] In this embodiment, the self-correction method for predicting the remaining number of stimulations is applied to a transcranial magnetic stimulation device, which includes a stimulation coil, a TMS system, and a cooling system. In order to more accurately record the temperature of the device, a temperature sensor is installed inside or outside the stimulation coil, wherein the cooling system adopts liquid cooling. The standard scheme is a pre-configured standard control group, that is, the control variable data needs to be configured. For example, the standard scheme includes a determined room temperature condition, a fixed heat value, a preset device initial temperature, a device continuous stimulation time, and a periodic recording of the device temperature change. The operating data includes the heat dissipation of the device. The heat dissipation of the device after applying the standard scheme is calculated as:

[0044] H o =H i -ΔT×c×V;

[0045] Among them, H o is the heat dissipation of the equipment, H i is a fixed heating value, ΔT is the temperature change of the equipment recorded every 30 seconds, c is the specific heat capacity of the refrigerant used by the equipment to dissipate heat, and V is the volume of the refrigerant.

[0046] S102, receiving a user plan, applying the user plan to the device, processing the operation data, and calculating actual stimulation data of the user plan;

[0047] In this embodiment, the user scheme is specifically a device execution scheme configured by the user. Since TMS stimulation has different stimulation modes and stimulation schemes, including different intensities, frequencies, etc., the heating power of each mode is different; the current temperature of the stimulation coil will also change with different working environments, so the actual stimulation data includes the heat generation. The calculation of the actual stimulation data of the user scheme is:

[0048] H i1 =U2×tu×f / R;

[0049] Among them, H i1 is the calorific value of the user's solution, U is the pulse voltage set by the user's solution, tu is the pulse width set by the user's solution, f is the solution frequency set by the user's solution, and R is the impedance of the device;

[0050] It should be noted that the impedance of the device refers to the impedance from the stimulation coil of the transcranial magnetic stimulation device to the boost module, and the calorific value refers to the heat generated by the device when applying a certain scheme. As mentioned above, the heat generated by the device is different for different schemes. In this embodiment, we need to use the calorific value data, so the actual stimulation data obtained mainly includes the calorific value of the user's scheme.

[0051] S103, calculating the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted remaining number of stimulations;

[0052] In this embodiment, the prediction algorithm is:

[0053] N=K×(H i1 -H o ) / (T max -T0)×c×V;

[0054] Where N is the predicted number of remaining stimulations, K is the initial correction coefficient, and T max is the maximum operating temperature allowed by the equipment, and T0 is the initial temperature of the equipment.

[0055] S104, determining whether the predicted remaining number of stimulations can continuously complete the user plan;

[0056] In this embodiment, the predicted remaining number of stimulations is the estimated number of times the device is executed in the user plan. However, since the execution methods of the user plan and the standard plan are different, the heating power of the device is also different. Therefore, the predicted remaining number of stimulations may not be able to complete the user plan.

[0057] S105. If not, based on a preset correction algorithm, the actual stimulation data and the predicted remaining number of stimulations are processed, a correction coefficient is calculated and output in the form of an image, the adjusted user plan is received, and step S102 is re-executed.

[0058] In this embodiment, when it is determined that the user plan cannot be completed continuously, a correction coefficient needs to be calculated accordingly. It should be noted that the initial correction system generally takes a value of 1. The calculated correction system will be displayed in the form of a graphic so that the user can intuitively understand whether the selected plan can be completed continuously by the device, and can quickly adjust the plan. The adjusted plan is received, and step S102 is re-executed to recalculate the predicted remaining number of stimulations until the user plan can be completed continuously. The continuous completion time here is controlled to 10 minutes.

[0059] The correction factor is calculated as:

[0060] K i =K×(N+N r ) / 2N

[0061] Among them, K i is the correction factor, N r The number of stimulations actually output by the device.

[0062] The device's operating data after applying the standard plan is obtained in advance, the user plan is received and the actual stimulation data of the device is calculated, and the prediction algorithm is used to obtain the predicted remaining number of stimulations. If the number is not enough to complete the user plan, the correction algorithm is used to calculate the correction coefficient and display it in an image. According to the correction coefficient, the user is required to adjust the plan and re-execute the above-mentioned calculation of the actual stimulation data of the device and subsequent steps until the user plan can be completed continuously. This enables the user to self-correct the predicted remaining number of stimulations in real time during actual use, thereby reducing the prediction error.

[0063] The above describes the self-correction method for predicting the remaining number of stimulations in the embodiment of the present invention. The following describes the self-correction system for predicting the remaining number of stimulations in the embodiment of the present invention. Figure 2 , the self-correction system for predicting the remaining number of stimulations comprises:

[0064] The acquisition module 201 is used to configure the standard solution and obtain the operation data of the preset equipment after applying the standard solution;

[0065] The calculation module 202 is used to receive the user plan, apply the user plan to the device, process the operation data, and calculate the actual stimulation data of the user plan;

[0066] Prediction module 203, used to calculate the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted remaining number of stimulations;

[0067] A judgment module 204 is used to judge whether the predicted remaining number of stimulations can continuously complete the user plan;

[0068] The correction module 205 is used to process the actual stimulation data and the predicted remaining stimulation times based on a preset correction algorithm, calculate the correction coefficient and output it in the form of an image, receive the adjusted user plan, and re-execute the calculation module.

[0069] Specifically, the operating data of the device after applying the standard plan is obtained in advance, the user plan is received and the actual stimulation data of the device is calculated, and the prediction algorithm is used to obtain the predicted remaining number of stimulations. If the number is not enough to complete the user plan, the correction algorithm is used to calculate the correction coefficient and display it in an image. According to the correction coefficient, the user is required to adjust the plan and re-execute the above-mentioned calculation of the actual stimulation data of the device and subsequent steps until the user plan can be completed continuously. This enables the user to self-correct the predicted remaining number of stimulations in real time during actual use, thereby reducing the prediction error.

[0070] Among them, the standard plan includes determined room temperature conditions, fixed heating value, preset device initial temperature, device continuous stimulation time, and periodic recording of device temperature changes.

[0071] The operating data includes the heat dissipation of the equipment. The heat dissipation of the equipment after applying the standard solution is calculated as:

[0072] H o =H i -ΔT×c×V;

[0073] The actual stimulation data includes the heat value, and the actual stimulation data of the user scheme is calculated as:

[0074] H i1 =U2×tu×f / R;

[0075] Among them, H i1 is the calorific value of the user's plan, U is the pulse voltage set in the user's plan, tu is the pulse width set in the user's plan, f is the plan frequency set in the user's plan, and R is the impedance of the device.

[0076] Among them, the prediction algorithm is:

[0077] N=K×(H i1 -H o ) / (T max -T0)×c×V;

[0078] Where N is the predicted number of remaining stimulations, K is the initial correction coefficient, and T max is the maximum operating temperature allowed by the equipment, and T0 is the initial temperature of the equipment.

[0079] The calculation of the correction coefficient is:

[0080] K i=K×(N+N r ) / 2N

[0081] Among them, K i is the correction factor, N r The number of stimulations actually output by the device.

[0082] above Figure 2 The self-correction system for predicting the remaining number of stimulations in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The self-correction device for predicting the remaining number of stimulations in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0083] Figure 3 1 is a schematic diagram of the structure of a self-correction device for predicting the number of remaining stimulations provided by an embodiment of the present invention. The self-correction device 300 for predicting the number of remaining stimulations may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage devices) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be temporary storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the self-correction device 300 for predicting the number of remaining stimulations. Furthermore, the processor 310 may be configured to communicate with the storage medium 330 to execute a series of instruction operations in the storage medium 330 on the self-correction device 300 for predicting the number of remaining stimulations.

[0084] The self-correction device 300 based on the prediction of the remaining number of stimulations can also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It can be understood by those skilled in the art that Figure 3 The self-correction device structure for predicting the remaining number of stimulations shown does not constitute a limitation on the communication protocol device based on LAN screen projection, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0085] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein instructions are stored in the computer, and when the instructions are executed on a computer, the computer executes the steps of the self-correction method for predicting the remaining number of stimulations.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0087] The above is a self-correction method or multiple implementations for predicting the remaining number of stimulations provided in combination with specific content, and it is not intended that the specific implementation of the present invention is limited to these descriptions. Any method, structure, etc. similar to or identical to the method, structure, etc. of the present invention, or a number of technical deductions or replacements based on the concept of the present invention, should be considered as the scope of protection of the present invention.

Claims

1. A self-correction method for predicting the number of remaining stimulations, characterized in that: include: S101, configuring a standard solution, and obtaining operation data of a pre-installed device after applying the standard solution; S102, receiving a user plan, applying the user plan to the device, processing the operation data, and calculating actual stimulation data of the user plan; S103, calculating the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted remaining number of stimulations; S104, determining whether the predicted remaining number of stimulations can continuously complete the user plan; S105. If not, based on a preset correction algorithm, the actual stimulation data and the predicted remaining number of stimulations are processed, a correction coefficient is calculated and output in the form of an image, an adjusted user plan is received, and step S102 is re-executed.

2. A self-correction method for predicting the remaining number of stimulations according to claim 1, characterized in that: The standard protocol includes a defined room temperature condition, a fixed heating value, a preset initial device temperature, a continuous stimulation time of the device, and periodic recording of device temperature changes.

3. A self-correction method for predicting the remaining number of stimulations according to claim 2, characterized in that: The operation data includes the heat dissipation of the device. The heat dissipation of the device after applying the standard solution is calculated as: H o =H i -ΔT×c×V; Among them, the H o is the heat dissipation of the device, the H i is the fixed heating value, ΔT is the temperature change of the device recorded every 30 seconds, c is the specific heat capacity of the refrigerant used by the device for heat dissipation, and V is the volume of the refrigerant.

4. A self-correction method for predicting the remaining number of stimulations according to claim 3, characterized in that: The actual stimulation data includes heat generation, and the actual stimulation data of the user scheme is calculated as: H i1 =U2×tu×f / R; Among them, the H i1 is the heat generated by the user scheme, U is the pulse voltage set by the user scheme, tu is the pulse width set by the user scheme, f is the scheme frequency set by the user scheme, and R is the impedance of the device.

5. A self-correction method for predicting the remaining number of stimulations according to claim 4, characterized in that: The prediction algorithm is: N=K×(H i1 -H o ) / (T max -T0)×c×V; Wherein, N is the predicted number of remaining stimulations, K is the initial correction coefficient, and T max is the maximum operating temperature allowed by the device, and T0 is the initial temperature of the device.

6. A self-correction method for predicting the remaining number of stimulations according to claim 5, characterized in that: The correction factor is calculated as: <h2 style=";text-align:left;direction:ltr">K<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> (K×(N+N<h2 style=";text-align:left;direction:ltr"> r <h2 style=";text-align:left;direction:ltr"> ) / 2N Among them, the K i is the correction factor, the N r is the number of stimulations actually output by the device.

7. A self-correction system for predicting the number of remaining stimulations, characterized in that: include: A collection module, used to configure a standard solution and obtain the operation data of the preset equipment after applying the standard solution; A calculation module, used for receiving a user scheme, applying the user scheme to the device, processing the operation data, and calculating actual stimulation data of the user scheme; A prediction module, configured to calculate the device operation data and the actual stimulation data based on a preset prediction algorithm to obtain a predicted remaining number of stimulations; A judgment module, used to judge whether the predicted remaining number of stimulations can continuously complete the user plan; The correction module is used to process the actual stimulation data and the predicted remaining number of stimulations based on a preset correction algorithm, calculate the correction coefficient and output it in the form of an image, receive the adjusted user plan, and re-execute the calculation module.

8. A self-calibration device for predicting the number of remaining stimulations, characterized in that: The self-correction device for predicting the remaining number of stimulations comprises: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a line; The at least one processor calls the instructions in the memory to enable the self-calibration device for predicting the remaining number of possible stimulations to perform the self-calibration method for predicting the remaining number of possible stimulations according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the self-correction method for predicting the remaining number of stimulations according to any one of claims 1 to 6 is implemented.

Images