Flexible sensor network design method, computer device and storage medium

Abstract

The invention discloses a flexible sensor network design method, a computer device and a storage medium, and the method can configure the characteristic data of a sub-curved-surface model in a flexible sensor network patch, so that the characteristic data of each sub-curved-surface model can be evolved in a direction in which the sub-curved-surface model can be well attached to a corresponding skin area. The obtained flexible sensing network patch can be attached to a skin area where the flexible sensing network patch is to be placed more finely; therefore, by executing the flexible sensing network design method in the embodiment, improvement of the fitting performance of the flexible sensing network patch is facilitated, personalized customization of the flexible sensing network patch is achieved, and therefore monitoring equipment applying the flexible sensing network patch can better monitor data of a patient, and miniaturization and portability of the monitoring equipment are achieved. The method is widely applied to the technical field of electronic device design automation.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic device design automation, in particular to a flexible sensor network design method, a computer device and a storage medium. Background Art

[0002] Flexible patches can be used in technical fields such as medical monitoring, mechanical assistance, and tracking and rescue. For example, patients who have undergone gastrointestinal surgery face the risk of complications such as bleeding and infection, and need to monitor their vital signs throughout the perioperative period. The professional monitoring equipment used by medical institutions is not suitable for home use. Some current home monitoring equipment has disadvantages such as being bulky and inconvenient to carry, which limits real-time monitoring of patients. Monitoring equipment based on flexible patches can install sensors and other devices on the flexible patches, and attach the flexible patches to the parts of the patient that need to be monitored to monitor the patient. In addition, flexible patches have the advantages of being small in size and light in weight, thereby achieving portability of monitoring equipment, which is conducive to increasing the effective use time of monitoring equipment and achieving real-time monitoring of patients.

[0003] Flexible patches must adhere well to the patient's skin to enable the sensors to accurately monitor data. Current flexible patch design is often guided by experience, such as a rough classification based on factors like patient age and gender, which then results in the selection of a corresponding flexible patch shape template. This results in poor skin conformity, impacting the monitoring device's effectiveness. Summary of the Invention

[0004] In view of the technical problems such as poor adhesion of flexible patches designed by current flexible patch design technology to the patient's skin, the purpose of the present invention is to provide a flexible sensor network design method, a computer device and a storage medium.

[0005] In one aspect, an embodiment of the present invention includes a flexible sensor network design method, the flexible sensor network design method comprising the following steps:

[0006] Establishing a plurality of sub-surface models; each of the sub-surface models corresponds to a corresponding surface portion on the flexible sensor network patch to be designed;

[0007] executing at least one cyclic process;

[0008] Determining the parameters of the flexible sensor network patch based on the characteristic data adjusted in the last cycle;

[0009] Wherein, any cycle process includes the following steps:

[0010] configuring each of the sub-surface models respectively to obtain characteristic data corresponding to each of the sub-surface models;

[0011] For any of the sub-surface models, determining a local fitting size threshold corresponding to the sub-surface model according to the characteristic data corresponding to the sub-surface model;

[0012] Determining a global fitting size threshold according to each of the local fitting size thresholds;

[0013] Determining the local conformal state corresponding to each of the sub-surface models according to the global fitting size threshold;

[0014] The characteristic data corresponding to the sub-surface model is maintained or adjusted according to the local conformal state.

[0015] Furthermore, configuring each of the sub-surface models to obtain characteristic data corresponding to each of the sub-surface models includes:

[0016] When the loop process is the first loop process, initially configuring the characteristic data for each of the sub-surface models;

[0017] When the cycle process is another cycle process after the first cycle process, the characteristic data adjusted in the previous cycle process is obtained as the characteristic data of the current cycle process.

[0018] Furthermore, the initial configuration of the characteristic data for each of the sub-surface models includes:

[0019] For any of the subsurface models:

[0020] performing surface curvature measurement on the skin area corresponding to the sub-surface model to obtain surface characteristic data corresponding to the sub-surface model;

[0021] Initializing geometric dimension data and mechanical property data corresponding to the sub-surface model; the geometric dimension data includes the width and thickness of the sub-surface model, and the mechanical property data includes the Poisson's ratio and modulus of the sub-surface model;

[0022] The surface characteristic data, the geometric dimension data and the mechanical characteristic data are used to determine the characteristic data after initial configuration.

[0023] Furthermore, determining a local fitting size threshold corresponding to the sub-surface model according to the characteristic data corresponding to the sub-surface model includes:

[0024] Inputting the characteristic data into a trained artificial intelligence network for processing;

[0025] Obtaining adhesion energy, strain energy, and maximum stress corresponding to the sub-surface model output by the artificial intelligence network;

[0026] Setting dual-condition bonding determination constraint conditions; the dual-condition bonding determination constraint conditions include: adhesion energy + strain energy ≤ 0, maximum stress ≤ fracture stress;

[0027] Determining an energy critical width and a stress critical width according to the dual-condition fitting constraint; the energy critical width and the stress critical width are critical widths for the sub-surface model to satisfy the dual-condition fitting constraint;

[0028] The local fitting size threshold is determined according to a minimum value between the energy critical width and the stress critical width.

[0029] Furthermore, determining a global fit size threshold according to each of the local fit size thresholds includes:

[0030] The global fitting size threshold is determined according to the minimum value of all the local fitting size thresholds.

[0031] Furthermore, determining the local conformal state corresponding to each of the sub-surface models according to the global fitting size threshold includes:

[0032] For any of the sub-surface models, obtaining the width of the sub-surface model before adjustment during this cycle;

[0033] When the width before adjustment is less than or equal to the global fitting size threshold, the local conformal state of the sub-surface model is determined to be fitable; otherwise, the local conformal state of the sub-surface model is determined to be unfittable.

[0034] Furthermore, maintaining or adjusting the characteristic data corresponding to the sub-surface model according to the local conformal state includes:

[0035] Determining a global conformal state corresponding to the current cycle according to each of the local conformal states;

[0036] When the global conformal state is the first state, the width of the sub-surface model whose local conformal state is conformable is maintained, and the width of the sub-surface model whose local conformal state is unconformable is adjusted.

[0037] Furthermore, the cycle process further comprises the following steps:

[0038] When the global conformal state is the second state, all the loop processes are terminated.

[0039] On the other hand, an embodiment of the present invention further includes a computer device including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the flexible sensor network design method of the embodiment.

[0040] On the other hand, an embodiment of the present invention further includes a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to execute the flexible sensor network design method in the embodiment.

[0041] The beneficial effects of the present invention are as follows: the flexible sensor network design method in the embodiment can configure the characteristic data of the sub-surface model in the flexible sensor network patch, so that the characteristic data of each sub-surface model evolves in the direction of being able to fit well with the corresponding skin area, and the obtained flexible sensor network patch can fit more finely with the skin area to be placed; therefore, by executing the flexible sensor network design method in the embodiment, it is beneficial to improve the fit of the flexible sensor network patch and realize the personalized customization of the flexible sensor network patch, so that the monitoring equipment using the flexible sensor network patch can better monitor the patient's data and realize the miniaturization and portability of the monitoring equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the steps of the flexible sensor network design method in the embodiment;

[0043] Figure 2 Schematic diagram of the flexible sensor network patch and sub-surface model in the embodiment. DETAILED DESCRIPTION

[0044] In this embodiment, Figure 1 As shown, the flexible sensor network design method includes the following steps:

[0045] S1. Establish multiple sub-surface models;

[0046] S2. Execute at least one cycle;

[0047] S3. Determine the parameters of the flexible sensor network patch based on the characteristic data adjusted during the last cycle.

[0048] Steps S1-S3 are the process of designing a flexible sensor network patch (also referred to as a flexible patch), specifically designing parameters such as geometric (dimensional) parameters and material (mechanical) parameters of the flexible sensor network patch.

[0049] In step S1, Figure 2As shown, multiple sub-surface models are established. Each sub-surface model can be represented by a surface in space. Each sub-surface model has its own characteristic data, including surface characteristics, geometric dimensions, and mechanical properties. Each sub-surface model corresponds to a surface portion of the flexible sensor network patch to be designed, and all sub-surface models constitute the entire flexible sensor network patch.

[0050] In step S2, Figure 2 As shown, multiple loop processes are executed, and after each loop process is executed, a check is made to see whether a loop end condition is satisfied. If the loop end condition is not satisfied, the next loop process is executed. If the loop end condition is satisfied, the next loop process is not executed, and all loop processes are terminated. In this embodiment, the principles of each loop process are the same, and one of the loop processes is used as an example for description.

[0051] In this embodiment, any cycle process (for example, the i-th cycle process) includes the following steps:

[0052] S201. Configure each sub-surface model separately to obtain the characteristic data corresponding to each sub-surface model;

[0053] S202. For any sub-surface model, determine a local fitting size threshold corresponding to the sub-surface model based on characteristic data corresponding to the sub-surface model;

[0054] S203. Determine the global fit size threshold based on each local fit size threshold;

[0055] S204. Determine the local conformal state corresponding to each sub-surface model according to the global fitting size threshold;

[0056] S205. Maintain or adjust characteristic data corresponding to the sub-surface model according to the local conformal state.

[0057] In step S201 , if the i-th loop process is the first loop process, each sub-surface model is initially configured so that each sub-surface model obtains an initial value of its corresponding characteristic data.

[0058] Specifically, for any sub-surface model (for example, the nth sub-surface model), when performing its initial configuration, a three-coordinate measuring machine or a digital speckle interferometer or other equipment can be used to measure the surface curvature of the skin area corresponding to the nth sub-surface model (specifically, it can be the skin area covered by the nth sub-surface model after the flexible sensing network patch to be designed is attached to the patient's chest, abdomen, joints and other skin parts), and obtain the surface curvature κ1 (specifically, it can represent the curvature in one direction) and κ2 (specifically, it can represent the curvature in another perpendicular direction) of this skin area, thereby serving as the surface characteristic data corresponding to the nth sub-surface model; then, the geometric dimension data (specifically, including the width w and thickness t of the nth sub-surface model, etc.) and mechanical characteristic data (specifically, including Poisson's ratio ν and modulus E1, E2, etc.) corresponding to the sub-surface model can be initialized. Specifically, during the initial configuration, random values ​​can be generated within a certain range as the initial values ​​of the geometric dimension data and the mechanical characteristic data. The surface characteristic data κ1 and κ2, geometric dimension data w and t, and mechanical characteristic data ν, E1 and E2 obtained through actual measurement constitute the characteristic data of the nth sub-surface model after initial configuration.

[0059] In this embodiment, since the characteristic data corresponding to the sub-surface model is maintained or adjusted each time the loop process executes step S205, the characteristic data maintained or adjusted each time the loop process executes step S205 is used as the characteristic data when the next loop process executes step S201. Therefore, if the i-th loop process is not the first loop process (for example, it is a loop process after the first loop process), then when the i-th loop process executes step S201, the characteristic data maintained or adjusted in the (i-1)-th loop process is used.

[0060] Since there will be no ambiguity, in this embodiment, both the characteristic data obtained by initial configuration and the characteristic data obtained after maintenance or adjustment can be expressed as surface characteristic data κ1 and κ2, geometric dimension data w and t, and mechanical characteristic data ν, E1 and E2, etc.

[0061] In step S202, taking the nth sub-surface model as an example, the current adhesion energy, strain energy and maximum stress of the nth sub-surface model can be determined based on the current surface characteristic data κ1 and κ2, geometric dimension data w and t, and mechanical characteristic data ν, E1 and E2 of the nth sub-surface model.

[0062] Specifically, the characteristic data can be processed using a trained artificial intelligence network that has the capability of identifying corresponding adhesion energy, strain energy, and maximum stress based on the characteristic data.

[0063] In this embodiment, the processing of data by the trained artificial intelligence network is represented as f(), then for the first sub-surface model,

[0064] f1(κ1,κ2,E1,w,t,v,E2) (κ1,κ2,E1,w,t,v,E2, etc. are the characteristic data of the first sub-surface model)

[0065] The adhesion energy, strain energy and maximum stress performance of the first sub-surface model are obtained. Similarly, the adhesion energy, strain energy and maximum stress performance of the second sub-surface model, the third sub-surface model... the nth sub-surface model can be obtained respectively by

[0066] f2(κ1,κ2,E1,w,t,v,E2) (κ1,κ2,E1,w,t,v,E2, etc. are the characteristic data of the second sub-surface model)

[0067] f3(κ1,κ2,E1,w,t,v,E2) (κ1,κ2,E1,w,t,v,E2, etc. are the characteristic data of the third sub-surface model)

[0068] …

[0069] f n (κ1,κ2,E1,w,t,v,E2) (κ1,κ2,E1,w,t,v,E2, etc. are the characteristic data of the nth sub-surface model)

[0070] Waiting for processing.

[0071] In step S202, taking the nth sub-surface model as an example, after obtaining the current adhesion energy, strain energy and maximum stress of the nth sub-surface model, a dual-condition fitting judgment constraint condition can be set, and the energy critical width w corresponding to the nth sub-surface model can be determined according to the dual-condition fitting judgment constraint condition. energy and stress critical width w stress .

[0072] Specifically, the constraints for the two-condition fit determination include:

[0073] Minimum energy condition: adhesion energy + strain energy ≤ 0

[0074] Minimum local stress condition: maximum stress ≤ fracture stress

[0075] In this embodiment, the energy critical width w can be determined according to the minimum energy condition. energy For example, for the nth sub-surface model, determine the width w of the nth sub-surface model when the minimum energy condition is just established, so as to obtain the energy critical width w corresponding to the nth sub-surface model energy .

[0076] In this embodiment, the critical stress width w can be determined according to the minimum local stress condition. stress For example, for the nth sub-surface model, determine the width w of the nth sub-surface model when the minimum local stress condition is just established, so as to obtain the stress critical width w corresponding to the nth sub-surface model stress .

[0077] After obtaining the energy critical width w of the nth subsurface model energy and stress critical width w stress After that, the energy critical width w can be taken energy and stress critical width w stress The minimum value among them is used as the local fitting size threshold w of the nth sub-surface model n .

[0078] Since the nth sub-surface model is any sub-surface model, similar steps can be used to obtain the local fitting size thresholds w1, w2, w3…w of each sub-surface model. n .

[0079] In step S203, according to each local fitting size threshold w1, w2, w3...w n , determine the global fitting size threshold w critical . Global fit size threshold w critical It represents the fitting size threshold of the flexible sensor network patch as a whole as a complex surface during the current iteration.

[0080] In this embodiment, all local fit size thresholds w1, w2, w3…w n The minimum value among them is determined as the global fitting size threshold w critical ,Right now

[0081] w critical =min(w1,w2,w3…w n )

[0082] In step S204, according to the global fit size threshold w critical , determine the local conformal state of each sub-surface model. The local conformal state of a sub-surface model indicates whether the part of the flexible sensing network patch corresponding to this sub-surface model is well aligned with the corresponding skin area.

[0083] In this embodiment, taking the nth sub-surface model as an example, when executing step S204, step S205 of this loop process has not yet been executed. At this time, the width w of the nth sub-surface model is the width before adjustment in this loop process. If the width w of the nth sub-surface model is less than or equal to the global fitting size threshold w critical, that is, w≤w critical , then it means that the nth sub-surface model can fit its corresponding skin area well, so the local conformal state of the nth sub-surface model is judged to be "fittable"; on the contrary, if the width w of the nth sub-surface model is greater than the global fitting size threshold w critical , that is, w>w critical , this indicates that the nth sub-surface model cannot fit its corresponding skin area well, and therefore the local conformal state of the nth sub-surface model is determined to be "unfittable". Since the nth sub-surface model is an arbitrary sub-surface model, similar steps can be used to determine the local conformal state of each sub-surface model during the i-th cycle.

[0084] In this embodiment, when executing step S205 in the i-th loop process, that is, when maintaining or adjusting the characteristic data corresponding to the sub-surface model according to the local conformal state, the global conformal state corresponding to the i-th loop process can be determined first based on each local conformal state.

[0085] In this embodiment, the global conformal state corresponding to the i-th loop process can be the number of sub-surface models whose local conformal state is "fittable" during the i-th loop process (specifically, the absolute number or the proportion of the total number of sub-surface models). In this embodiment, a threshold value can be set. If the global conformal state is greater than or equal to the threshold value, it can be determined that the global conformal state is large and the global conformal state is the first state. In this case, when step S205 is executed, the width w of the sub-surface models whose local conformal state is "fittable" is maintained (i.e., their values ​​are not adjusted), while the width w of the sub-surface models whose local conformal state is "unfittable" is adjusted. Specifically, the width w that needs to be adjusted can be adjusted by randomly modifying the value. After executing step S205 of the i-th loop process, the i-th loop process ends and the i+1-th loop process begins. When executing step S201 of the i+1-th loop process, the characteristic data obtained by executing step S205 of the i-th loop process can be directly called.

[0086] If the global conformal state is less than the threshold, it can be determined that the global conformal state is small and the global conformal state is in the second state. At this time, when executing step S205 in the i-th loop, the width w of all sub-surface models can be maintained (i.e., their values ​​are not adjusted), and the current loop, i.e., the i-th loop, and all loops can be terminated. In other words, the "global conformal state is less than the threshold" can be used as the end condition for the loop, and the i-th loop is the last loop.

[0087] In this embodiment, by executing a loop process, the characteristic data (especially the width) of the sub-surface models in the flexible sensing network patch can be configured so that the characteristic data of each sub-surface model evolves in the direction of being able to fit well with the corresponding skin area; specifically, "the global conformal state is less than the quantity threshold" can be used as the end condition of the loop process. When this end condition is not met, it means that the number of sub-surface models that cannot fit well with the corresponding skin area is large, and the next loop process is continued to optimize the characteristic data of the sub-surface model; when this end condition is met, it means that the number of sub-surface models that cannot fit well with the corresponding skin area is small, or all sub-surface models can fit well with the corresponding skin area, so all loop processes are terminated and the optimization of the characteristic data of the sub-surface model is completed. According to the characteristic data of the optimized sub-surface model, a flexible sensor network patch can be manufactured using materials such as PDMS (Polydimethylsiloxane) using 3D printing or laser cutting technology. Specifically, a PDMS substrate with a bionic topological structure can be prepared, and each area on the PDMS substrate corresponds to each sub-surface model, and has characteristic parameters such as surface characteristic data κ1 and κ2, geometric dimension data w and t, and mechanical characteristic data ν, E1 and E2 of the corresponding sub-surface model. The resulting flexible sensor network patch can fit the skin area to be placed more finely; therefore, by executing the flexible sensor network design method in this embodiment, it is beneficial to improve the conformability of the flexible sensor network patch and realize personalized customization of the flexible sensor network patch, so that the monitoring equipment using the flexible sensor network patch can better monitor the patient's data and realize the miniaturization and portability of the monitoring equipment.

[0088] In this embodiment, in addition to adjusting the width of the sub-surface model when executing step S205, other characteristic data of the sub-surface model can also be adjusted, such as one or more of the surface characteristic data κ1 and κ2, geometric dimension data t, and mechanical characteristic data ν, E1 and E2.

[0089] A computer program that executes the flexible sensor network design method in this embodiment can be written and written into a computer device or storage medium. When the computer program is read out and run, the flexible sensor network design method in this embodiment is executed, thereby achieving the same technical effect as the flexible sensor network design method in the embodiment.

[0090] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. In addition, the descriptions of up, down, left, right, etc. used in this disclosure are only relative to the relative positional relationship of the components of the present disclosure in the accompanying drawings. The singular forms of "a" and "the" used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as those generally understood by those skilled in the art. The terms used in the specification of this embodiment are only for describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in this embodiment includes any combination of one or more related listed items.

[0091] It should be understood that, although the present disclosure may adopt the term first, second, third etc. to describe various elements, these elements should not be limited to these terms.These terms are only used to distinguish the elements of the same type from each other.For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element.The use of any and all examples or exemplary language ("for example", "such as" etc.) provided by the present embodiment is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0092] It should be appreciated that embodiments of the present invention can be implemented or practiced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The methods can be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and figures described in the specific embodiments. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, the program can be run on a programmed application-specific integrated circuit for this purpose.

[0093] In addition, the operations of the process described in this embodiment may be performed in any suitable order, unless otherwise indicated in this embodiment or otherwise clearly contradicted by the context. The process described in this embodiment (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as a code (e.g., executable instructions, one or more computer programs, or one or more applications) executed on one or more processors, by hardware or a combination thereof. A computer program includes a plurality of instructions that may be executed by one or more processors.

[0094] Furthermore, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the above steps in conjunction with a microprocessor or other data processor, the invention of this embodiment includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0095] The computer program can be applied to input data to perform the functions of the present embodiment, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0096] The above are merely preferred embodiments of the present invention. The present invention is not limited to the aforementioned embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods may be made.

Claims

1. A flexible sensor network design method, characterized in that: The flexible sensor network design method comprises: Establishing a plurality of sub-surface models; each of the sub-surface models corresponds to a corresponding surface portion on the flexible sensor network patch to be designed; executing at least one cyclic process; Determining the parameters of the flexible sensor network patch based on the characteristic data adjusted in the last cycle; Wherein, any cycle process includes the following steps: configuring each of the sub-surface models respectively to obtain characteristic data corresponding to each of the sub-surface models; For any of the sub-surface models, determining a local fitting size threshold corresponding to the sub-surface model according to the characteristic data corresponding to the sub-surface model; Determining a global fitting size threshold according to each of the local fitting size thresholds; Determining the local conformal state corresponding to each of the sub-surface models according to the global fitting size threshold; The characteristic data corresponding to the sub-surface model is maintained or adjusted according to the local conformal state.

2. The flexible sensor network design method according to claim 1, characterized in that: The configuring each of the sub-surface models to obtain characteristic data corresponding to each of the sub-surface models includes: When the loop process is the first loop process, initially configuring the characteristic data for each of the sub-surface models; When the cycle process is another cycle process after the first cycle process, the characteristic data adjusted in the previous cycle process is obtained as the characteristic data of the current cycle process.

3. The flexible sensor network design method according to claim 2, characterized in that: The initial configuration of the characteristic data for each of the sub-surface models comprises: For any of the subsurface models: performing surface curvature measurement on the skin area corresponding to the sub-surface model to obtain surface characteristic data corresponding to the sub-surface model; Initializing geometric dimension data and mechanical property data corresponding to the sub-surface model; the geometric dimension data includes the width and thickness of the sub-surface model, and the mechanical property data includes the Poisson's ratio and modulus of the sub-surface model; The surface characteristic data, the geometric dimension data and the mechanical characteristic data are used to determine the characteristic data after initial configuration.

4. The flexible sensor network design method according to claim 1, characterized in that: Determining the local fitting size threshold corresponding to the sub-surface model according to the characteristic data corresponding to the sub-surface model includes: Inputting the characteristic data into a trained artificial intelligence network for processing; Obtaining adhesion energy, strain energy, and maximum stress corresponding to the sub-surface model output by the artificial intelligence network; Setting dual-condition bonding determination constraint conditions; the dual-condition bonding determination constraint conditions include: adhesion energy + strain energy ≤ 0, maximum stress ≤ fracture stress; Determining an energy critical width and a stress critical width according to the dual-condition fitting constraint; the energy critical width and the stress critical width are critical widths for the sub-surface model to satisfy the dual-condition fitting constraint; The local fitting size threshold is determined according to a minimum value between the energy critical width and the stress critical width.

5. The flexible sensor network design method according to claim 4, characterized in that: Determining the global fit size threshold according to each of the local fit size thresholds includes: The global fitting size threshold is determined according to the minimum value of all the local fitting size thresholds.

6. The flexible sensor network design method according to any one of claims 1 to 5, characterized in that: Determining the local conformal state corresponding to each of the sub-surface models according to the global fitting size threshold includes: For any of the sub-surface models, obtaining the width of the sub-surface model before adjustment during this cycle; When the width before adjustment is less than or equal to the global fitting size threshold, the local conformal state of the sub-surface model is determined to be fitable; otherwise, the local conformal state of the sub-surface model is determined to be unfittable.

7. The flexible sensor network design method according to claim 6, characterized in that: Maintaining or adjusting the characteristic data corresponding to the sub-surface model according to the local conformal state includes: Determining a global conformal state corresponding to the current cycle according to each of the local conformal states; When the global conformal state is the first state, the width of the sub-surface model whose local conformal state is conformable is maintained, and the width of the sub-surface model whose local conformal state is unconformable is adjusted.

8. The flexible sensor network design method according to claim 7, characterized in that: The cycle also includes the following steps: When the global conformal state is the second state, all the loop processes are terminated.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the flexible sensor network design method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to execute the flexible sensor network design method according to any one of claims 1 to 8 when executed by the processor.

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