Multi-physical field data fusion system and method, product, and medium for biological probes

Abstract

The present invention provides a multi-physical field data fusion system and method, product, and medium for a biological probe, belonging to the fields of biosensing and tissue feature analysis, and solving the technical problem of how to accurately and effectively adapt to different biological tissues to fuse the multi-physical field data collected by the biological probe and extract key information, and then quickly and accurately determine the state of the biological tissue. By combining multi-physical field data fusion, parameter optimization, and adaptive model construction, the present invention can significantly improve the overall performance of the biological probe measurement system, fully explore the internal correlation between the physical field data for the state detection of biological tissues, avoid information loss and deviation caused by single physical field data, and provide multi-dimensional support for subsequent biometric analysis.

Description

Technical Field

[0001] The present invention relates to the fields of biosensing and tissue characterization analysis in medical engineering, and particularly to a multi-physical field data fusion system and method for a biological probe, a computer program product, and a computer-readable storage medium. Background Art

[0002] With the development of biomedical technology, as an important detection tool, biological probes have been widely used in the state monitoring of biological tissues, disease diagnosis, and medical research. The perception and data acquisition of biological probes for characteristic tissues provide key information about the state of biological tissues for doctors and researchers.

[0003] However, for different biological tissues and the detection results of biological probes for different biological tissues, there are complex interaction relationships. How to accurately and effectively adapt to different biological tissues to fuse multi-source data, that is, multi-physical field data collected by biological probes, and extract key information, and then quickly and accurately determine the state of biological tissues has become an urgent technical problem to be solved. Summary of the Invention

[0004] An object of the present invention is to solve the technical problem of how to accurately and effectively adapt to different biological tissues to fuse multi-physical field data (including impedance data, pressure data, temperature data, and angle data) collected by biological probes, extract key information, and then quickly and accurately determine the state of biological tissues.

[0005] According to one aspect of an embodiment of the present invention, a multi-physical field data fusion system for a biological probe is disclosed. The system includes:

[0006] A biological probe for collecting data from the currently measured biological tissue to obtain multi-physical field data of the biological tissue, where the multi-physical field data includes environmental parameters and intrinsic characteristic parameters of the biological tissue;

[0007] A memory, a processor, and a computer program stored on the memory, where the computer program is executed by the processor to implement the following steps:

[0008] Determine key parameters of the currently measured biological tissue from the multi-physical field data through a biological probe measurement model, find a set of optimal parameter values for the key parameters, and a weighted coefficient combination mapped by the optimal parameter values;

[0009] Adapt to the contact between the biological probe and the biological tissue, and calibrate and fuse the optimal parameter values through the mapped weighted coefficient combination to obtain characteristic parameters of the biological tissue;

[0010] Construct a biological tissue feature analysis model that adaptively constructs the feature parameter mapping for the measured biological tissue, and output the state evaluation result of the biological tissue.

[0011] According to one aspect of the embodiments of the present application, the biological probe senses different types of physical signals, and performs real-time sensing and acquisition of multi-physical field data corresponding to multiple physical fields on the biological tissue.

[0012] According to one aspect of the embodiments of the present application, the biological probe measurement model is used to determine the key parameters of the currently measured biological tissue from the multi-physical field data, and find a set of optimal parameter values for the key parameters, as well as the weighted coefficient combination mapped by the optimal parameter values, including:

[0013] For the biological tissue currently contacted and measured by the biological probe, based on the discretization processing of the multi-physical field data by the biological probe measurement model and the random sampling on the discretization processing, perform sensitivity calculation to determine the key parameters in the multi-physical field data;

[0014] Perform parameter identification on the key parameters in the multi-physical field data to obtain a set of optimal parameter values corresponding to the key parameters, as well as the weighted coefficient combination mapped by the optimal parameter values.

[0015] According to one aspect of the embodiments of the present application, for the biological tissue currently contacted and measured by the biological probe, based on the discretization processing of the multi-physical field data by the biological probe measurement model and the random sampling on the discretization processing, perform sensitivity calculation to determine the key parameters in the multi-physical field data, including:

[0016] Initialize the parameter range of the parameter mapping in the multi-physical field data for the biological tissue and the number of sampling points of the parameter within the parameter range;

[0017] Adapt the number of sampling points to generate a series of discrete sampling values within the parameter range, and use the sampling values as the possible values of the corresponding parameters to form the vector representation of the parameters;

[0018] For the vector representation of the parameters, evaluate the sensitivity of each parameter by sequentially changing the parameters, and take the parameter with the greater sensitivity as the key parameter in the multi-physical field data.

[0019] According to one aspect of the embodiments of the present application, the parameter ranges of each parameter mapping are different for different biological tissues.

[0020] According to one aspect of the embodiments of the present application, for the vector representation of the parameters, evaluating the sensitivity of each parameter by sequentially changing the parameters and taking the parameter with the greater sensitivity as the key parameter in the multi-physical field data includes:

[0021] Calculate the local sensitivity and global sensitivity of the sampled trajectories to obtain the local sensitivity and global sensitivity of each parameter;

[0022] Identify the parameters that have a significant impact on the state change of the biological tissue under its own physiological activities based on the local sensitivity and global sensitivity as the key parameters of the multi-physical field data in the biological tissue.

[0023] According to one aspect of the embodiments of the present application, the parameter identification of the key parameters in the multi-physical field data to obtain a set of optimal parameter values corresponding to the key parameters, and the weighted coefficient combination mapped by the optimal parameter values, includes:

[0024] Perform a genetic algorithm on the multi-physical field data corresponding to each parameter to iteratively optimize the biological probe measurement model, and obtain a set of optimal parameter values corresponding to the key parameters and the weighted coefficient combination mapped by the optimal parameter values.

[0025] According to one aspect of the embodiments of the present invention, a method for fusing multi-physical field data of a biological probe is disclosed, characterized in that the method performs the steps implemented in the system described above.

[0026] According to one aspect of the embodiments of the present invention, a computer program product is disclosed, including a computer program, characterized in that the computer program, when executed by a processor, implements the system described above.

[0027] According to one aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed, on which a computer program is stored, and the program, when executed by a processor, implements the system described above.

[0028] By combining multi-physical field data fusion, parameter optimization, and adaptive model construction, the embodiments of the present invention can significantly improve the overall performance of the biological probe measurement system.

[0029] Specifically, the system implemented by the embodiments of the present invention comprehensively considers the influence of different physical fields on biological tissues and implements the fusion of multi-physical field data (including impedance data, pressure data, temperature data, and angle data) obtained by biological probes, so as to adapt to the currently measured biological tissues and obtain more comprehensive and accurate measurement results. Moreover, the search for the optimal values of key parameters and the optimization of the weighted coefficient combination effectively improve the recognition ability of the biological probe measurement model for the characteristics of complex biological tissues, greatly reducing errors and improving the stability of measurements.

[0030] The optimization of the biological probe measurement model adapted to biological tissues can be dynamically adjusted according to different environmental conditions and biological tissue characteristics, so that excellent performance can be maintained for various biological tissues and various changes of biological tissues. Finally, it can adaptively and accurately respond to the changes of physical tissues and the environment, and the system adaptability and robustness are enhanced.

[0031] Thus, the efficient fusion of multi-physical field data (including impedance data, pressure data, temperature data, and angle data) is achieved, fully exploring the internal relationships between the physical field data for the state detection of biological tissues, avoiding information loss and deviation caused by single physical field data, and providing multi-dimensional support for subsequent biometric analysis.

[0032] Other features and advantages of the present invention will become apparent from the following detailed description or will be partly learned through the practice of the present invention.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other objects, features, and advantages of the present invention will become more apparent.

[0035] Figure 1 It is a schematic diagram of the implementation of a multi-physical field data fusion system of a biological probe shown according to an exemplary embodiment.

[0036] Figure 2 It is according to Figure 1 The corresponding embodiment shows a method flowchart for determining the key parameters of the currently measured biological tissue for multi-physical field data through a biological probe measurement model, finding a set of optimal parameter values for the key parameters, and the weighted coefficient combination steps mapped by the optimal parameter values.

[0037] Figure 3 It is according to Figure 2 The corresponding embodiment shows a method flowchart for discretizing multi-physical field data based on a biological probe measurement model and randomly sampling on the discretization process for the biological tissue currently contacted and measured by the biological probe, and implementing sensitivity calculation to determine the key parameters in the multi-physical field data.

[0038] Figure 4 It is according to Figure 3 The corresponding embodiment shows a method flowchart for generating a series of discrete sampling values within the parameter range for the number of adaptive sampling points, and forming a vector representation of the parameter with the sampling values as the possible values of the corresponding parameter.

[0039] Figure 5is based on Figure 3 The method flowchart is described by the steps of obtaining the vector representation of the parameter-oriented according to the corresponding embodiment, evaluating the sensitivity of each parameter by sequentially changing the parameter, and taking the parameter with the greater sensitivity as the key parameter in the multi-physical field data.

[0040] Figure 6 is based on Figure 3 The method flowchart is described by the steps of obtaining the vector representation of the parameter-oriented according to the corresponding embodiment, evaluating the sensitivity of each parameter by sequentially changing the parameter, and taking the parameter with the greater sensitivity as the key parameter in the multi-physical field data.

[0041] Figure 7 It shows the mapping diagram of the weighted fusion value (the evaluation result of the biological tissue health state) corresponding to the sample of each skin tissue to the health state. Detailed implementation mode

[0042] Now, the example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present invention will be more complete and thorough, and the concept of the example embodiments will be fully conveyed to those skilled in the art. The drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted.

[0043] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to give a thorough understanding of the example embodiments of the present invention. However, those skilled in the art will realize that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, steps, etc. can be adopted. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring the various aspects of the present invention.

[0044] Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0045] Refer to Figure 1 , Figure 1 which is a schematic diagram of the implementation of the multi-physical field data fusion system of a biological probe shown according to an exemplary embodiment.

[0046] The multi - physical - field data fusion system of the biological probe provided by the embodiment of the present invention includes a biological probe and a control terminal. The control terminal includes a memory, a processor, and a computer program stored on the memory. It should be understood that the control terminal interacts with the biological probe to control the biological probe and process the data collected by the biological probe, and output the corresponding evaluation result of the biological tissue state.

[0047] The control terminal can be various terminal devices, such as Figure 1 the shown control terminal 1, control terminal 2, and control terminal 3. Any terminal device that can access the biological probe and run the computer program can be used as the control terminal of the system implemented by the present invention.

[0048] The biological probe is used to collect data from the currently measured biological tissue to obtain the multi - physical - field data of the biological tissue. The multi - physical - field data includes the environmental parameters and the intrinsic characteristic parameters of the biological tissue.

[0049] The computer program is executed by the processor to implement the following steps:

[0050] Step S410: Determine the key parameters of the currently measured biological tissue from the multi - physical - field data through the biological probe measurement model, find a set of optimal parameter values for the key parameters, and the combination of weighting coefficients mapped by the optimal parameter values.

[0051] Step S420: Adapt to the contact between the biological probe and the biological tissue, and calibrate and fuse the optimal parameter values through the mapped combination of weighting coefficients to obtain the characteristic parameters of the biological tissue.

[0052] Step S430: Adaptively construct a biological tissue characteristic analysis model that maps the characteristic parameters for the measured biological tissue, and output the evaluation result of the biological tissue state.

[0053] The following elaborates in detail on this system and the steps implemented by the operation of this system.

[0054] The biological probe can be configured with sensors, sensor arrays, etc., and is configured according to the full dimension required for parameter detection, so that the biological probe can perceive and collect the biological tissue in multiple physical fields.

[0055] For the currently required characteristic tissues to be measured, such as skin, liver, spine, etc., the multi - physical - field data can be perceived and collected under the action of the biological probe. The biological probe is used to collect relevant parameters in the environment and / or biological tissue in real - time, that is, environmental parameters and their intrinsic characteristic parameters.

[0056] That is to say, the multi-physical field data obtained by the biosensor through data acquisition of the currently measured biological tissue, including the parameters corresponding to physical quantities such as impedance, pressure, temperature, and angle, and the parameter values corresponding to various parameters constitute a physical field data.

[0057] The biosensor can sense different types of physical signals of the biological tissue, and perform real-time sensing of multi-physical signals on the biological tissue, so as to perform real-time sensing and acquisition of multi-physical field data corresponding to multiple physical fields on the biological tissue.

[0058] For any biological tissue to be measured, the biosensor performs data acquisition for multiple physical fields, that is, through built-in sensors, sensor arrays, etc., it performs parameter acquisition of physical quantities such as impedance, pressure, temperature, and angle, obtains physical field data corresponding to various parameters, and the physical field data corresponding to all types of parameters forms the multi-physical field data output by the biosensor.

[0059] The biosensor acquires different types of multi-physical field data through the interaction with the biological tissue by physical principles (such as impedance, temperature, pressure, heat conduction, electromagnetic wave reflection, conductivity, etc.). The multi-physical field data reflects the physical, chemical, and physiological characteristics of the biological tissue.

[0060] The biosensor that performs data acquisition in the system transmits the acquired multi-physical field data to the computer program running on the processor, and then the computer program performs multi-physical field data fusion adapted to the currently measured biological tissue on the multi-physical field data, and finally, on this basis, efficiently and accurately analyzes the characteristics of the biological tissue and outputs the state evaluation result of the currently measured biological tissue.

[0061] Under the action of the embodiments of the present invention, it is possible to perform state detection on different biological tissues without replacing and adjusting the biosensor, and achieve adaptive high-precision data fusion and state evaluation for different biological tissues.

[0062] For the multi-physical field data obtained by the biosensor, through the interaction between the biosensor and the computer program running in the system, it is transmitted to the computer program in a streaming manner by the biosensor, and the measurement model of the biosensor is optimized by executing steps S410 to S430 in the computer program, so as to adaptively and accurately fuse the multi-physical field data and output the state evaluation result for the measured biological tissue.

[0063] In step S410, on the one hand, the biosensor measurement model is used to identify the key parameters of the multi-physical field data that belong to the currently measured biological tissue and determine the corresponding parameter values, and on the other hand, the weighted coefficient combination of the identified various key parameters is optimized.

[0064] Specifically, the biological probe collects multi-physical field data of biological tissue by contacting the biological tissue. The collected multi-physical field data include but are not limited to environmental parameters such as temperature and pressure, and parameters describing the intrinsic characteristics of biological tissue such as impedance, mechanical stress, and electrical layer ratio.

[0065] The collected multi-physics field data are mostly high-dimensional and noisy, and there may be complex interactions between the physical fields. Therefore, it is necessary to use the biological probe measurement model to accurately obtain the optimal parameter values ​​of key parameters.

[0066] Key parameters refer to the parameters that have the greatest impact on the final results during the state assessment of biological tissues. The key parameters of different biological tissues are often different.

[0067] The bioprobe measurement model is a mathematical or computational model used to describe and analyze the interaction between the bioprobe and biological tissue. The bioprobe measurement model obtains the optimal parameter values ​​and weighted coefficient combinations of the adapted key parameters based on the multi-physical field data (such as temperature, conductivity, pressure, optical properties, etc.) sensed by the bioprobe.

[0068] The bioprobe measurement model extracts useful information from multi-physics field data to identify key parameters that have a significant impact on the state of the currently measured biological tissue. The bioprobe measurement model has adaptive capabilities for different biological tissues and can dynamically adjust according to different biological tissues and their environmental conditions to ensure high-precision evaluation of biological tissues.

[0069] Through the biological probe measurement model and even the discrete processing of each parameter and random sampling based on the discrete processing, the vector representation corresponding to each parameter is finally obtained. The vector representation of each parameter changes the parameter successively in each sampling, and then the sensitivity of each parameter is calculated, and the key parameters are identified based on the sensitivity.

[0070] For the identified key parameters, an optimization algorithm, such as a genetic algorithm, is used in the biological probe measurement model to iterate to find a set of optimal parameter values ​​corresponding to the key parameters and a weighted coefficient combination of the optimal parameter value mapping.

[0071] The optimal set of parameter values ​​found best matches the actual state of the biological tissue, and the corresponding weighted coefficient combination is obtained by trying different weighted coefficient combinations in the iterative optimization process, which can minimize the error in the biological tissue state assessment.

[0072] By combining the obtained weighted coefficients, the multi-physics field data is weightedly fused, and the characteristics of each physical field data are combined to improve the accuracy of the fusion results. For example, some parameters (such as impedance) may play a dominant role in a specific biological tissue state, while other parameters (such as humidity and angle) participate in the fusion as auxiliary information.

[0073] Please also refer to Figure 2 , Figure 2 which is a method flowchart for describing the steps of determining the key parameters of the currently measured biological tissue from multi-physical field data through a biological probe measurement model, finding a set of optimal parameter values for the key parameters, and the weighted coefficient combination mapping of the optimal parameter values, according to the corresponding embodiments shown in Figure 1 Figure 1

[0074] Step S410 of determining the key parameters of the currently measured biological tissue from multi-physical field data through a biological probe measurement model, finding a set of optimal parameter values for the key parameters, and the weighted coefficient combination mapping of the optimal parameter values provided by the embodiments of the present invention includes:

[0075] Step S411: For the biological tissue currently contacted and measured by the biological probe, based on the discretization processing of multi-physical field data by the biological probe measurement model and random sampling on the discretization processing, perform sensitivity calculation to determine the key parameters in the multi-physical field data;

[0076] Step S412: Perform parameter identification on the key parameters in the multi-physical field data to obtain a set of optimal parameter values corresponding to the key parameters and the weighted coefficient combination mapping of the optimal parameter values.

[0077] The following elaborates on these two steps in detail.

[0078] Based on the biological probe measurement model, perform discretization processing and random sampling on multi-physical field data, and perform sensitivity calculation to determine the key parameters in the multi-physical field data, ensuring the accuracy, stability, and efficiency of biological tissue measurement.

[0079] As mentioned above, through contact with biological tissue, the biological probe can sense and collect multi-physical field data of biological tissue. These data cover environmental parameters of biological tissue (such as temperature, humidity, pressure, etc.) and intrinsic characteristic parameters (such as conductivity, optical properties, mechanical properties, etc.). These multi-physical field data are often continuous, complex, and usually contain noise.

[0080] Therefore, in practical applications, it is first necessary to perform discretization processing on the collected continuous data to facilitate simple data processing and analysis.

[0081] Exemplarily, the discretization processing is to convert continuous physical quantities into a finite number of discrete values or intervals, obtaining the discretized representation of various parameters in the currently measured biological tissue, that is, the sampling values for each sampling point.

[0082] Random sampling is implemented based on discretization processing. Exemplarily, the implemented random sampling can be Monte Carlo simulation. Random sampling is performed within the parameter ranges defined by various parameters through Monte Carlo simulation to cover various possibilities, making the realization of sampling more accurate and balanced.

[0083] Specifically, the execution process of Monte Carlo simulation includes: randomly sampling the discretized sampling values of various types of parameters, performing a simulation once after obtaining a set of sampling values of parameters, that is, obtaining the sensitivity calculation of this set of sampling values, and improving and optimizing the biological probe measurement model based on the obtained calculation results, and so on. Iteratively perform Monte Carlo simulation to obtain a vector representation that can characterize significant influences and the state of biological tissues for implementing sensitivity.

[0084] Refer to Figure 3 , Figure 3 is based on Figure 2 The corresponding embodiment shows a method flowchart for describing the biological tissue currently contacted and measured by a biological probe, performing discretization processing on multi-physical field data based on the biological probe measurement model, and random sampling on the discretization processing, and implementing sensitivity calculation to determine key parameters in the multi-physical field data.

[0085] Step S411 of the embodiment of the present invention for the biological tissue currently contacted and measured by a biological probe, performing discretization processing on multi-physical field data based on the biological probe measurement model, and random sampling on the discretization processing, and implementing sensitivity calculation to determine key parameters in the multi-physical field data, includes:

[0086] Step S4111, initialize and configure the parameter ranges of various parameter mappings in the multi-physical field data for the biological tissue and the number of sampling points of the parameters within the parameter ranges;

[0087] Step S4112, adapt the number of sampling points to generate a series of discrete sampling values within the parameter ranges, and form a vector representation of the parameters with the sampling values as the possible values of the corresponding parameters;

[0088] Step S4113, for the vector representation of the parameters, evaluate the sensitivity of each parameter by successively changing the parameters, and take the parameter with greater sensitivity as the key parameter in the multi-physical field data.

[0089] The following elaborates on these steps in detail.

[0090] For different biological tissues, the parameters have different parameter ranges. Therefore, for the currently measured biological tissue, it is necessary to initialize and configure the parameter ranges of various parameter mappings in the multi-physical field data to control the reliability of the implemented discretization processing and random sampling.

[0091] In addition, the reasonable number of sampling points will be selected according to the parameter ranges and correlations of various parameters to ensure that the parameter ranges can be covered during the subsequent model operation, so as to obtain the most comprehensive results.

[0092] Exemplarily, the larger the parameter range, the more sampling points are required. And the stronger the correlation between various parameters, the number of sampling points can be appropriately reduced. Therefore, for different biological tissues and different types of parameters, the number of configured sampling points can be dynamically configured.

[0093] By dynamically configuring the number of sampling points, while ensuring the accuracy of the biological probe measurement model, the computational complexity is not overly increased, that is, the accuracy and computational efficiency are balanced through the dynamically configured number of sampling points.

[0094] For example, assume that the parameter range of each parameter is L i , U i . On the one hand, when the parameter range is small and the change trend is relatively linear, the parameter range can be divided into p equally spaced points as sampling points, and the number of sampling points is p ; on the other hand, sampling points can be added in the sensitive intervals existing in the parameter ranges of some parameters. Therefore, the number of sampling points will be dynamically configured to adapt to various situations.

[0095] After dynamically configuring the parameter ranges and the number of sampling points for biological tissues and various parameters, the continuous data in the mapped parameter ranges of various parameters is discretized into finite sampling points to obtain the corresponding sampling values as the vector representation of the parameters.

[0096] The sampling values, as the available values of the parameters, represent the possible discrete states of this type of parameter within its parameter range, and the obtained sampling values will be combined into a parameter vector, that is, the vector representation of this type of parameter.

[0097] For example, for k types of parameters, each type of parameter i has p i discrete sampling points within its parameter range, and the obtained vector representation of the parameter is:

[0098] X i = X i,0 , X i,1 , X i,2 , ……, X i, p i-1, ];

[0099] X i is a parameter i vector representation, and the elements in the vector X i,j represent the parameter i sampling value at the j-th sampling point.

[0100] And so on, for various types of parameters, their vector representations are obtained, and then successive variable sampling of the parameters is performed based on this. Finally, in the vector representations of all the parameters obtained, only the sampling value corresponding to one parameter changes among the vector representations of the parameters.

[0101] Furthermore, referring to Figure 4 , Figure 4 is based on Figure 3 The corresponding embodiment shows a method flowchart described for generating a series of discrete sampling values within the parameter range for the adapted number of sampling points and forming a vector representation of the parameter with the sampling values as the possible values of the corresponding parameter.

[0102] Step S4112 of generating a series of discrete sampling values within the parameter range for the adapted number of sampling points and forming a vector representation of the parameter with the sampling values as the possible values of the corresponding parameter provided by the embodiment of the present invention includes:

[0103] Step S501, generating an equally spaced normalized sequence according to the number of sampling points;

[0104] Step S502, scaling the normalized sequence proportionally to the parameter range of the corresponding type of parameter to generate sampling values corresponding to the number of sampling points for various types of parameters, and the sampling values are used to generate the vector representation of the parameter.

[0105] This is the process of generating a discretized sampling set within the parameter range mapped by the parameter. The obtained vector representation of the parameter is the discretized sampling set within the parameter range. By executing this process, the parameter range can be divided into a series of discrete points, and then the influence of the parameter can be estimated through these discrete points to determine the influence degree of each parameter.

[0106] The sampling points of the parameter are defined by uniformly discretizing the value range of the parameter into several equally spaced points. Specifically, the sampling points are obtained by multiplying the normalized discrete value by the parameter range mapped by the parameter and adding the lower limit value.

[0107] Exemplarily, the number of sampling points is p , and the sampling value of each type of parameter is .

[0108] For k parameters atp One sampling point is sampled once to obtain the parameters i The vector representations of are respectively X i = X i,0 , X i,1 , X i,2 , ……, X i, p i-1 , X i is the vector representation of the parameter i The elements in the vector X i,j represent the sampling value of the parameter i at the j th sampling point.

[0109] With the obtaining of the vector representations of each parameter, the parameters are gradually changed by executing step S4113, and then the sensitivities of each parameter are evaluated based on this, and the key parameters are determined based on the sensitivities.

[0110] Gradual change refers to the execution process of changing the sampling value of each parameter one by one, keeping the sampling values of other parameters unchanged to obtain several sampling vectors, and each sampling vector is composed of the sampling values of each parameter.

[0111] One sampling can obtain the sampling values corresponding to a set of parameters, and this set of parameters is all the parameters corresponding to the multi-physical field data. The sampling values obtained by one sampling form a sampling vector, and only the sampling value corresponding to one parameter changes between adjacent sampling vectors.

[0112] Thus, for the parameter i , the set formed by its sampling values is the vector representation of the aforementioned parameter i , that is:

[0113] X i = X i,0 , X i,1 , X i,2 , ……, X i, p i-1 ,];

[0114] The vector X is composed of the vector representation of the parameter, specifically:

[0115] X = X 1 , X 2 , X 3 ,……, X n ;

[0116] Among them, n is the number of parameters. Exemplarily, for k parameters, n = k . X i is the value of parameter i at a specific sampling point.

[0117] So far, through the vector representation of each parameter, that is, in the vector X , k successive change samplings are performed to obtain the corresponding sampling vectors successively. And for the sampling vector obtained from one change, its adjacent vectors are the sampling vectors obtained from the next change and the previous change. Therefore, only the sampling value corresponding to one parameter has a numerical change among them.

[0118] The change applied to each change is related to the parameter range corresponding to the changed sampling value and the number of sampling points. The sampling value, as an element in the sampling vector, corresponds to a parameter, and this parameter also initializes its parameter range and the number of sampling points. Therefore, the change that needs to be applied to the sampling value can be calculated accordingly.

[0119] Exemplarily, the applied change Δ is calculated by the following formula, that is:

[0120] ;

[0121] The vector representation of the parameter comes from the discretization of the parameter space. Each vector representation is a specific parameter value of a parameter, and the sampling vector is obtained by performing successive change samplings on the vectors of all parameters. Each element (sampling value) in the sampling vector represents the value of a parameter. In other words, a sampling vector represents a set of specific parameter values.

[0122] During the execution of the successive change sampling process, first, independent sampling is performed on the vector representation of the parameter to obtain a sampling vector, and then based on this, the data of one element is successively changed, that is, a change is applied to a sampling value to form a new sampling vector.

[0123] Specifically, in the successive change sampling strategy, for the vector X = X 1 ,X 2 , X 3 ,……, X n , implement independent sampling to select a specific sampling vector, such as (X 1,0 , X 2,0 , X 3,0 ,……, X n,0 );

[0124] Then, taking this as the initial value, in a way of successive change, each change only changes the value of one parameter, that is, applying a change to the value of a parameter in the previous vector to obtain a new sampling vector, and so on, successively changing each parameter to obtain a series of sampling vectors.

[0125] For a series of sampling vectors obtained by successive change sampling, since adjacent sampling vectors only differ in the change of one element and other elements remain unchanged, therefore, only one independent sampling is needed to obtain the initial sampling vector, and then gradually adjust each element in this sampling vector, thereby generating multiple adjacent sampling vectors, without the need to perform completely independent sampling again.

[0126] Thus, by performing successive change sampling, the number of samplings is greatly reduced, and a clear parameter change trend is constructed, thereby being able to clearly present the influence of a single parameter, without worrying about the interference of the interaction of multiple parameters on the result, which helps to identify key parameters through sensitivity.

[0127] And it should be understood that if multiple parameters are randomly sampled simultaneously each time, it may increase the dimension of the search space and the difficulty of optimization. The successive change sampling method can "decompose" the optimization space into one-dimensional problems, and it is easier to find the optimal solution when performing step-by-step optimization.

[0128] After obtaining a series of sampling vectors, the sensitivity of each parameter can be calculated accordingly, and then the parameter with a large sensitivity is taken as the key parameter in the multi-physical field data.

[0129] Refer to Figure 5 , Figure 5 which is a method flowchart described according to the vector representation for parameters corresponding to the embodiments shown in Figure 3 , and describes the steps of evaluating the sensitivity of each parameter by successive change of parameters and taking the parameter with a large sensitivity as the key parameter in the multi-physical field data.

[0130] The parameter-oriented vector representation in the embodiments of the present invention, by successively changing parameters to evaluate the sensitivity of each parameter, and taking the parameter with high sensitivity as the key parameter in the multi-physical field data in step S4113, includes:

[0131] Step S601: For each type of parameter, respectively, sample the initial sampling points with the corresponding vector representation as the initial vector to obtain all the parameter values at the start of sampling, forming an initial vector.

[0132] Step S602: After obtaining the initial vector, perform sampling a specified number of times through the successive change of the sampling values between parameters. A number of adjacent vectors obtained by sampling form a trajectory, and the specified number is the number of types of the parameters.

[0133] That is to say, a series of sampling vectors obtained by successive change sampling form a trajectory, which is then used to implement sensitivity calculation.

[0134] By executing step S601 and step S602, only the value of one parameter is changed while other parameters remain unchanged, avoiding repeated sampling, obtaining a sufficient number of representative samples with fewer sampling times, and thus improving the calculation efficiency.

[0135] Moreover, compared with the completely random or independent multiple sampling methods, the successive change sampling makes the difference between the vectors generated by each sampling smaller, can reduce unnecessary calculation repetition, while ensuring the diversity and breadth of sampling, and saving calculation and storage resources.

[0136] Under the action of successive change sampling, since only the sampling value of one parameter is changed each time and others remain the same, the controllability of the sampling vectors is ensured, and the interaction complexity brought by the simultaneous change of multiple parameters is also avoided. The effect of each parameter change can be clearly controlled, and the interference of the interaction on the result can be reduced.

[0137] Furthermore, it is possible to capture the influence between different physical field parameters in the multi-physical field data in detail, and then accurately evaluate the roles and relationships of different parameters in actual biological tissue measurement, enhancing the accuracy and reliability of biological tissue measurement.

[0138] In another embodiment, for the sensitivity analysis implemented, local sensitivity and global sensitivity will be calculated, and finally key parameters will be identified based on this.

[0139] Refer to Figure 6 , Figure 6 is the method flowchart described according to Figure 3 the corresponding embodiment for the parameter-oriented vector representation, by successively changing parameters to evaluate the sensitivity of each parameter, and taking the parameter with high sensitivity as the key parameter in the multi-physical field data in the step.

[0140] For the parameter-oriented vector representation according to the embodiments of the present invention, by successively changing the parameters to evaluate the sensitivity of each parameter, and taking the parameter with large sensitivity as the key parameter in the multi-physical field data in step S4113, it further includes:

[0141] Step S701, calculate the local sensitivity and global sensitivity of the trajectory to obtain the local sensitivity and global sensitivity of each parameter;

[0142] Step S702, identify the parameters that have a significant impact on the state change of the biological tissue under its own physiological activities according to the local sensitivity and global sensitivity as the key parameters of the multi-physical field data in the biological tissue.

[0143] For the trajectory formed by a series of obtained sampling vectors, calculate the sensitivity index of each parameter, and the sensitivity index includes local sensitivity and global sensitivity.

[0144] Exemplarily, the elementary effect (EE) is the result calculated by evaluating the change of each parameter one by one, and estimates the importance of the parameter by evaluating the local change of each parameter within its parameter range. Therefore, the elementary effect (EE) is the local sensitivity of the parameter.

[0145] After calculating the elementary effect of each parameter, the global sensitivity of each parameter will also be obtained through the calculation of the global sensitivity. Exemplarily, the global sensitivity may include the mean value of EE and the variance of EE. For example, the mean value of EE is used to measure the overall impact of the corresponding parameter, and then the variance of EE is used to evaluate the uncertainty and volatility of the impact generated by the corresponding parameter. By combining the mean value and variance of EE, the accuracy and reliability of key parameter identification are more precisely improved to ensure the biological tissue measurement carried out with high reliability.

[0146] Thus, after determining the key parameters in the multi-physical field data by executing step S411, the optimal parameter values and the applicable weighted coefficient combinations corresponding to the identified key parameters can be obtained by executing step S412.

[0147] In an exemplary embodiment, the solution of the optimal parameter values and the acquisition of the applicable weighted coefficient combinations are realized by iterative optimization implemented by a genetic algorithm.

[0148] Specifically, the execution of step S412 includes: executing the genetic algorithm iterative optimization on the biological probe measurement model with the multi-physical field data corresponding to each parameter to obtain a set of optimal parameter values corresponding to the key parameters and the weighted coefficient combinations mapped by the optimal parameter values.

[0149] Through the model iterative optimization performed by the genetic algorithm, the parameter identification implemented outputs a set of optimal parameter values, which is closer to the actual biological tissue measurement.

[0150] Adapt to the contact between the biological probe and the biological tissue to obtain a set of optimal parameter values of the currently applicable key parameters, and a combination of weighting coefficients mapped by the optimal parameter values, that is, after step S410 is executed, then adapt to the contact between the biological probe and the biological tissue to implement calibration fusion, as shown in step S420.

[0151] During the execution of step S420, the calibration of the optimal parameter values is implemented by collecting multi-physical field data obtained by the biological probe. It should be understood that on the one hand, the multi-physical field data describes the characteristics of the biological tissue itself and the environmental parameters where it is located, such as temperature, angle, etc. The angle referred to is the contact angle of the biological probe. For example, when the biological probe contacts the body horizontally (0 degrees), the contact area is the largest, and the multi-physical field data collected at this time is the most accurate. However, as the angle increases, the obtained multi-physical field data will deviate. Therefore, it is necessary to calibrate the optimal parameter values output by the multi-physical field data for the key parameters.

[0152] The calibration implemented is accompanied by numerical fusion. For a set of obtained optimal parameter values, calibration based on the angle can be performed first, and then the calibrated values are fused according to the combination of weighting coefficients to obtain the characteristic parameters of the biological tissue.

[0153] In addition, it is also possible to first fuse the optimal values according to the combination of weighting coefficients to obtain a fusion value, and then perform calibration on the fusion value based on the angle.

[0154] Exemplarily, using the angle angle in the multi-physical field data as an aid, the cosine value cos(angle) corresponding to the angle angle is used as an adjustment factor to describe the influence of the angle on the measurement.

[0155] For example, for the optimal parameter values corresponding to each key parameter, obtain the corresponding angle, and then obtain the calibrated values of the three key parameters of impedance, pressure, and temperature, as shown in the following formula:

[0156] impedance_adj = impedance×cos(angle);

[0157] pressure_adj = pressure×cos(angle);

[0158] temperature_adj = temperature×cos(angle);

[0159] Among them, impedance is the optimal parameter value corresponding to the key parameter of impedance, and impedance_adj is the calibrated value of impedance; pressure is the optimal parameter value corresponding to the key parameter of pressure, and pressure_adj is the calibrated value of pressure; temperature is the optimal parameter value corresponding to the key parameter of temperature, and temperature_adj is the calibrated value of temperature.

[0160] After calibration is completed with the assistance of the angle, fuse the calibrated values based on the applicable combination of weighting factors to obtain the characteristic parameters of the currently measured characteristic tissue.

[0161] Generally speaking, by way of example, the execution process of step S420 includes: adapting to the contact between the biological probe and the biological tissue to obtain an auxiliary physical quantity, where the auxiliary physical quantity is used to describe the contact state of the biological probe with the biological tissue; calibrating and fusing the optimal parameter values according to the auxiliary physical quantity and the combination of weighting factors to obtain the characteristic parameters of the biological tissue.

[0162] Thus, by using the auxiliary physical quantity, such as the angle, to describe the contact state between the biological probe and the currently measured characteristic tissue, it helps to accurately reflect the response characteristics of the biological tissue under different contact conditions, and further can significantly improve the accuracy of biological tissue measurement and avoid errors caused by poor contact or environmental interference.

[0163] Combining the auxiliary physical quantity with the combination of weighting factors, calibrating and fusing the optimal parameter values can effectively eliminate unnecessary interference factors and improve the accuracy of the characteristic parameters of the biological tissue. By weighting different data sources with different weights, it is ensured that the finally output characteristic parameters are optimal and can more truly reflect the state of the biological tissue.

[0164] Under the action of step S420, the adaptability of the contact between the biological probe and the biological tissue is enhanced, and it can dynamically adapt to different contact conditions and is suitable for multiple types of biological tissue. Further explained, the contact state between the biological probe and the biological tissue is dynamically changing, and with the action of the auxiliary physical quantity, it can help the system to understand the changes in the contact situation in real time, and then adjust itself to obtain a consistent and high-quality result output. In addition, for different biological tissues, their physical properties, such as hardness, conductivity, and temperature, are different and often vary due to different types of biological tissues. Therefore, by means of the auxiliary physical quantity, adapting to the currently measured biological tissue for calibration and fusion enables the system to adapt to multiple types of biological tissues and provide high-precision measurements.

[0165] With the realization of calibration fusion, external interference is removed. Even if the contact of the biological probe is affected by the environment, it will effectively shield the interference under the action of the combination of the auxiliary physical quantity and the applicable weighting coefficient, ensuring the reliability of the measurement and the stable operation of the system, and providing support for precision medicine and personalized health assessment.

[0166] For the characteristic parameters obtained by calibration fusion, a biological tissue characteristic analysis model for mapping the characteristic parameters is constructed by executing step S430, so as to obtain the state evaluation result of the currently measured biological tissue.

[0167] In step S430, for each obtained characteristic parameter, a biological tissue characteristic analysis model for its mapping is adaptively constructed. Exemplarily, the biological tissue characteristic analysis model can be in the form of a mapping function.

[0168] For each characteristic parameter, its corresponding mapping function can be fitted, and the mapping function is used to describe the relationship between the characteristic parameter and the biological tissue state. For example, some characteristic parameters can linearly correlate with the health state of the biological tissue, while some characteristic parameters may be non-linear. Therefore, a set of mapping functions will be generated for the obtained characteristic parameters to provide multi-dimensional state evaluation for the currently measured biological tissue.

[0169] Illustratively, key parameters such as the impedance change rate, pressure response coefficient, and temperature regulation ability of the biological tissue are obtained through calibration fusion, and these key parameters are closely related to the health state and functional state of the currently measured biological tissue.

[0170] Based on these key parameters, corresponding characteristic parameters are obtained, their respective weights and mapping functions are configured, and then the state of the biological tissue is evaluated through the weights and mapping functions to obtain the current health state and / or functional state of the biological tissue.

[0171] For a characteristic parameter, the stronger its correlation with the health state of the biological tissue and the higher the accuracy of the lesion judgment of the biological tissue, the greater the corresponding weight. Exemplarily, for the characteristic parameter α and characteristic parameter β of the key parameter, their weight calculation is:

[0172] ;

[0173] Where w α is the weight of the characteristic parameter α and w β is the weight of the characteristic parameter β n is the number of sampling points, and e is a constant.

[0174] Finally, the calculation of the state of the currently measured biological tissue can be carried out, that is:

[0175] ;

[0176] Among them, f(α) and f(β) are mapping functions constructed according to the relationship between characteristic parameters and biological tissue characteristics, so as to provide strong support for biomedical research.

[0177] Taking the measurement of skin, a kind of biological tissue, as an example, the system implementation of the present invention will be described below.

[0178] Skin tissue is affected by external environments such as pressure and temperature, and internal characteristics such as impedance. For the multi-physical field data y output by the biological probe, which exists in the form of the intensity of skin bioelectric signals. At this time, the biological probe measurement model is used to identify key parameters such as impedance, pressure and temperature, as Figure 7 shown, Figure 7 is a curve graph of the multi-physical field data output by the biological probe 10 in an example. Among the multi-physical field data output by the biological probe 10, the data curves corresponding to the three types of parameters of impedance, temperature and pressure are respectively as Figure 7 shown.

[0179] The parameter ranges involved in the three types of parameters of impedance, pressure and temperature are as follows:

[0180] Impedance x1, parameter range [10, 1000] Ω;

[0181] Pressure x2, parameter range [0, 5] N;

[0182] Temperature x3, parameter range [20, 40] °C.

[0183] Configure the number of sampling points as p = 5; The vector expressions obtained for each parameter after discretization processing and random sampling are:

[0184] x1 = {10, 257.5, 505, 752.5, 1000} Ω;

[0185] x2 = {0, 1.25, 2.5, 3.75, 5} N;

[0186] x3 = {20, 25, 30, 35, 40} °C.

[0187] Then, the initial value is selected by the independent sampling method of Monte Carlo simulation, and a specific sampling vector (initial vector) obtained by independent sampling is obtained: X = (x1, x2, x3) = (505 Ω, 2.5 N, 30 °C).

[0188] Perform successive changes, i.e., successive perturbations, on this specific sampling vector X=(x1, x2, x3)=(505Ω, 2.5N, 30℃). Starting from the initial point, perturb a single parameter in turn (increase or decrease by one step size (i.e., the applied change)), while keeping the other parameters unchanged.

[0189] The perturbation step size is Δ:

[0190] For x1: Δ=(1000 - 10) / (5 - 1)=247.5Ω.

[0191] For x2: Δ=(5 - 0) / (5 - 1)=1.25N.

[0192] For x3: Δ=(40 - 20) / (5 - 1)=5℃.

[0193] Thus, perform the first perturbation, the second perturbation, and the third perturbation to obtain a series of sampling vectors, as follows:

[0194] The first perturbation of x1: X=(752.5Ω, 2.5N, 30℃);

[0195] The second perturbation of x2: X=(505Ω, 3.75N, 30℃);

[0196] The third perturbation of x3: X=(505Ω, 2.5N, 35℃).

[0197] A series of sampling vectors obtained from the three perturbations form a sampling trajectory, and perform the elementary effect (EE) on the sampling trajectory. After repeating the sampling multiple times, the following average sensitivity effect EE is obtained:

[0198] Impedance x1: EEx1=-0.15μV / Ω (the negative value indicates that an increase in the parameter will cause a decrease in the output);

[0199] Pressure x2: EEx2=22.5μV / N;

[0200] Temperature x3: EEx3=3.8μV / ℃.

[0201] It can be seen from this that pressure x2 is the key parameter. Search for the optimal parameter values with pressure x2 as the key parameter, and output the state evaluation result of the skin tissue under the action of the feature parsing model of the mapped feature tissue through the subsequent calibration fusion performed.

[0202] Furthermore, perform calibration fusion on a set of optimal parameter values corresponding to the key parameter. In the calibration fusion performed, increase the angle to assist the calibration data as described above, that is, first perform the fusion of this set of optimal parameter values and then calibrate.

[0203] In addition, calibration and re - fusion can also be implemented by adding angular assistance for the optimal parameter values corresponding to each key parameter.

[0204] And so on. Finally, the state evaluation result of the skin tissue is output under the action of the feature parsing model of the feature organization mapped by the fused characteristic parameters, as shown in Figure 7 shown.

[0205] Figure 7 Figure shows the mapping diagram of the weighted fusion value (biological tissue health state evaluation result) corresponding to each skin tissue sample to the health state, that is, the biological tissue health state evaluation result calculated by the formula is used to determine whether the skin tissue sample is in a healthy state, a sub - healthy state, or even an unhealthy state.

[0206] In an exemplary embodiment, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the steps of the method as described above.

[0207] In an exemplary embodiment, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the method as described above.

[0208] In an exemplary embodiment, the present invention also provides a computer - readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the method as described above.

[0209] From the description of the above - mentioned embodiments, those skilled in the art can easily understand that the exemplary embodiments described here can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solution according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non - volatile storage medium (such as a CD - ROM, a USB flash drive, a mobile hard disk, etc.) or on the network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0210] In an exemplary embodiment of the present invention, there is also provided a computer program medium, on which computer - readable instructions are stored, and when the computer - readable instructions are executed by a processor of a computer, the computer is enabled to execute the method described in the method embodiment part above.

[0211] According to an embodiment of the present invention, there is also provided a program product for implementing the method in the above method embodiment. It may be a portable compact disc read-only memory (CD-ROM), include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0212] The program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0213] The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal may take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0214] The program code contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0215] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0216] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present invention, the features and functions of two or more of the above-described modules or units may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above may be further divided and embodied by multiple modules or units.

[0217] In addition, although the steps of the methods in the present invention are described in a specific order in the drawings, this does not require or imply that the steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0218] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a portable hard drive, etc.) or on a network, including several instructions to cause a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the methods according to the embodiments of the present invention.

[0219] Other embodiments of the present invention will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the invention following the general principles of the present invention and including known common general knowledge or conventional technical means in the technical field not disclosed by the present invention. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present invention are pointed out by the appended claims.

Claims

1. A multi-physics field data fusion system for biological probes, characterized in that: The system comprises: A biological probe, used for collecting data of a currently measured biological tissue to obtain multi-physical field data of the biological tissue, wherein the multi-physical field data includes environmental parameters of the biological tissue and intrinsic characteristic parameters thereof; A memory, a processor, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the following steps: Determine the key parameters of the currently measured biological tissue by the multi-physical field data through the biological probe measurement model, and find a set of optimal parameter values ​​for the key parameters, as well as a weighted coefficient combination of the optimal parameter value mapping; Adapting to the contact between the biological probe and the biological tissue, the characteristic parameters of the biological tissue are obtained by combining and calibrating the optimal parameter values ​​through the mapped weighted coefficients; A biological tissue feature analysis model of the feature parameter mapping is adaptively constructed for the measured biological tissue, and a state evaluation result of the biological tissue is output.

2. The system according to claim 1, characterized in that The biological probe senses different types of physical signals to sense and collect multi-physical field data corresponding to multiple physical fields in real time on biological tissues.

3. The system according to claim 1, characterized in that The method of determining the key parameters of the currently measured biological tissue by the multi-physical field data through the biological probe measurement model, and finding a set of optimal parameter values ​​for the key parameters, and a weighted coefficient combination of the optimal parameter value mapping, includes: For the biological tissue currently contacted and measured by the biological probe, based on the discretization processing of the multi-physical field data by the biological probe measurement model and the random sampling on the discretization processing, sensitivity calculation is performed to determine the key parameters in the multi-physical field data; Parameter identification is performed on key parameters in the multi-physical field data to obtain a set of optimal parameter values ​​corresponding to the key parameters, and a weighted coefficient combination of the optimal parameter value mapping.

4. The system according to claim 3, characterized in that The method of performing sensitivity calculation on the biological tissue currently contacted and measured by the biological probe based on the discretization processing of the multi-physical field data by the biological probe measurement model and random sampling on the discretization processing to determine the key parameters in the multi-physical field data includes: Initializing the configuration of the parameter range of each parameter mapping in the multi-physical field data and the number of sampling points of the parameter within the parameter range for the biological tissue; Adapting the number of sampling points to generate a series of discrete sampling values ​​within the parameter range, and using the sampling values ​​as possible values ​​of the corresponding parameters to form a vector representation of the parameters; The parameter-oriented vector representation evaluates the sensitivity of each parameter by successively changing the parameters, and the parameter with the largest sensitivity is taken as the key parameter in the multi-physical field data.

5. The system according to claim 4, characterized in that The parameter range of each parameter mapping is different for different biological tissues.

6. The system according to claim 4, characterized in that The parameter-oriented vector representation evaluates the sensitivity of each parameter by successively changing the parameters, and takes the parameter with the largest sensitivity as the key parameter in the multi-physics field data, including: Calculate the local sensitivity and global sensitivity of the sampled trajectory to obtain the local sensitivity and global sensitivity of each parameter; According to the local sensitivity and the global sensitivity, the parameters that have a significant impact on the state change of the biological tissue under its own physiological activities are identified as the key parameters of the multi-physical field data in the biological tissue.

7. The system according to claim 3, characterized in that The step of performing parameter identification on the key parameters in the multi-physics field data to obtain a set of optimal parameter values ​​corresponding to the key parameters, and a weighted coefficient combination of the optimal parameter value mapping, includes: The genetic algorithm is used to iteratively optimize the biological probe measurement model using multi-physical field data corresponding to each parameter to obtain a set of optimal parameter values ​​corresponding to key parameters and a weighted coefficient combination of the optimal parameter value mapping.

8. A multi-physics field data fusion method for a biological probe, characterized in that: The method executes the steps implemented in the system of any one of claims 1-7.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the system according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the system according to any one of claims 1 to 7.

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