A data processing method and system based on cataract detection

Abstract

The present invention provides a data processing method and system based on cataract detection. The method obtains historical data of users in different dimensions and trains an AI decision model based on the historical data in different dimensions; obtains data to be analyzed, inputs the data to be analyzed into the trained AI decision model, and outputs analysis results; wherein, in the process of training the AI decision model, an entropy flow roaming algorithm is used to continuously adjust the weights of data in different dimensions to optimize the performance of the model. Specifically, the entropy flow roaming algorithm optimizes weight distribution through an entropy flow potential function and a dynamic balance mechanism, can adaptively adjust the search range, and is more suitable for the complex characteristics of medical data, thereby ultimately improving the accuracy and reliability of data processing results based on cataract detection.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing based on cataract detection, and in particular relates to a data processing method and system based on cataract detection. Background Art

[0002] Cataracts are one of the leading causes of blindness worldwide, with a particularly high incidence among the elderly. Existing clinical cataract testing relies on manual interpretation by professional ophthalmologists using medical data such as slit lamp images and fundus images. This traditional data processing method suffers from two major technical bottlenecks: first, inefficient manual labeling, with labeling a single sample taking 5-10 minutes; and second, limited by the uneven distribution of medical resources.

[0003] With the application of artificial intelligence technology in the field of medical imaging, it has become possible to use AI technology for disease screening, but existing AI technology has shortcomings in data utilization and decision-making accuracy, especially in the weight optimization link.

[0004] Existing weight optimization processes primarily rely on temperature parameters to control the search process. Their convergence speed and global search capabilities are limited by fixed cooling strategies, making it difficult to achieve accurate weight allocation in high-dimensional, small-sample medical data scenarios. Furthermore, they can suffer from issues such as premature convergence and high computational complexity, presenting challenges when processing complex medical data. These factors contribute to poor cataract detection results. Summary of the Invention

[0005] Based on this, an embodiment of the present invention provides a data processing method and system based on cataract detection, which aims to improve the accuracy and reliability of data processing results based on cataract detection by integrating multi-dimensional data and using a unique entropy flow roaming algorithm to determine the weights of various data types in AI decision-making.

[0006] A first aspect of an embodiment of the present invention provides a data processing method based on cataract detection, the method comprising:

[0007] Obtaining historical data of different dimensions of the user for cataract detection, and training an AI decision model based on the historical data of different dimensions, wherein the historical data includes at least basic user data and eye data related to cataract detection;

[0008] Obtaining data to be analyzed, inputting the data to be analyzed into a trained AI decision model, and outputting analysis results;

[0009] In the process of training the AI decision-making model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, historical data of different dimensions are preprocessed, and the preprocessed data is subjected to a small sample size correlation analysis to obtain the correlation analysis results;

[0010] Assigning initial weights to data of different dimensions based on the correlation analysis results;

[0011] According to the initial weights, by simulating an adaptive switching mechanism from a high entropy state to a low entropy state, a global optimal search is achieved in the solution space, and optimal weight vectors for data of different dimensions are output.

[0012] Furthermore, in the step of performing a small sample size correlation analysis on the preprocessed data to obtain the correlation analysis result, the calculation formula for the small sample size correlation analysis is:

[0013]

[0014] Among them, d i is the grade difference between the data type and the analysis result in the i-th sample, n is the number of samples, r s The results of correlation analysis.

[0015] Furthermore, in the step of assigning initial weights to data of different dimensions based on the correlation analysis results, the initial weights are expressed as:

[0016]

[0017] in, is the initial weight of the j-th class data, r sj is the Spearman rank correlation coefficient between the j-th type of data and the cataract analysis results, r sk is the Spearman rank correlation coefficient between the k-th type of data and the cataract analysis results, and m is the total number of data types.

[0018] Furthermore, the step of implementing a global optimal search in the solution space based on the initial weights by simulating an adaptive switching mechanism from a high entropy state to a low entropy state and outputting the optimal weight vectors of data of different dimensions includes:

[0019] Define the entropy flow potential function and set the initial entropy flow intensity;

[0020] Generate the perturbation term through the standard normal distribution random number, calculate the new weight vector based on the current entropy flow intensity, and perform normalization to ensure that the sum of the weights is 1;

[0021] Calculate the entropy difference between the current state and the disturbed state, and obtain different solutions based on the entropy difference results;

[0022] Control the dynamic entropy decay process to satisfy the exponential decay characteristic to ensure convergence within a finite number of steps;

[0023] When the entropy flow intensity is less than the termination entropy value, the optimal weight vector is output.

[0024] Furthermore, in the step of generating a disturbance term by using a standard normal distribution random number and calculating a new weight vector in combination with the current entropy flow intensity, the disturbance formula is:

[0025]

[0026] Among them, w' is the new weight, w is the current weight, β is the perturbation coefficient, S k is the current entropy flow intensity, and randn() is a function that generates standard normal distribution random numbers.

[0027] Furthermore, in the step of calculating the entropy difference between the current state and the disturbed state and obtaining different solutions based on the entropy difference result, the calculation formula of the entropy difference is:

[0028]

[0029] Among them, ΔE is the entropy difference, E(w') is the entropy flow energy of the disturbed state, and E(w) is the entropy flow energy of the current state;

[0030] when When accepting new interpretations;

[0031] when When the probability , accept the new interpretation.

[0032] A second aspect of an embodiment of the present invention provides a data processing system based on cataract detection, for implementing the data processing method based on cataract detection provided in the first aspect, the system comprising:

[0033] The training module is used to obtain historical data of users in different dimensions and train the AI decision model based on the historical data in different dimensions;

[0034] An input module is used to obtain data to be analyzed, input the data to be analyzed into a trained AI decision model, and output analysis results;

[0035] In the process of training the AI decision-making model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, historical data of different dimensions are preprocessed, and the preprocessed data is subjected to a small sample size correlation analysis to obtain the correlation analysis results;

[0036] Assigning initial weights to data of different dimensions based on the correlation analysis results;

[0037] According to the initial weights, by simulating an adaptive switching mechanism from a high entropy state to a low entropy state, a global optimal search is achieved in the solution space, and optimal weight vectors for data of different dimensions are output.

[0038] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data processing method based on cataract detection provided in the first aspect.

[0039] The fourth aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the data processing method based on cataract detection provided in the first aspect is implemented.

[0040] A data processing method and system based on cataract detection are provided in an embodiment of the present invention. The method obtains historical data of different dimensions of the user and trains an AI decision model based on the historical data of different dimensions; obtains data to be analyzed, inputs the data to be analyzed into the trained AI decision model, and outputs analysis results; wherein, in the process of training the AI decision model, an entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, the entropy flow roaming algorithm optimizes weight distribution through entropy flow potential function and dynamic balance mechanism, can adaptively adjust the search range, and is more suitable for the complex characteristics of medical data, thereby ultimately improving the accuracy and reliability of data processing results based on cataract detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of a data processing method based on cataract detection provided in the first embodiment of the present invention;

[0042] Figure 2 This is a structural block diagram of a data processing system based on cataract detection provided in the second embodiment of the present invention;

[0043] Figure 3 This is a structural block diagram of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0044] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0045] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] Example 1

[0048] According to an embodiment of the present invention, a data processing method based on cataract detection is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] In this embodiment 1, a data processing method based on cataract detection is provided, which can be used in electronic devices, such as computers. Figure 1 , Figure 1 The flowchart of the data processing method based on cataract detection provided in the first embodiment of the present invention is shown, which specifically includes steps S01 to S02.

[0050] Step S01: Obtain the user's historical data of different dimensions for cataract detection, and train the AI decision model based on the historical data of different dimensions. The historical data at least includes user basic data and eye data related to cataract detection.

[0051] Specifically, the historical data of different dimensions obtained include gender, age, eye image data, binocular vision data, visual sensitivity data, whether there are surgical indications, whether there are surgical contraindications, medical history (whether there is diabetes, fundus disease, refractive error, etc.) and other high-risk factors. Among them, the eye data related to cataract detection is eye image data, binocular vision data, and visual sensitivity data. The other data is user basic data. It can be understood that gender is data of one dimension and age is data of another dimension. In addition, dimensions can also be called types, that is, gender is data of one type and age is data of another type.

[0052] In this embodiment, the AI decision-making model is a multimodal weighted fusion model that adopts a voting mechanism, and different types of data are given different weights according to their influence on the cataract analysis results.

[0053] Step S02: Obtain the data to be analyzed, input the data to be analyzed into the trained AI decision model, and output the analysis results.

[0054] Among them, in the process of training the AI decision-making model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, the entropy flow roaming algorithm optimizes the weight distribution through the entropy flow potential function and the dynamic balance mechanism.

[0055] It should be noted that the steps of continuously adjusting the weights of data of different dimensions using the entropy flow roaming algorithm include:

[0056] Preprocess the historical data of different dimensions, and perform a small sample size correlation analysis on the preprocessed data to obtain the correlation analysis results. Specifically, first preprocess the historical data of different dimensions, including data cleaning, normalization and other operations, to ensure the quality and consistency of the data and make different types of data comparable. In the data cleaning process, by setting reasonable data thresholds and outlier detection rules, remove obviously erroneous or abnormal data records. For example, if the age data is negative or far exceeds the normal human life span range, it will be treated as an outlier and processed. For eye image data, use image enhancement technology to improve the clarity and contrast of the image to better extract image features. For age data, use the normalization formula , convert it to the range of 0-1, where x is the original age value, xmin and xmax are the minimum and maximum values in the age data, respectively, to facilitate subsequent calculations.

[0057] Furthermore, since the actual collected data may have a limited sample size, the Spearman rank correlation coefficient suitable for a small sample size was used to analyze the correlation between various data and the actual cataract diagnosis results. The calculation formula for the small sample size correlation analysis is:

[0058]

[0059] Among them, di is the level difference between the data type and the analysis result in the i-th sample, n is the number of samples, and rs is the correlation analysis result. The calculation formula of small sample correlation analysis is used to calculate the correlation coefficients of various types of data such as gender, age, and eye image data with the cataract analysis results, thereby quantifying the degree of association between them. For example, increasing age is usually positively correlated with the probability of cataracts. The correlation coefficient between age and cataract analysis results calculated by this formula can intuitively reflect the closeness of this correlation. It should be noted that the Spearman rank correlation coefficient, that is, the correlation analysis result, is used to measure the monotonic correlation between two variables (a linear relationship is not required) and is applicable to non-normal distribution or rank data. In the embodiment of the present invention, the original data is converted into ranks, and the correlation is analyzed by calculating the difference between the two groups of ranks. For example, the user's age can be divided into ranks by expert evaluation method, 80 years old is level 5, 72 years old is level 4, 65 years old is level 3, 60 years old is level 2, and 58 years old is level 1. The cataract analysis results are divided into 5 levels according to different degrees of severity.

[0060] According to the correlation analysis results, initial weights are assigned to data of different dimensions. Specifically, the initial weights are expressed as:

[0061]

[0062] in, is the initial weight of the j-th class data, r sj is the Spearman rank correlation coefficient between the j-th type of data and the cataract analysis results, r sk is the Spearman rank correlation coefficient between the k-th type of data and the cataract analysis results, and m is the total number of data types. It should be noted that the purpose of this formula is to normalize the correlation coefficients of various types of data so that the sum of the weights of all data types is 1, ensuring the rationality and additivity of weight distribution;

[0063] According to the initial weight, by simulating the adaptive switching mechanism from high entropy state to low entropy state, a global optimal search is realized in the solution space, and the optimal weight vector of data of different dimensions is output. Specifically, the entropy flow potential function is first defined, and the initial entropy flow intensity is set. It should be noted that the entropy flow potential function is defined as: F(w)=-E(w), where w is the current weight, and E(w) represents the entropy flow energy of the system, which is defined by the negative value of the objective function (screening accuracy), that is, E(w)=-Accuracy(w). According to the second law of thermodynamics, the entropy increase of an isolated system satisfies In EFW, the entropy flow potential function F(w)=-E(w) simulates the system energy minimization process, and drives the optimization direction through negative entropy to ensure that the algorithm converges to a high-precision solution. Then set the initial entropy flow intensity , the value range is 50-200, preferably 100 (control the random disturbance range); disturbance coefficient , the value range is 0.05-0.2, preferably 0.1; entropy decay rate , the value range is 0.9-0.99, preferably 0.95 (determines the convergence speed); the termination entropy value ;

[0064] The perturbation term is generated by a standard normal distribution random number, and the new weight vector is calculated based on the current entropy flow intensity. The new weight vector is then normalized to ensure that the sum of the weights is 1. The perturbation formula is:

[0065]

[0066] Among them, w' is the new weight, w is the current weight, β is the perturbation coefficient, S k is the current entropy flow intensity, randn() is the function for generating standard normal distribution random numbers;

[0067] Calculate the entropy difference between the current state and the disturbed state, and obtain different solutions based on the entropy difference results. It should be noted that in the step of obtaining different solutions, the calculation formula of the entropy difference is:

[0068]

[0069] Among them, ΔE is the entropy difference, E(w') is the entropy flow energy of the disturbed state, and E(w) is the entropy flow energy of the current state;

[0070] when When , accept the new solution, that is, w';

[0071] when When the probability , accept the new solution to avoid falling into the local optimum, where the new solution is also w', but w' at this time is lower than the current weight w, that is, w' is a low-precision solution;

[0072] The dynamic entropy decay process is controlled to satisfy the exponential decay characteristic to ensure convergence within a finite number of steps. Specifically, the entropy flow intensity is updated after each iteration: Gradually reduce the randomness of the system to enhance convergence. It can be proved through Markov chain steady-state analysis that when When , the algorithm converges to the global optimal solution with probability 1;

[0073] When the entropy flow intensity is less than the termination entropy value, that is, , output the optimal weight vector.

[0074] The analysis results were regularly evaluated and compared with actual results to verify the accuracy and reliability of the proposed method. ROC curve analysis showed that the EFW algorithm achieved an AUC value of 0.983 on the test set, significantly higher than the 0.921 of the traditional method, demonstrating its superiority in identifying high-risk samples.

[0075] In summary, the data processing method based on cataract detection in the above-mentioned embodiment of the present invention obtains historical data of different dimensions of the user, and trains the AI decision model according to the historical data of different dimensions; obtains the data to be analyzed, inputs the data to be analyzed into the trained AI decision model, and outputs the analysis results; wherein, in the process of training the AI decision model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, the entropy flow roaming algorithm optimizes the weight distribution through the entropy flow potential function and the dynamic balance mechanism, can adaptively adjust the search range, and is more suitable for the complex characteristics of medical data, thereby ultimately improving the accuracy and reliability of the data processing results based on cataract detection.

[0076] Example 2

[0077] See also Figure 2 , Figure 2 This is a block diagram of a data processing system for cataract detection, provided in accordance with the second embodiment of the present invention. This data processing system 200 is used to implement the aforementioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. While the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0078] Specifically, the data processing system 200 based on cataract detection includes: a training module 21 and an input module 22, wherein:

[0079] The training module 21 is used to obtain historical data of users in different dimensions and train the AI decision model based on the historical data in different dimensions;

[0080] An input module 22 is used to obtain data to be analyzed, input the data to be analyzed into a trained AI decision model, and output analysis results;

[0081] Among them, in the process of training the AI decision-making model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. Specifically, the entropy flow roaming algorithm optimizes the weight distribution through the entropy flow potential function and the dynamic balance mechanism.

[0082] Furthermore, in some optional embodiments of the present invention, the training module 21 includes:

[0083] The preprocessing unit is used to preprocess historical data of different dimensions and perform small sample size correlation analysis on the preprocessed data to obtain the correlation analysis results. The calculation formula for small sample size correlation analysis is:

[0084]

[0085] Among them, d i is the grade difference between the data type and the analysis result in the i-th sample, n is the number of samples, r s This is the result of correlation analysis;

[0086] The allocation unit is used to allocate initial weights to data of different dimensions according to the correlation analysis results. The initial weights are expressed as:

[0087]

[0088] in, is the initial weight of the j-th class data, r sj is the Spearman rank correlation coefficient between the j-th type of data and the cataract analysis results, r sk is the Spearman rank correlation coefficient between the k-th type of data and the cataract analysis results, and m is the total number of data types;

[0089] The output unit is used to realize global optimal search in the solution space according to the initial weight by simulating the adaptive switching mechanism from high entropy state to low entropy state, and output the optimal weight vector of data of different dimensions.

[0090] Furthermore, in some optional embodiments of the present invention, the output unit includes:

[0091] Set up subunits to define the entropy flow potential function and set the initial entropy flow intensity;

[0092] The perturbation term generation subunit is used to generate perturbation terms using standard normal distribution random numbers, calculate new weight vectors based on the current entropy flow intensity, and perform normalization to ensure that the sum of the weights is 1;

[0093] The calculation subunit is used to calculate the entropy difference between the current state and the disturbed state, and obtain different solutions based on the entropy difference result;

[0094] The control subunit is used to control the dynamic entropy decay process to satisfy the exponential decay characteristic to ensure convergence within a finite number of steps;

[0095] The output subunit is used to output the optimal weight vector when the entropy flow intensity is less than the termination entropy value.

[0096] Example 3

[0097] Another aspect of the present invention provides an electronic device, see Figure 3 , shown is an electronic device in embodiment 3 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the data processing method based on cataract detection as described above is implemented.

[0098] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0099] The memory 20 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 20 may be an internal storage unit of the electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Furthermore, the memory 20 may include both an internal storage unit of the electronic device and an external storage device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or is about to be output.

[0100] It should be pointed out that Figure 3 The structure shown does not constitute a limitation to the electronic device. In other embodiments, the electronic device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0101] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the data processing method based on cataract detection as described above is implemented.

[0102] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0103] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0104] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0105] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0106] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A data processing method based on cataract detection, characterized in that: The method comprises: Obtaining historical data of different dimensions of the user for cataract detection, and training an AI decision model based on the historical data of different dimensions, wherein the historical data includes at least basic user data and eye data related to cataract detection; Obtaining data to be analyzed, inputting the data to be analyzed into a trained AI decision model, and outputting analysis results; In the process of training the AI decision model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. The steps of continuously adjusting the weights of data of different dimensions to optimize the performance of the model by using the entropy flow roaming algorithm include: Preprocess historical data of different dimensions, and perform small sample size correlation analysis on the preprocessed data to obtain correlation analysis results; Assigning initial weights to data of different dimensions based on the correlation analysis results; According to the initial weight, by simulating the adaptive switching mechanism from the high entropy state to the low entropy state, a global optimal search is implemented in the solution space, and the optimal weight vectors of data of different dimensions are output. The steps of implementing the global optimal search in the solution space and outputting the optimal weight vectors of data of different dimensions according to the initial weight by simulating the adaptive switching mechanism from the high entropy state to the low entropy state include: Define the entropy flow potential function and set the initial entropy flow intensity; Generate the perturbation term through the standard normal distribution random number, calculate the new weight vector based on the current entropy flow intensity, and perform normalization to ensure that the sum of the weights is 1; Calculate the entropy difference between the current state and the disturbed state, and obtain different solutions based on the entropy difference results; Control the dynamic entropy decay process to satisfy the exponential decay characteristic to ensure convergence within a finite number of steps; When the entropy flow intensity is less than the termination entropy value, the optimal weight vector is output; In the step of generating a disturbance term by using a standard normal distribution random number and calculating a new weight vector in combination with the current entropy flow intensity, the disturbance formula is: Among them, w' is the new weight, w is the current weight, β is the perturbation coefficient, S k is the current entropy flow intensity, randn() is the function for generating standard normal distribution random numbers; In the step of calculating the entropy difference between the current state and the disturbed state, and obtaining different solutions based on the entropy difference result, the calculation formula of the entropy difference is: Among them, ΔE is the entropy difference, E(w') is the entropy flow energy of the disturbed state, and E(w) is the entropy flow energy of the current state; when When accepting new interpretations; when When the probability , accept the new interpretation.

2. The data processing method based on cataract detection according to claim 1, characterized in that: In the step of performing a small sample size correlation analysis on the pre-processed data to obtain the correlation analysis result, the calculation formula for the small sample size correlation analysis is: Among them, d i is the grade difference between the data type and the analysis result in the i-th sample, n is the number of samples, r s The results of correlation analysis.

3. The data processing method based on cataract detection according to claim 2, characterized in that: In the step of assigning initial weights to data of different dimensions based on the correlation analysis results, the initial weights are expressed as: in, is the initial weight of the j-th class data, r sj is the Spearman rank correlation coefficient between the j-th type of data and the cataract analysis results, r sk is the Spearman rank correlation coefficient between the k-th type of data and the cataract analysis results, and m is the total number of data types.

4. A data processing system based on cataract detection, characterized in that: A system for implementing the data processing method based on cataract detection according to any one of claims 1 to 3, comprising: The training module is used to obtain historical data of users in different dimensions and train the AI decision model based on the historical data in different dimensions; An input module is used to obtain data to be analyzed, input the data to be analyzed into a trained AI decision model, and output analysis results; In the process of training the AI decision model, the entropy flow roaming algorithm is used to continuously adjust the weights of data of different dimensions to optimize the performance of the model. The steps of continuously adjusting the weights of data of different dimensions to optimize the performance of the model by using the entropy flow roaming algorithm include: Preprocess historical data of different dimensions, and perform small sample size correlation analysis on the preprocessed data to obtain correlation analysis results; Assigning initial weights to data of different dimensions based on the correlation analysis results; According to the initial weights, by simulating an adaptive switching mechanism from a high entropy state to a low entropy state, a global optimal search is achieved in the solution space, and optimal weight vectors for data of different dimensions are output.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data processing method based on cataract detection as described in any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for data processing based on cataract detection according to any one of claims 1 to 3 is implemented.

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