Medical equipment benefit analysis and prediction method and system based on historical data
By preprocessing and building a regression analysis model for multi-dimensional historical data of medical equipment, the problem of low accuracy of medical equipment benefits assessment in the existing technology is solved, and accurate prediction of equipment benefits and optimal resource allocation are achieved.
Patent Information
- Application Number
- CN202411933934.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-26
- Publication Date
- 2025-04-04
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The effectiveness evaluation of existing medical equipment relies on static data, and cannot fully consider the actual use of the equipment and repair history, resulting in low prediction accuracy and insufficient decision support.
By obtaining multi-dimensional historical data of medical equipment, pre-processing and benefit score calculations, using regression analysis algorithm to build a prediction model, conduct model training and parameter adjustments, and accurately predict future equipment benefits.
It realizes accurate prediction and dynamic multi-dimensional assessment of medical equipment benefits, helping hospitals allocate resources more effectively, improve equipment usage efficiency, and optimize return on investment.
Smart Images
Figure CN119763799A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of medical equipment management, and in particular to a medical equipment benefit analysis and prediction method, system, equipment and medium based on historical data. Background Art
[0002] Medical equipment plays an important role in hospital operations, usually including key equipment such as CT, MRI, ultrasound diagnostic equipment, and monitors. The efficient use and benefit evaluation of medical equipment is the key to the rationality and efficiency of medical resource allocation. However, the current benefit evaluation of medical equipment mainly relies on static data such as simple frequency of use of equipment and manually counted maintenance records. This approach has the following limitations:
[0003] Incomplete usage data: The actual usage of the equipment and the impact of maintenance history on the equipment benefits cannot be fully considered.
[0004] Low prediction accuracy: There is a lack of scientific models based on historical data to accurately predict future benefits.
[0005] Insufficient decision support: Existing analysis methods cannot support hospital equipment managers to make reasonable resource allocation decisions, resulting in a low return on equipment investment. Summary of the invention
[0006] The purpose of the present invention is to provide a medical equipment benefit analysis and prediction method, system, device and medium based on historical data to solve the technical problems existing in the prior art.
[0007] The present invention is achieved through the following technical solutions:
[0008] In a first aspect, an embodiment of the present invention provides a medical equipment benefit analysis and prediction method based on historical data, comprising:
[0009] Obtain multi-dimensional historical data of medical equipment;
[0010] Preprocess the multi-dimensional historical data to obtain preprocessed data;
[0011] Calculate the historical benefit score of each device based on the preprocessed data;
[0012] The prediction model is constructed using the regression analysis algorithm, and the model training and parameter adjustment are performed based on the preprocessed data and historical benefit scores to obtain a trained benefit prediction model;
[0013] Obtain new medical equipment parameters, use the trained benefit prediction model to predict future equipment benefits, and obtain the predicted equipment benefit score;
[0014] The predicted equipment benefit score is displayed graphically.
[0015] Furthermore, the specific method for preprocessing the multi-dimensional historical data includes:
[0016] Detect and remove outliers from multi-dimensional historical data;
[0017] Use K-nearest neighbor algorithm to fill missing values in the data set;
[0018] The data are standardized to obtain data with consistent characteristic scale.
[0019] Furthermore, the formula for calculating the historical benefit score of each device based on the preprocessed data is:
[0020]
[0021] Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight.
[0022] Furthermore, the specific methods of constructing a prediction model using a regression analysis algorithm and performing model training and parameter adjustment based on preprocessed data and historical benefit scores include:
[0023] Select key features based on historical data of the equipment;
[0024] The prediction model was constructed using the multivariate linear regression model;
[0025] Historical data is input into the prediction model to train the model, and the model parameters are adjusted using cross-validation method.
[0026] In a second aspect, an embodiment of the present invention provides a medical equipment benefit analysis and prediction system based on historical data, comprising: a data acquisition module, a preprocessing module, a benefit evaluation module, a model building module, a prediction module and a display module.
[0027] The data acquisition module is used to acquire multi-dimensional historical data of medical equipment;
[0028] The preprocessing module is used to preprocess the multi-dimensional historical data to obtain preprocessed data;
[0029] The benefit evaluation module is used to calculate the historical benefit score of each device based on the preprocessed data;
[0030] The model building module uses a regression analysis algorithm to build a prediction model, performs model training and parameter adjustment based on preprocessed data and historical benefit scores, and obtains a trained benefit prediction model;
[0031] The prediction module is used to obtain new medical equipment parameters, predict future equipment benefits using the trained benefit prediction model, and obtain a predicted equipment benefit score;
[0032] The display module is used to display the predicted equipment benefit score in a graphical manner.
[0033] Further, the preprocessing module includes a detection unit, a filling unit and a standardization processing unit, and the detection unit detects and removes outliers from the multi-dimensional historical data;
[0034] The filling unit is used to fill the missing values in the data set using a K-nearest neighbor algorithm;
[0035] The standardization processing unit is used to perform standardization processing on the data to obtain data with consistent feature scales.
[0036] Furthermore, the benefit evaluation module includes a benefit score calculation unit, and the formula for calculating the historical benefit score of each device by the benefit score calculation unit is:
[0037]
[0038] Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight.
[0039] Furthermore, the model construction module includes a feature selection unit, a construction unit and a training unit. The feature selection unit is used to select key features based on the historical data of the equipment. The construction unit uses a multivariate linear regression model to build a prediction model. The training unit inputs historical data into the prediction model to train the model and uses a cross-validation method to adjust the model parameters.
[0040] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, an input device, an output device and a memory, wherein the processor is connected to the input device, the output device and the memory, respectively, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method described in the above embodiment.
[0041] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the above embodiment.
[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:
[0043] The embodiments of the present invention provide a medical equipment benefit analysis and prediction method, system, equipment and medium based on historical data, which utilize regression analysis models and equipment benefit evaluation algorithms to achieve accurate prediction and dynamic multi-dimensional evaluation of equipment benefits. The predicted equipment benefit scores obtained can help hospitals allocate resources more effectively and improve equipment utilization efficiency, thereby optimizing the return on investment of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:
[0045] Figure 1 A flowchart of a medical equipment benefit analysis and prediction method based on historical data provided by the first embodiment of the present invention;
[0046] Figure 2 A structural block diagram of a medical equipment benefit analysis and prediction system based on historical data provided by another embodiment of the present invention;
[0047] Figure 3 A structural block diagram of an electronic device provided in another embodiment of the present invention. DETAILED DESCRIPTION
[0048] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.
[0049] Example 1
[0050] like Figure 1 As shown, a medical equipment benefit analysis and prediction method based on historical data provided by an embodiment of the present invention is applicable to a medical equipment benefit analysis and prediction system based on historical data, and the method includes the following steps:
[0051] Obtain multi-dimensional historical data of medical equipment;
[0052] Preprocess the multi-dimensional historical data to obtain preprocessed data;
[0053] Calculate the historical benefit score of each device based on the preprocessed data;
[0054] The prediction model is constructed using the regression analysis algorithm, and the model training and parameter adjustment are performed based on the preprocessed data and historical benefit scores to obtain a trained benefit prediction model;
[0055] Obtain new medical equipment parameters, use the trained benefit prediction model to predict future equipment benefits, and obtain the predicted equipment benefit score;
[0056] The predicted equipment benefit score is displayed graphically.
[0057] In this embodiment, the medical equipment benefit analysis and prediction method based on historical data provided by the embodiment of the present invention obtains multi-dimensional historical data of medical equipment including usage frequency (number of times used daily / weekly / monthly), maintenance records (number of maintenance, average repair time), usage time (total usage time, single usage time), diagnostic effect (accuracy, equipment failure rate), etc. The multi-dimensional historical data is preprocessed, and the data obtained after preprocessing is of high quality compared with the original data. The historical benefit score of each device is calculated based on the preprocessed data to provide a comprehensive data basis for the benefit prediction model. The new medical equipment parameters are input into the benefit prediction model to predict the future benefits to obtain the predicted benefit score, which is then graphically displayed.
[0058] Specifically, the specific method for preprocessing multi-dimensional historical data includes:
[0059] 1. Anomaly detection: Use the Z-score method to detect and remove outliers. The formula is as follows:
[0060]
[0061] Among them, X is the observed value, u is the mean value of the data, and σ is the standard deviation. If Z>3, the data is considered abnormal and needs to be deleted.
[0062] 2. Missing value processing: Use KNN (K-nearest neighbor) algorithm to fill the missing values in the data set. KNN is a similarity-based algorithm that can estimate missing values by finding neighboring data similar to the missing data. The specific process is as follows:
[0063] 1) Preparation
[0064] Divide all data sets into complete data and data with missing values, so that missing values can be filled with complete data. Select appropriate features to fill missing values, for example, features that are highly relevant to the business can be selected.
[0065] 2) Distance metric selection
[0066] Choose an appropriate distance metric to evaluate the similarity between different data points. Common distance metrics include Euclidean distance (for numerical features) and Manhattan distance (suitable for datasets containing discrete features).
[0067] For numerical features, the Euclidean distance calculation formula is usually as follows:
[0068]
[0069] Among them, x and y are two sample vectors, x i and i They are the values of x and y corresponding to the i-th feature dimension.
[0070] Determine the K value: Select an appropriate K value, that is, find the K neighbors that are most similar to the missing value. Generally, a better K value can be selected through cross-validation or empirical rules. The usual practice is to try different K values and select the K value with the best filling effect through the mean squared error (MSE).
[0071] Find K nearest neighbors: For each missing value data point, calculate its distance to other complete data points based on the distance metric, and select the K nearest complete data points as the nearest neighbors.
[0072] Calculate the missing value filling: Use the data of K neighbors to fill the missing values. Depending on the type of missing value, the filling method will also be different:
[0073] Numerical features: Missing values are usually filled using the average of K neighbor values:
[0074]
[0075] Classification feature: Use the most frequent category among the K neighbors as the filling value (mode).
[0076] Filling missing values for multiple features: For data points with multiple missing features, you can fill each missing value in turn, or use an iterative approach to gradually update the missing values to obtain a more accurate estimate. In the iterative approach, first fill the least missing feature, and then use the updated data to recalculate the distance to fill the remaining features.
[0077] Verify the filling effect: After the filling is completed, the filling effect can be verified by cross-validation. If the filling effect is not good, try to adjust the K value or choose other more suitable data preprocessing methods.
[0078] Application example: For example, for the usage data of a medical device, the feature with missing values is "weekly usage frequency". Suppose we select 5 data points with similar features (such as equipment type, usage time, and maintenance record), apply the KNN algorithm to calculate the similarity to obtain the nearest neighbor data point set, and use the average value of the "weekly usage frequency" of the 5 similar devices to fill the current missing value.
[0079] 3) Data standardization or normalization
[0080] Before calculating the Euclidean distance, the data is standardized or normalized to ensure that all features have the same scale. Common normalization methods are:
[0081] Standardization: Convert the value of each feature into data with a mean of 0 and a standard deviation of 1, that is, through the formula:
[0082]
[0083] Among them, μ is the mean of the feature, σ is the standard deviation of the feature, and x is the multi-dimensional historical data.
[0084] Normalization: Scale the value of each feature to a specific range (such as 0 to 1):
[0085]
[0086] Among them, max(x) and min(x) are the maximum and minimum values in the multi-dimensional historical data respectively.
[0087] In this embodiment, the formula for calculating the historical benefit score of each device based on the preprocessed data is:
[0088]
[0089] Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight. α, β, and γ are weights adjusted according to the equipment type and hospital needs. This benefit evaluation algorithm ensures that multi-dimensional factors such as equipment utilization rate, diagnostic accuracy, and maintenance records are comprehensively considered, providing a comprehensive data foundation for subsequent prediction models.
[0090] In this embodiment, the prediction model is constructed using a regression analysis algorithm, and the specific method for model training and parameter adjustment based on the preprocessed data and historical benefit scores includes:
[0091] Feature selection: Select key features based on the historical data of the equipment, including frequency of use, historical benefit score, number of maintenance times, etc.
[0092] Model selection: Multiple linear regression model is used to build the prediction model. For cases with strong nonlinearity, decision tree regression or support vector machine model can be used.
[0093] Model training and validation: Input historical data into the prediction model to train the model, and use the cross-validation method to adjust the model parameters to obtain a trained benefit prediction model. The cross-validation method can improve the generalization ability of the model.
[0094] Benefit prediction: Input new equipment parameters and input the new equipment parameters into the trained benefit prediction model to predict future equipment benefits. The benefit prediction model formula is:
[0095]
[0096] in, is the predicted benefit score, x1,x2,...x n are device characteristics, θ0, θ1, θ2, θ n is the regression coefficient.
[0097] The predicted equipment benefit score is displayed in a graphical form, including benefit change curves, recommended equipment configuration plans, etc., to help decision makers understand the trend of equipment benefits and provide decision support.
[0098] The medical equipment benefit analysis and prediction method based on historical data provided by the embodiment of the present invention has the following advantages:
[0099] 1. High-precision prediction: Accurately predict equipment benefits based on historical data through regression analysis models, making resource allocation more scientific.
[0100] 2. Multi-dimensional benefit evaluation: By calculating the benefit score based on a combination of multiple indicators, a more comprehensive analysis perspective is provided for equipment benefits.
[0101] 3. Optimize resource allocation: The prediction results can provide scientific resource allocation suggestions for equipment configuration, improve equipment utilization and return on investment.
[0102] 4. Graphical display: visualize the forecast results to help decision makers make reasonable management decisions more quickly.
[0103] Example 2
[0104] like Figure 2 As shown, an embodiment of the present invention provides a medical equipment benefit analysis and prediction system based on historical data, including: a data acquisition module, a preprocessing module, a benefit evaluation module, a model building module, a prediction module and a display module.
[0105] The data acquisition module is used to acquire multi-dimensional historical data of medical equipment;
[0106] The preprocessing module is used to preprocess the multi-dimensional historical data to obtain preprocessed data;
[0107] The benefit evaluation module is used to calculate the historical benefit score of each device based on the preprocessed data;
[0108] The model building module uses a regression analysis algorithm to build a prediction model, performs model training and parameter adjustment based on preprocessed data and historical benefit scores, and obtains a trained benefit prediction model;
[0109] The prediction module is used to obtain new medical equipment parameters, predict future equipment benefits using the trained benefit prediction model, and obtain a predicted equipment benefit score;
[0110] The display module is used to display the predicted equipment benefit score in a graphical manner.
[0111] In this embodiment, the preprocessing module includes a detection unit, a filling unit and a standardization processing unit, wherein the detection unit detects and removes outliers from multi-dimensional historical data;
[0112] The filling unit is used to fill the missing values in the data set using a K-nearest neighbor algorithm;
[0113] The standardization processing unit is used to perform standardization processing on the data to obtain data with consistent feature scales.
[0114] In this embodiment, the benefit evaluation module includes a benefit score calculation unit, and the formula for calculating the historical benefit score of each device by the benefit score calculation unit is:
[0115]
[0116] Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight.
[0117] In this embodiment, the model construction module includes a feature selection unit, a construction unit and a training unit. The feature selection unit is used to select key features based on the historical data of the equipment. The construction unit uses a multivariate linear regression model to build a prediction model. The training unit inputs historical data into the prediction model to train the model and uses cross-validation method to adjust the model parameters.
[0118] The medical equipment benefit analysis and prediction system based on historical data provided by the embodiment of the present invention has the following advantages:
[0119] 1. High-precision prediction: Accurately predict equipment benefits based on historical data through regression analysis models, making resource allocation more scientific.
[0120] 2. Multi-dimensional benefit evaluation: By calculating the benefit score based on a combination of multiple indicators, a more comprehensive analysis perspective is provided for equipment benefits.
[0121] 3. Optimize resource allocation: The prediction results can provide scientific resource allocation suggestions for equipment configuration, improve equipment utilization and return on investment.
[0122] 4. Graphical display: visualize the forecast results to help decision makers make reasonable management decisions more quickly.
[0123] Example 3
[0124] like Figure 3 As shown, a structural block diagram of an electronic device according to another embodiment of the present invention, the device includes a processor, an input device, an output device and a memory, the processor is connected to the input device, the output device and the memory respectively, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method described in the first embodiment above.
[0125] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.
[0126] Input devices may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and output devices may include a display (LCD, etc.), a speaker, etc.
[0127] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.
[0128] In a specific implementation, the processor, input device, and output device described in the embodiments of the present invention may execute the implementation described in the method embodiment provided in the embodiments of the present invention, or may execute the implementation described in the system embodiment described in the embodiments of the present invention, which will not be described in detail here.
[0129] Example 4
[0130] Another embodiment of the present invention further provides an embodiment of a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method described in the first embodiment.
[0131] The computer-readable storage medium may be an internal storage unit of the terminal described in the foregoing embodiments, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0133] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.
[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.
[0135] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A medical equipment benefit analysis and prediction method based on historical data, characterized in that: include: Obtain multi-dimensional historical data of medical equipment; Preprocess the multi-dimensional historical data to obtain preprocessed data; Calculate the historical benefit score of each device based on the preprocessed data; The prediction model is constructed using the regression analysis algorithm, and the model training and parameter adjustment are performed based on the preprocessed data and historical benefit scores to obtain a trained benefit prediction model; Obtain new medical equipment parameters, use the trained benefit prediction model to predict future equipment benefits, and obtain the predicted equipment benefit score; The predicted equipment benefit score is displayed graphically.
2. The medical equipment benefit analysis and prediction method based on historical data according to claim 1 is characterized in that: The specific method for preprocessing multi-dimensional historical data includes: Detect and remove outliers from multi-dimensional historical data; Use K-nearest neighbor algorithm to fill missing values in the data set; The data are standardized to obtain data with consistent characteristic scale.
3. The medical equipment benefit analysis and prediction method based on historical data according to claim 1 is characterized in that: The formula for calculating the historical benefit score of each device based on the preprocessed data is: Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight.
4. The method for analyzing and predicting the benefits of medical equipment based on historical data according to claim 3 is characterized in that: The specific method of using the regression analysis algorithm to build a prediction model and perform model training and parameter adjustment based on preprocessed data and historical benefit scores includes: Select key features based on historical data of the equipment; The prediction model was constructed using the multivariate linear regression model; Historical data is input into the prediction model to train the model, and the model parameters are adjusted using cross-validation method.
5. A medical equipment benefit analysis and prediction system based on historical data, characterized in that: include: Data acquisition module, preprocessing module, benefit evaluation module, model building module, prediction module and display module, The data acquisition module is used to acquire multi-dimensional historical data of medical equipment; The preprocessing module is used to preprocess the multi-dimensional historical data to obtain preprocessed data; The benefit evaluation module is used to calculate the historical benefit score of each device based on the preprocessed data; The model building module uses a regression analysis algorithm to build a prediction model, performs model training and parameter adjustment based on preprocessed data and historical benefit scores, and obtains a trained benefit prediction model; The prediction module is used to obtain new medical equipment parameters, predict future equipment benefits using the trained benefit prediction model, and obtain a predicted equipment benefit score; The display module is used to display the predicted equipment benefit score in a graphical manner.
6. The medical equipment benefit analysis and prediction system based on historical data according to claim 5 is characterized in that: The preprocessing module includes a detection unit, a filling unit and a standardization processing unit, wherein the detection unit detects and removes outliers from multi-dimensional historical data; The filling unit is used to fill the missing values in the data set using a K-nearest neighbor algorithm; The standardization processing unit is used to perform standardization processing on the data to obtain data with consistent feature scales.
7. The medical equipment benefit analysis and prediction system based on historical data according to claim 5 is characterized in that: The benefit evaluation module includes a benefit score calculation unit, and the formula for calculating the historical benefit score of each device by the benefit score calculation unit is: Among them, α is the equipment usage weight, β is the diagnosis weight, and γ is the maintenance record weight.
8. The medical equipment benefit analysis and prediction system based on historical data according to claim 7 is characterized in that: The model construction module includes a feature selection unit, a construction unit and a training unit. The feature selection unit is used to select key features according to the historical data of the equipment. The construction unit uses a multivariate linear regression model to build a prediction model. The training unit inputs historical data into the prediction model to train the model and uses a cross-validation method to adjust the model parameters.
9. An electronic device, characterized in that: It includes a processor, an input device, an output device and a memory, the processor is connected to the input device, the output device and the memory respectively, the memory is used to store a computer program, the computer program includes program instructions, and is characterized in that the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 4.