Disease population middle-aged and elderly category structure prediction method based on grey component prediction modeling

Abstract

The invention provides a disease population middle-aged and elderly category structure prediction method based on gray component prediction modeling, which comprises the following steps: collecting multi-component elderly population structure data influenced by a specific disease, and analyzing component data features; building a modeling framework integrating a time elastic mechanism and component structure analysis according to the data features; establishing a novel dynamic nonlinear gray component prediction model of multi-parameter combination optimization adaptive to the irregular time sequence component data based on the framework; and gray component prediction model parameters are solved, and the structural evolution of the old-age disease crowd with each component is estimated, so that the problems of old-age category structure prediction and health management of the disease crowd based on small sample sequence modeling are solved. According to the method provided by the invention, the proportion change rule of the old people in different health states of the disease population can be accurately described, so that the health management personnel can more accurately grasp the change trend of the old people in the disease burden, and thus a more reasonable resource allocation and intervention strategy is formulated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disease health management system, in particular to a disease population old age category structure prediction method based on grey component prediction modeling. BACKGROUND

[0002] Old-age population disease prediction is an important research direction in the cross field of public health and health management, and the prediction results have important decision value for optimizing medical policy, improving resource allocation efficiency and developing intervention strategies. Old-age population disease prediction research is usually based on historical health data and predicts the prevalence rate of a specific disease in the old age population through various statistical and calculation models, mainly focusing on chronic diseases and acute diseases related to age.

[0003] Various models are used for old-age population disease prediction, mainly including statistical models, machine learning and system modeling, etc. Statistical methods such as exponential smoothing method, Cox regression model, Logistic regression analysis. Machine learning such as using SVM; random forest prediction. System modeling methods such as using dynamic modeling technology, state transition model, dynamic Markov model. On the one hand, existing old-age disease prediction research mainly focuses on the overall prevalence rate of the old age population, however, with the aggravation of the old age population problem, more refined disease prevention and resource allocation are needed, and it is crucial to focus on the component dynamic analysis of the disease structure within the old age population. On the other hand, most of the existing methods are based on single sequence and time interval assumptions, which are difficult to adapt to the characteristics of small sample, non-equidistant and component constraints commonly existing in refined survey data, resulting in the limitation of traditional time series model in practical application. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a disease population old age category structure prediction method based on grey component prediction modeling, and to construct a non-equidistant grey component prediction model for predicting the structure of different old age categories in the disease population. On the one hand, the system research of grey component model is an important breakthrough for the expansion of grey system theory to more complex structure data, which not only helps to break the dependence of component data on ideal time interval and enhance the adaptability of the model to non-ideal data environment, but also provides theoretical support for constructing a prediction framework for structure data. On the other hand, this method is used for the evolution prediction of the structure of the old age population with a certain disease under the background of population aging, which helps to identify the evolution trend, optimize resource allocation, and shows significant practical significance in health risk warning and precise management.

[0005] To achieve the above purpose, the present application adopts the following technical solution: a disease population old age category structure prediction method based on grey component prediction modeling, comprising: S1, collect multi-component old population structure data affected by a certain type of disease, and analyze the characteristics of the component data; S2, according to the data characteristics, build a modeling framework integrating time elasticity mechanism and component structure analysis; S3, based on the modeling framework, establish a non-equidistant gray dynamic component prediction model suitable for irregular time series component data, optimize the parameters and mine the implicit cumulative law, background value coefficient and nonlinear characteristics through parameter combination, and the expression contains the order, index, background value and fixed parameters and dynamic parameters to be solved which can be optimized; S4, solve the parameters of the gray component prediction model based on logarithmic transformation, and perform anti-logarithmic transformation to estimate the structure evolution of each component old disease population, including: solving the fixed parameters of the model by least squares method, using particle swarm algorithm to globally optimize the dynamic parameters to minimize the global average relative simulation error, obtaining the cumulative sequence prediction value of each sequence based on the solved parameters and restoring it to the original sequence prediction value through cumulative reduction, and finally obtaining the simulation or prediction results of the original proportion sequence through anti-logarithmic transformation, connecting the prediction values at each discrete time to obtain the component evolution curve of different categories of old population in the disease population.

[0006] Further, in S1, multi-component old population structure data affected by a certain type of disease is collected, the data is derived from a database containing multiple surveys, and the survey time points form a non-equidistant time series; the old people with the disease are divided into four clear component categories according to whether they are disabled and the degree of disability, and after obtaining the original proportion sequence of the four categories, the core characteristics of the sequence are identified and quantified, namely the time non-equidistance and the component structure constraint; By calculating the actual interval length between consecutive time points in the sequence and analyzing the coefficient of variation of these interval lengths, the non-uniformity degree of the time series is numerically characterized; at the same time, the addition consistency verification is performed on all category proportion data at each time point; the change pattern of each component sequence with time is analyzed, the absolute change amount and its direction of each component proportion between adjacent time points are calculated, and the heterogeneity of the evolution trend of the proportion of different disabled population categories is identified, that is, some categories show monotonic change while other categories show fluctuation or turning.

[0007] Further, in S2, the original multi-component old population structure data collected is subjected to asymmetric logarithmic ratio transformation to convert the original proportion sequence into a modeling sequence, wherein the transformation process is realized by calculating the natural logarithm of the ratio of each component proportion to the reference component proportion, thereby eliminating the constant and constraint of the component data, and making the transformed sequence suitable for gray modeling; The transformed component structure data is used as the modeling sequence to build a gray dynamic component prediction model, which processes irregular time intervals and integrates component structure analysis to capture the dynamic evolution characteristics of the multi-component system; The asymmetric inverse logarithmic transformation is performed on the output analog and prediction sequence to restore the sequence to the original proportion sequence, wherein the inverse transformation process is processed through exponential operation and normalization to ensure that the restored sequence satisfies the constraint condition that the sum of proportions of all components is 1; The average relative percentage error, the standard deviation of the relative percentage error, and the average absolute scaled error are used as evaluation criteria to evaluate the prediction curve of the elderly category structure in the disease population obtained by the gray component model, wherein the average relative percentage error is calculated as the average of the absolute values of the relative errors of the predicted values and the actual values at each time point, the standard deviation of the relative percentage error is calculated as the dispersion index of the relative errors, and the average absolute scaled error is calculated as the ratio of the prediction error to the average absolute value of the first-order difference of the sequence.

[0008] Further, in S3, based on the modeling framework, the system structural characteristics reflected by the elderly category component data in the disease population, and the characteristics that the data is disturbed by the system as a whole and each component and presents different change rules in different periods, a non-equidistant gray dynamic component prediction model is constructed; The gray dynamic component prediction model includes the optimized parameters, the index parameters, and the parameters of the background value of the generated sequence in the vicinity, and simultaneously includes the fixed parameters and the dynamic parameters to be solved. In the specific model construction process, based on the non-uniformity of the time interval of the non-equidistant sequence, a new sequence is defined as the accumulated generated sequence of the original sequence, wherein the accumulation order is dynamically determined by an intelligent optimization algorithm to capture the implicit accumulation law of the data; secondly, the coefficient of the background value of the gray dynamic component prediction model is adjusted by the index parameter to reflect the nonlinear characteristics of the sequence, and the dynamic parameter is used to represent the uncertainty of the evolution over time; The gray dynamic component prediction model integrates the fixed parameters to describe the basic trend of the system, and the dynamic parameters to adapt to the variability of the component structure, and through parameter combination optimization, the accumulation law size, the background value coefficient size, and the nonlinear characteristics hidden in the non-equidistant component data are mined, thereby forming a gray prediction model structure that adapts to irregular time series component data.

[0009] Further, the intelligent optimization algorithm mines the accumulation law size, the background value coefficient size, and the nonlinear characteristics to construct the non-equidistant gray component prediction model, and the formula expression is: ; wherein, , is the number of components, is the number of points included in each sequence, represents a certain time point corresponding to the sequence; The order of the operator for imposing the treatment on the original sequence, used to describe the strength of the grey operator in the generation, is defined as a real number, that is , is an index, is the background value of the sequence immediately adjacent to the generation, and is a dynamic parameter to be solved; is a fixed parameter to be solved, wherein is called the development coefficient, the size and sign of which represent the development trend of the original sequence, represents the grey action amount, the size of which is related to the time point ; the parameter is a random disturbance term, used to represent information with accidental or weak factors; and is a new sequence, , which satisfies the formula: ; represents the interval between the time point and the time point , , and have the same meaning, wherein is a summation index, used to represent each historical data point from time 1 to time .

[0010] Further, in the S4, based on the non-equidistant grey dynamic component prediction model, the fixed parameter estimation matrix in the model is solved by using the least square method, and the solving process is realized by constructing a matrix operation containing non-equidistant time intervals; Then, the particle swarm algorithm is used to globally combine and optimize the dynamic parameters in the grey dynamic component prediction model, the optimization process takes the minimization of the global average relative simulation error as the objective function, and the optimal parameter combination is determined through iterative optimization; After obtaining the fixed parameters and the dynamic parameters, they are substituted into the grey dynamic component prediction model, and the accumulated sequence prediction values of each sequence at the prediction time are calculated; Then, the cumulative reduction operator is used as the recursive formula to restore the accumulated sequence prediction values to the original sequence prediction values, and the restoration process is realized by the difference operation.

[0011] Further, the original sequence prediction values obtained by the cumulative reduction are subjected to an asymmetric inverse logarithmic transformation, and the modeling sequence is converted back to the original proportion sequence, and the transformation process is realized by the exponential operation and the normalization processing to ensure that the sum of the proportions of each component satisfies the unit constraint; By connecting the predicted values of each discrete time point, the component evolution curves of different age categories in the disease population are formed, and the accuracy of the prediction results is quantitatively evaluated by taking the average relative percentage error, the standard deviation of the relative percentage error and the average absolute scaling error as evaluation indexes, wherein the average relative percentage error reflects the average accuracy of the prediction, the standard deviation of the relative percentage error represents the stability of the prediction, and the average absolute scaling error is compared with the error of the random walk benchmark to depict whether the model prediction is better than the basic variation level of the sequence itself.

[0012] The application provides a disease population old age category structure prediction method based on grey component prediction modeling, which has the following beneficial effects: 1、The application introduces a time elasticity mechanism, constructs a non-equidistant grey component modeling framework and a new type of grey component prediction model, breaks through the dependence of component data on equidistant data, can realize high-precision modeling of structural data, and effectively deals with the component data modeling problem of inconsistent time intervals in actual observation.

[0013] 2、The structural curve obtained according to the grey component prediction model can effectively depict the structural dynamic evolution trend of the old population with diseases, realize the analysis of the structural old population health data, and support fine resource allocation.

[0014] 3、The application solves the decision blind spot that it is difficult to dynamically predict the disability structure of the old disease population under the conditions of sparse and irregular data, improves the grasping ability of the health management department for the disease burden change trend of the old population. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The method flowchart provided by the application is shown in the figure; Figure 2 The specific flowchart of the disease population old age category structure component prediction is shown in the figure; Figure 3 The prediction effect of the old age category structure in the cardiovascular disease under different models is shown in the figure; Figure 4 The old age category structure prediction evolution result graph of the cardiovascular disease is shown in the figure. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application, and are not used to limit the application.

[0017] Although the steps in this invention are arranged by reference numerals, this is not intended to limit the order of the steps. Unless the order of the steps is explicitly stated or the execution of a step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" as used herein refers to and covers any and all possible combinations of one or more of the associated listed items.

[0018] Please refer to Figures 1 to 4 A specific implementation method of this embodiment is given: like Figure 1 As shown, this invention presents a method for predicting the category structure of elderly populations in disease populations based on grey component prediction modeling, as proposed in this paper. Figure 2 As shown, this method comprises two parts: a modeling framework for component structure analysis and the construction of a novel dynamic gray component prediction model; specifically, it includes the following steps: S1. Collect multi-component elderly population structure data affected by specific diseases and analyze the characteristics of the component data; As a preferred embodiment of step S1, it specifically includes: Data on the multi-component elderly population structure affected by diseases were collected and organized. Based on the degree and status of disability, elderly people with diseases were divided into four categories: healthy elderly, mildly disabled elderly, moderately disabled elderly, and severely disabled elderly. The number of elderly people with cardiovascular diseases under different disability conditions in different years nationwide was statistically analyzed, and their proportions were calculated as follows: ,satisfy: ; ; in, The sequence is non-equally spaced, representing the percentage of elderly people in different years under different disability conditions. Each group must satisfy the constraint that the sum is 1 in a specific year.

[0019] S2. Based on the data characteristics, construct a modeling framework that integrates time elasticity mechanism and component structure analysis; As a preferred embodiment of step S2, it specifically includes: S21. Based on the original data obtained in S1, perform an asymmetric transformation (alr) on the component data to obtain the modeling sequence: ,and The formula is expressed as: ; S22. Use 85% of the transformed component structure data as the modeling sequence and 15% as the simulation prediction sequence to construct a non-equidistant gray dynamic component model.

[0020] S3, based on the modeling framework, establish a non-equidistant gray dynamic component prediction model suitable for irregular timing component data, through parameter combination optimization and mining of implicit cumulative law, background value coefficient and nonlinear characteristics, its expression contains the order, index, background value and fixed parameters and dynamic parameters to be solved which can be optimized; As a preferred embodiment of step S3, specifically includes: The elderly category component data in the disease population reflects the structural characteristics of the system, which is disturbed by the entire system and each component of the system, and presents different change laws at different times. For non-equidistant component data, considering the trend difference of component data and the irregularity of data interval at different times, through parameter combination optimization, a new nonlinear non-equidistant gray component prediction model is constructed by using intelligent optimization algorithm to mine the implicit cumulative law size, background value coefficient size and nonlinear characteristics. The formula expression is: ; Among them, , is the number of components, is the number of points contained in each sequence, and represents a certain time point corresponding to the sequence; is the order of the operator applied to the original sequence, which is used to describe the generation of the gray operator. Its definition is a real number, that is, , is the index, is the background value of the generated sequence, which is a dynamic parameter to be solved; is a fixed parameter to be solved, wherein is called a development coefficient, which size and sign of the original sequence development trend, then represents the gray action amount, which size and time point related; parameter is a random disturbance term, which is used to represent information with accidental or weak factors; and is a new sequence, , satisfies the formula: ; , represents the interval between time point and time point , , and have the same meaning, wherein is a summation index, which is used to represent each historical data point from time 1 to time .

[0021] S4, solve the parameters of the grey component prediction model based on the logarithmic transformation, and perform the anti-logarithmic transformation, and then estimate the structural evolution of the component elderly disease population, including: solving the fixed parameters of the model by the least squares method, using the particle swarm algorithm to globally combine and optimize the dynamic parameters to minimize the global average relative simulation error, obtaining the cumulative sequence prediction value of each sequence based on the solved parameters and restoring it to the original sequence prediction value by cumulative reduction, and finally obtaining the simulation or prediction result of the original proportion sequence through the anti-logarithmic transformation, connecting the prediction values of each discrete time to obtain the component evolution curve of different categories of the elderly population in the disease population.

[0022] As a preferred embodiment of step S4, it specifically includes: S41, according to steps S21 and S22, obtain the modeling sequence and the corresponding sequence grouping for simulation and prediction; S42, the new nonlinear non-equidistant grey component model contains fixed parameters and dynamic parameters, and the least error square sum is taken as the objective function, and the fixed parameter estimation matrix is first solved by the least squares method , and the calculation formula is specifically: ; Among them, The specific formula is: ; ; Among them, Indicates the interval between time sequence time point j and the previous time, that is, the four parameter estimates ; S43, use the particle swarm algorithm (the Particle Swarm Optimization, PSO) to globally combine and optimize the to-be-determined parameters. Specifically, based on different model groups, select the common parameter combination optimization principle and the condition of the minimum global average relative simulation error to seek the optimal parameter combination of the model, which satisfies the formula: ; S44, bring the parameters solved in steps S41 and S42 into the constructed grey component prediction model to obtain the R-order cumulative sequence prediction value of each sequence at time point ; Among them, These parameters are not additional parameters, but a combination of the solved model parameters, which makes the prediction update formula present in a more concise form. As an index variable in the summation process, it itself does not have a specific physical or model meaning, and its role is to traverse the historical data item by item. S45. Using the R-order cumulative decrease operator as the recurrence relation, we obtain... The predicted values ​​of each sequence at time point are expressed by the formula: ; S46. Based on the sequences obtained in step S45, perform asymmetric antilogarithmic transformation (Realr) component data transformation to obtain the simulated / predicted sequences of the original sequences. The formula is expressed as: ; S47. Using the mean relative percentage error (MRPE), the standard deviation of the relative percentage error (STD), and the mean absolute scaling error (MASE) as evaluation criteria, the accuracy of the prediction curves of the elderly category structure in the disease population obtained by the proposed novel grey component prediction model is evaluated. The specific formula is as follows: ; ; ; in, For the number of components, The sum of the number of points contained in each sequence. and They have the same meaning, representing the specific position in the sequence at a particular point in time. This represents a specific point in time corresponding to the sequence. , indicating time point With time point The spacing between them; Representative modeling sequence corresponding time point The corresponding original value, This represents the value predicted by the model at the corresponding point in time.

[0023] In this embodiment, based on the CLHLS database, structural data on cardiovascular diseases among the elderly nationwide were extracted and processed as the research object in actual experiments. Simultaneously, based on the database data, more refined classification structural data of cardiovascular diseases was obtained, categorized into four groups according to disability status and degree: no disability, mild disability, moderate disability, and severe disability. To compare the advantages of the proposed model, the model of this invention is abbreviated as NLUGM. Advanced non-equidistant grey component models were selected, including the non-equidistant GM(1,1) model (UGM), the non-equidistant fractional GM(1,1) model (UFGM), the non-equidistant GM(1,1) power model (UPGM), the exponential nonlinear regression model (ENLR), and the support vector regression model (SVR), which are feature-friendly for this type of data and robust to time intervals and proportions, for modeling and prediction.

[0024] The transformed structure data of 85% is taken as a modeling sequence, and 15% is taken as a simulation prediction sequence. After simulation, the structure simulation result of the cardiovascular disease of the old people in the whole country is as shown in Figure 3 The accuracy evaluation is as shown in Table 1.

[0025] Table 1. Accuracy evaluation table of cardiovascular disease old class structure prediction

[0026] The results show that the six types of models have the best fitting and prediction effect in each component under different component data conversion methods. Further, the results of each model in different scenarios are calculated by three types of model test methods of MAPE, STD and MASE, each component is calculated and the corresponding comprehensive results are obtained, and the results show that in each type of component data model, the NLUGM model proposed in the application has the smallest comprehensive MAPE and comprehensive STD, and the MAPE is 4.167%, which is lower than 5%, belonging to the first level of accuracy (> 95%). Based on the performance of the prediction model, such accuracy can meet the requirements, which reflects the effectiveness and rationality of the method proposed in the application. Further, the prediction results are displayed by using Figure 4

[0027] In summary, the method proposed in the application constructs a new gray component prediction model according to the structure component characteristics of the specific disease old population. Based on small sample data, the new model can well capture the trend of each sequence and obtain high prediction accuracy. The gray prediction model does not need to understand the internal evolution experience of the disease in advance, and does not need to give a rule assumption and select a specific model form.

[0028] According to the evolution curve obtained by the gray component prediction model, a new technical path and specific numerical analysis result are provided for the modeling and prediction of the cardiovascular disease prevalence structure of the old population in China, and health resources are allocated.

[0029] In the present specification, the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" and the like describe at least one embodiment or example described in connection with a specific feature, structure, material or characteristic. These described specific features, structures, materials or characteristics can be combined in one or more embodiments or examples in an appropriate manner. In addition, the skilled person can combine and integrate different embodiments or examples described in the present specification and their features without contradiction.

[0030] ​The logic and / or steps shown in the flowcharts or otherwise described herein can be considered a sequence of instructions, which, when executed by a processor system, cause the processor system to perform a process. These instructions can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device. Such systems, apparatuses, or devices include, but are not limited to, a processor system or other system that can fetch and execute instructions.

[0031] The above embodiments describe the principles and implementation of the present application in detail, and the working principle is described by specific examples. These examples are only used to help understand the method of the present application and its core idea. At the same time, according to the idea of the present application, the actual implementation and application range can be changed. Therefore, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for predicting the structure of the elderly category in a disease population based on grey component predictive modeling, characterized by, The method comprises the following steps: S1, collecting multi-component old population structure data affected by a certain type of disease, and analyzing the component data characteristics; S2, according to the data characteristics, building a modeling framework integrating time elasticity mechanism and component structure analysis; S3, based on the modeling framework, establishing a non-equidistant grey dynamic component prediction model suitable for irregular time series component data, optimizing the parameters and mining the implicit cumulative law, background value coefficient and nonlinear characteristics, and the expression contains the order, index, background value and fixed parameters and dynamic parameters to be solved which can be optimized; S4, solving the parameters of the grey component prediction model based on logarithmic transformation, and performing anti-logarithmic transformation to estimate the structure evolution of each component old disease population, including solving the fixed parameters of the model by the least square method, using particle swarm algorithm to globally optimize the dynamic parameters to minimize the global average relative simulation error, obtaining the cumulative sequence prediction value of each sequence based on the solved parameters and restoring it to the original sequence prediction value through cumulative reduction, and finally obtaining the simulation or prediction results of the original proportion sequence through anti-logarithmic transformation, and connecting the prediction values at each discrete time to obtain the component evolution curve of different categories of old population in the disease population.

2. The method of claim 1, wherein the method is a method of predicting the structure of the elderly class in a disease population based on grey component predictive modeling. In S1, multi-component old population structure data affected by a certain type of disease is collected, and the data is derived from a database containing multiple surveys, and the survey time points form a non-equidistant time series; the old people with the disease are divided into four clear component categories according to whether they are disabled and the degree of disability, and after obtaining the original proportion sequence of the four categories, the core characteristics of the sequence are identified and quantified, namely the time non-equidistance and the component structure constraint; The non-uniformity degree of the time series is numerically characterized by calculating the actual interval length between consecutive time points in the sequence and analyzing the coefficient of variation of these interval lengths; at the same time, the addition consistency verification is performed on all category proportion data at each time point; the change mode of each component sequence with time is analyzed, the absolute change amount and direction of each component proportion between adjacent time points are calculated, and the heterogeneity of the evolution trend of the proportion of different disabled population categories is identified, that is, some categories show monotonic change while other categories show fluctuation or turning. 3.The disease population old age class structure prediction method based on grey component prediction modeling according to claim 1, characterized in that: In S2, the original multi-component old population structure data collected is subjected to asymmetric logarithmic ratio transformation, and the original proportion sequence is converted into a modeling sequence, wherein the transformation process is realized by calculating the natural logarithm of the ratio of each component proportion to the reference component proportion, thereby eliminating the constant and constraint of the component data, and making the transformed sequence suitable for grey modeling; The transformed component structure data is used as the modeling sequence to build a grey dynamic component prediction model, which processes irregular time intervals and integrates component structure analysis to capture the dynamic evolution characteristics of the multi-component system; The simulation and prediction sequences output are subjected to asymmetric anti-logarithmic transformation to restore the sequence to the original proportion sequence, wherein the anti-transformation process is realized by exponential operation and normalization processing to ensure that the restored sequence satisfies the constraint condition that the sum of all component proportions is 1. The accuracy of the prediction curve of the elderly category structure in the disease population obtained by the grey component model is evaluated by taking the average relative percentage error, the standard deviation of the relative percentage error and the average absolute scaled error as evaluation criteria, wherein the average relative percentage error is calculated as the average of the absolute values of the relative errors of the predicted values and the actual values at each time point, the standard deviation of the relative percentage error is calculated as the dispersion index of the relative errors, and the average absolute scaled error is calculated as the ratio of the prediction error to the average absolute value of the first-order difference of the sequence.

4. The method of claim 1, wherein the method is a method of predicting the structure of the elderly class in a disease population based on grey component predictive modeling. In S3, based on the modeling framework, the system structural characteristics reflected by the elderly category component data in the disease population, and the characteristics that the data presents different change rules in different periods due to the interference of the system as a whole and each component, a non-equidistant grey dynamic component prediction model is constructed; The grey dynamic component prediction model passes through the parameter combination optimization process, through the optimizable order parameter, the exponential parameter and the background value parameter of the adjacent generated sequence, and simultaneously contains the fixed parameters and dynamic parameters to be solved; In the specific model construction process, based on the non-uniformity of the time intervals of the non-equidistant sequence, a new sequence is defined as the accumulated generated sequence of the original sequence, wherein the accumulation order is dynamically determined by an intelligent optimization algorithm to capture the implicit accumulation rule of the data; secondly, the coefficient of the background value of the grey dynamic component prediction model is adjusted by the exponential parameter to reflect the nonlinear characteristics of the sequence, and the dynamic parameter is used to represent the uncertainty of its evolution over time; The grey dynamic component prediction model integrates the fixed parameters to describe the basic trend of the system, and the dynamic parameters to adapt to the variability of the component structure, and through parameter combination optimization, the accumulation rule size, the background value coefficient size and the nonlinear characteristics hidden in the non-equidistant component data are mined, so as to form a grey prediction model structure that adapts to irregular time series component data.

5. The method of claim 4, wherein the method is characterized by: The intelligent optimization algorithm mines the accumulation rule size, the background value coefficient size and the nonlinear characteristics to construct the non-equidistant grey component prediction model, and the formula expression is: ； wherein, , is the number of components, is the number of points contained in each sequence, denotes a certain time point corresponding to the sequence; is the order of the operator applied to the original sequence, used to describe the intensity of the grey operator in the generation process, which is defined as a real number, i.e. , is the index, is the background value of the sequence immediately generated, which is a dynamic parameter to be solved; is a fixed parameter to be solved, wherein is called the development coefficient, the size and sign of which represent the development trend of the original sequence, denotes the grey action amount, the size of which is related to the time point ; the parameter is a random disturbance term, used to represent information with accidental or weak factors; and is a new sequence, satisfying the formula: ； , denotes a time point between time points , , and have the same meaning, wherein is a summation index used to denote each historical data point from time 1 to time .

6. The method of claim 1, wherein the method is a method of predicting the structure of the elderly class in a disease population based on grey component predictive modeling. In S4, based on the non-equidistant grey dynamic component prediction model, the least square method is used to solve the fixed parameter estimation matrix in the model, and the solving process is realized by constructing a matrix operation containing non-equidistant time intervals; Then, the particle swarm algorithm is used to globally combine and optimize the dynamic parameters in the grey dynamic component prediction model, and the optimization process takes the minimization of the global average relative simulation error as the objective function, and determines the optimal parameter combination through iterative optimization; After obtaining the fixed parameters and the dynamic parameters, they are substituted into the grey dynamic component prediction model to calculate the accumulated sequence prediction values of each sequence at the prediction time; Then, the cumulative decrease operator is used as the recursive formula to restore the accumulated sequence prediction values to the original sequence prediction values, and the restoration process is realized by the first-order difference operation.

7. The method of claim 6, wherein the method is a method of predicting the structure of the elderly class in a disease population based on grey component predictive modeling. The original sequence prediction values obtained by the cumulative decrease restoration are subjected to an asymmetric anti-logarithmic transformation to convert the modeling sequence back to the original proportion sequence, and the transformation process ensures that the sum of the proportions of each component satisfies the unit constraint through exponential operation and normalization processing; By connecting the predicted values of each discrete time point, the component evolution curves of different age categories in the disease population are formed, and the precision of the prediction results is quantitatively evaluated by taking the average relative percentage error, the standard deviation of the relative percentage error and the average absolute scaling error as evaluation indexes, wherein the average relative percentage error reflects the average accuracy of the prediction, the standard deviation of the relative percentage error represents the stability of the prediction, and the average absolute scaling error is compared with the error of the random walk benchmark to depict whether the model prediction is better than the basic variation level of the sequence itself.

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