Cardiovascular and cerebrovascular disease information management method and system

Abstract

The invention discloses a cardiovascular and cerebrovascular disease information management method and system, and relates to the field of disease information management.The disease severity level data of patients with cardiovascular and cerebrovascular diseases to be managed are obtained by constructing a final cardiovascular and cerebrovascular disease severity level mapping equation; judging whether the cardiovascular and cerebrovascular disease patient to be managed needs to take medicine or not; the disease severity data obtained in the aspect of medicine taking of the patient with the cardiovascular and cerebrovascular diseases to be managed is compared with the disease severity data obtained in the aspect of the disease information data of the patient, so that whether the current medicine taking of the patient with the cardiovascular and cerebrovascular diseases to be managed is reasonable or not is judged, and timely adjustment is facilitated; the disease recovery of the patient is facilitated; wherein a quantitative judgment basis is provided for judging whether the mapping accuracy of an initial cardiovascular and cerebrovascular disease severity level mapping equation meets requirements or not by setting a severity level mapping data error threshold value.

Description

Technical Field

[0001] The present invention belongs to the field of disease information management, and in particular, relates to a cardiovascular and cerebrovascular disease information management method and system. Background Art

[0002] Patients with cardiovascular and cerebrovascular diseases need to take medication during treatment; however, as the medication progresses and patients feel that their condition has improved, they may arbitrarily adjust the dosage, frequency and other information data of the medication. If the dosage and frequency cannot be adjusted to a reasonable level in time, the heart may face excessive pressure again, causing the disease to relapse or worsen; and it may affect the doctor's judgment of the disease and evaluation of the treatment effect. The doctor may not be able to accurately understand the patient's true condition, resulting in untimely or inaccurate adjustment of the treatment plan, delaying the best treatment time, and increasing the difficulty of treatment. Summary of the invention

[0003] In view of the problems in the related art, the present invention proposes a cardiovascular and cerebrovascular disease information management method and system to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention is a method for managing cardiovascular and cerebrovascular disease information, comprising the following steps: S1. Collecting cardiovascular and cerebrovascular disease information data of patients with cardiovascular and cerebrovascular diseases to be managed, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed; S2. Collecting cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; S3, using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct a final cardiovascular and cerebrovascular disease severity level mapping equation and map the cardiovascular and cerebrovascular disease information data set to be managed to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; S4, collecting drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients and classifying them according to the existing cardiovascular and cerebrovascular disease severity level data matrix and calculating the central data of each classification to obtain the existing drug taking characteristic classification central data matrix; S5. Classify the characteristic data of the cardiovascular and cerebrovascular disease patients to be managed when taking the drugs according to the existing drug taking characteristic classification center data matrix to obtain the second cardiovascular and cerebrovascular disease severity level data to be managed; adjust the characteristic data of the cardiovascular and cerebrovascular disease patients to be managed when taking the drugs according to the second cardiovascular and cerebrovascular disease severity level data to be managed and the first cardiovascular and cerebrovascular disease severity level data to be managed; In this scheme, the cardiovascular and cerebrovascular disease information data of multiple existing cardiovascular and cerebrovascular disease patients and the corresponding cardiovascular and cerebrovascular disease severity level data are collected to construct a final cardiovascular and cerebrovascular disease severity level mapping equation, the disease severity level data of the cardiovascular and cerebrovascular disease patients to be managed are obtained, and it is determined whether the cardiovascular and cerebrovascular disease patients to be managed need to take medicine; when it is determined that the cardiovascular and cerebrovascular disease patients to be managed need to take medicine, this scheme collects the drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients and calculates several classification center data thereof, each classification center data corresponds to a cardiovascular and cerebrovascular disease severity level; therefore, by calculating the cardiovascular and cerebrovascular disease patients to be managed The Euclidean distance between the drug taking characteristic data of cardiovascular and cerebrovascular diseases and the central data of each drug taking classification is calculated, so as to classify the drug taking characteristic data of the patients with cardiovascular and cerebrovascular diseases to be managed to obtain the corresponding cardiovascular and cerebrovascular disease severity level data; when the cardiovascular and cerebrovascular disease severity level data is different from the disease severity level data of the cardiovascular and cerebrovascular disease patients to be managed obtained by using the final cardiovascular and cerebrovascular disease severity level mapping equation, it means that the current drug taking is unreasonable, and therefore the drug taking characteristic data of the patients with cardiovascular and cerebrovascular diseases to be managed is adjusted so that the drug taking characteristic data is most matched with the severity of their diseases, thereby improving the effect of drug treatment.

[0005] Preferably, the S1 comprises the following steps: S11. Set multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set. , a 1i represents the i-th information type of the set cardiovascular and cerebrovascular diseases, Indicates the total number of information types of set cardiovascular and cerebrovascular diseases; then set a number of severity levels of cardiovascular and cerebrovascular diseases to obtain a set of severity levels of cardiovascular and cerebrovascular diseases , a 21 、a 22 、a 23 、a 24 、a 25 They represent the first severity level of cardiovascular and cerebrovascular diseases, i.e. low risk, the second severity level, i.e. moderate risk, the third severity level, i.e. high risk, the fourth severity level, i.e. extremely high risk, and the fifth severity level, i.e. critical, with values ​​of 1, 2, 3, 4, and 5 respectively; S12, setting patients with cardiovascular and cerebrovascular diseases to be managed; collecting cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed according to the cardiovascular and cerebrovascular disease information type set, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed , Information data representing the i-th type of cardiovascular and cerebrovascular disease of the patient with the cardiovascular and cerebrovascular disease to be managed; The cardiovascular and cerebrovascular disease information types include blood pressure data type, blood lipid data type, blood sugar data type, morbidity data type, etc. By collecting information data on cardiovascular and cerebrovascular diseases from patients with cardiovascular and cerebrovascular diseases to be managed, data support is provided for the subsequent acquisition of disease severity level data from patients with cardiovascular and cerebrovascular diseases to be managed.

[0006] Preferably, S2 comprises the following steps: S21, set a plurality of existing cardiovascular and cerebrovascular disease patients and historical data collection time points to obtain a set of existing cardiovascular and cerebrovascular disease patients And the historical data collection time point set , , They respectively represent the i-th existing cardiovascular and cerebrovascular disease patient and the i-th historical data collection time point, and b1 and b2 respectively represent the total number of existing cardiovascular and cerebrovascular disease patients and historical data collection time points; S22, according to the cardiovascular and cerebrovascular disease information type set, the cardiovascular and cerebrovascular disease severity level set, the existing cardiovascular and cerebrovascular disease patient set and the historical data collection time point set, the cardiovascular and cerebrovascular disease information data of each existing cardiovascular and cerebrovascular disease patient and the corresponding cardiovascular and cerebrovascular disease severity level data are collected at each historical data collection time point to obtain the existing cardiovascular and cerebrovascular disease information data set matrix And the existing cardiovascular and cerebrovascular disease severity level data matrix ; They are as follows: ; ; in, , Respectively represent the cardiovascular and cerebrovascular disease information dataset and cardiovascular and cerebrovascular disease severity level data of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , express The kth type of cardiovascular and cerebrovascular disease information data in; The methods for collecting data on the severity of cardiovascular and cerebrovascular diseases include clinical assessment, which is to assess the severity of a patient's cardiovascular and cerebrovascular disease through detailed medical history inquiries, physical examinations, and laboratory tests; imaging examinations, which can assess the structure and function of the heart and blood vessels through imaging examinations such as echocardiography, carotid ultrasound, CT angiography (CTA), and magnetic resonance angiography (MRA), thereby determining the severity of the disease; By collecting cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data for each existing cardiovascular and cerebrovascular disease patient, data support is provided for the subsequent construction of the final cardiovascular and cerebrovascular disease severity level mapping equation.

[0007] Preferably, S3 comprises the following steps: S31, constructing a final cardiovascular and cerebrovascular disease severity level mapping equation using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix; S32, substituting each cardiovascular and cerebrovascular disease information data in the cardiovascular and cerebrovascular disease information data set to be managed into the final cardiovascular and cerebrovascular disease severity level mapping equation for mapping, to obtain first cardiovascular and cerebrovascular disease severity level data to be managed; when the first cardiovascular and cerebrovascular disease severity level data to be managed is 1, the cardiovascular and cerebrovascular disease patient to be managed does not need to take medication; otherwise, the cardiovascular and cerebrovascular disease patient to be managed needs to take medication; By constructing the final cardiovascular and cerebrovascular disease severity level mapping equation, a mapping tool is provided for the subsequent mapping of information data of cardiovascular and cerebrovascular disease patients to be managed, thereby determining whether the cardiovascular and cerebrovascular disease patients to be managed need to take medication.

[0008] Preferably, the S31 comprises the following steps: S311. Construct an initial cardiovascular and cerebrovascular disease severity level mapping equation as follows: ; In the formula, is the dependent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the cardiovascular and cerebrovascular disease severity level data; is the i-th independent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the i-th type of cardiovascular and cerebrovascular disease information data, for The coefficient of the independent variable ; is the offset of the initial cardiovascular and cerebrovascular disease severity level mapping equation; ceil represents the rounding function; S312: Substitute each cardiovascular disease information data in each existing cardiovascular disease information data set in the existing cardiovascular disease information data set matrix into the initial cardiovascular disease severity level mapping equation for mapping, to obtain an existing severity level initial mapping data matrix. ;as follows: ; in, Indicates that Substituting each cardiovascular and cerebrovascular disease information data into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain cardiovascular and cerebrovascular disease severity level data; S313, setting a severity level mapping data error threshold; calculating the Euclidean distance between the existing severity level initial mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing severity level mapping error data c; the calculation formula is as follows: ; When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, the initial cardiovascular and cerebrovascular disease severity level mapping equation is adjusted until the existing severity level mapping error data is less than the severity level mapping data error threshold, thereby obtaining a final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, the initial cardiovascular and cerebrovascular disease severity level mapping equation does not need to be adjusted, and the initial cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation; By setting the error threshold of severity level mapping data, a quantitative basis is provided for determining whether the mapping accuracy of the initial cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements.

[0009] Preferably, adjusting the initial cardiovascular and cerebrovascular disease severity level mapping equation in S313 includes the following steps: S3131, Constructing the Cardiovascular and Cerebrovascular Disease Level Mapping Equation Locust Population , represents the i-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation, represents the size of the locust population of the cardiovascular and cerebrovascular disease level mapping equation; sets the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to And the current number of iterations is ; They are recorded as the maximum number of iterations of the grade equation adjustment and the current number of iterations of the grade equation adjustment; The search space dimension of the locust population of the cardiovascular and cerebrovascular disease grade mapping equation is ; S3132, setting each independent variable coefficient and the bias value interval of the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the independent variable value interval set d1 and the bias value interval [d 21 ,d 22 ];d 21 ,d 22 They respectively represent the lower limit and upper limit of the bias value of the initial cardiovascular and cerebrovascular disease severity level mapping equation; d1 is as follows: ; in, , respectively represent the lower limit and upper limit of the value of the i-th independent variable coefficient of the initial cardiovascular and cerebrovascular disease severity level mapping equation; The initial position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation is set according to the independent variable value interval set and the bias value interval, and the first initial position matrix set is obtained. , The initial position matrix of the jth locust in the locust population representing the cardiovascular and cerebrovascular disease grade mapping equation is as follows: ; in, express The position component on the i-th independent variable coefficient dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; express The position component on the bias dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; , The calculation formulas are as follows: ; ; In the formula, rand 1j1i 、rand 1j2 Respectively for , Generate a random number between 0 and 1; S3133, constructing the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the severity level mapping error data c ;as follows: ; S3134, start iteration, before iteration, adjust the current iteration number of the grade equation to 1; in the first round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease grade mapping equation Calculate the fitness value of the initial position matrix of each locust in the first initial position matrix set to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the locust as the first global optimal fitness and the first global optimal position respectively; update the initial position matrix of each locust in the first initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, adjust the current iteration number of the level equation by 1 and enter the next round of iteration; In each other iteration process, the fitness function of the locust population using the cardiovascular and cerebrovascular disease level mapping equation is Calculate the fitness value of the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated during the previous round of iterations to obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding locust position matrix as the second global optimal fitness and the second global optimal position respectively; update the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated during the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, adjust the current iteration number of the grade equation by 1 and enter the next round of iterations; S3135, when When , stop the iteration and output the first final global optimal position; otherwise, continue to iterate until ; Substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the existing severity level optimized mapping data matrix; Calculate the Euclidean distance between the existing severity level optimized mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized existing severity level mapping error data; When the optimized existing severity level mapping error data is less than the severity level mapping data error threshold, the optimized cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, return to S3134 and continue to iterate until the optimized existing severity level mapping error data is less than the severity level mapping data error threshold; The locust optimization algorithm can effectively explore different areas of the solution space. Locust individuals will be affected by other individuals during the search process, thus avoiding falling into the local optimal solution; usually a better solution can be found within a fewer number of iterations; and it has fewer parameters and is easy to implement; based on the above advantages, the locust optimization algorithm is used in this scheme to simultaneously perform multiple iterative adjustments on multiple independent variable coefficients and biases of the initial cardiovascular and cerebrovascular disease severity level mapping equation, and the error between the mapping data of the initial cardiovascular and cerebrovascular disease severity level mapping equation and the actual data is used as the fitness function; therefore, as the iteration proceeds, the error between the mapping data of the initial cardiovascular and cerebrovascular disease severity level mapping equation and the actual data becomes smaller and smaller, and ultimately the mapping accuracy of the cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements.

[0010] Preferably, S4 comprises the following steps: S41, setting a plurality of drug taking feature types to obtain a drug taking feature type set; according to the drug taking feature type set, the historical data collection time point set and the existing cardiovascular and cerebrovascular disease patient set, collecting drug taking feature data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point to obtain an existing drug taking feature data set matrix ;as follows: ; in, represents the drug taking feature dataset of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , express The k-th type of drug taking characteristic data, e represents the total number of set drug taking characteristic types; S42, classifying the existing drug taking feature data set matrix according to the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing drug taking feature data classification matrix set ; It represents the kth drug taking feature data classification matrix obtained by classifying the existing drug taking feature data set matrix; as follows: ; in, express The j-th type of drug taking feature data in the i-th group of existing drug taking feature data sets; S43, digitally numbering the non-numerical data in the existing drug taking characteristic data classification matrix set to obtain a numbered existing drug taking characteristic data classification matrix set; calculating the central data of each existing drug taking characteristic data classification matrix in the numbered existing drug taking characteristic data classification matrix set to obtain an existing drug taking characteristic classification central data matrix ;as follows: ; in, Representing the j-th type of drug taking characteristic data in the classification center data set of the k-th drug taking characteristic data classification matrix in the existing drug taking characteristic data classification matrix set; The drug taking feature types include drug name, single drug dosage, and drug taking frequency, etc.; By calculating the classification center data of each existing drug taking characteristic data classification matrix in the existing drug taking characteristic data classification matrix set, it provides a classification basis for the subsequent classification of the drug taking characteristic data of patients with cardiovascular and cerebrovascular diseases to be managed, and secondly, it provides a basis for one-to-one correspondence with the disease severity level data, thereby providing a basis for obtaining the disease severity level data corresponding to the drug taking characteristic data of patients with cardiovascular and cerebrovascular diseases to be managed.

[0011] Preferably, the S5 comprises the following steps: S51, when the patient with cardiovascular and cerebrovascular diseases to be managed needs to take medicine, feature data of the patient with cardiovascular and cerebrovascular diseases to be managed when he needs to take medicine is collected according to the drug taking feature type set to obtain a drug taking feature data set to be managed; the non-numerical data in the drug taking feature data set to be managed is digitally numbered in the manner in S43 to obtain a numbered drug taking feature data set to be managed; Calculate the Euclidean distance between the numbered drug taking feature data set to be managed and each row of data in the existing drug taking feature classification center data matrix to obtain a drug taking feature Euclidean distance set; S52, obtaining the cardiovascular and cerebrovascular disease severity level data corresponding to the minimum Euclidean distance data in the drug taking feature Euclidean distance set according to the existing cardiovascular and cerebrovascular disease severity level data matrix, and obtaining the second cardiovascular and cerebrovascular disease severity level data to be managed; S53, when the second cardiovascular disease severity level data to be managed is different from the first cardiovascular disease severity level data to be managed, adjusting the drug taking characteristic data set to be managed until the second cardiovascular disease severity level data to be managed is the same as the first cardiovascular disease severity level data to be managed; otherwise, there is no need to adjust the drug taking characteristic data set to be managed; By comparing the disease severity data obtained from the medication of the patients with cardiovascular and cerebrovascular diseases to be managed with the disease severity data obtained from the patient's disease information data, it is possible to determine whether the current medication of the patients with cardiovascular and cerebrovascular diseases to be managed is reasonable, so that timely adjustments can be made, which is beneficial to the patient's recovery.

[0012] A cardiovascular and cerebrovascular disease information management system, comprising a cardiovascular and cerebrovascular disease information type setting module, a cardiovascular and cerebrovascular disease information data collection module for patients to be managed, an existing cardiovascular and cerebrovascular disease patient data collection module, a cardiovascular and cerebrovascular disease severity level mapping equation construction module, a patient severity level mapping module for patients to be managed, an existing patient medication taking characteristic data collection module, a classification center calculation module, a medication taking characteristic data classification module for patients to be managed, and a medication taking characteristic data adjustment module for patients to be managed; The cardiovascular and cerebrovascular disease information type setting module is used to set multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set; The cardiovascular and cerebrovascular disease information data collection module for patients to be managed is used to collect cardiovascular and cerebrovascular disease information data of patients to be managed with cardiovascular and cerebrovascular diseases according to the cardiovascular and cerebrovascular disease information type set, to obtain a cardiovascular and cerebrovascular disease information data set to be managed; The existing cardiovascular and cerebrovascular disease patient data collection module is used to collect cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The cardiovascular and cerebrovascular disease severity level mapping equation construction module is used to construct a final cardiovascular and cerebrovascular disease severity level mapping equation using an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The patient severity level mapping module to be managed is used to map the cardiovascular and cerebrovascular disease information data set to be managed using the final cardiovascular and cerebrovascular disease severity level mapping equation to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; The existing patient drug taking characteristic data collection module is used to collect drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing drug taking characteristic data set matrix; The classification center calculation module is used to classify the existing drug taking feature data set matrix in cooperation with the existing cardiovascular and cerebrovascular disease severity level data matrix and calculate the center data of each classification to obtain the existing drug taking feature classification center data matrix; The drug taking characteristic data classification module for patients to be managed is used to classify the characteristic data of the drug taking patients to be managed according to the existing drug taking characteristic classification center data matrix to obtain the second severity level data of the cardiovascular and cerebrovascular diseases to be managed; The module for adjusting characteristic data of medication taking of patients to be managed is used to adjust characteristic data of medication taking of patients with cardiovascular and cerebrovascular diseases to be managed according to the second severity level data of cardiovascular and cerebrovascular diseases to be managed and the first severity level data of cardiovascular and cerebrovascular diseases to be managed.

[0013] The present invention has the following beneficial effects: 1. In the present invention, by constructing a final cardiovascular and cerebrovascular disease severity level mapping equation, the disease severity level data of the cardiovascular and cerebrovascular disease patient to be managed is obtained to determine whether the cardiovascular and cerebrovascular disease patient to be managed needs to take medicine; and then by comparing the disease severity data obtained from the medication of the cardiovascular and cerebrovascular disease patient to be managed with the disease severity data obtained from the patient's disease information data, it is determined whether the current medication of the cardiovascular and cerebrovascular disease patient to be managed is reasonable, so as to make timely adjustments, which is conducive to the patient's recovery; 2. The present invention provides a quantitative basis for determining whether the mapping accuracy of the initial cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements by setting the severity level mapping data error threshold; 3. The present invention adopts the locust optimization algorithm to simultaneously perform multiple iterative adjustments on multiple independent variable coefficients and bias of the initial cardiovascular and cerebrovascular disease severity level mapping equation, and uses the error between the mapping data of the initial cardiovascular and cerebrovascular disease severity level mapping equation and the actual data as the fitness function; therefore, as the iteration proceeds, the error between the mapping data of the initial cardiovascular and cerebrovascular disease severity level mapping equation and the actual data becomes smaller and smaller, and ultimately the mapping accuracy of the cardiovascular and cerebrovascular disease severity level mapping equation meets the requirements.

[0014] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0016] FIG1 is a schematic flow chart of the cardiovascular and cerebrovascular disease information management method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0018] Embodiment 1, please refer to FIG1 , this embodiment is a method for managing cardiovascular and cerebrovascular disease information, comprising the following steps: S1. Collecting cardiovascular and cerebrovascular disease information data of patients with cardiovascular and cerebrovascular diseases to be managed, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed; The S1 comprises the following steps: S11. Set multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set. , a 1i represents the i-th information type of the set cardiovascular and cerebrovascular diseases, Indicates the total number of information types of set cardiovascular and cerebrovascular diseases; then set a number of severity levels of cardiovascular and cerebrovascular diseases to obtain a set of severity levels of cardiovascular and cerebrovascular diseases , a 21 、a 22 、a 23 、a 24 、a 25 They represent the first severity level of cardiovascular and cerebrovascular diseases, i.e. low risk, the second severity level, i.e. moderate risk, the third severity level, i.e. high risk, the fourth severity level, i.e. extremely high risk, and the fifth severity level, i.e. critical, with values ​​of 1, 2, 3, 4, and 5 respectively; S12, setting patients with cardiovascular and cerebrovascular diseases to be managed; collecting cardiovascular and cerebrovascular disease information data of the patients with cardiovascular and cerebrovascular diseases to be managed according to the cardiovascular and cerebrovascular disease information type set, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed , Information data representing the i-th type of cardiovascular and cerebrovascular disease of the patient with the cardiovascular and cerebrovascular disease to be managed.

[0019] S2. Collecting cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The S2 comprises the following steps: S21, set a plurality of existing cardiovascular and cerebrovascular disease patients and historical data collection time points to obtain a set of existing cardiovascular and cerebrovascular disease patients And the historical data collection time point set , , They respectively represent the i-th existing cardiovascular and cerebrovascular disease patient and the i-th historical data collection time point, and b1 and b2 respectively represent the total number of existing cardiovascular and cerebrovascular disease patients and historical data collection time points; S22, according to the cardiovascular and cerebrovascular disease information type set, the cardiovascular and cerebrovascular disease severity level set, the existing cardiovascular and cerebrovascular disease patient set and the historical data collection time point set, the cardiovascular and cerebrovascular disease information data of each existing cardiovascular and cerebrovascular disease patient and the corresponding cardiovascular and cerebrovascular disease severity level data are collected at each historical data collection time point to obtain the existing cardiovascular and cerebrovascular disease information data set matrix And the existing cardiovascular and cerebrovascular disease severity level data matrix ; They are as follows: ; ; in, , Respectively represent the cardiovascular and cerebrovascular disease information dataset and cardiovascular and cerebrovascular disease severity level data of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , express The kth type of cardiovascular and cerebrovascular disease information data in .

[0020] S3, using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct a final cardiovascular and cerebrovascular disease severity level mapping equation and map the cardiovascular and cerebrovascular disease information data set to be managed to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; The S3 comprises the following steps: S31, constructing a final cardiovascular and cerebrovascular disease severity level mapping equation using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix; The S31 comprises the following steps: S311. Construct an initial cardiovascular and cerebrovascular disease severity level mapping equation as follows: ; In the formula, is the dependent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the cardiovascular and cerebrovascular disease severity level data; is the i-th independent variable of the initial cardiovascular and cerebrovascular disease severity level mapping equation, representing the i-th type of cardiovascular and cerebrovascular disease information data, for The coefficient of the independent variable ; is the offset of the initial cardiovascular and cerebrovascular disease severity level mapping equation; ceil represents the rounding function; S312: Substitute each cardiovascular disease information data in each existing cardiovascular disease information data set in the existing cardiovascular disease information data set matrix into the initial cardiovascular disease severity level mapping equation for mapping, to obtain an existing severity level initial mapping data matrix. ;as follows: ; in, Indicates that Substituting each cardiovascular and cerebrovascular disease information data into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain cardiovascular and cerebrovascular disease severity level data; S313, setting a severity level mapping data error threshold; calculating the Euclidean distance between the existing severity level initial mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing severity level mapping error data c; the calculation formula is as follows: ; When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, the initial cardiovascular and cerebrovascular disease severity level mapping equation is adjusted until the existing severity level mapping error data is less than the severity level mapping data error threshold, thereby obtaining a final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, the initial cardiovascular and cerebrovascular disease severity level mapping equation does not need to be adjusted, and the initial cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation; The adjustment of the initial cardiovascular and cerebrovascular disease severity level mapping equation in S313 includes the following steps: S3131, Constructing the Cardiovascular and Cerebrovascular Disease Level Mapping Equation Locust Population , represents the i-th locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation, represents the size of the locust population of the cardiovascular and cerebrovascular disease level mapping equation; sets the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to And the current number of iterations is ; They are recorded as the maximum number of iterations of the grade equation adjustment and the current number of iterations of the grade equation adjustment; The search space dimension of the locust population of the cardiovascular and cerebrovascular disease grade mapping equation is ; S3132, setting each independent variable coefficient and the bias value interval of the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the independent variable value interval set d1 and the bias value interval [d 21 ,d 22 ];d 21 ,d 22 They respectively represent the lower limit and upper limit of the bias value of the initial cardiovascular and cerebrovascular disease severity level mapping equation; d1 is as follows: ; in, , respectively represent the lower limit and upper limit of the value of the i-th independent variable coefficient of the initial cardiovascular and cerebrovascular disease severity level mapping equation; The initial position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation is set according to the independent variable value interval set and the bias value interval, and the first initial position matrix set is obtained. , The initial position matrix of the jth locust in the locust population representing the cardiovascular and cerebrovascular disease grade mapping equation is as follows: ; in, express The position component on the i-th independent variable coefficient dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; express The position component on the bias dimension of the initial cardiovascular and cerebrovascular disease severity level mapping equation; , The calculation formulas are as follows: ; ; In the formula, rand 1j1i 、rand 1j2 Respectively for , Generate a random number between 0 and 1; S3133, constructing the fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the severity level mapping error data c ;as follows: ; S3134, start iteration, before iteration, adjust the current iteration number of the grade equation to 1; in the first round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease grade mapping equation Calculate the fitness value of the initial position matrix of each locust in the first initial position matrix set to obtain a first fitness value set; use the maximum fitness value in the first fitness value set and the corresponding initial position matrix of the locust as the first global optimal fitness and the first global optimal position respectively; update the initial position matrix of each locust in the first initial position matrix set according to the first global optimal fitness and the first global optimal position; after the update is completed, adjust the current iteration number of the level equation by 1 and enter the next round of iteration; In each other iteration process, the fitness function of the locust population using the cardiovascular and cerebrovascular disease level mapping equation is Calculate the fitness value of the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated during the previous round of iterations to obtain a second fitness value set; use the maximum fitness value in the second fitness value set and the corresponding locust position matrix as the second global optimal fitness and the second global optimal position respectively; update the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated during the previous round of iterations according to the second global optimal fitness and the second global optimal position; after the update is completed, adjust the current iteration number of the grade equation by 1 and enter the next round of iterations; S3135, when When , stop the iteration and output the first final global optimal position; otherwise, continue to iterate until ; Substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the existing severity level optimized mapping data matrix; Calculate the Euclidean distance between the existing severity level optimized mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized existing severity level mapping error data; When the optimized existing severity level mapping error data is less than the severity level mapping data error threshold, the optimized cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, return to S3134 and continue to iterate until the optimized existing severity level mapping error data is less than the severity level mapping data error threshold; S32. Substitute each cardiovascular and cerebrovascular disease information data in the cardiovascular and cerebrovascular disease information data set to be managed into the final cardiovascular and cerebrovascular disease severity level mapping equation for mapping, and obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; when the first cardiovascular and cerebrovascular disease severity level data to be managed is 1, the cardiovascular and cerebrovascular disease patient to be managed does not need to take medication; otherwise, the cardiovascular and cerebrovascular disease patient to be managed needs to take medication.

[0021] S4, collecting drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients and classifying them according to the existing cardiovascular and cerebrovascular disease severity level data matrix and calculating the central data of each classification to obtain the existing drug taking characteristic classification central data matrix; The S4 comprises the following steps: S41, setting a plurality of drug taking feature types to obtain a drug taking feature type set; according to the drug taking feature type set, the historical data collection time point set and the existing cardiovascular and cerebrovascular disease patient set, collecting drug taking feature data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point to obtain an existing drug taking feature data set matrix ;as follows: ; in, represents the drug taking feature dataset of the i-th existing cardiovascular and cerebrovascular disease patient at the j-th historical data collection time point; , express The k-th type of drug taking characteristic data, e represents the total number of set drug taking characteristic types; S42, classifying the existing drug taking feature data set matrix according to the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the existing drug taking feature data classification matrix set ; It represents the kth drug taking feature data classification matrix obtained by classifying the existing drug taking feature data set matrix; as follows: ; in, express The j-th type of drug taking feature data in the i-th group of existing drug taking feature data sets; S43, digitally numbering the non-numerical data in the existing drug taking characteristic data classification matrix set to obtain a numbered existing drug taking characteristic data classification matrix set; calculating the central data of each existing drug taking characteristic data classification matrix in the numbered existing drug taking characteristic data classification matrix set to obtain an existing drug taking characteristic classification central data matrix ;as follows: ; in, Representing the j-th type of drug taking characteristic data in the classification center data set of the k-th drug taking characteristic data classification matrix in the existing drug taking characteristic data classification matrix set; The S43 comprises the following steps: S431, construct a classification center for drug taking characteristics to calculate locust populations , represents the i-th locust in the locust population calculated by the classification center of the drug taking characteristics, represents the scale of locust population calculated by the drug taking feature classification center; sets the maximum number of iterations of the drug taking feature classification center to calculate the locust population And the current number of iterations is ; are recorded as the maximum number of iterations for the classification center calculation and the current number of iterations for the classification center calculation respectively; the search space dimension of the locust population calculated by the classification center of drug taking characteristics is ; S432, randomly selecting a row of data from each classification matrix in the numbered existing drug taking characteristic data classification matrix set to form a drug taking characteristic classification center, calculating the initial position matrix of each locust in the locust population, and obtaining a second initial position matrix set , Indicates that the drug taking feature classification center calculates the first The initial position matrix of the locust is as follows: ; in, express The position component on the dimension of the j-th type of medication taking feature data in the classification center data set of the k-th medication taking feature data classification matrix in the existing medication taking feature data classification matrix set; S433, calculating the first locust species in the locust population according to the classification center of the drug taking characteristics The initial position matrix of the locusts and the existing drug taking feature data classification matrix set are used to construct the drug taking feature classification center to calculate the first The fitness function of a locust ;as follows: ; S434, start iteration, before iteration, set the current iteration number of the classification center calculation to 1; in the first round of iteration, use the classification center of the drug taking characteristics to calculate the first The fitness function of a locust Calculate the fitness value of the initial position matrix of each locust in the second initial position matrix set to obtain a third fitness value set; use the maximum fitness value in the third fitness value set and the corresponding initial position matrix of the locust as the third global optimal fitness and the third global optimal position respectively; update the initial position matrix of each locust in the second initial position matrix set according to the third global optimal fitness and the third global optimal position; after the update is completed, calculate the current iteration number of the classification center plus 1 and enter the next round of iteration; In each other round of iteration, the drug taking feature classification center is used to calculate the first The fitness function of a locust Calculate the fitness value of the position matrix of each locust in the locust population calculated by the classification center of drug taking characteristics updated in the previous round of iteration to obtain a fourth fitness value set; use the maximum fitness value in the fourth fitness value set and the corresponding locust position matrix as the fourth global optimal fitness and the fourth global optimal position respectively; update the position matrix of each locust in the locust population calculated by the classification center of drug taking characteristics updated in the previous round of iteration according to the fourth global optimal fitness and the fourth global optimal position; after the update is completed, add 1 to the current iteration number of the classification center calculation and enter the next round of iteration; S435, when When , stop the iteration and output the second final global best position; otherwise, continue to iterate until until the second final global optimal position is used as the existing drug taking feature classification center data matrix; By adopting the locust optimization algorithm, multiple iteration operations are simultaneously performed on the classification center data of each existing drug taking feature data classification matrix in the existing drug taking feature data classification matrix set, and the overall discrete degree of each existing drug taking feature data classification matrix in the existing drug taking feature data classification matrix set is used as the fitness function. As the iteration proceeds, the overall discrete degree of each existing drug taking feature data classification matrix in the existing drug taking feature data classification matrix set becomes smaller and smaller, which means that the classification center data of each existing drug taking feature data classification matrix obtained by iteration can better represent the classification center data of each existing drug taking feature data classification matrix.

[0022] S5. Classify the characteristic data of the cardiovascular and cerebrovascular disease patients to be managed when taking the drugs according to the existing drug taking characteristic classification center data matrix to obtain the second cardiovascular and cerebrovascular disease severity level data to be managed; adjust the characteristic data of the cardiovascular and cerebrovascular disease patients to be managed when taking the drugs according to the second cardiovascular and cerebrovascular disease severity level data to be managed and the first cardiovascular and cerebrovascular disease severity level data to be managed; The S5 comprises the following steps: S51, when the patient with cardiovascular and cerebrovascular diseases to be managed needs to take medicine, feature data of the patient with cardiovascular and cerebrovascular diseases to be managed when he needs to take medicine is collected according to the drug taking feature type set to obtain a drug taking feature data set to be managed; the non-numerical data in the drug taking feature data set to be managed is digitally numbered in the manner in S43 to obtain a numbered drug taking feature data set to be managed; Calculate the Euclidean distance between the numbered drug taking feature data set to be managed and each row of data in the existing drug taking feature classification center data matrix to obtain a drug taking feature Euclidean distance set; S52, obtaining the cardiovascular and cerebrovascular disease severity level data corresponding to the minimum Euclidean distance data in the drug taking feature Euclidean distance set according to the existing cardiovascular and cerebrovascular disease severity level data matrix, and obtaining the second cardiovascular and cerebrovascular disease severity level data to be managed; S53. When the second data on the severity level of the cardiovascular and cerebrovascular disease to be managed is different from the first data on the severity level of the cardiovascular and cerebrovascular disease to be managed, the data set of characteristic data of drug taking to be managed is adjusted until the second data on the severity level of the cardiovascular and cerebrovascular disease to be managed is the same as the first data on the severity level of the cardiovascular and cerebrovascular disease to be managed; otherwise, there is no need to adjust the data set of characteristic data of drug taking to be managed.

[0023] Embodiment 2. This embodiment discloses a cardiovascular and cerebrovascular disease information management system, which can implement the method of the above embodiment, including a cardiovascular and cerebrovascular disease information type setting module, a cardiovascular and cerebrovascular disease information data collection module for patients to be managed, an existing cardiovascular and cerebrovascular disease patient data collection module, a cardiovascular and cerebrovascular disease severity level mapping equation construction module, a patient severity level mapping module to be managed, an existing patient drug taking feature data collection module, a classification center calculation module, a drug taking feature data classification module for patients to be managed, and a drug taking feature data adjustment module for patients to be managed; The cardiovascular and cerebrovascular disease information type setting module is used to set multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set; The cardiovascular and cerebrovascular disease information data collection module for patients to be managed is used to collect cardiovascular and cerebrovascular disease information data of patients to be managed with cardiovascular and cerebrovascular diseases according to the cardiovascular and cerebrovascular disease information type set, to obtain a cardiovascular and cerebrovascular disease information data set to be managed; The existing cardiovascular and cerebrovascular disease patient data collection module is used to collect cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The cardiovascular and cerebrovascular disease severity level mapping equation construction module is used to construct a final cardiovascular and cerebrovascular disease severity level mapping equation using an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The patient severity level mapping module to be managed is used to map the cardiovascular and cerebrovascular disease information data set to be managed using the final cardiovascular and cerebrovascular disease severity level mapping equation to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; The existing patient drug taking characteristic data collection module is used to collect drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing drug taking characteristic data set matrix; The classification center calculation module is used to classify the existing drug taking feature data set matrix in cooperation with the existing cardiovascular and cerebrovascular disease severity level data matrix and calculate the center data of each classification to obtain the existing drug taking feature classification center data matrix; The drug taking characteristic data classification module for patients to be managed is used to classify the characteristic data of the drug taking patients to be managed according to the existing drug taking characteristic classification center data matrix to obtain the second severity level data of the cardiovascular and cerebrovascular diseases to be managed; The module for adjusting characteristic data of medication taking of patients to be managed is used to adjust characteristic data of medication taking of patients with cardiovascular and cerebrovascular diseases to be managed according to the second severity level data of cardiovascular and cerebrovascular diseases to be managed and the first severity level data of cardiovascular and cerebrovascular diseases to be managed.

[0024] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0025] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for managing cardiovascular and cerebrovascular disease information, characterized in that: The following steps are involved: S1. Collecting cardiovascular and cerebrovascular disease information data of patients with cardiovascular and cerebrovascular diseases to be managed, and obtaining a cardiovascular and cerebrovascular disease information data set to be managed; S2. Collecting cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; S3, using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to construct a final cardiovascular and cerebrovascular disease severity level mapping equation and map the cardiovascular and cerebrovascular disease information data set to be managed to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; S4, collecting drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients and classifying them according to the existing cardiovascular and cerebrovascular disease severity level data matrix and calculating the central data of each classification to obtain the existing drug taking characteristic classification central data matrix; S5. Classify the characteristic data of the cardiovascular and cerebrovascular disease patients when they take the drugs according to the existing drug taking characteristic classification center data matrix to obtain the second cardiovascular and cerebrovascular disease severity level data to be managed; adjust the characteristic data of the cardiovascular and cerebrovascular disease patients when they take the drugs according to the second cardiovascular and cerebrovascular disease severity level data to be managed and the first cardiovascular and cerebrovascular disease severity level data to be managed.

2. The cardiovascular and cerebrovascular disease information management method according to claim 1, characterized in that: The S1 comprises the following steps: S11, setting multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set; and then setting a number of severity levels of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease severity level set; S12, setting the cardiovascular and cerebrovascular disease patients to be managed; collecting the cardiovascular and cerebrovascular disease information data of the cardiovascular and cerebrovascular disease patients to be managed according to the cardiovascular and cerebrovascular disease information type set to obtain a cardiovascular and cerebrovascular disease information data set to be managed.

3. A cardiovascular and cerebrovascular disease information management method according to claim 2, characterized in that: The S2 comprises the following steps: S21, setting a plurality of existing cardiovascular and cerebrovascular disease patients and historical data collection time points to obtain an existing cardiovascular and cerebrovascular disease patient set and a historical data collection time point set; S22. According to the cardiovascular and cerebrovascular disease information type set, the cardiovascular and cerebrovascular disease severity level set, the existing cardiovascular and cerebrovascular disease patient set and the historical data collection time point set, the cardiovascular and cerebrovascular disease information data and the corresponding cardiovascular and cerebrovascular disease severity level data of each existing cardiovascular and cerebrovascular disease patient are collected at each historical data collection time point to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix.

4. The cardiovascular and cerebrovascular disease information management method according to claim 3, characterized in that: The S3 comprises the following steps: S31, constructing a final cardiovascular and cerebrovascular disease severity level mapping equation using the existing cardiovascular and cerebrovascular disease information data set matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix; S32. Substitute each cardiovascular and cerebrovascular disease information data in the cardiovascular and cerebrovascular disease information data set to be managed into the final cardiovascular and cerebrovascular disease severity level mapping equation for mapping, and obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; when the first cardiovascular and cerebrovascular disease severity level data to be managed is 1, the cardiovascular and cerebrovascular disease patient to be managed does not need to take medication; otherwise, the cardiovascular and cerebrovascular disease patient to be managed needs to take medication.

5. The cardiovascular and cerebrovascular disease information management method according to claim 4, characterized in that: The S31 comprises the following steps: S311. Constructing an initial cardiovascular and cerebrovascular disease severity level mapping equation; S312, substituting each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set in the existing cardiovascular and cerebrovascular disease information data set matrix into the initial cardiovascular and cerebrovascular disease severity level mapping equation for mapping, to obtain an existing severity level initial mapping data matrix; S313, setting a severity level mapping data error threshold; calculating the Euclidean distance between the existing severity level initial mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain existing severity level mapping error data; When the existing severity level mapping error data is greater than or equal to the severity level mapping data error threshold, the initial cardiovascular and cerebrovascular disease severity level mapping equation is adjusted until the existing severity level mapping error data is less than the severity level mapping data error threshold, and the final cardiovascular and cerebrovascular disease severity level mapping equation is obtained; otherwise, there is no need to adjust the initial cardiovascular and cerebrovascular disease severity level mapping equation, and the initial cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation.

6. The cardiovascular and cerebrovascular disease information management method according to claim 5, characterized in that: The adjustment of the initial cardiovascular and cerebrovascular disease severity level mapping equation in S313 includes the following steps: S3131, constructing a locust population of the cardiovascular and cerebrovascular disease level mapping equation; setting the maximum number of iterations of the locust population of the cardiovascular and cerebrovascular disease level mapping equation to And the current number of iterations is ; respectively recorded as the maximum number of iterations of the grade equation adjustment and the current number of iterations of the grade equation adjustment; S3132, setting each independent variable coefficient and bias value interval of the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain an independent variable value interval set and a bias value interval; The initial position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease level mapping equation is set according to the independent variable value interval set and the bias value interval to obtain a first initial position matrix set; S3133, constructing a fitness function of the locust population of the cardiovascular and cerebrovascular disease level mapping equation according to the severity level mapping error data; S3134, start iteration, and before iteration, set the current iteration number of the grade equation to 1; in each round of iteration, use the fitness function of the locust population of the cardiovascular and cerebrovascular disease grade mapping equation to calculate the fitness value of the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated in the previous round of iteration, and update the position matrix of each locust in the locust population of the cardiovascular and cerebrovascular disease grade mapping equation updated in the previous round of iteration; S3135, when When , stop the iteration and output the first final global optimal position; otherwise, continue to iterate until ; Substitute each position component in the first final global optimal position into the initial cardiovascular and cerebrovascular disease severity level mapping equation to obtain the optimized cardiovascular and cerebrovascular disease severity level mapping equation; Substitute each cardiovascular and cerebrovascular disease information data in each existing cardiovascular and cerebrovascular disease information data set matrix in S312 into the optimized cardiovascular and cerebrovascular disease severity level mapping equation for mapping to obtain the existing severity level optimized mapping data matrix; Calculate the Euclidean distance between the existing severity level optimized mapping data matrix and the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain the optimized existing severity level mapping error data; When the optimized existing severity level mapping error data is less than the severity level mapping data error threshold, the optimized cardiovascular and cerebrovascular disease severity level mapping equation is used as the final cardiovascular and cerebrovascular disease severity level mapping equation; otherwise, return to S3134 and continue to iterate until the optimized existing severity level mapping error data is less than the severity level mapping data error threshold.

7. The cardiovascular and cerebrovascular disease information management method according to claim 6, characterized in that: The S4 comprises the following steps: S41, setting a plurality of drug taking feature types to obtain a drug taking feature type set; according to the drug taking feature type set, the historical data collection time point set and the existing cardiovascular and cerebrovascular disease patient set, collecting drug taking feature data of each existing cardiovascular and cerebrovascular disease patient at each historical data collection time point to obtain an existing drug taking feature data set matrix; S42, classifying the existing drug taking characteristic data set matrix according to the existing cardiovascular and cerebrovascular disease severity level data matrix to obtain an existing drug taking characteristic data classification matrix set; S43, digitally numbering the non-numerical data in the existing drug taking characteristic data classification matrix set to obtain a numbered existing drug taking characteristic data classification matrix set; calculating the central data of each existing drug taking characteristic data classification matrix in the numbered existing drug taking characteristic data classification matrix set to obtain an existing drug taking characteristic classification central data matrix.

8. The cardiovascular and cerebrovascular disease information management method according to claim 7, characterized in that: In S43, the central data of each existing drug administration characteristic data classification matrix in the numbered existing drug administration characteristic data classification matrix set is calculated using the locust optimization algorithm.

9. The cardiovascular and cerebrovascular disease information management method according to claim 8, characterized in that: The S5 comprises the following steps: S51, when the patient with cardiovascular and cerebrovascular diseases to be managed needs to take medicine, feature data of the patient with cardiovascular and cerebrovascular diseases to be managed when he needs to take medicine is collected according to the drug taking feature type set to obtain a drug taking feature data set to be managed; the non-numerical data in the drug taking feature data set to be managed is digitally numbered in the manner in S43 to obtain a numbered drug taking feature data set to be managed; Calculate the Euclidean distance between the numbered drug taking feature data set to be managed and each row of data in the existing drug taking feature classification center data matrix to obtain a drug taking feature Euclidean distance set; S52, obtaining the cardiovascular and cerebrovascular disease severity level data corresponding to the minimum Euclidean distance data in the drug taking feature Euclidean distance set according to the existing cardiovascular and cerebrovascular disease severity level data matrix, and obtaining the second cardiovascular and cerebrovascular disease severity level data to be managed; S53. When the second data on the severity level of the cardiovascular and cerebrovascular disease to be managed is different from the first data on the severity level of the cardiovascular and cerebrovascular disease to be managed, the data set of characteristic data of drug taking to be managed is adjusted until the second data on the severity level of the cardiovascular and cerebrovascular disease to be managed is the same as the first data on the severity level of the cardiovascular and cerebrovascular disease to be managed; otherwise, there is no need to adjust the data set of characteristic data of drug taking to be managed.

10. A cardiovascular and cerebrovascular disease information management system, based on the cardiovascular and cerebrovascular disease information management method according to any one of claims 1 to 9, characterized in that: It includes a cardiovascular and cerebrovascular disease information type setting module, a cardiovascular and cerebrovascular disease information data collection module for patients to be managed, an existing cardiovascular and cerebrovascular disease patient data collection module, a cardiovascular and cerebrovascular disease severity level mapping equation construction module, a severity level mapping module for patients to be managed, an existing patient drug taking feature data collection module, a classification center calculation module, a drug taking feature data classification module for patients to be managed, and a drug taking feature data adjustment module for patients to be managed; The cardiovascular and cerebrovascular disease information type setting module is used to set multiple information types of cardiovascular and cerebrovascular diseases to obtain a cardiovascular and cerebrovascular disease information type set; The cardiovascular and cerebrovascular disease information data collection module for patients to be managed is used to collect the cardiovascular and cerebrovascular disease information data of patients to be managed according to the cardiovascular and cerebrovascular disease information type set to obtain the cardiovascular and cerebrovascular disease information data set to be managed; The existing cardiovascular and cerebrovascular disease patient data collection module is used to collect cardiovascular and cerebrovascular disease information data and corresponding cardiovascular and cerebrovascular disease severity level data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The cardiovascular and cerebrovascular disease severity level mapping equation construction module is used to construct a final cardiovascular and cerebrovascular disease severity level mapping equation using an existing cardiovascular and cerebrovascular disease information data set matrix and an existing cardiovascular and cerebrovascular disease severity level data matrix; The patient severity level mapping module to be managed is used to map the cardiovascular and cerebrovascular disease information data set to be managed using the final cardiovascular and cerebrovascular disease severity level mapping equation to obtain the first cardiovascular and cerebrovascular disease severity level data to be managed; The existing patient drug taking characteristic data collection module is used to collect drug taking characteristic data of multiple existing cardiovascular and cerebrovascular disease patients to obtain an existing drug taking characteristic data set matrix; The classification center calculation module is used to classify the existing drug taking feature data set matrix in conjunction with the existing cardiovascular and cerebrovascular disease severity level data matrix and calculate the center data of each classification to obtain the existing drug taking feature classification center data matrix; The module for classifying characteristic data of drug taking of patients to be managed is used to classify characteristic data of drug taking of patients with cardiovascular and cerebrovascular diseases to be managed according to the existing central data matrix of drug taking characteristic classification, so as to obtain the second data of severity level of cardiovascular and cerebrovascular diseases to be managed; The module for adjusting characteristic data of drug taking of patients to be managed is used to adjust characteristic data of drug taking of patients with cardiovascular and cerebrovascular diseases to be managed according to the second data of severity level of cardiovascular and cerebrovascular diseases to be managed and the first data of severity level of cardiovascular and cerebrovascular diseases to be managed.

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