Health state assessment method and system and electronic equipment

Abstract

The invention provides a health status assessment method and system and electronic equipment, and relates to the field of health status assessment, and the method can collect and process heterogeneous data from multiple sources, and carry out deep fusion analysis on the heterogeneous data to obtain user physique identification data. Therefore, the physical condition of the user is obtained, the health state of the user is analyzed and evaluated, and then an accurate health evaluation result is obtained, so that the processing flow from data acquisition to health state evaluation is completed.

Description

Technical Field

[0001] The present invention relates to the field of health status assessment, and particularly to a health status assessment method, system and electronic device. Background Art

[0002] In the process of assessing the health status of users, traditional assessment methods use methods such as questionnaire processing, four diagnostic data acquisition, and daily monitoring data collection to collect data from users. Since it involves different fields, the data collection and processing process is complex, and it is difficult to uniformly process the health status assessment process of users. In addition, for the assessment process of users' health status, the existing technology mainly realizes it based on the semantic analysis results, lacking the fusion analysis of health data, resulting in poor accuracy of the health status assessment of users. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a health status assessment method, system and electronic device. This method can collect and process heterogeneous data from multiple sources, and conduct in-depth fusion analysis on it to obtain the physique identification data of the user to be evaluated, so as to obtain the physique situation of the user and conduct health status analysis and evaluation on it to obtain accurate health assessment results, realizing the complete processing flow from data collection to health status assessment, thus solving the above problems existing in the prior art.

[0004] In the first aspect, an embodiment of the present invention provides a health status assessment method, which includes: Health data collection step: Collect the physique monitoring data of the user to be evaluated based on a preset multi-source heterogeneous database, and determine the health data of the user to be evaluated based on the semantic result of the physique monitoring data; Physique identification processing step: Obtain the feature vector corresponding to the health data, determine the weight matrix and bias vector corresponding to the feature vector, and use the feature vector, weight matrix and bias vector to determine the physique identification data corresponding to the user to be evaluated; Health data analysis step: Perform density clustering calculation on the physique identification data to obtain the health index clustering result of the user to be evaluated, and calculate the health index score of the user to be evaluated using the health index clustering result; Assessment result generation step: Determine the health risk level of the user to be evaluated according to the health index score, and generate the health assessment result of the user to be evaluated based on the judgment matrix corresponding to the health risk level.

[0005] Optionally, the health data collection step includes: Determine the physique questionnaire database, four diagnostic database and health monitoring database based on the multi-source heterogeneous database; Obtain the physical fitness monitoring data corresponding to the user to be evaluated by using the physical fitness questionnaire database, the four diagnostic databases, and the health monitoring database; Construct a random forest corresponding to the physical fitness monitoring data, calculate the anomaly score of each data point in the random forest, and after updating and processing the physical fitness monitoring data according to the anomaly score, obtain the semantic relationship corresponding to the physical fitness monitoring data; Use the semantic relationship to determine the semantic result corresponding to the physical fitness monitoring data, and determine the health data of the user to be evaluated according to the semantic result.

[0006] Optionally, obtaining the physical fitness monitoring data corresponding to the user to be evaluated by using the physical fitness questionnaire database, the four diagnostic databases, and the health monitoring database includes: Obtain the physical fitness questionnaire data corresponding to the user to be evaluated by using the physical fitness questionnaire database, and determine the first physical fitness monitoring data corresponding to the user to be evaluated according to the weight value and semantic result corresponding to the physical fitness questionnaire data; Obtain the pulse condition data, tongue diagnosis data, voice data, and body temperature data corresponding to the user to be evaluated by using the four diagnostic databases, and determine the second physical fitness monitoring data corresponding to the user to be evaluated according to the feature vectors corresponding to the pulse condition data, tongue diagnosis data, voice data, and body temperature data; Obtain the health monitoring data and environmental data corresponding to the user to be evaluated by using the health monitoring database, and determine the third physical fitness monitoring data corresponding to the user to be evaluated according to the health monitoring data and environmental data; Determine the physical fitness monitoring data according to the first physical fitness monitoring data, the second physical fitness monitoring data, and the third physical fitness monitoring data.

[0007] Optionally, the physical fitness identification processing steps include: Obtain the text data corresponding to the health data, generate the label sequence data corresponding to the text data based on the initialized bidirectional long short-term memory model and conditional random field model, and use the label sequence data to determine the feature vector corresponding to the health data; Input the feature vector into the trained deep belief network, control the deep belief network to obtain the modal data corresponding to the feature vector, and use the joint representation result between the modal data to determine the weight matrix and bias vector corresponding to the feature vector; Determine the hidden units corresponding to the deep belief network according to the preset activation function and by using the feature vector, weight matrix, and bias vector, and use the hidden units to output the physical fitness identification data corresponding to the user to be evaluated under the health data.

[0008] Optionally, the health data analysis steps include: Obtain the physical fitness type data and disease type data corresponding to the user to be evaluated based on the physical fitness identification data, and obtain the association rules corresponding to the physical fitness type data and disease type data; Use association rules to perform density clustering calculations on the physical constitution identification data, obtain the characteristic distribution results between the physical constitution type data and the disease type data of the user to be evaluated, and determine the health index clustering results of the user to be evaluated based on the characteristic distribution results; Construct a prediction model corresponding to the physical constitution type data and the disease type data according to the initialized Bayesian network, and calculate the health index scores corresponding to the health index clustering results using the prediction model.

[0009] Optionally, constructing a prediction model corresponding to the physical constitution type data and the disease type data according to the initialized Bayesian network includes: Obtain the first node set corresponding to the physical constitution type data and the second node set corresponding to the disease type data; Construct the corresponding conditional probability table of the Bayesian network based on the first node set and the second node set, and determine the structure parameters of the Bayesian network using the conditional probability table; Construct a prediction model based on the structure parameters and the Bayesian network.

[0010] Optionally, the evaluation result generation step includes: Obtain the window function corresponding to the health index clustering results, calculate the mean result of the health index scores under the window function, and determine the health risk level of the user to be evaluated according to the mean result; Construct an evaluation index strategy corresponding to the user to be evaluated based on the health index clustering results, and determine the judgment matrix using the evaluation index strategy; Determine the health evaluation result corresponding to the user to be evaluated according to the judgment matrix.

[0011] Optionally, determining the health evaluation result corresponding to the user to be evaluated according to the judgment matrix includes: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; Obtain the index weight vector of the judgment matrix using the maximum eigenvalue and the corresponding eigenvector; Determine the membership degree vector corresponding to the index weight vector according to the health risk level, and construct the fuzzy relation matrix corresponding to the judgment matrix according to the membership degree vector; Generate the evaluation vector corresponding to the user to be evaluated according to the fuzzy relation matrix, and determine the health evaluation result using the evaluation vector.

[0012] In a second aspect, the present invention provides a health status evaluation system, which includes: A health data collection unit, configured to collect the physical constitution monitoring data of the user to be evaluated based on a preset multi-source heterogeneous database, and determine the health data of the user to be evaluated based on the semantic results of the physical constitution monitoring data; A physical constitution identification processing unit, configured to obtain a feature vector corresponding to health data, determine a weight matrix and a bias vector corresponding to the feature vector, and determine physical constitution identification data corresponding to a user to be evaluated by using the feature vector, the weight matrix, and the bias vector; A health data analysis unit, configured to perform density clustering calculation on the physical constitution identification data to obtain a health index clustering result of the user to be evaluated, and calculate a health index score of the user to be evaluated by using the health index clustering result; An evaluation result generation unit, configured to determine a health risk level of the user to be evaluated according to the health index score, and generate a health evaluation result of the user to be evaluated based on a judgment matrix corresponding to the health risk level.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, where the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the health status evaluation method provided in the first aspect.

[0014] In a fourth aspect, an embodiment of the present invention further provides a storage medium, where the storage medium stores computer executable instructions, and when the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to implement the steps of the health status evaluation method provided in the first aspect.

[0015] A health status evaluation method, system, and electronic device provided by an embodiment of the present invention, in the process of evaluating the health status of a user, first collect physical constitution monitoring data of the user to be evaluated based on a preset multi-source heterogeneous database, and determine the health data of the user to be evaluated based on the semantic result of the physical constitution monitoring data, so as to complete the health data collection process; further obtain a feature vector corresponding to the health data, determine a weight matrix and a bias vector corresponding to the feature vector, and determine physical constitution identification data corresponding to the user to be evaluated by using the feature vector, the weight matrix, and the bias vector, so as to complete the physical constitution identification processing process; then perform density clustering calculation on the physical constitution identification data to obtain a health index clustering result of the user to be evaluated, and calculate a health index score of the user to be evaluated by using the health index clustering result, so as to complete the health data analysis process; finally, determine the health risk level of the user to be evaluated according to the health index score, and generate a health evaluation result of the user to be evaluated based on a judgment matrix corresponding to the health risk level. This method can collect and process heterogeneous data from multiple sources, perform in-depth fusion analysis on the data to obtain the physical constitution identification data of the user, thereby obtaining the physical constitution of the user and performing health status analysis and evaluation on the user to obtain an accurate health evaluation result, thus realizing the complete processing flow from data collection to health status evaluation.

[0016] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention are realized and attained by the structure particularly pointed out in the specification, claims as well as the drawings.

[0017] To make the above objectives, features and advantages of the present invention more comprehensible, the following specific preferred embodiments are given, in conjunction with the accompanying drawings, and are described in detail as follows. Description of the Drawings

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 A flowchart of a health status assessment method provided by an embodiment of the present invention; Figure 2 A flowchart of the health data collection step S101 in a health status assessment method provided by an embodiment of the present invention; Figure 3 A flowchart of step S202 in a health status assessment method provided by an embodiment of the present invention; Figure 4 A flowchart of the physical constitution identification processing step S102 in a health status assessment method provided by an embodiment of the present invention; Figure 5 A flowchart of the health data analysis step S103 in a health status assessment method provided by an embodiment of the present invention; Figure 6 A flowchart of constructing a prediction model corresponding to physical constitution type data and disease type data according to the initialized Bayesian network in a health status assessment method provided by an embodiment of the present invention; Figure 7 A flowchart of the assessment result generation step S104 in a health status assessment method provided by an embodiment of the present invention; Figure 8 A flowchart of step S703 in a health status assessment method provided by an embodiment of the present invention; Figure 9 A schematic diagram of a health status assessment system provided by an embodiment of the present invention; Figure 10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0020] Icon: 910 - Health data acquisition unit; 920 - Constitution identification processing unit; 930 - Health data analysis unit; 940 - Evaluation result generation unit; 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed implementation manners

[0021] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] In the process of evaluating the health status of users, traditional evaluation methods use methods such as questionnaire processing, four diagnostic data acquisition, and daily monitoring data collection to collect data from users. Since it involves different fields, the data collection and processing process is complex, and it is difficult to uniformly process the health status evaluation process of users. In addition, for the evaluation process of the health status of users, the existing technology mainly realizes it based on the semantic analysis results, lacking the fusion analysis of health data, resulting in poor accuracy of the health status evaluation of users. Based on this, the embodiments of the present invention provide a health status evaluation method, system and electronic device. This method can collect and process heterogeneous data from multiple sources, and perform in-depth fusion analysis on it to obtain the constitution identification data of the user, so as to obtain the constitution of the user and perform health status analysis and evaluation on it to obtain accurate health evaluation results, thus realizing the complete processing flow from data collection to health status evaluation.

[0023] To facilitate the understanding of this embodiment, first, a health status evaluation method disclosed in the embodiments of the present invention will be introduced in detail. This method is as Figure 1 shown, and includes: Health data acquisition step S101: Based on a preset multi-source heterogeneous database, collect the physical fitness monitoring data of the user to be evaluated, and determine the health data of the user to be evaluated based on the semantic results of the physical fitness monitoring data; Constitution identification processing step S102: Obtain the feature vector corresponding to the health data, determine the weight matrix and bias vector corresponding to the feature vector, and use the feature vector, weight matrix and bias vector to determine the constitution identification data corresponding to the user to be evaluated; Health data analysis step S103: Perform density clustering calculation on the constitution identification data to obtain the health index clustering result of the user to be evaluated, and calculate the health index score of the user to be evaluated using the health index clustering result; Evaluation result generation step S104: Determine the health risk level of the user to be evaluated based on the health index score, and generate the health evaluation result of the user to be evaluated based on the judgment matrix corresponding to the health risk level.

[0024] The main purpose of the health data collection step is to implement the collection process of the health data of the user to be evaluated, mainly relying on a multi-source heterogeneous database to collect the user's physical fitness monitoring data. In the actual scenario, the collected physical fitness monitoring data can be cleaned and integrated, and the health data of the user to be evaluated can be obtained based on the semantic results of the physical fitness monitoring data.

[0025] Subsequently, the physical constitution identification processing step is executed. The main purpose of this step is to identify the user's physical constitution. After vectorizing the health data, the corresponding feature vector is obtained, and then the weight matrix and bias vector corresponding to the feature vector are obtained. Using the above vectors and matrices, a relevant physical constitution identification model is constructed, and the physical constitution identification data of the user to be evaluated is obtained using this model.

[0026] The main purpose of the health data analysis step is to obtain the specific health condition of the user to be evaluated after analyzing and processing the physical constitution identification data. The health condition is specifically characterized by the health index score. Specifically, by performing density clustering calculation on the physical constitution identification data, the health index score of the user in the current physical constitution state is obtained using the obtained health index clustering result, realizing the process of quantifying the health index represented by the physical constitution identification data of the user.

[0027] Finally, the evaluation result generation step is executed. This step mainly performs grade division according to the quantitative score result of the health index, thereby obtaining the health risk level of the user to be evaluated. Furthermore, based on different health risk levels, the health evaluation result of the user is generated according to its corresponding judgment matrix. It is worth mentioning that the judgment matrix represents the parameters of the importance of relevant health indicators in the health risk level. Using the relevant eigenvectors and eigenvalue results of the judgment matrix for fuzzy operation, the health evaluation result of the user to be evaluated is obtained.

[0028] Optionally, the health data collection step S101, as Figure 2 shown, includes: Step S201, determine the physical constitution questionnaire database, four diagnostic databases, and health monitoring database based on the multi-source heterogeneous database; Step S202, use the physical constitution questionnaire database, four diagnostic databases, and health monitoring database to obtain the physical fitness monitoring data corresponding to the user to be evaluated; Step S203, construct a random forest corresponding to the physical fitness monitoring data, calculate the anomaly score of each data point in the random forest, and after updating the physical fitness monitoring data according to the anomaly score, obtain the semantic relationship corresponding to the physical fitness monitoring data; Step S204: Determine the semantic result corresponding to the physical fitness monitoring data using semantic relationships, and determine the health data of the user to be evaluated based on the semantic result.

[0029] Specifically, the health data collection step mines the physical fitness questionnaire data, four diagnostic data, and health monitoring data, which are respectively implemented based on the corresponding physical fitness questionnaire database, four diagnostic database, and health monitoring database in the multi-source heterogeneous database. After the physical fitness questionnaire database, four diagnostic database, and health monitoring database are determined, the corresponding physical fitness questionnaire data, four diagnostic data, and health monitoring data can be obtained through the corresponding databases. Then, after collecting, optimizing, and mining these data, the physical fitness monitoring data of the user to be evaluated is obtained.

[0030] After obtaining the physical fitness monitoring data, perform data cleaning on it. After constructing a random forest corresponding to the physical fitness monitoring data, determine the outliers in the physical fitness monitoring data by calculating the anomaly score of each data point in the random forest. Specifically, calculate the anomaly score s(a, b) of each data point by constructing a random forest. The formula used is , where E(h(a)) is the average path length of data point a in the random forest, and c(b) is the normalization factor, which is determined according to the data volume b. The higher the score, the more abnormal the data point, so as to accurately identify and eliminate incorrect data.

[0031] In the actual scenario, a data consistency check algorithm can be used to verify the consistency of data from different sources. For example, compare the height and weight data filled in the questionnaire with the data collected by devices such as body fat scales. If the difference exceeds a certain threshold, conduct secondary verification or mark it as suspicious data for further manual review.

[0032] After updating the physical fitness monitoring data, map the physical fitness questionnaire data, four diagnostic data, and health monitoring data to a unified semantic framework to obtain the semantic relationships between these data. For example, semantic associations such as "has symptoms" and "corresponding pulse condition" can be defined between "yang deficiency constitution" and data such as "fear of cold symptoms" and "weak and thready pulse". Through semantic reasoning, deep integration and correlation analysis of the data are realized, so as to obtain the semantic result of its connotation based on the semantic relationship, and thus obtain the health data of the user to be evaluated.

[0033] After obtaining health data, its security can be enhanced by encrypting it and storing it in a specific storage architecture. Specifically, a hybrid encryption algorithm can be adopted. For example, first use an asymmetric encryption algorithm (such as RSA) to encrypt and transmit the AES encryption key to ensure the security of the key; then use the AES algorithm to encrypt and store the data efficiently. When decrypting the data, first use the private key to decrypt the AES key, and then use the AES key to decrypt the data, improving the security and efficiency of the encryption system. In actual scenarios, homomorphic encryption technology can also be introduced to allow specific operations on encrypted data. For example, perform statistical analysis on encrypted health data (such as calculating the average blood pressure of a certain physical constitution type population) without decrypting the data first, further protecting data privacy.

[0034] For the storage architecture, a distributed storage architecture can be constructed to store the encrypted data dispersedly on multiple nodes. For example, use the distributed ledger of blockchain technology to store some key data digests, and utilize its immutable feature to ensure data integrity and traceability. At the same time, store different copies on the cloud and local servers respectively, improving the reliability and availability of the data through redundant storage to prevent data loss. In actual scenarios, a strict access control policy model can also be established, combining role-based access control (RBAC) with attribute-based encryption (ABE). For example, set different access permission attributes for different roles such as medical staff, health managers, and users. Only users who meet specific attribute conditions (such as doctor role and having specific department qualifications) can decrypt and access the corresponding data, further refining access permission management to ensure data security.

[0035] Thus, in the health data collection step, it is crucial how to obtain the physical constitution monitoring data corresponding to the user to be evaluated by using the physical constitution questionnaire database, the four diagnostic databases, and the health monitoring database. Optionally, step S202 of obtaining the physical constitution monitoring data corresponding to the user to be evaluated by using the physical constitution questionnaire database, the four diagnostic databases, and the health monitoring database is as Figure 3 shown and includes: Step S301: Use the physical constitution questionnaire database to obtain the physical constitution questionnaire data corresponding to the user to be evaluated, and determine the first physical constitution monitoring data corresponding to the user to be evaluated according to the weight value and semantic result corresponding to the physical constitution questionnaire data; Step S302: Use the four diagnostic databases to obtain the pulse condition data, tongue diagnosis data, voice data, and body temperature data corresponding to the user to be evaluated, and determine the second physical constitution monitoring data corresponding to the user to be evaluated according to the eigenvectors corresponding to the pulse condition data, tongue diagnosis data, voice data, and body temperature data; Step S303: Use the health monitoring database to obtain the health monitoring data and environmental data corresponding to the user to be evaluated, and determine the third physical constitution monitoring data corresponding to the user to be evaluated according to the health monitoring data and environmental data; Step S304: Determine the physical fitness monitoring data based on the first physical fitness monitoring data, the second physical fitness monitoring data, and the third physical fitness monitoring data.

[0036] For the physical fitness questionnaire, after obtaining the physical fitness questionnaire data corresponding to the user to be evaluated using the physical fitness questionnaire database, the weight coefficient method can be used to assign different weights to each question according to its importance for physical fitness judgment. For example, for the key question "Do you have cold hands and feet all year round" for judging Yang deficiency constitution, a higher weight of 0.8 is assigned, while for the general question "Do you occasionally have headaches", the weight is set to 0.2. Calculate the questionnaire score through the weighted sum formula: , where Q is the total questionnaire score, is the weight of the i-th question, is the answer score of the i-th question (e.g., 0 means no, 1 means yes); then preliminarily divide the physical fitness tendency according to the total score range. In the actual processing process, a fuzzy logic algorithm can be used to handle some fuzzy problems. For example, for the question of "degree of dietary preference", set fuzzy options such as "very like", "like", "general", "dislike", "very dislike", etc., and convert these options into exact values through the fuzzy membership function to facilitate better quantitative analysis of the data.

[0037] For the four diagnostic data, the corresponding pulse condition data and tongue diagnosis data can be obtained through a pulse condition instrument and a tongue diagnosis instrument. On this basis, voice data and temperature data are also considered. Specifically, by adding an interface for the smelling diagnosis device to collect the user's voice (such as whether the voice is low, whether there is coughing and wheezing, etc.) and odor (such as breath, body odor, etc.) data, and then extracting the features of these data, such as the frequency and amplitude of the voice, the chemical composition analysis of the odor, etc., and finally converting them into digital feature vectors. For example, the voice frequency feature f can be calculated through the fast Fourier transform (FFT) formula where f(n) is the discrete sampling value of the voice signal, N is the number of sampling points, k is the frequency index, and j is the imaginary number. These feature vectors are stored together with other four diagnostic data to provide richer information for physical fitness identification.

[0038] For the health monitoring data, a time series analysis algorithm can be used to deeply mine the daily health monitoring data. For example, analyze the sleep data through the autoregressive moving average (ARMA) model , where S t is the sleep data at the current moment (such as sleep time, sleep depth, etc.), S t-i is the sleep data at the past i-th moment, and are the model parameters, is a white noise error term; the future sleep trend of the user is predicted through this model to discover potential sleep problems in advance. In addition, by obtaining environmental data, such as local air quality, temperature, humidity and other information, it is associated with the user's health monitoring data. For example, it is found that during haze weather, the respiratory symptoms of certain physical constitution groups (such as phlegm-dampness constitution) will worsen, and through correlation analysis, a more comprehensive basis is provided for health management.

[0039] Optionally, the physical constitution identification processing step S102, such as Figure 4 shown, includes: Step S401, obtain the text data corresponding to the health data, generate the label sequence data corresponding to the text data based on the initialized bidirectional long short-term memory model and conditional random field model, and use the label sequence data to determine the feature vector corresponding to the health data; Step S402, input the feature vector into the trained deep belief network, control the deep belief network to obtain the modal data corresponding to the feature vector, and use the joint representation result between the modal data to determine the weight matrix and bias vector corresponding to the feature vector; Step S403, determine the hidden units corresponding to the deep belief network according to the preset activation function and using the feature vector, weight matrix and bias vector, and use the hidden units to output the physical constitution identification data corresponding to the user to be evaluated under the health data.

[0040] In the process of obtaining the physical constitution identification data, a corresponding questionnaire can be set based on the theory of physical constitution for assistance. Specifically, the analytic hierarchy process (AHP) can be used to determine the weight distribution of the questionnaire questions. First, construct a judgment matrix A, where the element α ij in the matrix A represents the importance degree of question i relative to question j for physical constitution judgment. By calculating the largest eigenvalue of the matrix A and its corresponding eigenvector, the relative weight w i of each question is obtained. In this way, the contribution of each question in physical constitution identification can be measured more scientifically, making the analysis of the questionnaire results more accurate. In the actual scenario, situational question design can be introduced to simulate the user's physical reactions in different life scenarios. For example, "How is the warmth of your hands and feet in the cold winter?" Such situational questions can more intuitively obtain information related to physical constitution and reduce the user's understanding deviation.

[0041] After obtaining the text data corresponding to the health data using the questionnaire, natural language processing is performed on it. Specifically, the initialized bidirectional long short-term memory model and conditional random field model are used to obtain the label sequence data corresponding to the text data, and the label sequence data is used to determine the feature vector corresponding to the health data. Specifically, the bidirectional long short-term memory network (BiLSTM) can be combined with the conditional random field (CRF) for key information extraction. BiLSTM can effectively capture the context information of the text sequence, and CRF is used to optimize the output of BiLSTM to obtain a more accurate label sequence (i.e., key information). Let the input text sequence be X=(x 1 ,x 2 ,……,x n ), the hidden layer output of BiLSTM be H=(h 1 ,h 2 ,……,h n ), the transition matrix of CRF be T, then the probability calculation formula for the label sequence Y=(y 1 ,y 2 ,……,y n ) is: , where is the scoring function output by BiLSTM, is the transition scoring function of CRF, Y x is the set of all possible label sequences, is a specific label sequence in Y x . is to sum all possible label sequences x in the set Y .

[0042] The natural language processing model is pre-trained by establishing a corpus of physical constitution corresponding to the relevant medical system. The corpus can include text data such as traditional Chinese medicine ancient books, clinical cases, and physical constitution research literature. After annotation, it is used for model training to enable the model to better understand professional terms and semantic relationships, and improve the accuracy and efficiency of key information extraction. The generated model can be a physical constitution identification model constructed using the ensemble learning method. For example, multiple machine learning algorithms such as support vector machine (SVM), decision tree (DT), and random forest (RF) are integrated. Let M 1 ,M 2 ,……,M k be k different basic models. For the input user data x, the prediction result of each model is y i =M i (x). The final physical constitution identification result is obtained through the weighted voting method, where δ(y i ,j) is the indicator function, which is 1 when y i =j and 0 otherwise, wm i For the weights of model M i which are determined by methods such as cross-validation.

[0043] After the feature vectors are obtained, they are input into a deep belief network (DBN) for data fusion. In a specific scenario, the physical questionnaire data, traditional Chinese medicine four diagnostic data, daily health monitoring data, and lifestyle data are respectively used as different modalities and input into the DBN. Let v q , v s , v m , v h be the feature vectors of the questionnaire, four diagnostic, health monitoring, and lifestyle data respectively. The DBN learns the joint representation between different modality data through layer-by-layer unsupervised pre-training and supervised fine-tuning, and finally outputs the physical constitution identification result. During the pre-training process, the hidden units of each layer are calculated by the following formula: , where W l is the weight matrix, b l is the bias vector, and is the activation function.

[0044] Subsequently, a method combining feature-level fusion and decision-level fusion is adopted. In feature-level fusion, the features of different source data are normalized and then spliced into a joint feature vector, which is then input into the physical constitution identification model. In decision-level fusion, independent physical constitution identification sub-models are first trained using different source data respectively, and then the output results of the sub-models are fused through methods such as fuzzy integral to obtain the final physical constitution identification data.

[0045] Optionally, the health data analysis step S103, as Figure 5 shown, includes: Step S501, obtaining the physical constitution type data and disease type data corresponding to the user to be evaluated based on the physical constitution identification data, and obtaining the association rules corresponding to the physical constitution type data and disease type data; Step S502, using the association rules to perform density clustering calculation on the physical constitution identification data, obtaining the feature distribution result between the physical constitution type data and disease type data of the user to be evaluated, and determining the health index clustering result of the user to be evaluated based on the feature distribution result; Step S503, constructing a prediction model corresponding to the physical constitution type data and disease type data according to the initialized Bayesian network, and calculating the health index score corresponding to the health index clustering result using the prediction model.

[0046] In the steps of health data analysis, an improved Apriori algorithm can be adopted to perform association rule mining and find strong association rules between constitution types and diseases. Based on the traditional Apriori algorithm, an interest degree index is introduced. Let U be the item set of constitution types, V be the item set of diseases, the support degree be S(U, V), the confidence degree be C(U, V), and the interest degree I(U, V) = S(U, V) × (C(U, V) - P(V)), where P(V) is the prior probability of disease V. By setting the minimum interest degree threshold, more valuable constitution-disease association rules can be mined, avoiding the mining of a large number of meaningless associations.

[0047] Then, the density clustering algorithm (such as DBSCAN) is used to perform clustering analysis on the constitution-disease data. Let the data point set Z = {z 1 , z 2 , ……, z n}, for the data point z i , calculate the number of data points within its epsilon-neighborhood. If the number exceeds the set minimum number of points MinPts, then z i is a core point, and clusters are formed by continuously expanding the neighborhood of the core point. Through clustering analysis, the distribution characteristics of different constitution-disease groups are found, providing more detailed classification information for the prediction model.

[0048] Optionally, according to the initialized Bayesian network, a prediction model corresponding to the constitution type data and the disease type data is constructed. As Figure 6 shown, it includes: Step S601, obtaining the first node set corresponding to the constitution type data and the second node set corresponding to the disease type data; Step S602, constructing the corresponding conditional probability table of the Bayesian network based on the first node set and the second node set, and using the conditional probability table to determine the structure parameters of the Bayesian network; Step S603, constructing a prediction model based on the structure parameters and the Bayesian network.

[0049] The construction of the prediction model is based on the Bayesian network to construct a constitution-disease prediction model. Let the constitution type node set be T = {t 1 , t 2 , ……, t m}, the disease node set be D = {d 1 , d 2 , ……, d n}, and the other influencing factor node set be F = {f 1 , f 2 , ……, f k}. By learning a large amount of data, the structure and parameters of the Bayesian network are determined, that is, the conditional probability table P(d i |tj , f l ), that is, given the known physical constitution type is t j , and other influencing factors are f l , the probability of the occurrence of disease d i . P(d i , t j , f l ): The joint probability of the simultaneous occurrence of disease d i , physical constitution type t j , and other influencing factors f l . P(t j , f l |d i ): Given the occurrence of disease d i , the conditional probability that the physical constitution type is t j and other influencing factors are f l . P(d i ): The prior probability of the occurrence of disease d i , that is, the probability of the occurrence of disease d i itself without considering other conditions. d i : The i-th disease in the set of disease nodes D = {d 1 , d 2 , ……, d n}. t j : The j-th physical constitution type in the set of physical constitution type nodes T = {t 1 , t 2 , ……, t m}. f l : The l-th other influencing factor in the set of other influencing factor nodes F = {f 1 , f 2 , ……, f k}. : The summation symbol, indicating the summation of i from 1 to n, that is, the summation of the relevant probabilities of all possible diseases d i , which plays a role in normalization to make the finally calculated conditional probability between 0 and 1.

[0050] For the given user's physical constitution type and other influencing factor data, the probability of the occurrence of the disease is calculated using Bayes' formula as: .

[0051] To evaluate the performance of the prediction model using multiple indicators, in addition to the commonly used accuracy, recall rate, and F1 value, the Brier score is introduced. Let n be the number of prediction samples, p i be the probability of the occurrence of the disease for the i-th sample prediction, and y i be whether the disease actually occurs (1 indicates occurrence, 0 indicates non-occurrence). The Brier score formula is: Specifically, n is the number of prediction samples, that is, the number of samples participating in the model prediction evaluation. p i The probability that the model predicts the i-th sample has the disease, and the value range is between 0 and 1. y i The identifier indicating whether the i-th sample actually has the disease. 1 indicates that the sample actually has the disease, and 0 indicates that it does not. BS: Brier score, which is used to measure the difference between the model prediction probability and the actual result. The lower the Brier score, the better the model prediction effect. By comprehensively evaluating the index, continuously optimize the model parameters and structure to improve the prediction accuracy.

[0052] Optionally, the evaluation result generation step S104, as Figure 7 shown, includes: Step S701: Obtain the window function corresponding to the health index clustering result, calculate the mean result of the health index scores under the window function, and determine the health risk level of the user to be evaluated according to the mean result; Step S702: Construct an evaluation index strategy corresponding to the user to be evaluated based on the health index clustering result, and use the evaluation index strategy to determine the judgment matrix; Step S703: Determine the health evaluation result corresponding to the user to be evaluated according to the judgment matrix.

[0053] For the monitoring process of health index scores, the quantile regression method can be used to set the dynamic normal range of health indexes. Let the health index data be K, the vector of influencing factors (such as physical constitution type, age, gender, etc.) be J, and the quantile regression model be: , where q is the quantile level (such as 0.25, 0.5, 0.75, etc.), and β j,q is the corresponding regression coefficient. By establishing quantile regression models for different groups of people with different physical constitution types, ages, and genders respectively, determine the normal range boundaries of health indexes under different quantiles to adapt to individual differences and dynamic changes.

[0054] 2) Establish an adaptive threshold adjustment mechanism to dynamically adjust the normal range threshold according to the change trend of the user's health data. For example, using the exponential smoothing method, let the current threshold be T t , the new data point be x t , and the smoothing coefficient be α. Then the updated threshold is: T t+1 =αx t +(1 - α)T t , when the user's health index data shows a continuous upward or downward trend, the threshold can be adjusted accordingly to improve the sensitivity of abnormal fluctuation monitoring.

[0055] In an actual scenario, a streaming data processing framework such as Apache Flink can be used to efficiently process health data. In Flink, window functions are used to group and analyze data. For example, a sliding time window is adopted. If the window size is W and the sliding step is S, then statistical analysis (such as calculating the mean, standard deviation, etc.) is performed on the health indicator data within each time window. The formula used is: , where represents the sample mean of the health indicator data within a sliding time window; x i is the data point within the window, n is the number of data points within the window; s is the sample standard deviation of the health indicator data within the sliding time window. When the statistical indicator exceeds the dynamic normal range, an abnormal warning is triggered.

[0056] Specifically, a comprehensive evaluation model based on a fuzzy inference system can also be constructed. The physical constitution type, the degree of abnormality of health indicators, symptom information, etc. of the user are used as the inputs of the fuzzy inference system, and the output is the health risk level. For example, the fuzzy sets of the physical constitution type are defined as (very consistent, relatively consistent, general, less consistent, inconsistent), the fuzzy sets of the degree of abnormality of health indicators are (mild, moderate, severe), and the fuzzy sets of symptom information are (none, occasional, frequent). Fuzzy reasoning is carried out by establishing a fuzzy rule table (such as "if the physical constitution type is phlegm-dampness constitution and the degree of blood pressure abnormality is moderate and there are frequent dizziness symptoms, then the health risk level is high") to obtain a more comprehensive and accurate health risk assessment result, providing strong support for early warning decision-making.

[0057] Optionally, the steps S703 for determining the health assessment result corresponding to the user to be evaluated according to the judgment matrix, as Figure 8 shown, include: Step S801, calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; Step S802, obtaining the index weight vector of the judgment matrix by using the maximum eigenvalue and its corresponding eigenvector; Step S803, determining the membership degree vector corresponding to the index weight vector according to the health risk level, and constructing the fuzzy relation matrix corresponding to the judgment matrix according to the membership degree vector; Step S804, generating the evaluation vector corresponding to the user to be evaluated according to the fuzzy relation matrix, and determining the health assessment result by using the evaluation vector.

[0058] The generation of the health assessment result can be realized based on a relevant evaluation model. This model is an evaluation model combining the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method, such as physical constitution type (yang deficiency constitution, phlegm-dampness constitution, etc.), health status indicators (disease types, symptom severity, etc.), health data change trends (weight change rate, blood pressure fluctuation value, etc.). The relative weights of each index are determined by AHP. Let the judgment matrix be A, where aij Indicates the importance degree of index i relative to index j. The index weight vector W is obtained by calculating the maximum eigenvalue of matrix A and its corresponding eigenvector. ` =(w ` 1 ,w ` 2 ,……,w ` n ). Then, for each index, a fuzzy evaluation is carried out to determine the membership degree vector R that belongs to different evaluation levels (such as excellent, good, medium, poor). i =(r i1 ,r i2 ,……,r im ), and a fuzzy relation matrix is constructed. . Finally, through the fuzzy composition operation , where b j =max i min(w ` i, r ij ), the comprehensive evaluation result vector B is obtained. Based on this, the health assessment result of the user is determined, providing a basis for generating a personalized plan.

[0059] In the actual scenario, a survival analysis method can be introduced to evaluate the change of the user's health risk over time. Let T be the time of the health event occurrence (such as the occurrence of a disease, the deterioration of the health condition, etc.), and G=(g 1 ,g 2 ,……,g p ) be the influence factor vector (including the physical constitution type, health indicators, etc.). Through the Cox proportional hazards model h(t|G)=h 0 (t)exp(β 1 g 1 +β 2 g 2 +……+β p g p ), where h(t|G) is the risk function at time t under the given G condition, h 0 (t) is the baseline risk function, and β i is the regression coefficient. According to the model, calculate the health risk probability of the user at different future time points to plan preventive measures in advance in the health management plan.

[0060] As can be seen from the health status assessment method mentioned in the above embodiments, this method can collect and process heterogeneous data from multiple sources, and conduct in-depth fusion analysis on it to obtain the user's physical constitution identification data, thereby obtaining the user's physical constitution situation and conducting a health status analysis and assessment on it to obtain an accurate health assessment result, thus realizing the complete processing process from data collection to health status assessment.

[0061] Corresponding to the health status assessment method provided in the foregoing embodiments, an embodiment of the present invention provides a health status assessment system, as Figure 9 shown, the system includes: A health data acquisition unit 910, configured to collect physical monitoring data of a user to be evaluated based on a preset multi-source heterogeneous database, and determine the health data of the user to be evaluated based on the semantic result of the physical monitoring data; A physical constitution identification processing unit 920, configured to obtain a feature vector corresponding to the health data, determine a weight matrix and a bias vector corresponding to the feature vector, and use the feature vector, the weight matrix, and the bias vector to determine physical constitution identification data corresponding to the user to be evaluated; A health data analysis unit 930, configured to perform density clustering calculation on the physical constitution identification data to obtain a health index clustering result of the user to be evaluated, and calculate a health index score of the user to be evaluated by using the health index clustering result; An evaluation result generation unit 940, configured to determine the health risk level of the user to be evaluated according to the health index score, and generate a health evaluation result of the user to be evaluated based on a judgment matrix corresponding to the health risk level.

[0062] As can be seen from the health status assessment system mentioned in the foregoing embodiments, the system can collect and process heterogeneous data from multiple sources, perform in-depth fusion analysis on them to obtain the physical constitution identification data of the user, thereby obtaining the physical constitution of the user and performing health status analysis and evaluation on it to obtain an accurate health evaluation result, thus realizing the complete processing flow from data collection to health status evaluation.

[0063] The health status assessment system provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing embodiment of the health status assessment method. For a brief description, for the parts not mentioned in the system embodiment, reference may be made to the corresponding content in the foregoing embodiment of the health status assessment method.

[0064] This embodiment also provides an electronic device, and the structural schematic diagram of the electronic device is as Figure 10 shown, the device includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the steps of the above health status assessment method.

[0065] Figure 10 The electronic device shown also includes a bus 103 and a communication interface 104, and the processor 101, the communication interface 104, and the memory 102 are connected through the bus 103.

[0066] Among them, the memory 102 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory. The bus 103 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 10 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0067] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and send the encapsulated IPv4 packet or IPv4 packet to the user terminal through the network interface.

[0068] The processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 101 or the instructions in the form of software. The above-mentioned processor 101 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 102, and the processor 101 reads the information in the memory 102 and combines its hardware to complete the steps of the method in the foregoing embodiments.

[0069] The embodiment of the present invention also provides a storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the steps of the health status assessment method in the foregoing embodiments.

[0070] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, equipment, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0071] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0073] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0074] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the technical field of the present invention can still modify the technical solutions described in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A health status assessment method, characterized in that: The method comprises: Health data collection step: collecting the physical fitness monitoring data of the user to be evaluated based on a preset multi-source heterogeneous database, and determining the health data of the user to be evaluated based on the semantic results of the physical fitness monitoring data; Physical constitution identification processing step: obtaining a feature vector corresponding to the health data, determining a weight matrix and a bias vector corresponding to the feature vector, and determining physical constitution identification data corresponding to the user to be evaluated using the feature vector, the weight matrix and the bias vector; Health data analysis step: performing density clustering calculation on the physical identification data to obtain a health index clustering result of the user to be evaluated, and using the health index clustering result to calculate the health index score of the user to be evaluated; Evaluation result generating step: determining the health risk level of the user to be evaluated according to the health indicator score, and generating the health evaluation result of the user to be evaluated based on the judgment matrix corresponding to the health risk level.

2. The health status assessment method according to claim 1, characterized in that: The health data collection step includes: Determine a physical constitution questionnaire database, a four-diagnosis database and a health monitoring database based on the multi-source heterogeneous database; Acquire the physical fitness monitoring data corresponding to the user to be evaluated by using the physical fitness questionnaire database, the four diagnostic database and the health monitoring database; Constructing a random forest corresponding to the physical fitness monitoring data, calculating an abnormality score of each data point in the random forest, and updating the physical fitness monitoring data according to the abnormality score to obtain a semantic relationship corresponding to the physical fitness monitoring data; The semantic result corresponding to the physical fitness monitoring data is determined by utilizing the semantic relationship, and the health data of the user to be evaluated is determined according to the semantic result.

3. The health status assessment method according to claim 2, characterized in that: Acquiring the physical fitness monitoring data corresponding to the user to be evaluated using the physical fitness questionnaire database, the four diagnostic databases and the health monitoring database includes: Acquire the physical questionnaire data corresponding to the user to be evaluated by using the physical questionnaire database, and determine the first physical monitoring data corresponding to the user to be evaluated according to the weight value and semantic result corresponding to the physical questionnaire data; The pulse data, tongue diagnosis data, voice data and temperature data corresponding to the user to be evaluated are obtained by using the four-diagnosis database, and the second physical condition monitoring data corresponding to the user to be evaluated is determined according to the feature vectors corresponding to the pulse data, the tongue diagnosis data, the voice data and the temperature data; Acquire the health monitoring data and environmental data corresponding to the user to be evaluated by using the health monitoring database, and determine the third physical condition monitoring data corresponding to the user to be evaluated according to the health monitoring data and the environmental data; The physical fitness monitoring data is determined according to the first physical fitness monitoring data, the second physical fitness monitoring data and the third physical fitness monitoring data.

4. The health status assessment method according to claim 1, characterized in that: The constitution identification processing step includes: Acquire text data corresponding to the health data, generate label sequence data corresponding to the text data based on the initialized bidirectional long short-term memory model and conditional random field model, and determine the feature vector corresponding to the health data using the label sequence data; Inputting the feature vector into a trained deep belief network, controlling the deep belief network to obtain modal data corresponding to the feature vector, and determining the weight matrix and the bias vector corresponding to the feature vector using a joint representation result between the modal data; According to a preset activation function and using the feature vector, the weight matrix and the bias vector, the hidden unit corresponding to the deep belief network is determined, and the hidden unit is used to output the physical constitution identification data corresponding to the user to be evaluated under the health data.

5. The health status assessment method according to claim 1, characterized in that: The health data analysis step comprises: Based on the physical identification data, obtain physical type data and disease type data corresponding to the user to be evaluated, and obtain association rules corresponding to the physical type data and the disease type data; Performing density clustering calculation on the constitution identification data by using the association rule, obtaining a characteristic distribution result between the constitution type data and the disease type data of the user to be evaluated, and determining a health index clustering result of the user to be evaluated based on the characteristic distribution result; A prediction model corresponding to the constitution type data and the disease type data is constructed according to the initialized Bayesian network, and the health index score corresponding to the health index clustering result is calculated using the prediction model.

6. The health status assessment method according to claim 5, characterized in that: Constructing a prediction model corresponding to the constitution type data and the disease type data according to the initialized Bayesian network, including: Acquire a first node set corresponding to the constitution type data and a second node set corresponding to the disease type data; Constructing a corresponding conditional probability table of the Bayesian network based on the first node set and the second node set, and determining structural parameters of the Bayesian network using the conditional probability table; The prediction model is constructed according to the structural parameters and based on the Bayesian network.

7. The health status assessment method according to claim 1, characterized in that: The step of generating the evaluation result comprises: Obtaining a window function corresponding to the health indicator clustering result, calculating a mean result of the health indicator score under the window function, and determining the health risk level of the user to be evaluated according to the mean result; Constructing an evaluation indicator strategy corresponding to the user to be evaluated based on the health indicator clustering result, and determining the judgment matrix using the evaluation indicator strategy; The health assessment result corresponding to the user to be assessed is determined according to the judgment matrix.

8. The health status assessment method according to claim 7, characterized in that: Determining the health assessment result corresponding to the user to be assessed according to the judgment matrix includes: Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; Obtaining an indicator weight vector of the judgment matrix using the maximum eigenvalue and the corresponding eigenvector; Determine the membership vector corresponding to the indicator weight vector according to the health risk level, and construct a fuzzy relationship matrix corresponding to the judgment matrix according to the membership vector; An evaluation vector corresponding to the user to be evaluated is generated according to the fuzzy relationship matrix, and the health evaluation result is determined using the evaluation vector.

9. A health status assessment system, characterized in that: The system comprises: A health data collection unit, used to collect the physical fitness monitoring data of the user to be evaluated based on a preset multi-source heterogeneous database, and determine the health data of the user to be evaluated based on the semantic results of the physical fitness monitoring data; A constitution identification processing unit, used to obtain a feature vector corresponding to the health data, determine a weight matrix and a bias vector corresponding to the feature vector, and determine the constitution identification data corresponding to the user to be evaluated using the feature vector, the weight matrix and the bias vector; A health data analysis unit, configured to perform density clustering calculation on the physical identification data to obtain a health index clustering result of the user to be evaluated, and calculate a health index score of the user to be evaluated using the health index clustering result; An assessment result generating unit is used to determine the health risk level of the user to be assessed according to the health indicator score, and generate a health assessment result of the user to be assessed based on a judgment matrix corresponding to the health risk level.

10. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the steps of the health status assessment method described in any one of claims 1 to 8.

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