Intelligent detection system based on main trace elements of human body

Abstract

The invention discloses an intelligent detection system based on main trace elements of a human body, which relates to the technical field of human body detection and comprises a data acquisition module, a data conversion module, a combination analysis module and a suggestion generation module, the data acquisition module comprises a body fluid acquisition unit and a noninvasive optical blood acquisition unit, and is used for acquiring body fluid data and blood optical data of a human body based on wearable equipment; the data conversion module is used for carrying out component analysis on the collected body fluid data and carrying out statistics on the analysis data to obtain trace element data in the body fluid; and performing spectral analysis on the blood optical data to obtain trace element data in the blood. According to the system, targeted health suggestions can be given, real-time monitoring and accurate diagnosis are achieved, a convenient and personalized health management scheme is provided for a user, medical resource optimization and medical decision making can be assisted, the waiting time of conventional detection is shortened, detection is rapid and convenient, and improvement suggestions are made for the human body in time.

Description

Technical Field

[0001] The present invention relates to the technical field of human body detection, and specifically relates to an intelligent detection system based on the main trace elements of the human body. Background Art

[0002] With the continuous improvement of people's living standards, the attention to their own health status is increasing day by day. Trace elements in the human body play a crucial role in maintaining normal physiological functions of the human body. Essential trace elements in the human body (such as iron, zinc, copper, selenium, iodine, fluorine, etc.) play key roles in physiological functions. Iron is involved in oxygen transport, zinc supports immunity and wound healing, copper is involved in antioxidant and iron metabolism, selenium protects cells from oxidative damage, iodine synthesizes thyroid hormones to regulate metabolism, and fluorine enhances bone and dental health. These elements are also involved in enzyme activity, DNA synthesis, energy metabolism, and nervous system function. Deficiency or excess of these elements will affect health, such as anemia, growth retardation, and intellectual impairment. At present, the detection of trace elements mainly relies on laboratory analysis of blood and urine samples. This method has many disadvantages: on the one hand, the sampling process is relatively complex, which brings inconvenience to the examinees. For example, blood sampling requires professional medical staff to operate, which may cause discomfort to the examinees, and urine sampling requires the examinees to collect by themselves, and the samples are easily contaminated; on the other hand, the detection cycle is long, usually taking several hours or even several days to obtain the detection results, which cannot meet people's needs for instant health information, is not conducive to timely discovery of health hazards and adjustment of health management plans, resulting in people not being able to understand the lacking trace elements in their bodies in time and unable to supplement them in time. For this reason, we propose an intelligent detection system based on the main trace elements of the human body. Summary of the Invention

[0003] To solve the above technical problems, an intelligent detection system based on the main trace elements of the human body is provided. This technical solution solves the problems of complex sampling process, long detection cycle, and being not conducive to timely discovery of health hazards and adjustment of health management.

[0004] To achieve the above object, the technical solution adopted by the present invention is: an intelligent detection system based on the main trace elements of the human body, including: a data acquisition module, a data conversion module, a combined analysis module, and a suggestion generation module;

[0005] The data acquisition module includes a body fluid acquisition unit and a non-invasive optical blood acquisition unit, and acquires the body fluid data and blood optical data of the human body based on wearable devices;

[0006] The data conversion module performs component analysis on the acquired body fluid data, statistically analyzes the analyzed data, and obtains the trace element data in the body fluid; performs spectral analysis on the blood optical data to obtain the trace element data in the blood; and uploads it to the combined analysis module for analysis;

[0007] The combination analysis module performs a combined analysis on the obtained trace element data in body fluids and the trace element data in blood, calculates the correlation coefficient, determines whether the two sets of data are consistent, and after determining the consistency, calculates the ratio of trace elements between body fluids and blood in human health to determine whether the human body is healthy; among them, the calculation of the correlation coefficient and the determination of whether the two sets of data are consistent are used to reflect the status of human trace elements from different perspectives.

[0008] Based on the determined result, the recommendation generation module obtains scientific dietary and rest trace element supplement data based on big data and gives human health recommendations.

[0009] Preferably, the body fluid collection unit collects body fluids based on a multi-functional sensing array, which is composed of several sensing units that have specific responses to different trace elements. Each sensing unit includes a biorecognition element and a signal conversion element, which are used to convert the target trace element concentration signal into a detectable electrical signal and collect the electrical signal; the non-invasive optical blood collection unit collects based on infrared light. The spectral characteristics of the infrared light passing through human tissues change, and the spectral changes are monitored in real time, and the monitored data is collected; the body fluid collection unit and the non-invasive optical blood collection unit are located inside the wearable device, and the wearable device is intelligently connected to the user's mobile phone APP, and the user obtains the real-time detection results through the mobile phone APP.

[0010] Preferably, the data conversion module preprocesses the obtained data, determines the trace element concentration of the preprocessed body fluid data through amperometry. Set the measured current value of the body fluid as I, the target trace element concentration as c, and the expression of the calibration curve equation obtained by linear regression is: I = mc + b, where m is the slope of the calibration curve and b is the intercept. Substitute the measured current value Iw of the unknown sample into the calibration curve equation for calculation, and the calculation formula is:

[0011] cn = (Iw - b) / m

[0012] where cn is the target trace element concentration calculated by measuring sweat, and Iw is the measured current value of the unknown sample; the target trace element data measured in sweat is obtained through calculation.

[0013] Preferably, spectral analysis is performed on the blood optical data to obtain the trace element data in the blood. The spectral analysis obtains the infrared irradiation spectra of various trace elements in human blood through big data and constructs a database. Compare the real-time measured infrared irradiation spectrum of human blood with the spectra of various trace elements in the database, and judge by calculating the similarity between the two. If they are consistent, the specific value of the trace element content is obtained based on the database comparison.

[0014] Preferably, the images of the two spectra are vectorized in advance, processed based on the vectorization of pixel values, and then the similarity comparison calculation is performed, which is calculated by the Euclidean distance. The expression is:

[0015]

[0016] where d b is the Euclidean distance value between the spectrum of the b-th trace element and the spectrum of the real-time measurement, S bj is the value of the spectrum of the b-th trace element in the j-th dimension, R j is the spectrum of the infrared irradiation of human blood measured in real time in the j-th dimension, and a is the number of j; when the value of d b is 0, it is determined that the two spectral images are completely similar, and the specific value of the trace element content is obtained through the database.

[0017] Preferably, the analysis module calculates the correlation coefficient to determine whether there is consistency between the two sets of data, which is used to reflect the status of human trace elements from different angles and mutually verify the detection results. Among them, the content data of trace elements in the two sets of data liquids are X = (x1, x2,... x e ), and the content of trace elements in the blood is Y = (y1, y2,... y e ), where e is the number of samples. For each sample, a corresponding weight value w i is assigned based on the importance. If the sample weight is high, then w i = α n-i , where α ∈ (0, 1), and n is the total number of observations, and i is the current observation serial number; the weighted means of variables X and Y are defined, and the expression is:

[0018]

[0019] where and are the weighted means of variables X and Y respectively. The weighted covariance calculation is performed on the weighted means to obtain:

[0020]

[0021] where Cov w (X, Y) is the weighted covariance value of variables X and Y. The weighted standard deviation calculation is performed to obtain:

[0022]

[0023] where S w (X) and S w (Y) are the weighted standard deviation values of variables X and Y. Finally, the weighted dynamic correlation coefficient calculation is performed to obtain:

[0024]

[0025] where r w is the weighted dynamic correlation coefficient, which is used to measure the dynamic correlation between variables X and Y, and its value range is between (-1, 1). The closer its absolute value is to 1, the stronger the correlation; the closer it is to 0, the weaker the correlation. After judgment, the trace element ratio is calculated; if the judgment is inconsistent, the data collection and data conversion analysis are repeated.

[0026] Preferably, after it is determined that there is consistency, calculating the trace element ratio between body fluids and blood in human health includes calculating the single element ratio and the comprehensive multi-element ratio. For the single element ratio, the content of the current element in blood and body fluids is obtained in advance, and the ratio of the trace element between body fluids and blood is:

[0027] H = K y / K x , where H is the calculated ratio, K y is the content of the trace element in body fluids, and K x is the content of the trace element in blood; in the calculation of the comprehensive multi-element ratio, the sum of multiple elements in body fluids and the sum of multiple elements in blood are calculated in advance, and then the comprehensive ratio is calculated to obtain the comprehensive multi-element ratio.

[0028] Preferably, to determine whether a person is healthy, the results of medical research are obtained through big data, and a reference range for various trace element ratios is established. Based on the comparison between the calculated ratio and the ratio range obtained from big data, when it is greater than or less than the ratio range, it is prompted that the person is in a sub-healthy state or there are physiological changes. When the ratio significantly deviates from the reference range, it is determined that there is a disease condition, and the user is alerted through the mobile phone APP.

[0029] Preferably, the suggestion generation module is connected to the user's mobile phone APP through a wireless network, which is used to view the detection progress and results in real time based on the mobile phone APP, analyze the judgment results, classify according to the deviation of the trace element ratio, and for different categories, match different data retrieval and suggestion generation strategies. By connecting to the health database, corresponding matching and screening are performed, and health suggestions for the human body are given and displayed in real time on the mobile phone APP side.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] The data acquisition module of the present invention uses wearable devices to collect human body fluid and blood optical data in real time using a body fluid collection unit and a non-invasive optical blood collection unit. The data conversion module performs component analysis and statistics on the collected body fluid data, and performs spectral analysis on the blood optical data to obtain trace element data therein and upload it to the combined analysis module. The combined analysis module comprehensively analyzes the two trace element data, calculates the correlation coefficient to determine the consistency, and then calculates the ratio to evaluate human health. Finally, the recommendation generation module relies on big data to obtain scientific diet, work and rest, and trace element supplementation data based on the judgment results, and gives targeted health suggestions. The system realizes real-time monitoring and accurate diagnosis, provides users with convenient and personalized health management solutions, and can also help optimize medical resources and assist in medical decision-making, reducing the waiting time for routine tests. The test is fast and convenient, and timely suggestions for improvement are made for the human body. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a framework diagram of the intelligent detection system of the present invention;

[0033] Figure 2 This is a diagram of the steps for processing body fluid data in the data conversion module of the present invention;

[0034] Figure 3 This is a diagram of the blood data processing steps of the data conversion module of the present invention. DETAILED DESCRIPTION

[0035] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0036] Reference Figures 1 to 3 As shown, an intelligent detection system based on the main trace elements of the human body includes: a data acquisition module, a data conversion module, a combined analysis module and a suggestion generation module;

[0037] The data acquisition module includes a body fluid collection unit and a non-invasive optical blood collection unit, which collects body fluid data and blood optical data based on wearable devices;

[0038] The data conversion module performs component analysis on the collected body fluid data, performs statistics on the analysis data, and obtains the data on trace elements in the body fluid; performs spectral analysis on the blood optical data to obtain the data on trace elements in the blood; and uploads the data to the combined analysis module for analysis;

[0039] The combined analysis module combines and analyzes the acquired body fluid trace element data with the trace element data in the blood, calculates the correlation coefficient, and determines whether the two data are consistent. After determining whether there is consistency, the trace element ratio between the body fluids and blood in a healthy human body is calculated to determine whether the human body is healthy. The correlation coefficient calculation determines whether the two data are consistent and is used to reflect the trace element status of the human body from different angles.

[0040] The recommendation generation module obtains scientific diet, daily routine and trace element supplementation data based on the judged results and big data, and gives human health recommendations.

[0041] This application uses a body fluid collection unit and a non-invasive optical blood collection unit integrated into a wearable device to continuously collect body fluid data and blood optical data in real time. This allows monitoring of human health status to no longer be limited to regular physical examinations in hospitals, but to achieve daily and dynamic tracking. Once abnormal changes in trace elements in body fluids or blood occur, the system can quickly capture these signals. When the iron content in the blood continues to decline and reaches the threshold of mild imbalance, the system can promptly issue an early warning to remind the user to pay attention to their own health, thereby buying valuable time for early intervention and treatment, and effectively preventing the occurrence and development of diseases.

[0042] The data conversion module uses professional component analysis and spectral analysis technologies to accurately obtain trace element data in body fluids and blood. Combined with the analysis module, it calculates correlation coefficients and trace element ratios to comprehensively judge human health status. Compared with a single detection method, this multi-data source fusion analysis method is more comprehensive and accurate. At the same time, the system fully considers individual differences and conducts personalized health assessments based on each person's unique body fluid and blood trace element data. For people of different age groups, genders, and lifestyles, the system can make accurate judgments based on their corresponding normal reference ranges, providing a solid data foundation for subsequent health recommendations.

[0043] Wearable devices make data collection extremely convenient. Users can easily complete data collection in their daily lives, work, and even during exercise without having to go to a medical institution. The automated operation of the entire system, from data collection and conversion to analysis and recommendation generation, greatly improves the efficiency of testing and assessment. For users, simply by wearing the device, they can obtain professional and scientific health advice without the need for professional medical knowledge. This greatly improves the user experience and enhances their enthusiasm and initiative in managing their own health.

[0044] Based on big data and scientific analysis, the recommendation generation module provides targeted diet, work and rest, and trace element supplementation recommendations for users. These recommendations can help users adjust their lifestyles, improve their nutritional status, and maintain the balance of trace elements in the body. For users detected with mild zinc deficiency, the system recommends increasing the intake of zinc-rich foods such as lean meat and seafood, and arranging work and rest time reasonably to promote zinc absorption. Following these recommendations in the long term helps improve the overall health level of users and reduce the risks of various diseases caused by trace element imbalance, such as iron deficiency anemia and osteoporosis, achieving the transformation from disease treatment to disease prevention.

[0045] For medical institutions and medical staff, the large amount of health data collected by this system has important reference value. Through the analysis and research of these data, it is possible to deeply understand the health status and disease trends of different populations, providing a scientific basis for formulating public health policies and optimizing the allocation of medical resources. At the same time, the detailed health assessments and recommendations provided by the system can be used as auxiliary tools for medical staff in diagnosis and treatment, helping them understand the health status of patients more comprehensively, formulate more accurate treatment plans, and improve the quality and efficiency of medical services.

[0046] The body fluid collection unit collects body fluids based on a multi-functional sensing array, which consists of several sensing units that have specific responses to different trace elements. Each sensing unit includes a biometric recognition element and a signal conversion element, which are used to convert the target trace element concentration signal into a detectable electrical signal and collect the electrical signal. The non-invasive optical blood collection unit collects blood based on infrared light. The spectral characteristics of the infrared light passing through human tissues change, and the spectral changes are monitored in real time, and the monitored data is collected. The body fluid collection unit and the non-invasive optical blood collection unit are located inside the wearable device, and the wearable device is intelligently connected to the user's mobile phone APP. The user can obtain the real-time detection results through the mobile phone APP.

[0047] The wearable device of this application can be worn close to the body at any time. Whether in daily activities, work or rest, it can continuously collect body fluid and blood-related data. Users do not need to go to the hospital or testing institution specifically, breaking the limitations of time and space and realizing real-time tracking of their own health status. For example, during exercise, it can monitor the changes of body trace elements in real time. The body fluid collection unit uses a multi-functional sensing array, and its sensing units can accurately identify and convert different trace element concentration signals into electrical signals by virtue of the biometric recognition element and the signal conversion element, achieving accurate detection of multiple trace elements. The non-invasive optical blood collection unit captures blood information based on the infrared light technology and the change of spectral characteristics to ensure data accuracy.

[0048] Refer to Figure 2As shown in the figure, the data conversion module pre - processes the acquired data in advance, and determines the trace element concentration of the pre - processed body fluid data through current analysis method. Set the measured current value of the body fluid as I, and the target trace element concentration as c. The expression of the calibration curve equation obtained through linear regression is: I = mc + b, where m is the slope of the calibration curve and b is the intercept. Substitute the measured current value Iw of the unknown sample into the calibration curve equation for calculation, and the calculation formula is:

[0049] cn=(Iw - b) / m

[0050] where cn is the target trace element concentration calculated by measuring sweat, and Iw is the measured current value of the unknown sample; the target trace element data measured in sweat is obtained through calculation.

[0051] In the pre - processing link of this application, noise, outliers and interference information in the original data can be effectively removed, enabling subsequent analysis to be based on purer and more accurate data, greatly improving the credibility of the analysis results. Abnormal electrical signals generated by external electromagnetic interference during the acquisition process are removed, avoiding their misleading effect on the calculation of trace element concentration; the current analysis method is adopted, and a linear relationship between current and trace element concentration is established based on Faraday's law. Through the calibration curve equation I = mc + b, using the known slope m and intercept b, and substituting the measured current value Iw of the unknown sample, the target trace element concentration cn can be accurately calculated. This method has a clear principle and relatively simple operation, can accurately quantify the content of trace elements in body fluids, and provides accurate data support for health analysis;

[0052] The calibration curve equation is universal. Once the slope and intercept are determined, it can be used for multiple measurements of different unknown samples to quickly obtain the trace element concentration. Operators only need to measure the current value according to the established process and substitute it into the formula for calculation, without complex analysis steps, which reduces the detection threshold, improves the detection efficiency, and is convenient for large - scale application and promotion.

[0053] Refer to Figure 3 As shown in the figure, spectral analysis is performed on the blood optical data to obtain the trace element data in the blood. Through big data, the infrared irradiation spectra of various trace elements in human blood are obtained and constructed into a database. The infrared irradiation spectrum of the human blood measured in real - time is compared with the spectra of various trace elements in the database, and the similarity between the two is calculated for judgment. If they are consistent, the specific value of the trace element content is obtained based on the database comparison.

[0054] By comparing the real-time measured spectrum with the database, this application can quickly determine the types of trace elements contained therein, utilize the existing information in the database to quickly obtain their content values. Compared with traditional chemical analysis methods, it significantly shortens the detection time. Moreover, the database constructed based on big data makes the detection results more accurate and reliable, reducing human errors and detection biases. The database constructed by big data can integrate the infrared irradiation spectra of various trace elements in human blood, covering almost all possible trace elements. This enables spectral analysis to no longer be limited to the detection of a few common elements, and can comprehensively analyze multiple trace elements in blood, providing richer data for a comprehensive assessment of human health.

[0055] Previously, vectorization processing is performed on the images of the two spectra. Based on the vectorization of pixel values for processing, and then similarity comparison calculation is carried out, which is calculated through the Euclidean distance. The expression is:

[0056]

[0057] where d b is the Euclidean distance value between the spectrum of the b-th trace element and the real-time measured spectrum, S bj is the value of the spectrum of the b-th trace element in the j-th dimension, R j is the infrared irradiation spectrum of human blood measured in real time in the j-th dimension, and a is the number of j. When the value of d b is 0, it is determined that the two spectral images are completely similar, and the specific value of the trace element content is obtained through the database.

[0058] This application performs vectorization processing on the spectral image, which can convert complex image data into a unified vector form, facilitating computer storage, processing, and analysis, making subsequent calculations and operations more efficient, reducing the complexity of the algorithm. Vectorization based on pixel values can directly reflect the essential characteristics of the spectral image. Each pixel value carries the intensity information of the spectrum. After vectorization, this information is orderly integrated, which helps to extract and analyze the key features of the spectrum, providing a more accurate basis for similarity calculation.

[0059] The combined analysis module calculates the correlation coefficient to determine whether there is consistency between the two sets of data, which is used to reflect the human trace element status from different angles and mutually verify the detection results. Among them, the content data of trace elements in the two sets of data liquids is X = (x1, x2,... x e ), and the content of trace elements in blood is Y = (y1, y2,... y e ), where e is the number of samples. For each sample, a corresponding weight value w i is assigned based on the importance. If the sample weight is high, then w i = α n-i, where α ∈ (0, 1), and n is the total number of observations, and i is the current observation sequence number; define the weighted means of variables X and Y, and the expression is:

[0060]

[0061]

[0062] where and are the weighted means of variables X and Y respectively. Calculate the weighted covariance of the weighted means to obtain:

[0063]

[0064] where Cov w (X, Y) is the weighted covariance value of variables X and Y. Calculate the weighted standard deviation to obtain:

[0065]

[0066] where S w (X) and S w (Y) are the weighted standard deviation values of variables X and Y. Finally, calculate the weighted dynamic correlation coefficient to obtain:

[0067]

[0068] where r w is the weighted dynamic correlation coefficient, which is used to measure the dynamic correlation between variables X and Y. Its value range is between (-1, 1). The closer its absolute value is to 1, the stronger the correlation; the closer it is to 0, the weaker the correlation. After judgment, calculate the trace element ratio; if the judgment is inconsistent, then repeat the data collection and data conversion analysis.

[0069] The value range is between (-1, 1), which enables researchers to intuitively understand the association strength between two sets of data, whether it is a perfect positive correlation, a perfect negative correlation, or no linear correlation relationship, providing a clear quantitative index for further analysis. It can well reflect the linear trend between two sets of data. No matter what the specific distribution of the data is, as long as there is a linear relationship, the Pearson correlation coefficient can accurately capture the direction and strength of this relationship.

[0070] After judging consistency, calculate the trace element ratio between body fluids and blood in human health, including the calculation of single-element ratios and the calculation of multi-element comprehensive ratios. Among them, for the single-element ratio, first obtain the content of the current element in blood and body fluids, then the ratio of this trace element between body fluids and blood is: H = K y / K x , where H is the calculated ratio, and Ky is the content of trace elements in body fluids, and K x is the content of trace elements in blood; in the calculation of the multi-element comprehensive ratio, the sum of multi-elements in body fluids and the sum of multi-elements in blood are calculated in advance, and then the comprehensive ratio calculation is carried out to obtain the multi-element comprehensive ratio.

[0071] The ratio change of specific trace elements in body fluids and blood in this application may be related to the occurrence and development of certain diseases. Taking iron as an example, in patients with iron deficiency anemia, the iron content in blood decreases, while the change of iron in body fluids may be relatively small, resulting in an abnormal ratio of iron between body fluids and blood. Doctors can use this to assist in diagnosing diseases and evaluate the treatment effect and disease progression by monitoring the ratio change.

[0072] To determine whether a person is healthy, the results of medical research are obtained through big data, and the reference ranges of various trace element ratios are established. Based on the calculated ratio and the ratio range obtained from big data, when it is greater than or less than the ratio range, it indicates that the person is in a sub-healthy state or there are physiological changes. When the ratio significantly deviates from the reference range, it is determined that there is a disease condition, and the user is alerted through the mobile phone APP.

[0073] This application uses big data to obtain medical research results to establish the reference range of trace element ratios, which can integrate a large amount of sample data, covering the information of people in different regions, ages, and genders, making the reference range more scientific and universal. Compared with the conclusions drawn from single research or a small number of samples, the reference range supported by big data can more accurately reflect the normal range of trace element ratios in healthy people, providing a reliable basis for judging human health;

[0074] When the ratio significantly deviates from the reference range, it is determined that there is a disease condition. This data-based judgment method has a certain degree of accuracy. Different diseases may cause abnormal changes in specific trace element ratios. By monitoring and analyzing these ratios, doctors can be assisted to diagnose diseases more accurately, providing strong support for the early detection and treatment of diseases.

[0075] The suggestion generation module is connected to the user's mobile phone APP through a wireless network, used to view the progress and results of the detection in real time based on the mobile phone APP, analyze the judgment results, classify according to the deviation of the trace element ratio, and for different categories, match different data retrieval and suggestion generation strategies. By connecting to the health database, corresponding matching and screening are carried out, and health suggestions for the human body are given and displayed in real time on the mobile phone APP side.

[0076] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. An intelligent detection system based on the main trace elements of the human body, characterized in that, Including: A data acquisition module, a data conversion module, a combined analysis module, and a suggestion generation module; The data acquisition module includes a body fluid acquisition unit and a non-invasive optical blood acquisition unit, which collect human body fluid data and blood optical data based on a wearable device; The data conversion module conducts component analysis on the collected body fluid data, statistically analyzes the analyzed data, and obtains trace element data in the body fluid; Conduct spectral analysis on the blood optical data to obtain trace element data in the blood; Upload it to the combined analysis module for analysis; The combined analysis module conducts combined analysis on the obtained trace element data in the body fluid and the trace element data in the blood, calculates the correlation coefficient, judges whether the two sets of data are consistent, and after judging consistency, calculates the trace element ratio between the body fluid and the blood in human health to judge whether the human body is healthy; Among them, the calculation of the correlation coefficient and the judgment of whether the two sets of data are consistent are used to reflect the trace element status of the human body from different perspectives; The suggestion generation module, based on the judged result, obtains scientific dietary routine trace element supplement data based on big data and gives human health suggestions.

2. The intelligent detection system based on the main trace elements of the human body according to claim 1, wherein The body fluid acquisition unit collects body fluid based on a multi-functional sensor array, which is composed of several sensor units that have specific responses to different trace elements. Each sensor unit includes a biological recognition element and a signal conversion element, which are used to convert the target trace element concentration signal into a detectable electrical signal and collect the electrical signal; The non-invasive optical blood acquisition unit collects based on infrared light. The spectral characteristics of the infrared light passing through human tissue change, and the spectral changes are monitored in real time, and the monitored data is collected; The body fluid acquisition unit and the non-invasive optical blood acquisition unit are located inside the wearable device. The wearable device is intelligently connected to the user's mobile phone APP, and the user obtains the real-time detection result through the mobile phone APP.

3. An intelligent detection system based on the main trace elements of the human body according to claim 1, characterized in that, The data conversion module pre-processes the obtained data in advance. The pre-processed body fluid data is used to determine the trace element concentration through amperometry. Set the measured current value of the body fluid as I, the target trace element concentration as c, and the expression of the calibration curve equation obtained through linear regression is: I = mc + b, where m is the slope of the calibration curve and b is the intercept. Substitute the measured current value Iw of the unknown sample into the calibration curve equation for calculation, and the calculation formula is: cn = (Iw - b) / m Where cn is the target trace element concentration calculated by measuring sweat, and Iw is the measured current value of the unknown sample; the target trace element data measured in sweat is obtained through calculation.

4. An intelligent detection system based on the main trace elements of the human body according to claim 1, characterized in that, Conduct spectral analysis on the blood optical data to obtain the trace element data in the blood. The spectral analysis obtains the infrared irradiation spectra of various trace elements in human blood through big data, constructs a database, compares the real-time measured infrared irradiation spectrum of human blood with the spectra of various trace elements in the database, judges by calculating the similarity between the two, and if they are consistent, the specific value of the trace element content is obtained based on the database comparison.

5. An intelligent detection system based on the main trace elements of the human body according to claim 4, characterized in that, Pre-vectorize the images of the two spectra, process them based on the vectorization of pixel values, and then perform similarity comparison calculations, which are calculated through the Euclidean distance. The expression is: where d b is the Euclidean distance value between the spectrum of the b-th trace element and the real-time measured spectrum, and S bj is the value of the spectrum of the b-th trace element in the j-th dimension, and R j is the infrared irradiation spectrum of human blood measured in real time in the j-th dimension, and a is the number of j; where d b is 0, it is determined that the two spectral images are completely similar, and the specific value of the trace element content is obtained through the database.

6. The intelligent detection system based on the main trace elements of the human body according to claim 1, wherein, The correlation coefficient is calculated by the combination analysis module to determine whether there is consistency between the two sets of data, which is used to reflect the status of human trace elements from different perspectives and mutually verify the test results. The content data of trace elements in the liquid of the two sets of data is X = (x1, x2,... x e ), and the content of trace elements in the blood is Y = (y1, y2,... y e ), where e is the number of samples. For each sample, a corresponding weight value w i is assigned based on the importance level. If the sample weight is high, then w i = α n-i , where α ∈ (0, 1), n is the total number of observations, and i is the current observation sequence number; Define the weighted means of variables X and Y. The expression is: wherein and are the weighted means of variables X and Y respectively. By calculating the weighted covariance of the weighted means, we get: Among them, Cov w (X, Y) is the weighted skewness covariance value of variables X and Y. By performing weighted standard deviation calculation, we obtain: Among which S w (X) and S w (Y) The weighted standard deviation of variables X and Y, and finally the weighted dynamic correlation coefficient is calculated to obtain: where r w is the weighted dynamic correlation coefficient, which is used to measure the dynamic correlation between variables X and Y. Its value range is between (-1, 1). The closer its absolute value is to 1, the stronger the correlation is; the closer it is to 0, the weaker the correlation is. After judgment, the calculation of trace element ratio is carried out; if the judgment is inconsistent, the data collection and data conversion analysis are repeated.

7. An intelligent detection system based on the main trace elements of the human body according to claim 1, characterized in that, After determining the consistency, calculating the trace element ratio between body fluids and blood in human health includes the calculation of single element ratio and the calculation of multi-element comprehensive ratio. For the calculation of the single element ratio, the contents of the current element in blood and body fluids are obtained in advance, and the ratio of the trace element between body fluids and blood is: H = K y / K x , where H is the calculated ratio, and K y is the content of the trace element in body fluids, and K x is the content of the trace element in blood; in the calculation of the multi-element comprehensive ratio, the comprehensive ratio is obtained by calculating the sum of multiple elements in body fluids and the sum of multiple elements in blood in advance and then performing the comprehensive ratio calculation.

8. An intelligent detection system based on the main trace elements of the human body according to claim 1, characterized in that, To determine whether a person is healthy, obtain the results of medical research through big data, and establish a reference range for the ratios of various trace elements. Based on the comparison between the calculated ratio and the ratio range obtained from big data, when it is greater than or less than the ratio range, it is prompted that the person is in a sub-healthy state or there are physiological changes. When the ratio significantly deviates from the reference range, it is determined that there is a disease condition, and the user is alerted through the mobile phone APP.

9. The intelligent detection system based on the main trace elements of the human body according to claim 1, characterized in that, The recommendation generation module is connected to the user's mobile phone APP through a wireless network, used to view the progress and results of the detection in real time based on the mobile phone APP, analyze the judgment results, classify them based on the deviation of the trace element ratio, and match different data retrieval and recommendation generation strategies for different categories. Through connecting to the health database, perform corresponding matching and screening, and display the given health recommendations for the human body in real time on the mobile phone APP side.

Images