WO2022222197A1 - Procédé de surveillance personnalisée multimode - Google Patents

Procédé de surveillance personnalisée multimode Download PDF

Info

Publication number
WO2022222197A1
WO2022222197A1 PCT/CN2021/092129 CN2021092129W WO2022222197A1 WO 2022222197 A1 WO2022222197 A1 WO 2022222197A1 CN 2021092129 W CN2021092129 W CN 2021092129W WO 2022222197 A1 WO2022222197 A1 WO 2022222197A1
Authority
WO
WIPO (PCT)
Prior art keywords
component
value
monitoring
function
variable
Prior art date
Application number
PCT/CN2021/092129
Other languages
English (en)
Chinese (zh)
Inventor
丁贤根
丁远彤
肖苑辉
Original Assignee
港湾之星健康生物(深圳)有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 港湾之星健康生物(深圳)有限公司 filed Critical 港湾之星健康生物(深圳)有限公司
Publication of WO2022222197A1 publication Critical patent/WO2022222197A1/fr

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the invention relates to the field of industrial technology and the field of biomedicine, in particular to the measurement and monitoring of medical data and industrial data, in particular to a monitoring method for data obtained by indirect, multi-modal measurement that cannot be directly measured.
  • the measurement of physical and chemical quantities includes direct measurement and indirect measurement.
  • Direct measurement can be accomplished by improving the precision and accuracy of measuring physical and chemical quantities themselves. Indirect measurement is limited to the individual attributes of the person being measured. In many cases, it is difficult to directly measure its physical and chemical quantities. Sometimes even if indirect physical and chemical quantities are measured, it is difficult to monitor and convert them into sufficient precision and accuracy. The error meets the required physical and chemical quantity data.
  • the inventor also proposes a "multi-mode personalized calibration method", which attempts to measure a plurality of individualized, low-difficulty monitoring factor values associated with the measurement monitoring value by decomposing the measurement monitoring value, or multiple values related to the measurement monitoring value.
  • the monitoring value is related to the monitoring factor value and the measurement monitoring value with low accuracy and difficulty.
  • the measurement data is obtained, and then the correction of the measurement monitoring value is obtained indirectly by monitoring these data. Value - a reliable measurement result.
  • the physical and chemical quantities (monitoring values) that need to be measured are often related to other physical and chemical quantities (monitoring factor values), and there may be a relatively obvious functional relationship.
  • introducing sensors of various modes to measure the corresponding physical and chemical quantities, designing corresponding calculation methods, and incorporating the respective measured data into the monitoring calculation will bring obvious effects to the monitoring values.
  • the object algorithm proposed by the present invention is to monitor the individual of the measurement object according to its historical data.
  • the proposed group algorithm is to monitor each other horizontally for several objects with the same measurement properties. This method has obvious advantages for the monitoring of monitoring values that are related to the environment and to the subdivision classification of objects.
  • the current industry status of the measurement industry is mainly based on direct measurement, indirect measurement is relatively rare, and the method of individualization, multi-mode and monitoring has not been found yet.
  • the current status is:
  • the proposed human blood glucose monitoring is to monitor the millimolar concentration per liter (mmol/L) of glucose content in human veins.
  • the commonly used methods include:
  • Invasive single-point method draw venous blood for glucose testing
  • Minimally invasive single-point method puncture the finger to take the capillary blood of the finger and monitor the glucose with a test strip
  • Minimally invasive continuous method continuous monitoring of glucose by inserting an indwelling enzyme electrode probe into the arm
  • Non-invasive continuous methods electrophoresis is used to measure glucose in tissue fluid on the skin, infrared method to measure glucose through skin, microwave method to measure subcutaneous glucose, and contact lens with microcircuit to measure glucose in tears, etc.
  • infrared method to measure glucose through skin
  • microwave method to measure subcutaneous glucose
  • contact lens with microcircuit to measure glucose in tears
  • Single-point measurement cannot solve the problem of monitoring blood sugar fluctuations. For patients with type 1 diabetes, because the rapid drop in blood sugar in a short time cannot be alarmed, it will bring the patient's life in danger. Furthermore, for patients with type 2 diabetes, a single point measurement cannot address the optimal management and treatment of diabetes. Both invasive monitoring and minimally invasive monitoring bring pain and inconvenience to patients.
  • the measurement data itself has no information that can monitor itself, and cannot monitor itself.
  • the interference factors also include some personalized content, and it is impossible to use the same standard to eliminate these personalized interference.
  • the purpose and intent of the present invention are:
  • the present invention designs an object algorithm algorithm to carry out personalized monitoring according to historical records and agreed truth values.
  • the present invention proposes a group algorithm algorithm to monitor itself according to the data of other objects.
  • the agreed truth value referred to in the present invention is not limited to being obtained by measuring equipment with a higher precision, but can also be obtained by artificial intelligence and deep learning.
  • monitoring functions referred to in the present invention are not limited to these functions and formulas listed in the present application, including other functions and formulas designed by mid-level designers in the industry based on this idea.
  • step numbers of the present invention do not exist in the order of the numbers.
  • the scope of application of the present invention may include monitoring of measurement data and monitoring of other data.
  • the present invention emphasizes that the types of monitoring values, the types of monitoring factor values, and the division of objects and groups listed in the present invention are all derived from this idea. Due to space limitations and the basic spirit of the invention, the application of the present invention cannot list the types and associations of these data information one by one. The types of information proposed in the invention application are not meant to limit the idea of the present invention.
  • energy saving can be achieved through multi-mode measurement, object algorithm and group algorithm.
  • Figure 1 is a schematic diagram of multi-mode personalized monitoring
  • FIG. 1 Schematic diagram of blood glucose monitoring
  • Figure 7 Cargo compartment reefer layout.
  • Embodiment 1 External monitoring method of human body glucose data
  • One of the application embodiments of the present invention is an artificial intelligence-oriented personalized management method for diabetes, which is a typical application example of the present invention.
  • this embodiment only the description of the method of the present invention is involved, and it is not regarded as a complete design of an actual system, nor is it a limitation of the present invention.
  • FIG. 1 Schematic diagram of multi-mode personalized monitoring
  • each monitored person is regarded as each object, and in each monitor, the monitored components are monitored by a laser Raman analyzer and an infrared light analyzer, and a PPG/ECG analyzer (PPG: English full name Photoplethysmography, referred to as PPG.
  • PPG English full name Photoplethysmography
  • ECG English full name of Electrocardiography, referred to as ECG
  • a series of algorithms in the patent application of the present invention are used to eliminate the background noise, thereby obtaining accurate in vitro blood glucose monitoring values.
  • the method of venous blood blood test is required to obtain the agreed true value in the early stage, which is used as calibration.
  • the object algorithm proposed by the present invention is used to calculate Subject-corrected value, that is, blood glucose monitoring results.
  • the artificial intelligence big data analysis of many objects in the group can finally eliminate the step of venous blood drawing for the monitors and relieve the pain of the monitors.
  • FIG. 1 Schematic diagram of blood glucose monitoring
  • 2001 is a laser Raman spectrometer, which is used to irradiate human skin through infrared laser (the wavelengths of 785nm, 830nm, 1063nm, etc. are selected here), and transmit it to a depth of about 1mm to produce Raman effect, according to The characteristic value and amplitude of the Raman spectral shift of glucose, monitor the glucose content, and finally calculate the glucose content in the venous blood by using a series of algorithms of the present invention, that is, the blood glucose value determined by the World Health Organization.
  • 2002 is an infrared light analyzer, which acts as an auxiliary blood glucose monitoring device to monitor another blood glucose monitoring component.
  • PPG/ECG analyzer PPG: English full name Photoplethysmography, referred to as PPG.
  • ECG English full name Electrocardiography, referred to as ECG
  • monitoring blood pressure and pulse information monitoring blood pressure and pulse information, as an extended component of the present invention, participating in a series of algorithms of the present invention , which further enables the precision and accuracy of blood glucose monitoring.
  • 2004 is the real-time unit of the multi-mode personalized monitoring method of the present invention.
  • 2005 is a venous blood analyzer, which is one of the source channels of the agreed truth value of the present invention.
  • 2006 is the monitoring channel for the agreed truth value. It should be noted here that it is not a real-time online monitoring, but is used in the big data accumulation stage of the present invention. Once the big data accumulation is completed, this step can be omitted.
  • the present invention divides the monitoring components into a fixed component and a variable component.
  • the fixed component is the inherent part of the blood sugar content contained in the human skin and its subcutaneous tissue, which does not depend on the amount that changes with the blood sugar fluctuation of the human body, and the variable component is sensitive to the fluctuation of the glucose content in the human vein blood vessels. amount of change.
  • the present invention in order to further correlate the blood sugar fluctuations caused by changes in the blood flow rate of the human body, the present invention also introduces a cardiovascular blood pressure and pulse PPG/ECG analyzer to monitor the real-time blood pressure and pulse of the human body, and use this as an extension component.
  • the monitoring data of the present invention adopts the cloud computing mode, and the blood glucose monitoring data of each monitoring object is stored in the cloud server, forming a group big data mode.
  • the human body glucose value is detected based on Raman scattering spectrum, that is, the monitoring value described in the present invention. Since in the circuit of the Raman laser, all glucose will be detected, including at least the content of all glucose in the epidermis, intradermal, interstitial fluid, capillaries, venous blood vessels, etc., and even other The glucose at the unknown site, therefore, the detected glucose value is the sum of these glucose contents. According to the WHO definition, only glucose in the veins is needed. To this end, we design to use the changes in blood pressure and blood lipids as monitoring factor components, and according to the fluctuations in blood pressure and blood lipids, separate the glucose component in the venous blood vessels from the total glucose value, which is proposed by the present invention.
  • Longitudinal multi-mode monitoring monitor the monitoring value based on the historical data and real-time data of the object to make it a correction value.
  • the present invention also proposes horizontal multi-modal monitoring - monitoring the monitoring of one's own objects based on other historical statistical data in the group value to make it a correction value.
  • PDV is a variable component, which comes from the laser Raman analyzer and the infrared light analyzer in Figure 2, and mainly includes the change value of blood sugar.
  • PDF is a fixed component, also from laser Raman analyzer and infrared light analyzer, mainly including background noise and basic blood sugar content.
  • PDE is the extended component, which comes from a PCG/ECG analyzer.
  • MIF is the subject correction value, that is, the blood glucose value of the monitoring result.
  • DTM 1 (t), DTM 2 (t) to DTM m (t) are the analyzed blood sugar components, including epidermal blood sugar, intradermal blood sugar, interstitial fluid blood sugar, capillary blood sugar, venous blood sugar, etc. .
  • FIG. 4 is a spectrogram obtained by Raman scattering monitoring of mixed substances, that is, the synthesis of Raman waves of all substances detected at the Raman laser detection point.
  • 4010 is the horizontal axis, which represents the displacement wave of Raman scattering
  • 4020 is the vertical axis, which represents the intensity of the Raman wave.
  • 4001 is the first peak with a displacement of 1003cm -1
  • 4002 is the second peak with a displacement of 1125cm -1
  • 4003 is the third peak with a displacement of 1450cm-1
  • these three peaks constitute the characteristics of glucose "fingerprint”.
  • Other waveforms are characteristic of mixed non-glucose substances.
  • the method of the present invention needs to separate the signal quantity of glucose from the Raman scattering wave of the mixed substance in FIG. 4 .
  • FIG. 5 is the Raman shift spectrum of pure glucose, which is also the signal quantity of glucose that needs to be separated from FIG. 4 in the present invention.
  • 5010 is the horizontal axis, representing the displacement wave of Raman scattering
  • 5020 is the vertical axis, representing the intensity of the Raman wave
  • the curve in the figure is the component of the glucose Raman displacement wave
  • 5001 is the epidermal blood glucose value DTM 1 (t)
  • 5002 is the intradermal blood glucose value DTM 2 (t)
  • 5003 is the interstitial fluid blood glucose value DTM 3 (t)
  • 5004 is the capillary blood glucose value DTM 4 (t)
  • 5005 is the venous blood glucose value DTM 5 (t).
  • the final object correction value MIF(t) DTM 5 (t).
  • Methods of multimodal personalized monitoring including:
  • the monitoring value of the decomposition object is one or more monitoring components, the monitoring components are monitored, and the monitoring components are decomposed including one or more variable components, zero or more fixed components, and zero or more extended components that exist a monitoring function with the monitoring values.
  • the object algorithm is performed according to the historical and current monitoring components for the object to obtain an object correction value whose agreed true value error with the object is less than the allowable error.
  • the sensor for measuring blood glucose includes the monitoring data of the laser Raman spectrum analyzer as the monitoring component.
  • the monitoring data of the infrared light analyzer can also be added as the monitoring component.
  • the present invention divides the monitoring components into a fixed component and a variable component.
  • the fixed component is the inherent part of the blood sugar content contained in the human skin and its subcutaneous tissue, which does not depend on the amount that changes with the blood sugar fluctuation of the human body, and the variable component is sensitive to the fluctuation of the glucose content in the human vein blood vessels. amount of change.
  • the present invention in order to further correlate the blood sugar fluctuations caused by changes in the blood flow rate of the human body, the present invention also introduces a cardiovascular blood pressure and pulse PPG/ECG analyzer to monitor the real-time blood pressure and pulse of the human body, and use this as an extension component.
  • the monitoring data of the present invention adopts the cloud computing mode, and the blood glucose monitoring data of each monitoring object is stored in the cloud server, forming a group big data mode.
  • the present invention includes, but is not limited to, the steps of decomposing the monitoring value, and specifically, the following or various local improvement measures can be adopted:
  • the variable component is a monitoring component of the monitoring value that changes with environmental changes or a monitoring component specified by a user, and the variable component is obtained by monitoring with a sensor.
  • the fixed component is a type of the monitoring component that can be separated in the monitoring value, and within a sampling period equal to the variable component, the rate of change of the fixed component is the same as that of the variable component.
  • the ratio of the rate of change of the components is small, for the optimal selection of the biological health category, the ratio of the rate of change is less than 0.20, and for the optimal selection of the industrial category, the ratio of the rate of change is less than 0.10, and, under this condition, the fixed component
  • monitoring is performed using sensors or statistical predictions.
  • the other choice of the fixed component is to use the background noise as the fixed component, for example, the background noise of a laser Raman analyzer and the background noise of an infrared light analyzer.
  • Another option for the fixed component is to use the minimum value of the monitored component as the fixed component.
  • the selection of the change rate ratio range depends on the actual characteristics of the system. In some systems with high monitoring accuracy, the change rate ratio range can be smaller, for example, 0.01.
  • step S2030 the extended component is obtained by using sensor monitoring, and the monitoring function of the extended component and the monitoring value exists in a monitoring function including:
  • the change of the monitoring value affects the extended component in one direction, that is, the extended component is a function argument of the monitoring value.
  • the extended component is used as an auxiliary monitoring of the monitoring value.
  • the change of the blood glucose value locally affects the change of the blood oxygen content.
  • the blood glucose function becomes one of the independent variables of the blood oxygen function.
  • the change of the monitoring value affects the extended component in both directions, that is, the extended component and the monitoring value are mutually independent variables, and at this time, the extended component is used as the auxiliary monitoring and adjustment of the monitoring value.
  • the laser Raman shift amplitude and the change of the infrared wavelength are functions of mutual influence, which can be regarded as independent variables that are functions of each other.
  • the change of the extended component affects the monitoring value in one direction, that is, the monitoring value is a function argument of the extended component, and at this time, the extended component is used as an auxiliary adjustment of the monitoring value.
  • the blood glucose value is the mmol/L of the glucose content in the blood of human venous blood vessels, so the change of the pulse is important to the instantaneous blood glucose level. The value is influential.
  • the PPG/ECG pulse function is used as an independent variable of the blood glucose function, so as to more accurately monitor the blood glucose value.
  • step S2040 the monitoring value and the monitoring component are continuously monitored according to different continuous time series and specific moments, and the intermediate data and result data are stored in the information base.
  • the cloud center will continuously collect various useful data of the object and store it in the database.
  • the present invention includes, but is not limited to, the processing steps of the monitoring function. Specifically, the following or various local improvement measures can be adopted:
  • step S3010 a monitoring function between the monitoring value and the object is established according to formula (3.1), and the monitoring value is decomposed according to formula (3.2) to monitor components, including the variable component, the fixed component and the extended component component, the monitoring function of the monitoring value and the component is established according to formula (3.3), the variable component set is established according to formula (3.4), the fixed component set is established according to formula (3.5), and the extended component set is established according to formula (3.6).
  • MI f 3.1 (PD) (3.1)
  • MI f 3.3 (PDV, PDF, PDE) (3.3)
  • f 3.1 is the monitoring function of the monitoring value and the object
  • f 3.3 is the monitoring function of the monitoring value and the component
  • MI is the monitoring value or monitoring value data set
  • PD is the object or Object data set
  • PDV is the variable component or variable number component set
  • PDF is the fixed component or fixed component set
  • PDE is the extended component or the set of extended components
  • PFV ⁇ is the set of all the components numbered ⁇ .
  • variable component data or an element of the variable component data set n is the total number of the variable components
  • p is the total number of the fixed components, is numbered as The extended component data of or an element of the extended component data set, where Indicates that the PDE is an empty set, which means that the fixed component is not included
  • ⁇ is the total number of the extended components
  • the decomposition includes frequency domain decomposition from the perspective of monitoring convenience, and time domain decomposition from the perspective of continuous time.
  • f 3.3 is selected to be the glucose monitoring function of the 2001 laser Raman analyzer and the glucose monitoring function of the 2002 infrared light analyzer in FIG. 2, respectively, and the PPG/ECG analyzer monitoring function in 2003 is used as the extended component , in 2004 to do the decomposition of formula (3.4) to formula (3.6).
  • Step S3020 monitor the variable component according to the functional relationship between the variable component determined by the formula (3.7) and the output signal of the sensor, and establish a function set of the variable component according to the formula (3.8), Equation (3.9) is the output signal function of the sensor:
  • f 3.7 ⁇ is the function of the variable component numbered ⁇
  • F 3.8 is the function set of the variable component
  • f 3.9 ⁇ is the sensor function
  • n is the total number of functions of the variable component
  • PDV ⁇ is the variable component numbered ⁇
  • SS ⁇ is the signal output by the sensor numbered ⁇
  • S ⁇ is the sensor numbered ⁇ .
  • Step S3030 monitor the fixed component according to the functional relationship between the fixed component determined by the formula (3.10) and the output signal of the sensor or the statistical prediction, and establish the function of the fixed component according to the formula (3.11).
  • Set, formula (3.12) is the output signal function of the sensor or the statistical prediction:
  • f 3.10 ⁇ is the function of the fixed component numbered ⁇
  • F 3.11 is the function set of the fixed component
  • f 3.12 ⁇ is the output signal function of the sensor or the statistical prediction
  • p is the fixed component
  • PDF ⁇ is the fixed component numbered ⁇
  • SS ⁇ is the signal output by the sensor numbered ⁇ or the statistical prediction
  • S ⁇ is the sensor numbered ⁇ or the statistical prediction.
  • Step S3040 monitor the extended component according to the functional relationship between the extended component determined by the formula (3.13) and the output signal of the sensor, and establish a function set of the extended component according to the formula (3.14), the formula (3.15 ) is the output signal function of the sensor:
  • F 3.14 is the function set of the extended component
  • n is the total number of functions of the extended component
  • ⁇ are all natural numbers
  • the present invention includes, but is not limited to, the processing steps of background noise. Specifically, the following or various local improvement measures can be adopted:
  • Step S4010 for the object with the background noise, calculate according to the following steps:
  • the background noise is set as the fixed component
  • MI f 4.1 (PDV, PDF, PDE, PDN) (4.1)
  • MI f 4.6 (SV, SE, t) (4.6)
  • f 4.1 is the monitoring function including background noise
  • f 4.2 is the function with the background noise as the fixed component
  • f 4.3 is the extended component function including background noise
  • f 4.4 is the variable component sensor function
  • f 4.5 is the function of the background noise
  • f 4.6 is the monitoring value function
  • PDN is the background noise
  • SE is the sensor output signal of the extended component
  • SV is the sensor output of the variable component signal
  • t is the time series.
  • the background noise is selected as the electrical background noise of the CCD array and the glucose signal unique to the object of in vitro glucose monitoring, including epidermis, subcutaneous tissue, interstitial fluid, etc. Glucose in venous blood vessels as defined by the International Health Organization.
  • the intermediate technicians in the industry can make a detailed combination according to their own understanding, and use a laser Raman analyzer or other monitoring equipment for combined monitoring.
  • Step S4020 according to the actual application, in the case of single-variable monitoring, and in the case that the range of the variable-component sensor satisfies the application, adopt one of the variable-component sensors, and in the case of the variable-component sensor In the case that the range does not satisfy the application, a plurality of the variable component sensors are used to extend the range.
  • Another preferred solution is to divide the blood glucose value based on 4.2 as the boundary. For example, if the blood glucose value is greater than 4.2, one section of monitoring is designed, and if the blood glucose value is less than or equal to 4.2, another section of monitoring is designed.
  • step S4030 according to the actual application, in the case of multi-variable monitoring, each variable adopts one sensor of the variable component.
  • the use of a laser Raman analyzer and an infrared light analyzer is such a design solution.
  • Step S4040 according to the actual application, if the output data of the variable component sensor satisfies the application, it is not necessary to use the extended component sensor, and in the case that the output data of the variable component sensor cannot meet the application Next, use more than one sensor of the extended component described above.
  • the present invention includes, but is not limited to, the object algorithm. Specifically, the following or multiple local improvement measures can be adopted:
  • step S5010 standard or higher-level measurement accuracy monitoring equipment is used to monitor and obtain the monitoring value of the object as the agreed true value, record the monitoring time, and set the agreed true value
  • Step S5020 Set an initial parameter set, and calculate the object correction value, and then calculate the agreed true value according to the step S5010, calculate the error, if the error falls within the allowable error, assign the initial parameter set to the personalized parameter set, if If the error is greater than the allowable error, the initial parameter set is modified to be the personalized parameter set, so that the error falls within the allowable error.
  • Step S5030 using the personalized parameter set, using the current monitoring component and algorithms including extended Kalman filter algorithm, Monte Carlo particle algorithm, modern Bayesian algorithm, and the personalized parameter set , calculate the object correction value.
  • T-test or Z-test was used to analyze errors to remove outliers.
  • the support vector machine SVM and the convolutional neural network CNN algorithm are used to classify the historical values.
  • Step S5040 based on the setting, using the historical monitoring component, according to the set time interval, execute the step S5020 at the time interval point, and execute the step S5030 outside the time interval point to establish
  • the personalized parameter set and the time series corresponding to the monitoring components are recorded in the information database, and a deep learning algorithm is used to calculate the monitoring components and the personalized parameters of the objects in the information database set and the time series, find out the personalized parameter set with high probability and demarcate the corresponding monitoring component as the optimization point, and the high probability includes the probability specified by the user or the probability is greater than 30%.
  • step S5050 based on the personalized parameter set of the optimization point obtained in the step S5040, the object correction value is calculated by adopting a support vector machine algorithm and a convolutional neural network algorithm.
  • a medical-grade blood glucose and blood lipid blood test measurement device is used to obtain the measured values of the individual's blood glucose and blood lipids and record the measurement time, which are used as the agreed true values.
  • the values and time series of blood glucose and blood lipid measured by the equipment to be calibrated are used to calculate the vertical synchronization correction value, and the error between the vertical synchronization correction value and the agreed true value is calculated and verified. If the error If it is greater than the allowable error, modify the parameter set and iteratively calculate until the error is less than the allowable error.
  • the agreed truth value can also be obtained by statistics of other individuals and artificial intelligence algorithms. At this time, it is not necessary to use higher-level medical equipment to measure and obtain.
  • the calibration function can select algorithms such as mathematical statistics algorithm, support vector machine SVM, convolutional neural network CNN, etc., with the error smaller than the allowable error as the goal, for example, the convolution kernel or related parameters are trained, which are all summarized and personalized feature set.
  • the present invention includes, but is not limited to, the group algorithm. Specifically, the following or multiple locally improved measures can be adopted:
  • Step S6010 for all the objects in the group, calculate the optimization point and the object correction value, and record them in the information database.
  • Step S6020 according to the objects in the information database, for the optimization point and the object correction value, establish more than one object classification, and calibrate the object classification of the object, the individual classification of the object classification
  • the ratio of the number to the total number of objects is greater than the number specified by the user or greater than 0.2, and is recorded in the information base.
  • Step S6030 according to the object classification, for the optimization point and the object correction value, according to the principle of minimum error, calculate the personalized feature set of the object classification, and record it in the information database.
  • Step S6040 Calculate the group correction value of the object according to the personalized feature set of the object classification.
  • Step S6050 for the self-object, calculate the object classification to which the calibration belongs, and calculate the object correction value and the group correction value of the object according to the personalized feature set of the object classification.
  • Step S6060 according to the personalized feature set of the object classification, for the newly added object, determine its object classification according to the calculation of the monitoring value of the object, and directly adopt the personalized feature set of the classification to include: said agreed truth value without performing the step of monitoring said agreed truth value.
  • the personalized feature set includes the parameters of the object algorithm, the parameters of the population algorithm, the classification, and the agreed truth value of the object.
  • the use of the algorithm between groups can further improve the calibration efficiency and reduce the error.
  • targeted calibration is performed. , in order to obtain better results.
  • a group classification model is established, and a personalized feature set for classification is established. For individuals newly entering the group, calculation is performed to be included in the classification, and rapid calibration is performed with the personalized feature set corresponding to the classification.
  • the present invention includes, but is not limited to, a differential object algorithm, specifically as follows:
  • Step S7010 Calculate the difference between the monitoring value of the object at the optimization point and the correction value of the object, and record the difference in the information database.
  • Step S7020 if the difference value of each of the optimization points is a constant difference value, for the object correction value of the subsequent time series, use the monitoring value to subtract the constant difference value to calculate the object correction value .
  • Step S7030 if the difference value of each optimization point is a function difference value, then for the object correction value of the subsequent time series, subtract the function difference value from the monitoring value to calculate the object correction value .
  • Step S7040 if the difference value of the optimization point of the time series is an alternating difference value between the constant difference value and the function difference value, subtract the alternating difference value from the monitoring value to calculate the object correction value.
  • step S7050 for the Raman spectrum monitoring method, the object is scanned twice or more with excitation light with a slight frequency offset to generate two or more Raman spectra, and according to the Raman scattering characteristic shift of the object, the two or more
  • the Raman scattering spectrum generated by scanning is calculated by differential convolution or linear regression to eliminate the interference of background noise caused by fluorescence, and the wavelength difference of the excitation light with the slight frequency offset is between 0.2 and 100 nm.
  • the differential object algorithm is used to further counteract the influence of background noise and improve the monitoring accuracy.
  • the present invention includes, but is not limited to, the personalized feature set. Specifically, the following or various local improvement measures can be adopted:
  • Step S8010 set a constraint condition that the error of the corrected value of the object is less than the allowable error, establish the personalized feature set according to formula (8.1), and calculate the personalized feature set:
  • f 8.1 is the personalized feature set function
  • PF is the personalized feature set
  • MI is the monitoring value or the monitoring value data set
  • MIF is the object correction value or the object correction value set
  • ⁇ is the error
  • ⁇ E is the allowable error.
  • Step S8020 decompose the personalized feature set:
  • the personalization feature set is decomposed into more than one personalized feature set category
  • the personalized feature set category is decomposed into one or more personalized feature set category components.
  • f 8.2 is the category function of the personalized feature set
  • PFT is the category of the personalized feature set
  • the decomposition includes frequency domain decomposition from the perspective of convenient calculation, and also includes time domain decomposition from the perspective of continuous time.
  • Step S8030 monitoring the time domain value of the personalized feature set category:
  • f 8.3 is the personalized feature set category time domain function
  • t is the continuous time series.
  • Step S8040 monitor the specific moment value of the personalized feature set category:
  • f 8.3 is the time domain function of the personalized feature set category
  • T is the specific moment
  • PFT T is the specific moment value of the personalized feature set category at the specific moment.
  • Step S8050 monitor the time domain value of the category component of the personalized feature set:
  • the personalized feature set category is decomposed into more than one personalized feature set category component according to formula (8.5), and the personalized feature set category component and the continuous time series have the function determined by formula (8.6) relationship, monitor the time domain value of the category component of the personalized feature set according to formula (8.5):
  • PFT f 8.5 (PFT 1 , PFT 2 , . . . , PFT q ) (8.5)
  • f 8.5 is the category decomposition function of the personalized feature set
  • f 8.6 is the time domain function of the category component of the personalized feature set
  • PFT 1 , PFT 2 , ..., PFT q are the category components of the personalized feature set
  • q is the The total number of category components of the personalized feature set
  • ⁇ is the category component number of the personalized feature set
  • q and ⁇ are both natural numbers
  • 1 ⁇ q, PFT ⁇ is the category component of the personalized feature set numbered ⁇
  • Step S8060 monitor the specific moment value of the category component of the personalized feature set:
  • the specific time value of the category component of the personalized feature set at the specific time has a functional relationship determined by the formula (8.7) with the continuous time series, and the category component of the personalized feature set is monitored according to the formula (8.7).
  • the specific moment value at a specific moment :
  • PFT ⁇ T is the specific time value of the category component of the personalized feature set at the specific time.
  • the parameters and variables in all the algorithm formulas and the collected time series are taken as the content of the personalized parameter set, and are listed in the database using standardized work for subsequent algorithm use, especially the deep learning algorithm.
  • the present invention includes, but is not limited to, the mathematical model of the personalized feature set. Specifically, the following or various local improvement measures can be adopted:
  • Step S9010 fuzzy optimization method
  • the monitoring value and the monitoring component are optimized to obtain the optimal value of the object under the optimized situation, specifically including:
  • Step S9011 create a set:
  • a monitoring value set is established with the monitoring value elements, which is recorded as MIset.
  • a monitoring component set is established with the monitoring components as elements, denoted as MI ⁇ set, where ⁇ is the number of the monitoring components.
  • the monitoring value set is decomposed into a variable value set and a fixed value set.
  • variable value set is decomposed into a variable component set, the variable value set is denoted as PDVset, the variable component set is denoted as PDV ⁇ set, and ⁇ is the number of the variable component.
  • the fixed value set is decomposed into a fixed component set, the fixed value set is referred to as PDFset, the fixed component set is referred to as PDF ⁇ set, and ⁇ is the number of the fixed component.
  • Decompose the extended value set as the extended component set denote the extended value set as PDEset, and denote the extended value component set as is the number of the extended component.
  • the component personalized feature set set is a personalized feature set component set
  • the personalized feature set set is recorded as PFTset
  • the personalized feature set component set is recorded as PFT ⁇ set
  • ⁇ is the number of the individualized feature set component
  • the sets include fuzzy sets and non-fuzzy sets.
  • Step S9012 create a cut set:
  • the mapping relationship between the sets is established in turn, and the monitoring value set and the monitoring component set are used for sorting as the primary key to become an ordered set, and the first ⁇ is taken after positive sorting from large to small.
  • the elements are used as the ordered head cut set, and the first ⁇ elements are taken as the ordered tail cut set after reverse sorting from small to large, as follows:
  • MIset ⁇ (mi
  • MI ⁇ set ⁇ ⁇ mi ⁇
  • MIset ⁇ ⁇ mi
  • MI ⁇ set ⁇ ⁇ mi ⁇
  • MIset ⁇ , MI ⁇ set ⁇ , PDset ⁇ , PDVset ⁇ , PDV ⁇ set ⁇ , PDFset ⁇ , PDF ⁇ set ⁇ , PDEset ⁇ , PDE ⁇ set ⁇ , PFTset ⁇ , PFT ⁇ set ⁇ are the ordered heads Cut set, MIset ⁇ , MI ⁇ set ⁇ , PDset ⁇ , PDVset ⁇ , PDV ⁇ set ⁇ , PDFset ⁇ , PDF ⁇ set ⁇ , PDEset ⁇ , PDE ⁇ set ⁇ , PFTset ⁇ , PFT ⁇ set ⁇ are the ordering For the tail cut set, ⁇ and ⁇ are less than the number of elements in the respective sets, that is, the position of the cut set, which belongs to a natural number, ⁇ is the positive sorting number, and ⁇ is the reverse sorting number.
  • Step S9013 train the cutout:
  • Step S9014 search for optimization of the interception set:
  • the characteristic peaks of the laser Raman spectrum and the infrared light spectrum also adopt the interception operation, so as to enhance the monitoring effect.
  • Step S9020 establish a partial differential model:
  • MI′ 1 is the first derivative of the monitoring component numbered 1
  • MI′ m is the first order derivative of the monitoring component numbered m
  • PDV' ⁇ is the ⁇ th 1st derivative of the variable component that includes more than one associated with the MI ⁇
  • PDF' ⁇ is the ⁇ -th 1st derivative of the fixed component that includes zero or more of the fixed components associated with the MI ⁇ .
  • PDE' ⁇ is the ⁇ th 1st derivative of the extended component that includes zero or more associated with the MI ⁇ .
  • PFT' ⁇ is the ⁇ -th 1st derivative of the extended component that includes zero or more components associated with the MI ⁇ .
  • the monitoring component in function f 9.23 is also replaced by MI ⁇ in f 9.24 .
  • ⁇ is the highest order of the monitored component derivative
  • ⁇ is the highest order of the variable component derivative
  • ⁇ is the highest order of the extended component derivative
  • ⁇ , ⁇ , ⁇ , and ⁇ are all natural numbers.
  • the intercept operation is also used for the characteristic peaks of the laser Raman spectrum and the infrared light spectrum, so as to enhance the monitoring effect.
  • Step S9030 extreme value optimization method
  • the method of taking extreme values of the independent variable and the dependent variable in the partial differential equation including formula (9.23) and formula (9.24), and obtaining any
  • the method of calculating the monitoring value and the monitoring component is obtained, and the optimal value and the worst value of the monitoring value and the monitoring component are obtained to obtain This gets the optimal value for the object.
  • the characteristic peaks of the laser Raman spectrum, the infrared light spectrum, and the instantaneous value of blood glucose monitoring are all optimized according to their extreme values.
  • step S9031 according to the information base being continuously updated over time, in timed or irregular conditions, multiple learning and training are implemented, and methods including T test and Z test are used to select the monitoring value and the said monitoring value.
  • T test and Z test are used to select the monitoring value and the said monitoring value.
  • the optimal value of the component and the abnormal value of the monitoring value and the worst value of the monitoring component are monitored, and the abnormal value is eliminated, thereby obtaining the abnormal value of the object.
  • Step S9040 probability optimization method:
  • the object monitoring value and the object extension component collected in different time periods are selected, the monitoring value and the monitoring component are calculated, and the monitoring value and the monitoring component are calculated as the maximum value and the
  • the probability calculation method including the Bayesian algorithm is used, and when the statistics of the monitoring value and the monitoring component are the maximum value and the minimum value, the adjustable object in the object is monitored. The probability that the value and the extension component of the adjustable object have similar values, and the high probability verification is verified.
  • Calibrating the adjustable object monitoring value and the adjustable object extension component in the object when the monitoring value and the monitoring component are maximum values are the optimized adjustable object monitoring value and the optimized adjustable object extension component
  • the adjustable object monitoring value and the adjustable object extension component in the object when the monitoring value and the monitoring component are minimum values are calibrated to be a degraded adjustable object monitoring value and a degraded adjustable object extension component.
  • the sensitivity of the laser Raman spectrometer is very high, it is affected by the monitoring environment (such as external light, the pollution level of the monitoring point, the movement state of the monitoring individual, etc.), resulting in continuous time series, monitoring
  • the obtained laser Raman spectrum has certain fluctuations, so the method of probability optimization is adopted to eliminate the interference factors.
  • Step S9050 neural network optimization method:
  • Step S9051 according to including the mathematical model, for the relational information records in the information base, using the information records as neurons, and establishing a connection function between the neurons with the calculation results including the mathematical model, forming A neural network with more than one layer.
  • Step S9052 according to the connection function, according to the effect of the optimized adjustable object monitoring value and the optimized adjustable object extended component on the monitored value and the monitored component, divide and establish an excitatory type, an inhibitory type, and an explosive type.
  • a platform-type linker function the linker function includes a constant-type weight coefficient and a function-type weight coefficient.
  • Step S9053 using deep learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning algorithms, to optimize the connection sub-function.
  • step S9054 a support vector machine algorithm is used to classify and screen the monitoring value and the monitoring component, and screen out the optimized adjustable object monitoring value and the optimized adjustable object extended component.
  • Step S9055 using a convolutional neural network algorithm, for the condition of ignoring the association between the objects, implementing convolution, activation, pooling, full connection, and training the connection sub-function to filter out the monitoring including optimization. value and the monitored component.
  • step S9056 a cyclic neural network algorithm is used to establish an intra-layer correlation function under the condition that the objects need to be correlated, and the linker function is trained to screen out the monitoring value and the monitoring component including the optimization.
  • step S9057 a deep neural network algorithm is used to establish an inter-layer correlation function under the condition that the object, the monitoring value and the monitoring component between the layers of the neural network need to be correlated, and the connection is trained. sub-function to filter out the monitoring value and the monitoring component including optimization.
  • step S9058 a feedforward neural network algorithm is used to train the connection sub-function under the condition that each neuron is only connected to the neurons of the previous layer, so as to filter out the monitoring value including the optimized monitoring value and the Monitor quantities.
  • step S9059 a feedback neural network algorithm is used to train the connection sub-function under the condition that each neuron is only connected to the neuron of the next layer, so as to filter out the monitoring value including the optimized monitoring value and the monitoring value. weight.
  • the neural network optimization method is mainly applied in the calculation of the object algorithm, the group algorithm and the personalized feature set.
  • the object algorithm it can be used for the calibration of the historical data to the current data
  • the group algorithm it can be used for the calibration of other objects to its own object
  • the personalized feature set it can be used for the optimization of the personalized feature set .
  • Step S9060 reversible optimization method
  • the result information can reversibly reproduce the monitoring value and the monitoring component reaches the optimum or the specified value within its interval, and the reversible relationship between the monitoring components .
  • Step S9070 timing repetition method
  • the mathematical model and the reversible relationship calculate the timing time series value when the monitoring value and the monitoring component reach the optimal value or the specified value starting from the current moment, that is, starting from the current moment, after the timing time When the time of the sequence value is reached, the monitoring value and the monitoring component reach the optimal value or the specified value.
  • Step S9080 delay reproduction method:
  • the mathematical model when the timing time series value is less than the predetermined health assessment time domain value, calculate the required delay time difference, and add the delay time difference to the mathematical model.
  • the model is used to ensure that the monitoring value and the monitoring component reach the optimal value or the specified value at the time point of the predetermined health assessment time.
  • the present invention includes, but is not limited to, network processing steps. Specifically, the following or multiple local improvement measures can be adopted:
  • step SA110 a cloud center based on the Internet model is established, and all information monitored by the present invention, including all the groups, all the information of the objects, the intermediate calculation results, and the information base, are transmitted through the wide area network network in the form of cloud terminals, and all stored One or more cloud servers based on the Internet are used as one or more cloud centers, and cloud computing mode is adopted to manage, calculate and support the cloud terminal.
  • step SA120 one or more cloud centers are established in the blockchain mode to store, manage and support the information base and the aforementioned steps.
  • the users use anonymous records, and the information in the information base uses timestamped Chain structure, users access the information base through encryption and decryption communication, the information supports anti-tampering, and supports anti-repudiation, multi-center, and no-center modes.
  • a secure multi-party computing model is used to establish, manage and support more than one organization.
  • the content of the information base of the organization performs the agreed calculation, and the obtained calculation result is shared by the participating organizations.
  • the organization includes one or more of the cloud centers and manages one or more of the objects.
  • the secure multi-party computation includes: public key mechanisms, hybrid circuits, inadvertent transmission, secret sharing, privacy-preserving set intersection protocols, homomorphic encryption, zero-knowledge proofs, and methods without trusted centers to enhance information security and protection Object Privacy.
  • step SA140 the centralized learning mode is used to establish and train the model training when the object privacy protection is not emphasized, and the information database is stored in a cloud center.
  • step S9150 the federated learning mode is used to establish and train the model training when the privacy protection of the object needs to be emphasized.
  • the model training is performed between more than one stored cloud center, and the respective cloud centers do not exchange their respective cloud centers. information.
  • a local area network-based server is established to store and manage the support center, and all information including all groups and objects, intermediate calculation results, and the information base monitored by the present invention are transmitted through the local area network network in the form of network terminals, and all stored in the On the server of the local area network, in order to manage, calculate and support.
  • the single point is to monitor the steps of monitoring, storage, management, calculation and support of one of the objects, and all the information of the object monitored by the present invention, the intermediate calculation results, and the information base are all stored in the single point. storage, perform all steps.
  • the monitoring object is an individual, and a dedicated monitoring device that supports wireless movement is used.
  • the monitoring device is connected to the individual's smart phone, and then works with the cloud server of the public network.
  • the function fitting here includes arithmetic median, arithmetic mean, curve median, curve mean, least squares method, Gaussian method, neural network method, etc.
  • Embodiment 2 Monitoring method for energy saving of fan in cargo hold of container ship
  • the energy-saving control of the high-power fan in the cargo hold is a multi-input and multi-output control system, in which the input includes the ambient temperature, wind speed, and in-box settings of each refrigerated container stacked in the cargo hold.
  • the temperature, the overall wind field of the cargo hold, etc. the output control includes the frequency conversion speed regulation of all the cargo hold fans, the air duct air outlet damper, the cargo hold air inlet, the cargo hold air outlet, the refrigerated container control signal, etc.
  • the overall control index is to ensure that the refrigerated container is in the box Under the condition that the quality of the goods and the temperature in the box set by the user remain unchanged, the energy consumption of the fan in the cargo compartment is reduced as much as possible. According to the actual system operation data, using the multi-mode monitoring method of the present invention, the fan control can be more reasonable and stable, and the energy saving effect is as high as 58%.
  • FIG. 6 As shown in the number 6010 is the cargo hold, in which are stacked refrigerated containers (refrigerated containers), and in the refrigerated containers, the refrigerator has a closed-loop temperature control system, such as a PID regulator control temperature system.
  • a closed-loop temperature control system such as a PID regulator control temperature system.
  • signals including internal set temperature, supply air temperature, return air temperature, and external ambient temperature of the refrigerated container are obtained from the external interface of the refrigerated container through an external communication device, or these signals are obtained through a built-in communication circuit.
  • Signals which are output as indicated by number 6036 and input to the input of the system as negative feedback signals as indicated by number 6032, together with the system given signal as indicated by number 6031 and the AI given signal as indicated by number 6033 , form a complete feedback signal as shown in No. 6043, drive the fan control subsystem shown in No. 6020 to control the fan, and finally form a closed-loop control method in control theory as shown in No. 5035. That is to say, according to the collected data, the control function is generated as the closed-loop negative feedback data, and the fan or the fan and the gate are directly controlled.
  • the closed loop proposed here is for the process time from acquisition to control, and this time difference is relatively short, for example, less than 20 minutes. It should be noted that the 20 minutes here is just an example, this number can be longer or shorter depending on the size of the cabin.
  • FIG. 3 The control method associated with FIG. 6 , using the state-space control method with multiple inputs and multiple outputs as shown in FIG. 3 .
  • the numbers 7010 and 7020 are the vertical planes where the headers of the refrigerated containers stacked in a cargo hold on the ship are located respectively.
  • the headers of the refrigerated containers refer to the end of the refrigerated containers where the operation panel of the refrigerator is located.
  • the stacking of containers has a three-dimensional coordinate number, namely row number Bay, column number Row, layer number Tier, referred to as BRT coordinates.
  • the white squares indicated by numbers 7012 and 7022 are the positions of the bottom ballast tanks, and the gray squares indicated by numbers 7011 and 7021 are the headers of the refrigerated containers, where the air at the headers The temperature changes as the reefer container works.
  • C1 to C9 used in FIG. 5 to indicate that the continuous quantities vary from small to large, but this does not mean that the continuous quantities are only divided into 9 levels of C1 to C9.
  • the data of one frame picture refers to collecting once for a container in a cargo hold and storing it in the database of environmental data according to the BRT coordinates. And so on for other active containers, passive containers, or non-contained cargo.
  • the set monitoring value includes at least the position coordinates of the refrigerated container in the cargo hold, the ambient temperature, the wind speed, the set temperature in the container, the temperature of the air outlet, the temperature of the air supply outlet, the operating status of the fan in the cargo hold, and the overall energy consumption of the cargo hold.
  • the agreed truth value is selected as the historical record value, and the monitoring factor value is set as the control function of the fan in the cargo compartment and the overall energy consumption function of the cargo compartment.
  • the correlation functions are set as refrigerated container control function, cargo hold fan control function, cargo hold temperature control function, and cargo hold energy consumption function.
  • the set object is the brand and model of the reefer container, and the seed group is set, in which each cargo hold is a group, each ship is a group, and each route is a group.
  • each fan or the control of the damper on each fan is selected as the longitudinal correction value, and the space state equation group shown in Figure 2 is used as the correlation function and the control function.
  • each ship use each cargo hold as a group to perform a group algorithm.
  • Artificial intelligence algorithms are used for deep learning and labeling, and training is performed using, for example, convolutional neural network CNN algorithm, Bayesian Bayes algorithm, adversarial neural network GAN algorithm, firefly algorithm, and ant colony algorithm.
  • the optimized indicators include the minimum energy consumption of the fan, the least equipment action (for example, the minimum number of start and stop times, the minimum number of start and stop fans), the least disturbance to the internal temperature field, and the least disturbance to the air output data, and the actual control room.
  • the results are stored in the database according to time, and all relevant data such as ship route data, weather data, container loading and unloading data, and docked terminal data that can be obtained are input into the database.
  • Each ship uses data communication satellites to connect to the cloud big data center in real time, and uses blockchain and secure multi-party computing to protect the information of each ship and each cargo owner.

Abstract

La présente invention concerne un procédé de surveillance personnalisée multimode comprenant les étapes consistant à : décomposer une valeur de surveillance en un composant de surveillance variable et un composant de surveillance fixe ; introduire un composant étendu pour surveiller la valeur de surveillance et le bruit de fond ; calculer le composant variable, le composant fixe et le composant étendu au moyen d'une fonction de corrélation et d'un ensemble de caractéristiques personnalisées ; surveiller une véritable valeur réelle au moyen de données historiques et de surveillance de courant ; et exécuter un algorithme d'objet et d'un algorithme d'objet différentiel pour obtenir une valeur de correction d'objet ayant une erreur entre la valeur de correction et la valeur réelle classique d'un objet inférieure à une erreur admissible. De plus, au moyen de données de surveillance historiques d'un objet unique et d'une pluralité d'objets, l'algorithme d'objet et un algorithme de groupe sont utilisés pour optimiser l'ensemble de caractéristiques personnalisées. Des données de surveillance d'autres objets sont utilisées pour un calcul supplémentaire pour obtenir une valeur de correction de groupe de l'objet. Des modes tels qu'un mode de mégadonnées de réseau en nuage, un mode de réseau local et un mode point unique sont en outre inclus, et le dilemme amené par la mesure de tous les individus au moyen d'une norme dans l'état de la technique est résolu.
PCT/CN2021/092129 2021-04-20 2021-05-07 Procédé de surveillance personnalisée multimode WO2022222197A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110425470.2 2021-04-20
CN202110425470.2A CN113317782B (zh) 2021-04-20 2021-04-20 多模个性化监测的方法

Publications (1)

Publication Number Publication Date
WO2022222197A1 true WO2022222197A1 (fr) 2022-10-27

Family

ID=77414929

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/092129 WO2022222197A1 (fr) 2021-04-20 2021-05-07 Procédé de surveillance personnalisée multimode

Country Status (2)

Country Link
CN (1) CN113317782B (fr)
WO (1) WO2022222197A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114052676B (zh) * 2021-11-19 2024-05-07 南开大学 一种中医脉搏精简阵列传感器及其全阵列脉搏信息获取算法
CN115410419B (zh) * 2022-08-23 2024-02-02 交通运输部天津水运工程科学研究所 一种船舶系泊预警方法、系统、电子设备及存储介质
CN116627028B (zh) * 2023-07-21 2023-09-29 阳谷新太平洋电缆有限公司 交联电缆生产线控制方法

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2544124A1 (fr) * 2011-07-04 2013-01-09 Sabirmedical, S.L. Procédés et systèmes pour la mesure non invasive des taux de glucose
US20160029966A1 (en) * 2014-07-31 2016-02-04 Sano Intelligence, Inc. Method and system for processing and analyzing analyte sensor signals
US20170347894A1 (en) * 2016-06-03 2017-12-07 Fourth Frontier Technologies Pvt. Ltd. System and method for continuous monitoring of blood pressure
CN107771056A (zh) * 2015-09-10 2018-03-06 德克斯康公司 经皮分析物传感器和监视器、其校准以及相关联方法
CN107949883A (zh) * 2015-08-21 2018-04-20 美敦力迷你迈德公司 个性化参数建模方法及相关设备和系统
CN108042106A (zh) * 2017-11-14 2018-05-18 李明 一种提高人体体征无创检测设备检测精度的人工智能纠偏方法
CN108937955A (zh) * 2017-05-23 2018-12-07 广州贝塔铁克医疗生物科技有限公司 基于人工智能的个性化自适应可穿戴血糖校正方法及其校正装置
CN109520989A (zh) * 2017-09-18 2019-03-26 三星电子株式会社 估计葡萄糖暴露以及产生葡萄糖暴露估计模型的装置和方法
CN110123339A (zh) * 2019-05-10 2019-08-16 湖南龙罡智能科技有限公司 一种无创血糖测量装置与方法
US20200367833A1 (en) * 2019-05-22 2020-11-26 Research & Business Foundation Sungkyunkwan University Personalized non-invasive blood glucose measurement device using machine learning or deep learning and method using the measurement device
CN112466471A (zh) * 2020-12-16 2021-03-09 丁贤根 微智商监测调节的方法

Family Cites Families (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7010336B2 (en) * 1997-08-14 2006-03-07 Sensys Medical, Inc. Measurement site dependent data preprocessing method for robust calibration and prediction
US7406379B2 (en) * 2004-05-13 2008-07-29 Northrop Grumman Corporation System for interferometric sensing
JP5398156B2 (ja) * 2008-03-04 2014-01-29 キヤノン株式会社 ホワイトバランス制御装置およびその制御方法並びに撮像装置
WO2011013694A1 (fr) * 2009-07-28 2011-02-03 パナソニック電工株式会社 Dispositif d'estimation de taux de glycémie
DE102010037577A1 (de) * 2010-09-16 2012-03-22 Huf Electronics Gmbh Erfassungsverfahren für Betätigungsgesten und zugehöriges Kalibrierungsverfahren
JP5690688B2 (ja) * 2011-09-15 2015-03-25 クラリオン株式会社 外界認識方法，装置，および車両システム
JP6230437B2 (ja) * 2014-02-04 2017-11-15 東京エレクトロン株式会社 温度測定方法及びプラズマ処理システム
CN104865309A (zh) * 2015-04-07 2015-08-26 江苏省特种设备安全监督检验研究院 一种减小巴克豪森噪声检测误差的方法及其传感器系统
US10474113B2 (en) * 2017-03-09 2019-11-12 General Electric Company Power generation system control through adaptive learning
CN107036716B (zh) * 2017-04-25 2019-07-26 中国科学院微电子研究所 一种自校准红外热电堆温度传感器及自校准方法
CN107788994B (zh) * 2017-10-12 2019-10-01 微泰医疗器械(杭州)有限公司 一种基于云端大数据的智能实时动态血糖监测系统及方法
KR20240008409A (ko) * 2017-12-13 2024-01-18 메드트로닉 미니메드 인코포레이티드 연속 글루코오스 모니터링 방법 및 시스템
KR20200097144A (ko) * 2019-02-07 2020-08-18 삼성전자주식회사 생체정보 추정 장치 및 방법
KR102238472B1 (ko) * 2019-04-16 2021-04-09 광주과학기술원 오차 보정 방법 및 센서 시스템
KR20210034724A (ko) * 2019-09-20 2021-03-31 삼성전자주식회사 생체 신호를 추정하는 전자 장치 및 그 방법

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2544124A1 (fr) * 2011-07-04 2013-01-09 Sabirmedical, S.L. Procédés et systèmes pour la mesure non invasive des taux de glucose
US20160029966A1 (en) * 2014-07-31 2016-02-04 Sano Intelligence, Inc. Method and system for processing and analyzing analyte sensor signals
CN107949883A (zh) * 2015-08-21 2018-04-20 美敦力迷你迈德公司 个性化参数建模方法及相关设备和系统
CN107771056A (zh) * 2015-09-10 2018-03-06 德克斯康公司 经皮分析物传感器和监视器、其校准以及相关联方法
US20170347894A1 (en) * 2016-06-03 2017-12-07 Fourth Frontier Technologies Pvt. Ltd. System and method for continuous monitoring of blood pressure
CN108937955A (zh) * 2017-05-23 2018-12-07 广州贝塔铁克医疗生物科技有限公司 基于人工智能的个性化自适应可穿戴血糖校正方法及其校正装置
CN109520989A (zh) * 2017-09-18 2019-03-26 三星电子株式会社 估计葡萄糖暴露以及产生葡萄糖暴露估计模型的装置和方法
CN108042106A (zh) * 2017-11-14 2018-05-18 李明 一种提高人体体征无创检测设备检测精度的人工智能纠偏方法
CN110123339A (zh) * 2019-05-10 2019-08-16 湖南龙罡智能科技有限公司 一种无创血糖测量装置与方法
US20200367833A1 (en) * 2019-05-22 2020-11-26 Research & Business Foundation Sungkyunkwan University Personalized non-invasive blood glucose measurement device using machine learning or deep learning and method using the measurement device
CN112466471A (zh) * 2020-12-16 2021-03-09 丁贤根 微智商监测调节的方法

Also Published As

Publication number Publication date
CN113317782B (zh) 2022-03-22
CN113317782A (zh) 2021-08-31

Similar Documents

Publication Publication Date Title
WO2022222197A1 (fr) Procédé de surveillance personnalisée multimode
CN104665840B (zh) 无创血糖测量方法及指端测量探头
Mehbodniya et al. Fetal health classification from cardiotocographic data using machine learning
Patro et al. Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning
Yang et al. A new method for identifying the acute respiratory distress syndrome disease based on noninvasive physiological parameters
Salcedo-Bernal et al. Clinical data analysis: An opportunity to compare machine learning methods
Chen et al. AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction
CN114098724B (zh) 基于光学信号特征及代谢热特征的血糖预测方法和装置
Sivaganesan et al. Informatics for neurocritical care: challenges and opportunities
Kwon et al. RMExplorer: A visual analytics approach to explore the performance and the fairness of disease risk models on population subgroups
Deepa et al. IoT-enabled smart healthcare data and health monitoring based machine learning algorithms
WO2022222198A1 (fr) Procédé d'étalonnage d'aspect individualisé multimode
Tivay et al. Collective variational inference for personalized and generative physiological modeling: A case study on hemorrhage resuscitation
Patel Improving intrusion detection in cloud-based healthcare using neural network
CN112837804A (zh) 人工智能个性化慢病健康管疗的方法
Sperger et al. The future of precision health is data‐driven decision support
Gori et al. Predicting treatment outcome by combining different assessment tools: Toward an integrative model of decision support in psychotherapy.
Sel et al. Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation
Chakraborty Bayesian multiple response kernel regression model for high dimensional data and its practical applications in near infrared spectroscopy
Ghongade et al. A comparison of neural networks and machine learning methods for prediction of heart disease
Jadhav et al. A function‐based approach to model the measurement error in wearable devices
Chen et al. Understanding and exploring the whole set of good sparse generalized additive models
Mazing et al. Medical diagnostic intelligent system for recognizing of the functional state of a person
Valenza et al. Advanced computation in cardiovascular physiology: new challenges and opportunities
Gupta et al. A hybrid approach based stress monitoring system for office environment using IoT

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21937423

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE