WO2019206264A1 - 一种一次性采血器针头终末消毒方法及终末消毒系统 - Google Patents

一种一次性采血器针头终末消毒方法及终末消毒系统 Download PDF

Info

Publication number
WO2019206264A1
WO2019206264A1 PCT/CN2019/084488 CN2019084488W WO2019206264A1 WO 2019206264 A1 WO2019206264 A1 WO 2019206264A1 CN 2019084488 W CN2019084488 W CN 2019084488W WO 2019206264 A1 WO2019206264 A1 WO 2019206264A1
Authority
WO
WIPO (PCT)
Prior art keywords
needle
module
blood collection
score
disinfection
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/CN2019/084488
Other languages
English (en)
French (fr)
Inventor
李现红
王红红
陈嘉
张慈
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Central South University
Original Assignee
Central South University
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 Central South University filed Critical Central South University
Publication of WO2019206264A1 publication Critical patent/WO2019206264A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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
    • A61B5/15—Devices for taking samples of blood

Definitions

  • the invention belongs to the field of medical technology, and particularly relates to a terminal blood collection device needle terminal disinfection method and a terminal disinfection system.
  • the existing disposable blood collection devices are mainly used for: (1) clinical small blood sampling test; (2) blood glucose monitoring of diabetic patients; (3) rapid screening of some infectious diseases, such as AIDS, syphilis, hepatitis B and other diseases.
  • the needle After the blood collection device is used, since the needle still has blood remaining, it must be subjected to terminal disinfection treatment in a special sanitary institution.
  • terminal disinfection treatment in a special sanitary institution.
  • people can purchase disposable blood collection needles and use them outside the medical facilities, and often do not send them back to a specialized medical institution for terminal disinfection, which may lead to infection.
  • the disposable blood collection needle of the blood of the sex virus is retained in the living environment of people.
  • the technical solution adopted by the present invention is:
  • a method for terminal disinfection of a disposable blood collection needle which is characterized by:
  • the blood collection needle is subjected to terminal sterilization and disinfection by using a needle sterilization module;
  • the blood volume measurement module is also used to measure the volumetric information of the blood collection, including:
  • the harmonic wavelet function is as follows:
  • the horizontal plane of the three-dimensional time-frequency diagram is a base plane, and the two coordinate axes are time and harmonic wavelet decomposition layers, respectively, so that the base plane of the wavelet time-frequency diagram is divided into a grid composed of time and number of layers, each The square of the harmonic wavelet coefficient a s is used as the cylinder on the grid.
  • the harmonic wavelet decomposition results show that the harmonic wavelet energy of different frequencies and time contributes to the energy of the whole signal.
  • the harmonic wavelet time-frequency diagram is the decomposition result. Intuitively, the fluctuation corresponds to the relative magnitude of different harmonic wavelet energies.
  • the non-periodic small signal detection method using wavelet technology the harmonic wavelet decomposition algorithm is fast and has high precision, which can effectively overcome the shortcomings of the Fourier analysis method that the frequency component cannot be obtained with time evolution information, because it is extremely sensitive to tiny singular points in the signal. .
  • the disinfection evaluation module is used to evaluate the needle disinfection, including:
  • the positioning function is used to analyze the overall situation of the score obtained by needle sterilization to determine the position at the disinfection level; the selection method of the reference needle sterilization is determined by the positioning accuracy function, and then the appropriate needle sterilization item is selected as a reference to predict the needle after disinfection. And correctively adjust the prediction score based on the relationship between the prediction and the expectation of the reference item.
  • the evaluation method of the disinfection evaluation module includes:
  • Step one using the existing scoring data, using the disinfection expectation as a positioning function, calculating the relevant information of each needle disinfection expectation and scoring, and storing according to the needle disinfection item;
  • Step 2 Step 1 has calculated the stored needle disinfection expectation and score related information.
  • the score data changes and needs to be recalculated, only the needle disinfection item whose score data changes is processed, and the score data does not change.
  • the needle disinfection item does not need to be processed, and there is no need to deal with the relationship between the various needle disinfection items after the change of the score data;
  • Step 3 in the score prediction, according to the needle to be predicted and the score of the relevant needle that has been evaluated, the positioning function formula is used to determine the positioning accuracy ⁇ of the predicted needle sterilization;
  • Step 4 according to the positioning accuracy ⁇ of the needle to be predicted, among the relevant needles predicted and evaluated, find a needle whose desired value satisfies the accuracy requirement, and constitute a reference item set for performing the score prediction on the predicted needle;
  • step 5 the predicted score of the predicted needle is calculated by using the needle to be predicted to score the reference needle and predicting the expected value of the needle and the reference needle.
  • the positioning function is based on a desired positioning function; specifically:
  • the processing in the first step is performed in an off-line manner, including: processing all the information of the expectations of the needles and the number of the scored needles at a time, and then storing them in the form of a database or other data files for use in future scoring prediction; Store a needle as a record;
  • the precision function determines the positioning accuracy ⁇ of the predicted needle by using the needle to be predicted and the number of scoring needles of the relevant needle evaluated by the needle to be predicted, and the calculation formula is formula (2):
  • Needle For the needle to be predicted Existing score needle set, Needle The actual score of the obtained score is calculated and stored in step one; For the needle to be predicted The average score saturation of all needles evaluated, calculated as equation (3):
  • card(U(g)) is the actual number of scored needles obtained for each related item, and the values are calculated and stored in step one; card(U) is the total number of needles owned by the system;
  • the score saturation of a needle is the ratio of the number of actual score items that the needle has made to the total number of scores that the corresponding needle should receive.
  • equation (2) if the scores of all items evaluated by the predicted needle have low score saturation, The value of the positioning accuracy should be large, and vice versa; if the number of existing scores of the forecast item is small, the value of the positioning accuracy should be larger, otherwise it should be smaller;
  • the positioning fine ⁇ is satisfied, and the needle is used for prediction.
  • Reference set for scoring prediction Determined according to formula (3); the positioning accuracy ⁇ in step 3 gives a search for a predictive needle a neighborhood value of the reference item;
  • G(u) is the set of needles evaluated by the needle u, Calculated according to formula (1);
  • the score estimation function for calculating the predicted score of the predicted item is expressed by the formula (4):
  • the needle to be predicted is u
  • the needle is Vu,i is the score of the reference u for the needle u
  • the average of the scores given by all the needles in the equation is calculated as equation (5)
  • b is a correction amount for the deviation correction of the average value
  • the calculation formula is as shown in equation (6):
  • the deviation correction value is estimated by the average difference between the market expectation of the predicted item and the expectation of the reference item, and the calculation formula is Equation (6):
  • Equation (6) indicates that the deviation correction value is a predictive needle Market expectation Reference set The difference between the average expected market expectations of the reference needles;
  • the weighted Slope one is introduced, and the number of common scoring needles obtained between the items is used as a weight, and the score prediction result is further corrected; specifically, the number of common scoring needles obtained between the two needle items is weighted, and the expectation is a positioning function.
  • the predicted score of the needle is calculated by using the weighted fitting average formula of the formula (7);
  • the number of common scoring needles of any two needles is the number of elements of the intersection of their respective scoring needle sets
  • the weighted fitting average formula is used to predict the score.
  • the number of commonly scored needles between any two needles in the system is calculated according to formula (8); and if the score data of a needle changes, in the step In the second, the number of needles scored by the needle and the other needles is recalculated;
  • the number of needles in the sample file is m items, and the number of commonly scored needles between the two pairs of needles constitutes a m ⁇ m symmetric matrix.
  • the matrix is stored; the triangular matrix storage mode is adopted.
  • the terminal sterilization and disinfection of the blood collection needle by the needle sterilization module includes:
  • the boiling water heating time lasts for more than 20 minutes, so it can effectively kill common blood-borne viruses such as HIV, hepatitis B virus, hepatitis C virus, and Treponema pallidum.
  • the present invention also provides a disposable blood collection needle end disinfection system, which is characterized by:
  • Main control module used to control the work of blood collection module, elastic drive module, needle disinfection module and blood volume measurement module;
  • Blood collection module connected to the main control module for collecting blood for the patient by blood collection;
  • the elastic drive module is connected with the main control module and is used for providing blood collection power to the blood collection needle;
  • Needle disinfection module connected with the main control module, used for terminal sterilization and disinfection of the blood collection needle when the blood collection is completed;
  • Blood volume measurement module connected to the main control module for measuring the volume information of blood collection, including:
  • the harmonic wavelet function is as follows:
  • Recording module connected with the main control module, used for recording patient information and blood collection information data through the video recorder;
  • the main control module is also used to control the operation of the recording module.
  • Data export cloud module Connect to the recording module, export the recorded data, and save it in the cloud.
  • Disinfection evaluation module connected with the main control module for evaluating the disinfection of the needle; including:
  • the positioning function is used to analyze the overall situation of the score obtained by needle sterilization to determine the position at the disinfection level; the selection method of the reference needle sterilization is determined by the positioning accuracy function, and then the appropriate needle sterilization item is selected as a reference to predict the needle after disinfection. And correctively adjust the prediction score according to the relationship between the prediction item and the expectation of the reference item;
  • the main control module is also used to control the work of the disinfection evaluation module.
  • the disinfection evaluation module includes:
  • the pre-processing module is configured to use the existing scoring data to calculate the expected information of each needle and the number of scoring needles and the like according to the expectation of the formula (1), and store the information according to the needle item;
  • the scoring data change processing module is configured to process the needle item of the change of the scoring data when the scoring data needs to be recalculated when the scoring data needs to be recalculated by the pre-processing module having calculated the stored needle's expectation and the number of scoring needles and the like.
  • the needle item whose score data has not changed does not need to be processed, and there is no need to deal with the relationship between the respective needle items after the change of the score data;
  • a positioning accuracy calculation module configured to determine, in the score prediction, the positioning accuracy ⁇ of the disinfection of the predicted needle according to the number of needles of the relevant needle evaluated by the needle to be predicted and the needle to be predicted;
  • the reference set selection module is configured to determine, according to the positioning accuracy ⁇ of the needle to be predicted determined by the positioning accuracy calculation module, the related needles whose expected values meet the accuracy requirements, and constitute a reference for performing the score prediction on the predicted needle.
  • the predicted score calculation module is configured to calculate a predicted score of the predicted needle according to the score of the reference needle to be predicted by the reference needle selected by the reference set selection module.
  • the elastic drive module includes a twist drive module and an ejection module
  • a twisting drive module for separating the blood collection needle from the needle seat by twisting the driving block, and exposing the housing using the blood collection needle
  • a ejector module for providing ejection power through a spring.
  • the needle sterilization module includes a heating module for heating and disinfecting the used needle by heating the water by the heater:
  • the needle disinfection module further includes a liquid disinfection module for disinfecting the blood collection needle by inserting the blood collection needle into a sterilization chamber containing the disinfectant.
  • the invention sterilizes the blood collection needle through the needle disinfection module, and simplifies the terminal disinfection procedure of the disposable blood collection needle; at the same time, the blood collection device can facilitate the medical staff to provide the illumination function when collecting blood through the illumination module, thereby accurately determining the blood collection.
  • the position greatly facilitates the smooth blood collection; and the recording module can be conveniently used by the staff to store in the integrated memory of the recording module through the recording method, and store the patient information for tracking and monitoring.
  • the blood volume measurement module of the invention adopts a non-periodic small signal detection method of wavelet technology, and the harmonic wavelet decomposition algorithm has the advantages of high speed and high precision, and can effectively overcome the shortcoming that the Fourier analysis method cannot obtain the information of the frequency component evolution with time, because it is The tiny singular points in the signal are extremely sensitive, so they can be effectively used in fields such as intelligent analysis.
  • the MEbCF technology of the invention can obtain a better scoring prediction effect, and has the remarkable features of simple algorithm, easy implementation and low time and space overhead of the algorithm, and is a highly competitive recommendation technology in e-commerce.
  • the system and other scoring prediction fields have good application prospects.
  • the general similarity calculation and the item difference and the common score user number in the Slope one algorithm are calculated.
  • the time complexity is generally O(m 2 ⁇ n). /2), the space complexity is O(m 2 /2); the MEbCF technology of the present invention can be recommended only by calculating the market expectation of each commodity, and the time complexity is O(m ⁇ n), and the space is complicated.
  • the degree is O(m).
  • the concept of market effect proposed by the invention has the following meanings: (1) it conforms to the characteristics of people's scoring behavior; (2) the meaning is concise and clear, and is conducive to describing the characteristics of people's scoring behavior and related implementation and technology; (3) capable of It is different from the principles, concepts and methods embodied in other technical methods; (4) The method of scoring prediction based on market effects is simple and easy to implement.
  • Figure 1 is a block diagram showing the structure of an embodiment of the terminal disinfecting system of the present invention.
  • blood collection module 1, blood volume measurement module; 2, blood volume measurement module; 3, main control module; 4, elastic drive module; 5, needle disinfection module; 6, lighting module; 7, recording module; 8, disinfection evaluation module; Data export cloud module.
  • Fig. 3 is a graph showing the imaginary part of the harmonic wavelet function in the present invention.
  • FIG. 4 is a real part graph of a harmonic wavelet after adding a Blackman window in the present invention.
  • Fig. 5 is a graph showing the imaginary part of the harmonic wavelet after adding the Blackman window in the present invention.
  • the terminal disinfection system includes a blood collection module 1 , a blood volume measurement module 2 , a main control module 3 , an elastic drive module 4 , a needle sterilization module 5 , a lighting module 6 , and a recording module 7 . . among them:
  • the blood collection module 1 is connected to the main control module 3 for collecting blood for the patient by blood collection;
  • the blood volume measurement module 2 is connected to the main control module 3 for measuring the volume information of the blood collection by the scale;
  • the main control module 3 is connected with the blood collection module 1, the blood volume measurement module 2, the elastic drive module 4, the needle disinfection module 5, the illumination module 6, and the recording module 7, for controlling the normal operation of each module;
  • the elastic drive module 4 is connected to the main control module 3 for providing blood collection power to the blood collection needle;
  • the needle disinfection module 5 is connected to the main control module 3 for sterilizing and sterilizing the blood collection needle;
  • the lighting module 6 is connected to the main control module 3 for providing a lighting function for the blood collection process
  • the recording module 7 is connected to the main control module 3 for recording patient information and blood collection information data through the video recorder.
  • the data is exported to the cloud module 9, and is connected to the recording module 7, and the recorded data is exported for cloud preservation.
  • the elastic drive module 4 provided by the invention comprises a twist drive module and an ejection module
  • a twisting drive module for separating the blood collection needle from the needle seat by twisting the driving block, and exposing the housing using the blood collection needle
  • a ejector module for providing ejection power through a spring.
  • the needle disinfection module 5 provided by the invention comprises a heating module and a liquid disinfection module;
  • a heating module for heating and disinfecting the used needle through a heater
  • the illumination module 6 provides illumination function to the blood collection process; the worker pushes the blood collection needle out of the housing through the elastic drive module 4; then, the blood collection module 1 performs blood collection on the patient; and the blood volume measurement module 2 measures the scale by using the scale.
  • Fig. 3 is a graph showing the imaginary part of the harmonic wavelet function in the present invention.
  • FIG. 4 is a real part graph of a harmonic wavelet after adding a Blackman window in the present invention.
  • Fig. 5 is a graph showing the imaginary part of the harmonic wavelet after adding the Blackman window in the present invention.
  • the blood volume measurement module 2 is connected to the main control module 3 for measuring the volume information of the blood collection; specifically:
  • the harmonic wavelet function is as follows:
  • the harmonic wavelet is improved by the following function, and the frequency domain characteristics of the harmonic wavelet are improved using the Lower Blackman window function:
  • the harmonic wavelet time domain signal is improved by using the following function, which effectively reduces the time.
  • the influence of the finite length characteristic of the domain signal on the spectrum analysis improves the deviation of the harmonic wavelet decomposition coefficient
  • the horizontal plane of the three-dimensional time-frequency diagram is a base plane, and the two coordinate axes are time and harmonic wavelet decomposition layers, respectively, so that the base plane of the wavelet time-frequency diagram is divided into a grid composed of time and number of layers, each The square of the harmonic wavelet coefficient a s is used as the cylinder on the grid.
  • the harmonic wavelet decomposition results show that the harmonic wavelet energy of different frequencies and time contributes to the energy of the whole signal.
  • the harmonic wavelet time-frequency diagram is the decomposition result. Intuitively, the fluctuation corresponds to the relative magnitude of different harmonic wavelet energies.
  • the harmonic wavelet decomposition algorithm is fast and has high precision, which can effectively overcome the shortcomings of the Fourier analysis method that the frequency component cannot be obtained with time evolution information, because it is extremely sensitive to tiny singular points in the signal. ;
  • the main control module 3 is connected with the blood collection module 1, the blood volume measurement module 2, the elastic drive module 4, the needle disinfection module 5, and the recording module 7, for controlling the normal operation of each module;
  • the elastic drive module 4 is connected to the main control module 3 for providing blood collection power to the blood collection needle;
  • the needle disinfection module 5 is connected with the main control module 3, and is used for terminal sterilization and sterilization of the blood collection needle after the blood collection is completed; for heating and disinfecting the used needle by heating the water by the heater; the set temperature is 121 ° C, 20- 30 minutes; for medium components such as glucose that cannot withstand high temperatures, 115 ° C for 30-40 minutes.
  • the HIV inactivation is heated to 100 ° C for 20 minutes, and the virus death effect is significant and can be killed.
  • the recording module 7 is connected to the main control module 3 for recording patient information and blood collection information data through the video recorder.
  • the disinfection evaluation module 8 is connected to the main control module 3 for evaluating the disinfection condition of the needle;
  • the positioning function is used to analyze the overall situation of the score obtained by needle sterilization to determine the position at the disinfection level; the selection method of the reference needle sterilization is determined by the positioning accuracy function, and then the appropriate needle sterilization item is selected as a reference to predict the needle after disinfection. And correctively adjust the prediction score based on the relationship between the prediction and the expectation of the reference item.
  • the evaluation method of the disinfection evaluation module 8 specifically includes:
  • Step one using the existing scoring data, using the disinfection expectation as a positioning function, calculating the relevant information of each needle disinfection expectation and scoring, and storing according to the needle disinfection item;
  • Step 2 The information on the expected and score of the needle disinfection that has been stored in step 1 is changed, and when the score data needs to be recalculated, only the needle disinfection item whose score data changes is processed, and the score data does not occur.
  • the changed needle disinfection item does not need to be processed, and there is no need to deal with the relationship between the various needle disinfection items after the change of the score data;
  • Step 3 in the score prediction, according to the needle to be predicted and the score of the relevant needle to be predicted, the positioning function formula is used to determine the positioning accuracy ⁇ of the predicted needle sterilization;
  • Step 4 according to the positioning accuracy ⁇ of the needle to be predicted, among the relevant needles predicted and evaluated, find a needle whose desired value satisfies the accuracy requirement, and constitute a reference item set for performing the score prediction on the predicted needle;
  • step 5 the predicted score of the predicted needle is calculated by using the needle to be predicted to score the reference needle and predicting the expected value of the needle and the reference needle.
  • the positioning function is based on a desired positioning function; specifically:
  • the processing in the first step is performed in an off-line manner, including: processing all the information of the expectations of the needles and the number of the scored needles at a time, and then storing them in the form of a database or other data files for use in future scoring prediction; Store a needle as a record;
  • the precision function determines the positioning accuracy ⁇ of the predicted needle by using the needle to be predicted and the number of scoring needles of the relevant needle evaluated by the needle to be predicted, and the calculation formula is formula (2):
  • Needle For the needle to be predicted Existing score needle set, Needle The actual score of the obtained score is calculated and stored in step one; For the needle to be predicted The average score saturation of all needles evaluated, calculated as equation (3):
  • card(U(g)) is the actual number of scored needles obtained for each related item, and the values are calculated and stored in step one; card(U) is the total number of needles owned by the system;
  • the score saturation of a needle is the ratio of the number of actual score items that the needle has made to the total number of scores that the corresponding needle should receive.
  • equation (2) if the scores of all items evaluated by the predicted needle have low score saturation, The value of the positioning accuracy should be large, and vice versa; if the number of existing scores of the forecast item is small, the value of the positioning accuracy should be larger, otherwise it should be smaller;
  • the positioning fine ⁇ is satisfied, and the needle is used for prediction.
  • Reference set for scoring prediction Determined according to formula (3); the positioning accuracy ⁇ in step 3 gives a search for a predictive needle a neighborhood value of the reference item;
  • G(u) is the set of needles evaluated by the needle u, Calculated according to formula (1);
  • the score estimation function for calculating the predicted score of the predicted item is expressed by the formula (4):
  • the needle to be predicted is u
  • the needle is Vu,i is the score of the reference u for the needle u
  • the average of the scores given by all the needles in the equation is calculated as equation (5)
  • b is a correction amount for the deviation correction of the average value
  • the calculation formula is as shown in equation (6):
  • the deviation correction value is estimated by the average difference between the market expectation of the predicted item and the expectation of the reference item, and the calculation formula is Equation (6):
  • Equation (6) indicates that the deviation correction value is a predictive needle Market expectation Reference set The difference between the average expected market expectations of the reference needles;
  • the weighted Slope one is introduced, and the number of common scoring needles obtained between the items is used as a weight, and the score prediction result is further corrected;
  • the weight of the common score obtained between the two needle items is used as a weighting function, and the prediction function is calculated by using the weighted fitting average formula of the formula (7);
  • the number of common scoring needles of any two needles is the number of elements of the intersection of their respective scoring needle sets
  • the weighted fitting average formula is used to predict the score.
  • the number of commonly scored needles between any two needles in the system is calculated according to formula (8); and if the score data of a needle changes, in the step In the second, the number of needles scored by the needle and the other needles is recalculated;
  • the number of needles in the sample file is m items, and the number of commonly scored needles between the two pairs of needles constitutes a m ⁇ m symmetric matrix.
  • the matrix is stored; the triangular matrix storage mode is adopted.
  • the disinfection evaluation module 8 includes:
  • the pre-processing module is configured to use the existing scoring data to calculate the expected information of each needle and the number of scoring needles and the like according to the expectation of the formula (1), and store the information according to the needle item;
  • the scoring data change processing module is configured to process the needle item of the change of the scoring data when the scoring data needs to be recalculated when the scoring data needs to be recalculated by the pre-processing module having calculated the stored needle's expectation and the number of scoring needles and the like.
  • the needle item whose score data has not changed does not need to be processed, and there is no need to deal with the relationship between the respective needle items after the change of the score data;
  • a positioning accuracy calculation module configured to determine, in the score prediction, the positioning accuracy ⁇ of the disinfection of the predicted needle according to the number of needles of the relevant needle evaluated by the needle to be predicted and the needle to be predicted;
  • the reference set selection module is configured to determine, according to the positioning accuracy ⁇ of the needle to be predicted determined by the positioning accuracy calculation module, the related needles whose expected values meet the accuracy requirements, and constitute a reference for performing the score prediction on the predicted needle.
  • the predicted score calculation module is configured to calculate a predicted score of the predicted needle according to the score of the reference needle to be predicted by the reference needle selected by the reference set selection module.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Hematology (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

一种一次性采血器针头终末消毒方法及终末消毒系统，包括采血模块（1）用于通过采血针对患者进行采血；血量测量模块（2）用于测量采血的容量信息；主控模块（3）用于控制各个模块正常工作；弹力驱动模块（4）用于给采血针提供采血动力；针头消毒模块（5）用于采血完毕对采血针进行终末杀菌消毒；录音模块（7）用于通过录像器记录患者信息及采血信息数据；以及消毒评价模块（8）。该方法和系统通过针头消毒模块（5）对采血针进行终末消毒，简化了采血器的终末消毒处理；可以较准确控制采血量，有利于采血的顺利进行；以及通过录音模块（7）方便工作人员通过录音方式存储血液患者的信息，便于对患者的长期追踪监测。

Description

一种一次性采血器针头终末消毒方法及终末消毒系统 技术领域
本发明属于医疗技术领域，特别涉及一种一次性采血器针头终末消毒方法及终末消毒系统。
背景技术
现有一次性采血器主要用于：(1)临床少量采血检验；(2)糖尿病患者的血糖监测；(3)一些传染性疾病的快速筛查，如艾滋病、梅毒、乙肝等病。采血器使用后，由于针头仍残留血液，必须在专门的卫生机构进行终末消毒处理。但是对于后两种情况，人们可以自行购置一次性采血针头，并在医疗场所以外的地方使用，而往往并未将其送回专门的医疗机构进行终末消毒处理，从而有可能导致带有传染性病毒血液的一次性采血针头滞留在人们的生活环境中。
在当前社会计算、大数据应用等新一代互联网应用背景下，网络应用更加广泛，信息的产生和变化更加快速。例如，日常生活中人们使用各种商务系统的频率更加频繁，系统中的用户数量以及商品数量都在不断增加，因而引起了系统中数据量的急剧增加和变化，这给推荐技术的研究和应用带来了所谓的可扩展性问题。为帮助用户有效解决他们在网络应用中所面临的信息超载问题，迫切需要一种能够在这种数据规模不断扩大、信息的增加和变化日益频繁的环境下，能够对用户的请求作出及时、快速和准确响应的鲁棒性推荐技术。
综上所述，现有技术存在的问题是：
(1)现有技术中，使用者在没有严格按照产品使用要求收集使用后的针头到卫生机构进行集中消毒处理时，增大了带有传染性病毒的针头滞留于社会环境中的危险。如果能将使用后的采血针头自行进行终末的消毒处理，则人们在非医疗场所使用后则可以作为生活垃圾丢弃，则极大方便了人们的使用，将会减少传染性病毒可能残存针对而对社会环境带来的风险。
(2)现有的血量测量模块仅凭人工进行控制，不能实现自动化控制，控制精度低。
(3)在网络应用日益广泛深入的背景下，面对数据稀疏性、大数据处理与增量计算等问题，如何在采血推荐的准确性和时间效率、空间效率以及算法的可扩展性等方面进行权衡，研究开发出在时间效率、空间效率、准确性和可扩展性等方面综合性能良好的推荐技术，它直接关系到推荐技术的实际评价应用价值
发明内容
本发明的目的在于，针对上述现有技术的不足，提供一种一次性采血器针头终末消毒方法及终末消毒系统。
为解决上述技术问题，本发明所采用的技术方案是：
一种一次性采血器针头终末消毒方法，其特点是包括：
利用采血针对患者进行采血；
利用弹力驱动模块给采血针提供采血所需动力；
在采血完毕时，利用针头消毒模块对采血针进行终末杀菌消毒；
在采血的过程中，还包括利用血量测量模块测量采血的容量信息，包括：
(1)使用电容式振动加速度传感器对机械转轴振动加速度进行测量，得到滤波处理后的振动时频数据；
(2)利用傅立叶变换分析方法和加窗函数改进后的谐波小波分析方法，得出振动信号的频谱图和谐波小波系数分解的三维时频图，谐波小波函数如下：
Figure PCTCN2019084488-appb-000001
其傅立叶变换为：
Figure PCTCN2019084488-appb-000002
(3)分析此谐波小波系数分解三维时频图，得出信号中微小奇异波动所发生的时间点和频率点；
谐波小波频域特性改进使用下布莱克曼窗函数：
Figure PCTCN2019084488-appb-000003
(4)根据谐波小波分解结果计算血液容量。
经过加窗后的谐波小波函数的实部和虚部在|t|→∞时，其衰减速度要比原谐波小波快；
谐波小波频域特性改进使用上述函数，有效减小了时域信号有限长度特性对频谱分析的影响，改善了谐波小波分解系数的偏差；
所述三维时频图的水平面为基平面，两坐标轴分别为时间和谐波小波分解层数，这样小波时频图的基平面就被划分成由时间和层数构成的网格，每个网格上以谐波小波系数a s模的平方作柱体，谐波小波分解结果表明不同频率和时间的谐波小波能量对整个信号能量贡献的大小，谐波小波时频图是分解结果的直观表示，其起伏对应不同谐波小波能量的相对大小，通过谐波时频图，知道在什么时间什么频率成份对信号组成有重要影响；
采用小波技术的非周期性微小信号检测方法，谐波小波分解算法速度快，精度高，能有效克服傅立叶分析方法无法获得频率分量随时间演变信息的缺点，因其对信号中微小奇异点极其敏感。
进一步地，还包括：
利用录音模块记录患者信息及采血信息数据。
进一步地，还包括：
利用数据导出云端模块将录音数据导出，进行云端保存。
进一步地，还包括：
利用消毒评价模块对针头消毒情况进行评价，包括：
利用定位函数分析针头消毒得到的评分的整体情况来确定在消毒等级上所处的位置；通过定位精度函数确定参照针头消毒的选择方式，进而选择适当的针头消毒项为参照来预测针头消毒后评分；并根据预测项与参照项的期望之间的关系对预测评分进行修正调整。
作为一种优选方式，消毒评价模块的评价方法包括：
步骤一，利用现有的评分数据，以消毒期望为定位函数，计算每项针头消毒的期望及评分等相关信息，并按针头消毒项进行存储；
步骤二，由步骤一已经计算存储好的针头消毒的期望及评分相关信息，在评 分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头消毒项进行处理，而评分数据未发生变化的针头消毒项不需要再进行处理，也无需处理评分数据变化后的各个针头消毒项之间的关系；
步骤三，在评分预测中，根据要预测的针头以及评价过的相关针头的评分，利用定位函数式确定预测针头消毒的定位精度ε；
步骤四，根据要预测的针头的定位精度ε，在预测评价过的相关针头中，找出期望值满足精度要求的针头，构成用来对预测的针头进行评分预测的参照项目集；
步骤五，利用要预测的针头对参照针头的评分以及预测针头、参照针头的期望值计算预测针头的预测分值。
作为一种优选方式，所述定位函数为基于期望的定位函数；具体包括：
期望为一项针头得到的全部的评分的平均值，该平均值反映针头得到的评分的期望，用来区分每项针头在消毒等级上所处的位置；所述步骤一中，对一项针头
Figure PCTCN2019084488-appb-000004
其定位函数
Figure PCTCN2019084488-appb-000005
用其期望
Figure PCTCN2019084488-appb-000006
来计算，计算公式如式(1)：
Figure PCTCN2019084488-appb-000007
其中，
Figure PCTCN2019084488-appb-000008
为对针头
Figure PCTCN2019084488-appb-000009
进行过评分的全部针头组成的集合，
Figure PCTCN2019084488-appb-000010
为针头u对针头
Figure PCTCN2019084488-appb-000011
的评分，
Figure PCTCN2019084488-appb-000012
为集合
Figure PCTCN2019084488-appb-000013
中数据的个数；
所述步骤一中的处理采用离线方式进行，包括：一次处理完所有针头的期望及评分针头数等相关信息，然后以数据库或其它数据文件的形式存储起来，供以后的评分预测使用；数据的存储以一项针头为一个记录；
所述步骤三中，精度函数利用要预测的针头以及要预测的针头评价过的相关针头的评分针头数，来确定预测针头的定位精度ε，计算公式为式(2)：
Figure PCTCN2019084488-appb-000014
其中，
Figure PCTCN2019084488-appb-000015
为要预测的针头
Figure PCTCN2019084488-appb-000016
的现有评分针头集，
Figure PCTCN2019084488-appb-000017
即为针头
Figure PCTCN2019084488-appb-000018
得到的实际评分针头数，在步骤一中计算并存储，；
Figure PCTCN2019084488-appb-000019
为要预测的针头
Figure PCTCN2019084488-appb-000020
评价过的全部针头的平均评分饱和度，计算公式为式(3)：
Figure PCTCN2019084488-appb-000021
式(3)中
Figure PCTCN2019084488-appb-000022
为要预测的针头
Figure PCTCN2019084488-appb-000023
评价过的全部项目集，card(U(g))为各相关项目得到的实际评分针头数，其值均在步骤一中计算并存储；card(U)为系统所拥有的针头总数；
一个针头的评分饱和度为该针头进行过的实际评分项目数量与这些相应的 针头应得到的评分总数之比，式(2)中，若预测的针头评价过的全部项目的评分饱和度低，定位精度的取值应较大，反之应较小；若预测项目的现有评分数量少，则定位精度的取值应较大，反之则应较小；
所述步骤四中满足定位精ε、用来对预测的针头
Figure PCTCN2019084488-appb-000024
进行评分预测的参照集
Figure PCTCN2019084488-appb-000025
按式(3)来确定；所述步骤三中的定位精度ε给出寻找预测针头
Figure PCTCN2019084488-appb-000026
的参照项的一个邻域值；
Figure PCTCN2019084488-appb-000027
其中，G(u)为针头u评价过的针头集，
Figure PCTCN2019084488-appb-000028
按式(1)计算；
所述步骤五中，计算预测项目的预测分值的评分估算函数为式(4)：
Figure PCTCN2019084488-appb-000029
其中，要预测的针头为u，针头为
Figure PCTCN2019084488-appb-000030
vu,i为针头u对参照项i的评分
Figure PCTCN2019084488-appb-000031
Figure PCTCN2019084488-appb-000032
为针头u对参照集
Figure PCTCN2019084488-appb-000033
中的所有针头给出的评分的平均值，计算公式为式(5)，b为对平均值进行偏差校正的一个修正量，计算公式如式(6)：
Figure PCTCN2019084488-appb-000034
在期望为定位函数的情况下，偏差校正值用预测项目的市场期望与参照项目的期望之间的平均差来估计，计算公式为式(6)：
Figure PCTCN2019084488-appb-000035
式(6)表示，偏差校正值为预测针头
Figure PCTCN2019084488-appb-000036
的市场期望值
Figure PCTCN2019084488-appb-000037
与参照集
Figure PCTCN2019084488-appb-000038
中的参照针头的市场期望的平均值之差；
引入加权Slope one，以项目之间得到的共同评分针头数为权重，对评分预测结果进一步修正；具体包括：以两两针头项之间得到的共同评分针头数为权重，以期望为定位函数，采用式(7)加权拟合平均公式计算针头的预测评分；
Figure PCTCN2019084488-appb-000039
其中，
Figure PCTCN2019084488-appb-000040
为对要预测的针头
Figure PCTCN2019084488-appb-000041
和参照集中的针头i都进行过评分的针头数，称为针头
Figure PCTCN2019084488-appb-000042
和i的共同评分针头数；任意两个针头，其共同评分针头数计算公式如式(8)：
c i,j＝card(U(i,j))＝card(U(i)∩U(j))   (8)；
任意两个针头的共同评分针头数，是其各自的评分针头集的交集的元素个数；
采用加权拟合平均公式预测分值，在所述步骤一中，按式(8)计算系统中任意两两针头之间的共同评分针头数；且若一项针头的评分数据发生变化，在步骤二中，就重新计算该针头与其它针头的共同评分针头数；
样本χ中的针头数量为m项，全部针头两两之间的共同评分针头数构成一个m×m的对称矩阵，在所述步骤一和步骤二中，存储该矩阵；采用三角矩阵存储方式。
作为一种优选方式，利用针头消毒模块对采血针进行终末杀菌消毒包括：
对艾滋病病毒，灭活加热100℃，并持续20分钟；
对于不能耐高温的培养基成分，以115℃度的水持续加热30-40分钟；
对于除艾滋病病毒与不能耐高温的培养基成分的其它成份，以121℃度的水持续20分钟。
沸水加热时间持续20分钟以上，因此，可以有效地杀灭艾滋病病毒、乙肝病毒、丙肝病毒、梅毒螺旋体等常见的血液传播性病毒。
基于同一个发明构思，本发明还提供了一种一次性采血器针头终末消毒系统，其特点是包括：
主控模块：用于控制采血模块、弹力驱动模块、针头消毒模块、血量测量模块的工作；
采血模块：与主控模块连接，用于通过采血针对患者进行采血；
弹力驱动模块：与主控模块连接，用于给采血针提供采血动力；
针头消毒模块：与主控模块连接，用于在采血完毕时对采血针进行终末杀菌消毒；
血量测量模块：与主控模块连接，用于测量采血的容量信息，包括：
(1)使用电容式振动加速度传感器对机械转轴振动加速度进行测量，得到滤波处理后的振动时频数据；
(2)利用傅立叶变换分析方法和加窗函数改进后的谐波小波分析方法，得出振动信号的频谱图和谐波小波系数分解的三维时频图，谐波小波函数如下：
Figure PCTCN2019084488-appb-000043
其傅立叶变换为：
Figure PCTCN2019084488-appb-000044
(3)分析此谐波小波系数分解三维时频图，得出信号中微小奇异波动所发生的时间点和频率点；
谐波小波频域特性改进使用下布莱克曼窗函数：
Figure PCTCN2019084488-appb-000045
(4)根据谐波小波分解结果计算血液容量。
进一步地，还包括：
录音模块：与主控模块连接，用于通过录像器记录患者信息及采血信息数据；
主控模块还用于控制录音模块的工作。
进一步地，还包括：
数据导出云端模块：与录音模块连接，将录音数据导出，进行云端保存。
进一步地，还包括：
消毒评价模块：与主控模块连接，用于对针头消毒情况进行评价；包括：
利用定位函数分析针头消毒得到的评分的整体情况来确定在消毒等级上所处的位置；通过定位精度函数确定参照针头消毒的选择方式，进而选择适当的针头消毒项为参照来预测针头消毒后评分；并根据预测项与参照项的期望之间的关系对预测评分进行修正调整；
主控模块还用于控制消毒评价模块的工作。
作为一种优选方式，所述消毒评价模块包括：
预处理模块，用于利用现有的评分数据，以式(1)期望为定位函数，计算每项针头的期望及评分针头数等相关信息，并按针头项进行存储；
评分数据变化处理模块，用于由预处理模块已经计算储好存的针头的期望及评分针头数等相关信息在评分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头项进行处理，而评分数据未发生变化的针头项不需要再进行处理，也无需处理评分数据变化后的各个针头项之间的关系；
定位精度计算模块，用于在评分预测中，根据要预测的针头以及要预测的针头评价过的相关针头的评分针头数，确定预测针头的消毒的定位精度ε；
参照集选择模块，用于在评分预测中，根据定位精度计算模块确定的要预测的针头的定位精度ε，找出期望值满足精度要求的相关针头，构成用来对预测的针头进行评分预测的参照项目集；
预测分值计算模块，用于利用参照集选择模块选定的参照针头，根据要预测的针头对参照针头的评分，计算预测针头的预测分值。
作为一种优选方式，所述弹力驱动模块包括扭断驱动模块、弹射模块；
扭断驱动模块，用于通过扭断驱动块将采血针与针座分离，使用采血针露出壳体；
弹射模块，用于通过弹簧提供弹射动力。
作为一种优选方式，所述针头消毒模块包括加热模块，用于通过加热器加热水对使用后的针头进行加热消毒：
对艾滋病病毒，灭活加热100℃，并持续20分钟；
对于不能耐高温的培养基成分，以115℃度的水持续加热30-40分钟；
对于除艾滋病病毒与不能耐高温的培养基成分的其它成份，以121℃度的水持续20分钟。
进一步地，所述针头消毒模块还包括液体消毒模块，用于通过将采血针插入盛放有消毒液的消毒腔对采血针进行消毒。
本发明的优点及积极效果为：
本发明通过针头消毒模块对采血针进行消毒，简化了一次性采血针的终末消毒程序；同时在采血器通过照明模块可以方便医疗工作人员在采血时，进行提供照明功能，从而可以准确判断采血位置，大大便利采血的顺利进行；以及通过录音模块可以方便工作人员通过录音方式进行存储到录音模块集成的储存器中，将患者的信息储存，便于追踪监测。
本发明的血量测量模块采用小波技术的非周期性微小信号检测方法，谐波小 波分解算法速度快，精度高，能有效克服傅立叶分析方法无法获得频率分量随时间演变信息的缺点，因其对信号中微小奇异点极其敏感，故可有效用于智能分析等领域。
本发明的MEbCF技术能够获得较好的评分预测效果，同时又具有算法简单、便于实现以及算法的时间和空间开销较低等显著特点，是一种具有较强竞争力的推荐技术，在电子商务系统及其他评分预测领域具有良好的应用前景。
一般的相似度计算和Slope one算法中的项目分差和共同评分用户数计算，在推荐系统中有n个用户和m项商品的情况下，其时间复杂度一般均为O(m 2×n/2)，空间复杂度为O(m 2/2)；本发明的MEbCF技术，仅需计算每项商品的市场期望就能够进行推荐，其时间复杂度为O(m×n)，空间复杂度为O(m)。采用本发明的MEbCF技术，能够在数量级上巨大提高算法的时间和空间效率，在系统拥有大量用户和商品的情况下，巨大减轻推荐系统的运行负荷，为推荐系统及时响应用户需求和系统中数据的变化提供了有力的技术保障。在推荐效果上，能够达到甚至略优于基于相似度的k最近邻、Slope one等以简便易行著称的传统推荐技术。
本发明提出的市场效应概念，具有如下意义：(1)它符合人们的评分行为特点；(2)含义简洁、明确，有利于描述人们的评分行为特点和相关的实现和技术；(3)能够区别于其他技术方法中所体现的原理、概念和方法；(4)基于市场效应提出的评分预测方法，方法简单、易于实现。
附图说明
图1是本发明终末消毒系统一实施例结构框图。
图中：1、采血模块；2、血量测量模块；3、主控模块；4、弹力驱动模块；5、针头消毒模块；6、照明模块；7、录音模块；8、消毒评价模块；9、数据导出云端模块。
图2为本发明中谐波小波函数的实部曲线图。
图3为本发明中谐波小波函数的虚部曲线图。
图4为本发明中加布莱克曼窗后谐波小波的实部曲线图。
图5为本发明中加布莱克曼窗后谐波小波的虚部曲线图。
具体实施方式
为能进一步了解本发明的发明内容、特点及功效，兹例举以下实施例，并配合附图详细说明如下。
如图1所示，本发明的一实施例的终末消毒系统包括采血模块1、血量测量模块2、主控模块3、弹力驱动模块4、针头消毒模块5、照明模块6、录音模块7。其中：
采血模块1，与主控模块3连接，用于通过采血针对患者进行采血；
血量测量模块2，与主控模块3连接，用于通过刻度尺测量采血的容量信息；
主控模块3，与采血模块1、血量测量模块2、弹力驱动模块4、针头消毒模块5、照明模块6、录音模块7连接，用于控制各个模块正常工作；
弹力驱动模块4，与主控模块3连接，用于给采血针提供采血动力；
针头消毒模块5，与主控模块3连接，用于对采血针进行杀菌消毒；
照明模块6，与主控模块3连接，用于给采血过程提供照明功能；
录音模块7，与主控模块3连接，用于通过录像器记录患者信息及采血信息数据。
数据导出云端模块9，与录音模块7连接，将录音数据导出，进行云端保 存。
本发明提供的弹力驱动模块4包括扭断驱动模块、弹射模块；
扭断驱动模块，用于通过扭断驱动块将采血针与针座分离，使用采血针露出壳体；
弹射模块，用于通过弹簧提供弹射动力。
本发明提供的针头消毒模块5包括加热模块、液体消毒模块；
加热模块，用于通过加热器对使用后的针头进行加热消毒；
液体消毒模块，用于通过将采血针插入盛放有消毒液的消毒腔对采血针进行消毒。
本发明采血时，通过照明模块6给采血过程提供照明功能；工作人员通过弹力驱动模块4将采血针弹出壳体；接着通过采血模块1对患者进行采血；通过血量测量模块2利用刻度尺测量采血的容量信息；采血后，通过针头消毒模块5对采血针进行杀菌消毒；最后，通过录音模块7记录患者信息及采血信息数据。
图2为本发明中谐波小波函数的实部曲线图。
图3为本发明中谐波小波函数的虚部曲线图。
图4为本发明中加布莱克曼窗后谐波小波的实部曲线图。
图5为本发明中加布莱克曼窗后谐波小波的虚部曲线图。
下面结合具体分析对本发明作进一步描述。
血量测量模块2，与主控模块3连接，用于测量采血的容量信息；具体包括：
(1)使用电容式振动加速度传感器对机械转轴振动加速度进行测量，得到滤波处理后的振动时频数据；
(2)利用傅立叶变换分析方法和加窗函数改进后的谐波小波分析方法，得出振动信号的频谱图和谐波小波系数分解的三维时频图，谐波小波函数如下：
Figure PCTCN2019084488-appb-000046
其傅立叶变换为：
Figure PCTCN2019084488-appb-000047
(3)分析此谐波小波系数分解三维时频图，得出信号中微小奇异波动所发生的时间点和频率点；
所述谐波小波通过如下函数进行改进，谐波小波频域特性改进使用下布莱克曼窗函数：
Figure PCTCN2019084488-appb-000048
经过加窗后的谐波小波函数的实部和虚部在|t|→∞时，其衰减速度要比原谐波小波快；谐波小波时域信号改进使用如下函数，有效减小了时域信号有限长度特性对频谱分析的影响，改善了谐波小波分解系数的偏差；
所述三维时频图的水平面为基平面，两坐标轴分别为时间和谐波小波分解层数，这样小波时频图的基平面就被划分成由时间和层数构成的网格，每个网格上 以谐波小波系数a s模的平方作柱体，谐波小波分解结果表明不同频率和时间的谐波小波能量对整个信号能量贡献的大小，谐波小波时频图是分解结果的直观表示，其起伏对应不同谐波小波能量的相对大小，通过谐波时频图，知道在什么时间什么频率成份对信号组成有重要影响；
采用小波技术的非周期性微小信号检测方法，谐波小波分解算法速度快，精度高，能有效克服傅立叶分析方法无法获得频率分量随时间演变信息的缺点，因其对信号中微小奇异点极其敏感；
主控模块3，与采血模块1、血量测量模块2、弹力驱动模块4、针头消毒模块5、录音模块7连接，用于控制各个模块正常工作；
弹力驱动模块4，与主控模块3连接，用于给采血针提供采血动力；
针头消毒模块5，与主控模块3连接，用于采血完毕对采血针进行终末杀菌消毒；用于通过加热器加热水对使用后的针头进行加热消毒；设定温度是121℃，20-30分钟；对于不能耐高温的培养基成分比如葡萄糖，115℃30-40分钟。对艾滋病病毒灭活加热100℃持续20分钟，病毒死亡效果显著的，可以杀死。
录音模块7，与主控模块3连接，用于通过录像器记录患者信息及采血信息数据。
消毒评价模块8，与主控模块3连接，用于对针头消毒情况进行评价；具体包括：
利用定位函数分析针头消毒得到的评分的整体情况来确定在消毒等级上所处的位置；通过定位精度函数确定参照针头消毒的选择方式，进而选择适当的针头消毒项为参照来预测针头消毒后评分；并根据预测项与参照项的期望之间的关系对预测评分进行修正调整。
消毒评价模块8的评价方法具体包括：
步骤一，利用现有的评分数据，以消毒期望为定位函数，计算每项针头消毒的期望及评分等相关信息，并按针头消毒项进行存储；
步骤二，由步骤一已经计算储好存的针头消毒的期望及评分等相关信息在评分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头消毒项进行处理，而评分数据未发生变化的针头消毒项不需要再进行处理，也无需处理评分数据变化后的各个针头消毒项之间的关系；
步骤三，在评分预测中，根据要预测的针头以及要预测的评价过的相关针头的评分，利用定位函数式确定预测针头消毒的定位精度ε；
步骤四，根据要预测的针头的定位精度ε，在预测评价过的相关针头中，找出期望值满足精度要求的针头，构成用来对预测的针头进行评分预测的参照项目集；
步骤五，利用要预测的针头对参照针头的评分以及预测针头、参照针头的期望值计算预测针头的预测分值。
进一步，所述定位函数为基于期望的定位函数；具体包括：
期望为一项针头得到的全部的评分的平均值，该平均值反映针头得到的评分的期望，用来区分每项针头在消毒等级上所处的位置；所述步骤一中，对一项针头
Figure PCTCN2019084488-appb-000049
其定位函数
Figure PCTCN2019084488-appb-000050
用其期望
Figure PCTCN2019084488-appb-000051
来计算，计算公式如式(1)：
Figure PCTCN2019084488-appb-000052
其中，
Figure PCTCN2019084488-appb-000053
为对针头
Figure PCTCN2019084488-appb-000054
进行过评分的全部针头组成的集合，
Figure PCTCN2019084488-appb-000055
为针头u对针头
Figure PCTCN2019084488-appb-000056
的评分，
Figure PCTCN2019084488-appb-000057
为集合
Figure PCTCN2019084488-appb-000058
中数据的个数；
所述步骤一中的处理采用离线方式进行，包括：一次处理完所有针头的期望及评分针头数等相关信息，然后以数据库或其它数据文件的形式存储起来，供以后的评分预测使用；数据的存储以一项针头为一个记录；
所述步骤三中，精度函数利用要预测的针头以及要预测的针头评价过的相关针头的评分针头数，来确定预测针头的定位精度ε，计算公式为式(2)：
Figure PCTCN2019084488-appb-000059
其中，
Figure PCTCN2019084488-appb-000060
为要预测的针头
Figure PCTCN2019084488-appb-000061
的现有评分针头集，
Figure PCTCN2019084488-appb-000062
即为针头
Figure PCTCN2019084488-appb-000063
得到的实际评分针头数，在步骤一中计算并存储，；
Figure PCTCN2019084488-appb-000064
为要预测的针头
Figure PCTCN2019084488-appb-000065
评价过的全部针头的平均评分饱和度，计算公式为式(3)：
Figure PCTCN2019084488-appb-000066
式(3)中
Figure PCTCN2019084488-appb-000067
为要预测的针头
Figure PCTCN2019084488-appb-000068
评价过的全部项目集，card(U(g))为各相关项目得到的实际评分针头数，其值均在步骤一中计算并存储；card(U)为系统所拥有的针头总数；
一个针头的评分饱和度为该针头进行过的实际评分项目数量与这些相应的针头应得到的评分总数之比，式(2)中，若预测的针头评价过的全部项目的评分饱和度低，定位精度的取值应较大，反之应较小；若预测项目的现有评分数量少，则定位精度的取值应较大，反之则应较小；
所述步骤四中满足定位精ε、用来对预测的针头
Figure PCTCN2019084488-appb-000069
进行评分预测的参照集
Figure PCTCN2019084488-appb-000070
按式(3)来确定；所述步骤三中的定位精度ε给出寻找预测针头
Figure PCTCN2019084488-appb-000071
的参照项的一个邻域值；
Figure PCTCN2019084488-appb-000072
其中，G(u)为针头u评价过的针头集，
Figure PCTCN2019084488-appb-000073
按式(1)计算；
所述步骤五中，计算预测项目的预测分值的评分估算函数为式(4)：
Figure PCTCN2019084488-appb-000074
其中，要预测的针头为u，针头为
Figure PCTCN2019084488-appb-000075
vu,i为针头u对参照项i的评分
Figure PCTCN2019084488-appb-000076
Figure PCTCN2019084488-appb-000077
为针头u对参照集
Figure PCTCN2019084488-appb-000078
中的所有针头给出的评分的平均值，计算公式为式(5)，b为对平均值进行偏差校正的一个修正量，计算公式如式(6)：
Figure PCTCN2019084488-appb-000079
在期望为定位函数的情况下，偏差校正值用预测项目的市场期望与参照项目的期望之间的平均差来估计，计算公式为式(6)：
Figure PCTCN2019084488-appb-000080
式(6)表示，偏差校正值为预测针头
Figure PCTCN2019084488-appb-000081
的市场期望值
Figure PCTCN2019084488-appb-000082
与参照集
Figure PCTCN2019084488-appb-000083
中的参照针头的市场期望的平均值之差；
引入加权Slope one，以项目之间得到的共同评分针头数为权重，对评分预测结果进一步修正；
具体包括：以两两针头项之间得到的共同评分针头数为权重，以期望为定位函数，采用式(7)加权拟合平均公式计算针头的预测评分；
Figure PCTCN2019084488-appb-000084
其中，
Figure PCTCN2019084488-appb-000085
为对要预测的针头
Figure PCTCN2019084488-appb-000086
和参照集中的针头i都进行过评分的针头数，称为针头
Figure PCTCN2019084488-appb-000087
和i的共同评分针头数；任意两个针头，其共同评分针头数计算公式如式(8)：
c i,j＝card(U(i,j))＝card(U(i)∩U(j))   (8)；
任意两个针头的共同评分针头数，是其各自的评分针头集的交集的元素个数；
采用加权拟合平均公式预测分值，在所述步骤一中，按式(8)计算系统中任意两两针头之间的共同评分针头数；且若一项针头的评分数据发生变化，在步骤二中，就重新计算该针头与其它针头的共同评分针头数；
样本χ中的针头数量为m项，全部针头两两之间的共同评分针头数构成一个m×m的对称矩阵，在所述步骤一和步骤二中，存储该矩阵；采用三角矩阵存储方式。
消毒评价模块8包括：
预处理模块，用于利用现有的评分数据，以式(1)期望为定位函数，计算每项针头的期望及评分针头数等相关信息，并按针头项进行存储；
评分数据变化处理模块，用于由预处理模块已经计算储好存的针头的期望及评分针头数等相关信息在评分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头项进行处理，而评分数据未发生变化的针头项不需要再进行处理， 也无需处理评分数据变化后的各个针头项之间的关系；
定位精度计算模块，用于在评分预测中，根据要预测的针头以及要预测的针头评价过的相关针头的评分针头数，确定预测针头的消毒的定位精度ε；
参照集选择模块，用于在评分预测中，根据定位精度计算模块确定的要预测的针头的定位精度ε，找出期望值满足精度要求的相关针头，构成用来对预测的针头进行评分预测的参照项目集；
预测分值计算模块，用于利用参照集选择模块选定的参照针头，根据要预测的针头对参照针头的评分，计算预测针头的预测分值。
以上所述仅是对本发明的较佳实施例而已，并非对本发明作任何形式上的限制，凡是依据本发明的技术实质对以上实施例所做的任何简单修改，等同变化与修饰，均属于本发明技术方案的范围内。

Claims (15)

  1. 一种一次性采血器针头终末消毒方法，其特征在于，包括：
    利用采血针对患者进行采血；
    利用弹力驱动模块给采血针提供采血所需动力；
    在采血完毕时，利用针头消毒模块对采血针进行终末杀菌消毒；
    在采血的过程中，还包括利用血量测量模块测量采血的容量信息，包括：
    (1)使用电容式振动加速度传感器对机械转轴振动加速度进行测量，得到滤波处理后的振动时频数据；
    (2)利用傅立叶变换分析方法和加窗函数改进后的谐波小波分析方法，得出振动信号的频谱图和谐波小波系数分解的三维时频图，谐波小波函数如下：
    Figure PCTCN2019084488-appb-100001
    其傅立叶变换为：
    Figure PCTCN2019084488-appb-100002
    (3)分析此谐波小波系数分解三维时频图，得出信号中微小奇异波动所发生的时间点和频率点；
    谐波小波频域特性改进使用下布莱克曼窗函数：
    Figure PCTCN2019084488-appb-100003
    (4)根据谐波小波分解结果计算血液容量。
  2. 如权利要求1所述的一次性采血器针头终末消毒方法，其特征在于，还包括：
    利用录音模块记录患者信息及采血信息数据。
  3. 如权利要求2所述的一次性采血器针头终末消毒方法，其特征在于，还包括：
    利用数据导出云端模块将录音数据导出，进行云端保存。
  4. 如权利要求1至3任一项所述的一次性采血器针头终末消毒方法，其特征在于，还包括：
    利用消毒评价模块对针头消毒情况进行评价，包括：
    利用定位函数分析针头消毒得到的评分的整体情况来确定在消毒等级上所处的位置；通过定位精度函数确定参照针头消毒的选择方式，进而选择适当的针头消毒项为参照来预测针头消毒后评分；并根据预测项与参照项的期望之间的关系对预测评分进行修正调整。
  5. 如权利要求4所述的一次性采血器针头终末消毒方法，其特征在于，消毒评价模块的评价方法包括：
    步骤一，利用现有的评分数据，以消毒期望为定位函数，计算每项针头消毒的期望及评分等相关信息，并按针头消毒项进行存储；
    步骤二，由步骤一已经计算存储好的针头消毒的期望及评分相关信息，在评分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头消毒项进行处理，而评分数据未发生变化的针头消毒项不需要再进行处理，也无需处理评分数据变化后的各个针头消毒项之间的关系；
    步骤三，在评分预测中，根据要预测的针头以及评价过的相关针头的评分，利用定位函数式确定预测针头消毒的定位精度ε；
    步骤四，根据要预测的针头的定位精度ε，在预测评价过的相关针头中，找出期望值满足精度要求的针头，构成用来对预测的针头进行评分预测的参照项目集；
    步骤五，利用要预测的针头对参照针头的评分以及预测针头、参照针头的期望值计算预测针头的预测分值。
  6. 如权利要求5所述的一次性采血器针头终末消毒方法，其特征在于，所述定位函数为基于期望的定位函数；具体包括：
    期望为一项针头得到的全部的评分的平均值，该平均值反映针头得到的评分的期望，用来区分每项针头在消毒等级上所处的位置；所述步骤一中，对一项针头
    Figure PCTCN2019084488-appb-100004
    其定位函数
    Figure PCTCN2019084488-appb-100005
    用其期望
    Figure PCTCN2019084488-appb-100006
    来计算，计算公式如式(1)：
    Figure PCTCN2019084488-appb-100007
    其中，
    Figure PCTCN2019084488-appb-100008
    为对针头
    Figure PCTCN2019084488-appb-100009
    进行过评分的全部针头组成的集合，
    Figure PCTCN2019084488-appb-100010
    为针头u对针头
    Figure PCTCN2019084488-appb-100011
    的评分，
    Figure PCTCN2019084488-appb-100012
    为集合
    Figure PCTCN2019084488-appb-100013
    中数据的个数；
    所述步骤一中的处理采用离线方式进行，包括：一次处理完所有针头的期望及评分针头数等相关信息，然后以数据库或其它数据文件的形式存储起来，供以后的评分预测使用；数据的存储以一项针头为一个记录；
    所述步骤三中，精度函数利用要预测的针头以及要预测的针头评价过的相关针头的评分针头数，来确定预测针头的定位精度ε，计算公式为式(2)：
    Figure PCTCN2019084488-appb-100014
    其中，
    Figure PCTCN2019084488-appb-100015
    为要预测的针头
    Figure PCTCN2019084488-appb-100016
    的现有评分针头集，
    Figure PCTCN2019084488-appb-100017
    即为针头
    Figure PCTCN2019084488-appb-100018
    得到的实际评分针头数，在步骤一中计算并存储，；
    Figure PCTCN2019084488-appb-100019
    为要预测的针头
    Figure PCTCN2019084488-appb-100020
    评价过的全部针头的平均评分饱和度，计算公式为式(3)：
    Figure PCTCN2019084488-appb-100021
    式(3)中
    Figure PCTCN2019084488-appb-100022
    为要预测的针头
    Figure PCTCN2019084488-appb-100023
    评价过的全部项目集，card(U(g))为各 相关项目得到的实际评分针头数，其值均在步骤一中计算并存储；card(U)为系统所拥有的针头总数；
    一个针头的评分饱和度为该针头进行过的实际评分项目数量与这些相应的针头应得到的评分总数之比，式(2)中，若预测的针头评价过的全部项目的评分饱和度低，定位精度的取值应较大，反之应较小；若预测项目的现有评分数量少，则定位精度的取值应较大，反之则应较小；
    所述步骤四中满足定位精ε、用来对预测的针头
    Figure PCTCN2019084488-appb-100024
    进行评分预测的参照集
    Figure PCTCN2019084488-appb-100025
    按式(3)来确定；所述步骤三中的定位精度ε给出寻找预测针头
    Figure PCTCN2019084488-appb-100026
    的参照项的一个邻域值；
    Figure PCTCN2019084488-appb-100027
    其中，G(u)为针头u评价过的针头集，
    Figure PCTCN2019084488-appb-100028
    按式(1)计算；
    所述步骤五中，计算预测项目的预测分值的评分估算函数为式(4)：
    Figure PCTCN2019084488-appb-100029
    其中，要预测的针头为u，针头为
    Figure PCTCN2019084488-appb-100030
    v u,i为针头u对参照项i的评分
    Figure PCTCN2019084488-appb-100031
    为针头u对参照集
    Figure PCTCN2019084488-appb-100032
    中的所有针头给出的评分的平均值，计算公式为式(5)，b为对平均值进行偏差校正的一个修正量，计算公式如式(6)：
    Figure PCTCN2019084488-appb-100033
    在期望为定位函数的情况下，偏差校正值用预测项目的市场期望与参照项目的期望之间的平均差来估计，计算公式为式(6)：
    Figure PCTCN2019084488-appb-100034
    式(6)表示，偏差校正值为预测针头
    Figure PCTCN2019084488-appb-100035
    的市场期望值
    Figure PCTCN2019084488-appb-100036
    与参照集
    Figure PCTCN2019084488-appb-100037
    中的参照针头的市场期望的平均值之差；
    引入加权Slope one，以项目之间得到的共同评分针头数为权重，对评分预测结果进一步修正；具体包括：以两两针头项之间得到的共同评分针头数为权重，以期望为定位函数，采用式(7)加权拟合平均公式计算针头的预测评分；
    Figure PCTCN2019084488-appb-100038
    其中，
    Figure PCTCN2019084488-appb-100039
    为对要预测的针头
    Figure PCTCN2019084488-appb-100040
    和参照集中的针头i都进行过评分的针头数， 称为针头
    Figure PCTCN2019084488-appb-100041
    和i的共同评分针头数；任意两个针头，其共同评分针头数计算公式如式(8)：
    c i,j＝card(U(i,j))＝card(U(i)∩U(j))  (8)；
    任意两个针头的共同评分针头数，是其各自的评分针头集的交集的元素个数；
    采用加权拟合平均公式预测分值，在所述步骤一中，按式(8)计算系统中任意两两针头之间的共同评分针头数；且若一项针头的评分数据发生变化，在步骤二中，就重新计算该针头与其它针头的共同评分针头数；
    样本χ中的针头数量为m项，全部针头两两之间的共同评分针头数构成一个m×m的对称矩阵，在所述步骤一和步骤二中，存储该矩阵；采用三角矩阵存储方式。
  7. 如权利要求1所述的一次性采血器针头终末消毒方法，其特征在于，
    利用针头消毒模块对采血针进行终末杀菌消毒包括：
    对艾滋病病毒，灭活加热100℃，并持续20分钟；
    对于不能耐高温的培养基成分，以115℃度的水持续加热30-40分钟；
    对于除艾滋病病毒与不能耐高温的培养基成分的其它成份，以121℃度的水持续20分钟。
  8. 一种一次性采血器针头终末消毒系统，其特征在于，包括：
    主控模块：用于控制采血模块、弹力驱动模块、针头消毒模块、血量测量模块的工作；
    采血模块：与主控模块连接，用于通过采血针对患者进行采血；
    弹力驱动模块：与主控模块连接，用于给采血针提供采血动力；
    针头消毒模块：与主控模块连接，用于在采血完毕时对采血针进行终末杀菌消毒；
    血量测量模块：与主控模块连接，用于测量采血的容量信息，包括：
    (1)使用电容式振动加速度传感器对机械转轴振动加速度进行测量，得到滤波处理后的振动时频数据；
    (2)利用傅立叶变换分析方法和加窗函数改进后的谐波小波分析方法，得出振动信号的频谱图和谐波小波系数分解的三维时频图，谐波小波函数如下：
    Figure PCTCN2019084488-appb-100042
    其傅立叶变换为：
    Figure PCTCN2019084488-appb-100043
    (3)分析此谐波小波系数分解三维时频图，得出信号中微小奇异波动所发生的时间点和频率点；
    谐波小波频域特性改进使用下布莱克曼窗函数：
    Figure PCTCN2019084488-appb-100044
    (4)根据谐波小波分解结果计算血液容量。
  9. 如权利要求8所述的一次性采血器针头终末消毒系统，其特征在于，还包括：
    录音模块：与主控模块连接，用于通过录像器记录患者信息及采血信息数据；
    主控模块还用于控制录音模块的工作。
  10. 如权利要求9所述的一次性采血器针头终末消毒系统，其特征在于，还包括：
    数据导出云端模块：与录音模块连接，将录音数据导出，进行云端保存。
  11. 如权利要求8至10任一项所述的一次性采血器针头终末消毒系统，其特征在于，还包括：
    消毒评价模块：与主控模块连接，用于对针头消毒情况进行评价；包括：
    利用定位函数分析针头消毒得到的评分的整体情况来确定在消毒等级上所处的位置；通过定位精度函数确定参照针头消毒的选择方式，进而选择适当的针头消毒项为参照来预测针头消毒后评分；并根据预测项与参照项的期望之间的关系对预测评分进行修正调整；
    主控模块还用于控制消毒评价模块的工作。
  12. 如权利要求11所述的一次性采血器针头终末消毒系统，其特征在于，所述消毒评价模块包括：
    预处理模块，用于利用现有的评分数据，以式(1)期望为定位函数，计算每项针头的期望及评分针头数等相关信息，并按针头项进行存储；
    评分数据变化处理模块，用于由预处理模块已经计算储好存的针头的期望及评分针头数等相关信息在评分数据发生变化而需要重新计算时，仅对评分数据发生变化的针头项进行处理，而评分数据未发生变化的针头项不需要再进行处理，也无需处理评分数据变化后的各个针头项之间的关系；
    定位精度计算模块，用于在评分预测中，根据要预测的针头以及要预测的针头评价过的相关针头的评分针头数，确定预测针头的消毒的定位精度ε；
    参照集选择模块，用于在评分预测中，根据定位精度计算模块确定的要预测的针头的定位精度ε，找出期望值满足精度要求的相关针头，构成用来对预测的针头进行评分预测的参照项目集；
    预测分值计算模块，用于利用参照集选择模块选定的参照针头，根据要预测的针头对参照针头的评分，计算预测针头的预测分值。
  13. 如权利要求8所述的一次性采血器针头终末消毒系统，其特征在于，所述弹力驱动模块包括扭断驱动模块、弹射模块；
    扭断驱动模块，用于通过扭断驱动块将采血针与针座分离，使用采血针露出壳体；
    弹射模块，用于通过弹簧提供弹射动力。
  14. 如权利要求8所述的一次性采血器针头终末消毒系统，其特征在于，所述针头消毒模块包括加热模块，用于通过加热器加热水对使用后的针头进行加热消毒：
    对艾滋病病毒，灭活加热100℃，并持续20分钟；
    对于不能耐高温的培养基成分，以115℃度的水持续加热30-40分钟；
    对于除艾滋病病毒与不能耐高温的培养基成分的其它成份，以121℃度的水持续20分钟。
  15. 如权利要求8所述的一次性采血器针头终末消毒系统，其特征在于，所述针头消毒模块还包括液体消毒模块，用于通过将采血针插入盛放有消毒液的消毒腔对采血针进行消毒。
PCT/CN2019/084488 2018-04-27 2019-04-26 一种一次性采血器针头终末消毒方法及终末消毒系统 Ceased WO2019206264A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810390548.XA CN108888276B (zh) 2018-04-27 2018-04-27 一种能自动进行针头终末消毒的一次性采血器
CN201810390548.X 2018-04-27

Publications (1)

Publication Number Publication Date
WO2019206264A1 true WO2019206264A1 (zh) 2019-10-31

Family

ID=64342503

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/084488 Ceased WO2019206264A1 (zh) 2018-04-27 2019-04-26 一种一次性采血器针头终末消毒方法及终末消毒系统

Country Status (2)

Country Link
CN (1) CN108888276B (zh)
WO (1) WO2019206264A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108888276B (zh) * 2018-04-27 2021-06-18 中南大学 一种能自动进行针头终末消毒的一次性采血器

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101406397A (zh) * 2008-10-22 2009-04-15 陈国英 智能采血仪及采血方法
CN203609428U (zh) * 2013-11-15 2014-05-28 柳新荣 一次性采血器
CN105444870A (zh) * 2015-11-26 2016-03-30 黑龙江科技大学 一种采用小波技术的非周期性微小信号检测方法
CN206687716U (zh) * 2016-12-19 2017-12-01 厦门精配软件工程有限公司 一种自动便携抽血泵
CN107527236A (zh) * 2017-08-10 2017-12-29 云南财经大学 一种基于市场效应的协同过滤推荐方法及推荐系统
CN108888276A (zh) * 2018-04-27 2018-11-27 中南大学 一种能自动进行针头终末消毒的一次性采血器

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101406397A (zh) * 2008-10-22 2009-04-15 陈国英 智能采血仪及采血方法
CN203609428U (zh) * 2013-11-15 2014-05-28 柳新荣 一次性采血器
CN105444870A (zh) * 2015-11-26 2016-03-30 黑龙江科技大学 一种采用小波技术的非周期性微小信号检测方法
CN206687716U (zh) * 2016-12-19 2017-12-01 厦门精配软件工程有限公司 一种自动便携抽血泵
CN107527236A (zh) * 2017-08-10 2017-12-29 云南财经大学 一种基于市场效应的协同过滤推荐方法及推荐系统
CN108888276A (zh) * 2018-04-27 2018-11-27 中南大学 一种能自动进行针头终末消毒的一次性采血器

Also Published As

Publication number Publication date
CN108888276A (zh) 2018-11-27
CN108888276B (zh) 2021-06-18

Similar Documents

Publication Publication Date Title
CN107610779B (zh) 疾病评价及患病风险评估方法及装置
Peter et al. An empirical study on prediction of heart disease using classification data mining techniques
Sarasvananda et al. The k-means clustering algorithm with semantic similarity to estimate the cost of hospitalization
CN112786203A (zh) 一种机器学习糖尿病视网膜病变发病风险预测方法及应用
CN111581300A (zh) 一种基于健康医疗数据的标签矩阵构建及更新方法
CN119541840B (zh) 一种肿瘤患者智能化远程随访方法及系统
Assegie et al. Extraction of human understandable insight from machine learning model for diabetes prediction
WO2019206264A1 (zh) 一种一次性采血器针头终末消毒方法及终末消毒系统
CN114334053A (zh) 一种vte智能防控辅助决策系统
CN108877932A (zh) 智慧云医疗方法、计算机可读存储介质和终端
Yao et al. Long‐Term Used Continuous Blood Pressure Monitoring via Personalized Object Transfer Learning Architecture Enhanced Triboelectric Pulse Sensors
Chen et al. Dynamic feature selection in medical predictive monitoring by reinforcement learning
Sundas et al. Comprehensive study of machine learning-based systems for early warning of clinical deterioration
Lagunzad et al. Predicting the early sign of diabetes using ID3 as a data model
WO2023123913A1 (zh) 一种病情检测模型训练、检测方法、装置和电子设备
EP4609406A1 (en) Cancer progression assessment method and system thereof
Tang et al. A short recorded pulse dataset for vascular age prediction in China
CN120873703B (zh) 一种术后睡眠障碍关键因子识别方法
Chen et al. Hypothesis generation and data quality assessment through association mining
CN120413060B (zh) 一种呼吸疾病风险评估系统及方法
Datta et al. Towards Precision Medicine: Nature-Inspired Optimization Algorithms for Personalized Cholesterol Disease Prediction
Dong Research on Electronic Signal Conversion Based on DTW-K-means Clustering Algorithm and Transformer Model
Rao et al. Machine Learning Approaches for Diabetes Prediction: Comparative Analysis and Pre-processing Insights
Mehta et al. Smart Diagnosis: Leveraging Machine Learning for Early Detection of Hepatitis in Healthcare.
Xiong Design and research of non-invasive sleep monitor

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: 19792752

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19792752

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 14.05.2021)

122 Ep: pct application non-entry in european phase

Ref document number: 19792752

Country of ref document: EP

Kind code of ref document: A1