CN113536189A - Steam pressure sterilizer sterilization effect judgment method - Google Patents
Steam pressure sterilizer sterilization effect judgment method Download PDFInfo
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- CN113536189A CN113536189A CN202010319599.0A CN202010319599A CN113536189A CN 113536189 A CN113536189 A CN 113536189A CN 202010319599 A CN202010319599 A CN 202010319599A CN 113536189 A CN113536189 A CN 113536189A
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- 230000001954 sterilising effect Effects 0.000 title claims abstract description 153
- 238000004659 sterilization and disinfection Methods 0.000 title claims abstract description 153
- 238000000034 method Methods 0.000 title claims abstract description 85
- 230000000694 effects Effects 0.000 title claims abstract description 15
- 230000002159 abnormal effect Effects 0.000 claims abstract description 14
- 238000013075 data extraction Methods 0.000 claims abstract description 3
- 230000000630 rising effect Effects 0.000 claims description 16
- 238000005086 pumping Methods 0.000 claims description 7
- 230000001174 ascending effect Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000001816 cooling Methods 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 239000012634 fragment Substances 0.000 abstract description 2
- 239000000284 extract Substances 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000002459 sustained effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61L—METHODS OR APPARATUS FOR STERILISING MATERIALS OR OBJECTS IN GENERAL; DISINFECTION, STERILISATION OR DEODORISATION OF AIR; CHEMICAL ASPECTS OF BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES; MATERIALS FOR BANDAGES, DRESSINGS, ABSORBENT PADS OR SURGICAL ARTICLES
- A61L2/00—Methods or apparatus for disinfecting or sterilising materials or objects other than foodstuffs or contact lenses; Accessories therefor
- A61L2/02—Methods or apparatus for disinfecting or sterilising materials or objects other than foodstuffs or contact lenses; Accessories therefor using physical phenomena
- A61L2/04—Heat
- A61L2/06—Hot gas
- A61L2/07—Steam
Abstract
The invention belongs to the application field of sterilizers, and particularly relates to a sterilization effect judgment method of a steam pressure sterilizer, which comprises the steps of continuously extracting and judging data of a suspected sterilization process aiming at a section of data, and replacing the extracted data with a special value after judgment to show that the data are analyzed until the data of the suspected sterilization process do not exist in the data section; the data extraction method of the suspected sterilization process comprises the following steps: searching the highest point of the data in the data section, sequentially searching data turning points in two directions in front of and behind a time axis at the highest point until the initial point is found, segmenting the data section into a sterilization vacuumizing section, a high-temperature and high-pressure sterilization section and a temperature and pressure reduction section after sterilization according to each turning point, and sequentially judging the characteristic label of the section of data according to the characteristics of three sections of data types after the data section is segmented. The method can be well suitable for practical scenes, can identify a plurality of special sterilization abnormal waveforms, and prevents the fragments of a complete sterilization process from being mistakenly judged as sterilization abnormal.
Description
Technical Field
The invention belongs to the application field of sterilizers, and particularly relates to a method for judging the sterilization effect of a steam pressure sterilizer.
Background
National standards of temperature and pressure in the steam pressure sterilizer sterilization process:
a) in the whole sterilization cycle, the measured value of the sterilization temperature range is not lower than a set value (shown in the following table) and is not higher than a set value 3 ℃, and the difference value of any 2 points in a sterilization chamber does not exceed 2 ℃;
b) the measured pressure range should correspond to the measured temperature range;
c) the measured value of the sterilization time is not lower than the set value and not more than 10 percent of the set value.
| Temperature/. degree.C | Minimum sterilization time/min | Relative pressure/kPa |
| 121 | 15 | 103.6 |
| 132 | 4 | 185.4 |
| 134 | 3 | 202.8 |
Temperature and pressure values in a sterilization chamber are collected in real time through temperature and pressure sensors arranged on a sterilizer, a simple threshold judgment algorithm is too complex and redundant in logic rule and cannot be well applied to actual scenes, a plurality of special sterilization abnormal waveforms cannot be identified, and meanwhile, a complete sterilization process segment can be mistakenly judged as sterilization abnormality.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides the sterilization effect judgment method of the steam pressure sterilizer, which continuously extracts and judges data of a suspected sterilization process aiming at a section of data, replaces the extracted data with a special value after judgment, shows that the extracted data are analyzed until the data of the suspected sterilization process do not exist in the data section, can be well suitable for practical scenes, can identify a plurality of special sterilization abnormal waveforms, and prevents the error judgment of the section of a complete sterilization process as sterilization abnormal.
The technical scheme of the invention is as follows: a steam pressure sterilizer sterilization effect judging method comprises the steps of continuously extracting and judging suspected sterilization process data aiming at a section of data, and replacing the extracted data with a special value after judgment to indicate that the extracted data are analyzed until the data of the suspected sterilization process do not exist in the data section; the data extraction method of the suspected sterilization process comprises the following steps: searching the highest point of the data in the data section, sequentially searching data turning points in two directions in front of and behind a time axis at the highest point until the initial point is found, segmenting the data section into a sterilization vacuumizing section, a high-temperature and high-pressure sterilization section and a temperature and pressure reduction section after sterilization according to each turning point, and sequentially judging the characteristic label of the section of data according to the characteristics of three sections of data types after the data section is segmented.
Preferably, the logic for judging the feature tag of the three pieces of data is as follows:
aiming at each segmented data segment, the rising and falling speed of the temperature and the pressure is obtained through fitting, the duration time of the high temperature/high pressure is counted, corresponding attribute labels are further given to the data segment, the time sequence of the sterilization process is comprehensively considered, and the data segment sequentially comprises a vacuumizing spike segment, a high-temperature high-pressure sterilization segment and a suspected sterilization cooling and pressure reduction segment.
Preferably, the sterilization periodicity of the autoclave data is judged by the following process:
1) if the sterilization period exists, judging whether the left adjacent data segment has a spine characteristic, and if so, meeting the high-temperature and high-pressure period characteristic, indicating that the extracted data is a one-time complete sterilization data segment, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to data information if the sterilization process reaches the standard, generating a sterilization abnormal event if the sterilization event does not reach the standard, and replacing the extracted whole segment of data with a special value after the event is generated;
2) if the high-temperature and high-pressure sterilization section data have spine characteristics and the left section data also have spine characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information if the sterilization process reaches the national standard, generating a sterilization abnormal event if the sterilization process does not reach the national standard, and replacing the whole section of extracted data with a special value after the event is generated;
3) the high-temperature and high-pressure sterilization section data with other characteristics are meaningless, and the whole extracted section of data is replaced by a special value.
Preferably, the three-segment data is characterized as follows:
and (3) sterilization and vacuum-pumping section: the data rises and falls rapidly, and the duration time of high temperature and high pressure is short, like a sharp thorn;
high-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high temperature and high pressure duration is long and relatively stable;
temperature pressure drop section after completion of sterilization: the data rise and fall are rapid, the duration of the high-temperature section is long, and the characteristic exists in the sterilization process of a partial sterilization pot.
Preferably, the searching method of the high-temperature and high-pressure sterilization section data is as follows: and searching the highest point of the data, traversing towards two sides in sequence, and finding out the high-temperature/high-pressure left and right end points which meet the requirement that the numerical difference value is within a specified range.
Preferably, the method for searching the data turning point is as follows: and defining the point of a section of complete sterilization data which has a trend change in a specific direction and can be cut into a sterilization vacuum-pumping section, high-temperature high-pressure sterilization data and a sterilized complete segment as a turning point, continuously searching leftwards for the left end point of the high-temperature/high-pressure data, and recording the point of the trend change.
Preferably, the left end point of the high-temperature/high-pressure data is compared leftwards according to the opposite direction of the time axis, the left side of the turning point is in an ascending trend, and the right side of the turning point is in a descending trend; the right end points of the high-temperature/high-pressure data are compared rightwards according to the direction of a time axis, the left side of the turning point is in a descending trend, and the right side of the turning point is in an ascending trend.
Preferably, the method for determining the data rising and falling rates is as follows: the data of the high-temperature and high-pressure sterilization section can be subdivided into three parts, namely data rising, high-temperature and high-pressure and data falling, through turning points and high-temperature and high-pressure end points, and the rising and falling rates are calculated by adopting least square normal linear fitting aiming at the data rising and data falling.
Preferably, the least squares normative fit calculation procedure is as follows:
given a set of data (x)i,yi) I-0, 1., m-1, a fitted line p (x) a + bx is made, and the mean square error is:
in calculus theory, the minimum value of Q (a, b) is satisfied:
arranging into a matrix form:
this is called the normal equation of the fitted curve, which is solved by the elimination method or the gram method:
the method continuously extracts and judges the data of the suspected sterilization process aiming at a section of data, and replaces the extracted data with a special value after judgment, so that the analyzed data are shown, the method is well suitable for practical scenes until the data of the suspected sterilization process do not exist in the data section, a plurality of special sterilization abnormal waveforms can be identified, and the phenomenon that a complete sterilization process fragment is mistakenly judged as sterilization abnormal is prevented.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic of experimental data of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the attached drawings, but the present invention is not limited thereto.
As shown in fig. 1, a sterilization determination method for a pressure steam sterilizer continuously extracts data of a suspected sterilization process for a section of data, determines the data, and replaces the extracted data with a special value (indicating that the data has been analyzed) until the data of the suspected sterilization process does not exist in the data section. The suspected sterilization process extraction method comprises the following steps: searching the highest point of the data in the data section, sequentially searching data turning points in two directions before and after the time axis of the highest point until the initial point (the temperature and the pressure value of the normal environment) is found, and segmenting the data section into a sterilization vacuum-pumping section according to each turning point (the number of times of vacuumizing in the one-time complete sterilization process is not fixed according to different sterilization standards), a high-temperature high-pressure sterilization section and a temperature pressure reduction section after sterilization is finished. And after the data segment is segmented, sequentially judging the characteristic label of the segment of data according to the characteristics of the types of the three segments of data. The following logical decision is made:
and aiming at each segmented data segment, obtaining the rate of rise and fall of temperature and pressure (the speed of fall of temperature/pressure) through fitting, counting the duration time of high temperature/high pressure, and further giving corresponding attribute labels (spine, sterilization and suspected sterilization) to the data segment. The comprehensive consideration of the time sequence of the sterilization process includes a vacuum-pumping spine section (possibly multiple sections), a high-temperature high-pressure sterilization section and a suspected sterilization cooling and pressure reduction section.
First, the sterilization periodicity of the autoclave sterilization zone data (rapid rise and fall, sustained high temperature and high pressure for a certain period and stable) is judged
1) If the sterilization period exists, judging whether the left adjacent data segment has the spike characteristic, and if so, meeting the high-temperature and high-pressure period characteristic, indicating that the extracted data is a one-time complete sterilization data segment, further judging whether the sterilization process reaches the national standard (as described in the background technology), generating a sterilization event according to the data information when the sterilization event reaches the standard, generating a sterilization abnormal event when the sterilization event does not reach the standard, and replacing the extracted data of the whole segment with a special value after the event is generated;
2) if the high-temperature and high-pressure sterilization section data have spine characteristics and the left section data also have spine characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information when the sterilization process reaches the standard, generating a sterilization abnormal event when the sterilization abnormal event does not reach the standard, and replacing the whole section of extracted data with a special value after the event is generated;
3) the high-temperature and high-pressure sterilization section data with other characteristics are meaningless, and the whole extracted section of data is replaced by a special value.
Note: the following are characteristics of the three data segments (as shown in FIG. 2):
1. and (3) sterilization and vacuum-pumping section: the data is rising and falling rapidly, and the duration of high temperature and high pressure is short, like a sharp thorn.
2. High-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high temperature and high pressure duration is long and relatively stable.
3. Temperature pressure drop section after completion of sterilization: the data rise and fall are rapid, the duration of the high-temperature section is long, and the characteristic exists in the sterilization process of a partial sterilization pot.
The searching method of the high-temperature data segment is as follows: and searching the highest point of the data, traversing towards two sides in sequence, and finding out the high-temperature/high-pressure left and right end points which meet the requirement that the numerical difference value is within a specified range.
The searching method of the turning point comprises the following steps: a point of a section of complete sterilization data which changes in trend in a specific direction and can be divided into a sterilization vacuumizing section, high-temperature and high-pressure sterilization data and a sterilization complete section is defined as a turning point, a left end point of the high-temperature/high-pressure data is continuously searched leftwards, and the point of the change in trend is recorded (the left end point of the high-temperature/high-pressure data is compared leftwards according to the opposite direction of a time axis, the left side of the turning point is in an ascending trend, the right side of the turning point is in a descending trend, and the right end point of the high-temperature/high-pressure data is compared rightwards according to the direction of the time axis, the left side of the turning point is in a descending trend, and the right side of the turning point is in an ascending trend).
Judging the rising and falling rates of data: the data of the high-temperature and high-pressure sterilization section can be subdivided into three parts, namely data rising, high-temperature and high-pressure and data falling, through a turning point and a high-temperature and high-pressure endpoint. For data rise and data fall, the rate of rise and fall is calculated using least squares linear fit.
The least squares linear fit calculation procedure is as follows:
given a set of data (x)i,yi) I-0, 1.., m-1, a fitted straight line p (x) a + bx with a mean square error of
In calculus theory, the minimum value of Q (a, b) is satisfied
Arranging into a matrix form:
this is called the normal equation of the fitted curve, which is solved by the elimination method or the gram method:
Claims (9)
1. a sterilization effect judgment method of a steam pressure sterilizer is characterized by comprising the following steps: continuously extracting data of a suspected sterilization process from a section of data, judging, and replacing the extracted data with a special value after judgment, wherein the data is analyzed until the data of the suspected sterilization process does not exist in the data section; the data extraction method of the suspected sterilization process comprises the following steps: searching the highest point of the data in the data section, sequentially searching data turning points in two directions in front of and behind a time axis at the highest point until the initial point is found, segmenting the data section into a sterilization vacuumizing section, a high-temperature and high-pressure sterilization section and a temperature and pressure reduction section after sterilization according to each turning point, and sequentially judging the characteristic label of the section of data according to the characteristics of three sections of data types after the data section is segmented.
2. The sterilization effect judgment method of a steam sterilizer according to claim 1, wherein: the logic judgment of judging the feature labels of the three sections of data is as follows:
aiming at each segmented data segment, the rising and falling speed of the temperature and the pressure is obtained through fitting, the duration time of the high temperature/high pressure is counted, corresponding attribute labels are further given to the data segment, the time sequence of the sterilization process is comprehensively considered, and the data segment sequentially comprises a vacuumizing spike segment, a high-temperature high-pressure sterilization segment and a suspected sterilization cooling and pressure reduction segment.
3. The sterilization effect judgment method of a steam sterilizer according to claim 2, wherein: judging the sterilization periodicity of the high-temperature high-pressure sterilization section data, wherein the process is as follows:
1) if the sterilization period exists, judging whether the left adjacent data segment has a spine characteristic, and if so, meeting the high-temperature and high-pressure period characteristic, indicating that the extracted data is a one-time complete sterilization data segment, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to data information if the sterilization process reaches the standard, generating a sterilization abnormal event if the sterilization event does not reach the standard, and replacing the extracted whole segment of data with a special value after the event is generated;
2) if the high-temperature and high-pressure sterilization section data have spine characteristics and the left section data also have spine characteristics, further judging whether the sterilization process reaches the national standard, generating a sterilization event according to the data information if the sterilization process reaches the national standard, generating a sterilization abnormal event if the sterilization process does not reach the national standard, and replacing the whole section of extracted data with a special value after the event is generated;
3) the high-temperature and high-pressure sterilization section data with other characteristics are meaningless, and the whole extracted section of data is replaced by a special value.
4. The sterilization effect judgment method of a steam sterilizer according to claim 3, wherein: the characteristics of the three data sections are as follows:
and (3) sterilization and vacuum-pumping section: the data rises and falls rapidly, and the duration time of high temperature and high pressure is short, like a sharp thorn;
high-temperature high-pressure sterilization section: the data rising and falling process is rapid, and the high temperature and high pressure duration is long and relatively stable;
temperature pressure drop section after completion of sterilization: the data rise and fall are rapid, the duration of the high-temperature section is long, and the characteristic exists in the sterilization process of a partial sterilization pot.
5. The sterilization effect judgment method of a steam sterilizer according to claim 3, wherein: the searching method of the high-temperature and high-pressure sterilization section data comprises the following steps: and searching the highest point of the data, traversing towards two sides in sequence, and finding out the high-temperature/high-pressure left and right end points which meet the requirement that the numerical difference value is within a specified range.
6. The sterilization effect judgment method of a steam sterilizer according to claim 3, wherein: the method for searching the data turning point comprises the following steps: and defining the point of a section of complete sterilization data which has a trend change in a specific direction and can be cut into a sterilization vacuum-pumping section, high-temperature high-pressure sterilization data and a sterilized complete segment as a turning point, continuously searching leftwards for the left end point of the high-temperature/high-pressure data, and recording the point of the trend change.
7. The sterilization effect judgment method of a steam sterilizer according to claim 6, wherein: comparing the left end points of the high-temperature/high-pressure data leftwards according to the opposite direction of the time axis, wherein the left side of the turning point is in an ascending trend, and the right side of the turning point is in a descending trend; the right end points of the high-temperature/high-pressure data are compared rightwards according to the direction of a time axis, the left side of the turning point is in a descending trend, and the right side of the turning point is in an ascending trend.
8. The sterilization effect judgment method of a steam sterilizer according to claim 3, wherein: the method for judging the rising and falling rate of the data is as follows: the data of the high-temperature and high-pressure sterilization section can be subdivided into three parts, namely data rising, high-temperature and high-pressure and data falling, through turning points and high-temperature and high-pressure end points, and the rising and falling rates are calculated by adopting least square normal linear fitting aiming at the data rising and data falling.
9. The sterilization effect judgment method of a steam sterilizer according to claim 8, wherein: the least squares linear fit calculation procedure is as follows:
given a set of data (x)i,yi) I-0, 1., m-1, a fitted line p (x) a + bx is made, and the mean square error is:
in calculus theory, the minimum value of Q (a, b) is satisfied:
arranging into a matrix form:
this is called the normal equation of the fitted curve, which is solved by the elimination method or the gram method:
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