Method for judging cleaning process of general endoscope cleaning machine and application thereof

Abstract

The application discloses a method for judging a cleaning process of a general endoscope cleaning machine and application thereof, and comprises the following steps: adding a vibration data acquisition device with an acceleration sensor in the endoscope cleaning machine, and collecting acceleration data of x, y and z axes; performing correlation calculation of the acceleration data of any two axes of the x, y and z axes; obtaining correlation data of the acceleration data of the x, y and z axes, and selecting a fixed number of the correlation data to obtain a standard deviation; after performing feature extraction on the standard deviation, performing secondary feature extraction on the data obtained by the feature extraction again; and performing visual processing on the data obtained by the secondary feature extraction, and judging a cleaning stage of the endoscope cleaning machine. The method can intuitively identify the cleaning stage of the endoscope cleaning machine and the corresponding features of the stage through drawing, and realizes automatic data acquisition.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a method and application for determining a cleaning process of a universal endoscope cleaning machine. Background Art

[0002] In modern technology, endoscopes are common medical treatment devices, used in applications such as electronic gastroscopy, cholecystoscopy, enteroscopy, and laparoscopy. Because endoscopes come into direct contact with the patient's internal tissues during use, they must undergo strict handling procedures before being used on the next patient. In some workplaces, staff must manually enter report data into a designated information system after the endoscope cleaning process is completed in a medical endoscope cleaning machine.

[0003] After this process is completed, relevant information is typically printed out, including the time the decontamination process occurred, the specific steps in the decontamination process, the execution time for each step, the machine code, error messages, and reminders. Typically, management personnel will require operators to enter this information into the information system for subsequent use. However, in some cases, errors may occur during the input process, causing problems for subsequent use. This requires automated, unmanned collection of decontamination process report data and upload to the system.

[0004] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0005] The object of the present invention is to provide a universal method and application for judging the cleaning process of an endoscope cleaning machine, which can intuitively identify the cleaning stage of the endoscope cleaning machine and the corresponding characteristics of the stage through drawing.

[0006] To achieve the above objectives, an embodiment of the present invention provides a general method for determining the cleaning process of an endoscope cleaning machine.

[0007] In one or more embodiments of the present invention, a vibration data acquisition device with an acceleration sensor is added to an endoscope cleaning machine to collect acceleration data of the three axes x, y, and z; the correlation calculation of the acceleration data of any two axes of the acceleration data of the three axes x, y, and z is performed; the correlation data of the acceleration data of the three axes x, y, and z is obtained, and a fixed number of the correlation data are selected to calculate the standard deviation; after feature extraction of the standard deviation, secondary feature extraction is performed on the data obtained by feature extraction; and the data obtained by secondary feature extraction are visualized to determine the cleaning stage of the endoscope cleaning machine.

[0008] In one or more embodiments of the present invention, the acceleration data of the three axes x, y, and z are subjected to correlation calculation of the acceleration data of any two axes, and the correlation calculation formula is:

[0009] correlation(x i ,y i )=stdev(x i , x i+n ) / stdev(y i ,y i+n )

[0010] correlation(x i , z i )=stdev(x i , x i+n ) / stdev(z i , z i+n )

[0011] correlation(y i , z i )=stdev(y i ,y i+n ) / stdev(z i , z i+n )

[0012] where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, stdev(x i , x i+n ), stdev(y i ,y i+n ), stdev(z i , z i+n ) is the moving standard deviation of the acceleration data of the x, y, and z axes, and n is the number of items.

[0013] In one or more embodiments of the present invention, the feature extraction is performed on the standard deviation, and the calculation formula for the first feature extraction is:

[0014] Processed_x_std_1=round(std*n,-2),

[0015] That is, the standard deviation is magnified n times and the hundredth place of the magnified data is rounded off.

[0016] In one or more embodiments of the present invention, the data obtained by feature extraction is subjected to secondary feature extraction again, and the calculation formula for the secondary feature extraction is:

[0017] Processed_x_std_2=IF(Processed_x_std_1 <C1,n1,n2)-C2,

[0018] That is, determine whether processed_x_std_1 is greater than C1. If processed_x_std_1 is greater than C1, take n2. If processed_x_std_1 is less than C1, take n1. Among them, C1, C2, n1, and n2 are constants, and n1=-n2.

[0019] In one or more embodiments of the present invention, the visualizing the data obtained by the secondary feature extraction includes: graphing the data obtained by the secondary feature extraction to obtain a waveform graph of the data.

[0020] In one or more embodiments of the present invention, determining the cleaning stage of the endoscope cleaning machine includes: comparing and matching the waveform diagram based on the operating data of the endoscope cleaning machine, identifying the duration and characteristics of different cleaning stages of the endoscope cleaning machine, and determining the cleaning stage of the endoscope cleaning machine.

[0021] In one or more embodiments of the present invention, the cleaning process of the endoscope cleaning machine includes a water washing stage, an enzyme solution adding stage, an enzyme washing stage, an enzyme solution draining stage, a disinfectant adding stage, a disinfection stage, a disinfectant draining stage, and a drying stage.

[0022] In another aspect of the present invention, a general device for determining the cleaning process of an endoscope cleaning machine is provided, which includes a data acquisition module, a correlation calculation module, a standard deviation calculation module, a data processing module and a data determination module.

[0023] The data acquisition module is used to add a vibration data acquisition device with an acceleration sensor to the endoscope cleaning machine and collect acceleration data on the x, y, and z axes.

[0024] The correlation calculation module is used to perform correlation calculation on any two axes of the acceleration data of the three axes x, y, and z.

[0025] The standard deviation calculation module is used to obtain the correlation data of the three axes x, y, and z, and select a fixed number of nodes of the correlation data to calculate the standard deviation.

[0026] The data processing module is used to perform feature extraction on the data obtained by the feature extraction after the feature extraction is performed on the standard deviation, and to perform visualization processing on the data obtained by the secondary feature extraction.

[0027] The data judgment module is used to judge the cleaning stage of the endoscope cleaning machine through the visually processed data.

[0028] In one or more embodiments of the present invention, the correlation calculation module is further configured to: the correlation calculation formula is:

[0029] correlation(x i ,y i )=stdev(x i , x i+n ) / stdev(y i ,y i+n )

[0030] correlation(x i , z i )=stdev(x i , x i+n ) / stdev(z i , z i+n )

[0031] correlation(y i , z i )=stdev(y i ,y i+n ) / stdev(z i , z i+n )

[0032] where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, stdev(x i , x i+n ), stdev(y i ,y i+n ), stdev(z i , z i+n ) is the moving standard deviation of the acceleration data of the x, y, and z axes, and n is the number of items.

[0033] In one or more embodiments of the present invention, the standard deviation calculation module is further used to: the calculation formula for the first feature extraction is:

[0034] Processed_x_std_1=round(std*n,-2),

[0035] That is, the standard deviation is magnified 10 times and the hundredth place of the magnified data is rounded.

[0036] In one or more embodiments of the present invention, the data processing module is further configured to: calculate the secondary feature extraction formula as follows:

[0037] Processed_x_std_2=IF(Processed_x_std_1 <C1,n1,n2)-C2,

[0038] That is, determine whether processed_x_std_1 is greater than C1. If processed_x_std_1 is greater than C1, take n2. If processed_x_std_1 is less than C1, take n1. Among them, C1, C2, n1, and n2 are constants, and n1=-n2.

[0039] In one or more embodiments of the present invention, the data processing module is further configured to: perform graphing on the data obtained by the secondary feature extraction to obtain a waveform graph of the data.

[0040] In one or more embodiments of the present invention, the data judgment module is also used to: compare and match the waveform diagram based on the operating data of the endoscope cleaning machine, identify the duration and characteristics of different cleaning stages of the endoscope cleaning machine, and judge the cleaning stage of the endoscope cleaning machine.

[0041] In one or more embodiments of the present invention, the data judgment module is also used for: the stages of the cleaning process of the endoscope cleaning machine include water washing stage, enzyme solution adding stage, enzyme washing stage, enzyme solution emptying stage, disinfectant solution adding stage, disinfection stage, disinfectant solution emptying stage and drying stage.

[0042] In another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory, wherein the memory stores instructions, and when the instructions are executed by the at least one processor, the at least one processor executes the general method for determining the cleaning process of an endoscope cleaning machine as described above.

[0043] In another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining the cleaning process of a general endoscope cleaning machine as described above are implemented.

[0044] Compared with the prior art, the general method and application for judging the cleaning process of an endoscope cleaning machine according to the embodiment of the present invention can solve the errors in manually collecting the report data of the cleaning process of the cleaning machine. By collecting data through algorithms such as correlation calculation and feature extraction and visual processing, automatic data collection is realized, which avoids the errors caused by manual data collection and can effectively identify the cleaning stage of the endoscope cleaning machine and the corresponding characteristics of the stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of a method for determining a cleaning process of a general endoscope cleaning machine according to one embodiment of the present invention;

[0046] Figure 2 is an acceleration waveform diagram of a method for determining a cleaning process of a general endoscope cleaning machine according to an embodiment of the present invention;

[0047] Figure 3 This is a waveform diagram of correlation data of a method for determining a cleaning process of a general endoscope cleaning machine according to an embodiment of the present invention;

[0048] Figure 4 is a standard deviation waveform diagram of a method for determining a cleaning process of a universal endoscope cleaning machine according to an embodiment of the present invention;

[0049] Figure 5 is a square wave diagram of characteristic data of a method for determining a cleaning process of a universal endoscope cleaning machine according to an embodiment of the present invention;

[0050] Figure 6 This is a data comparison diagram of a method for determining a cleaning process of a general endoscope cleaning machine according to an embodiment of the present invention;

[0051] Figure 7 1 is a structural diagram of a general endoscope cleaning process determination device according to one embodiment of the present invention;

[0052] Figure 8 1 is a hardware structure diagram of a computing device for determining a cleaning process of a general endoscope cleaning machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0054] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations such as "include" or "comprising", etc., will be understood to include the stated elements or components but not to exclude other elements or other components.

[0055] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] Example 1

[0057] like Figure 1 As shown in the figure, a general method for determining the cleaning process of an endoscope cleaning machine in one embodiment of the present invention is introduced, and the method includes the following steps.

[0058] In step S101 , a vibration data acquisition device with an acceleration sensor is added to an endoscope cleaning machine to acquire acceleration data of the x, y, and z axes.

[0059] Specifically, a vibration data acquisition device is added to the endoscope cleaning machine. The vibration data acquisition device includes an acceleration sensor. The acceleration sensor is used to collect vibration parameters, namely acceleration data of the three axes x, y, and z.

[0060] like Figure 2 As shown, the waveform diagram of the acceleration data of the three axes x, y, and z is drawn with the acquisition time as the horizontal axis and the conversion value of the acceleration of the three axes x, y, and z as the vertical axis.

[0061] In step S102 , the acceleration data of the three axes x, y, and z are correlated with the acceleration data of any two axes.

[0062] Correlation analysis refers to the analysis of two or more correlated variable elements to measure the degree of correlation between the two factors. Correlation analysis can only be performed if there is a certain connection or probability between the correlated elements.

[0063] Depend on Figure 2 It can be seen that the acceleration data of the x, y, and z axes are clearly concentrated, that is, it is found that the acceleration data of the x, y, and z axes are correlated.

[0064] Through correlation calculation, we can get correlation(xi,yi), correlation(x i , z i ) and correlation(y i , z i ), the correlation calculation formula is as follows:

[0065] correlation(x i ,y i )=stdev(x i , x i+n ) / stdev(y i ,y i+n )

[0066] correlation(x i , z i )=stdev(x i , x i+n ) / stdev(z i , z i+n )

[0067] correlation(y i , z i )=stdev(y i ,y i+n ) / stdev(z i , z i+n )

[0068] where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, stdev(x i , x i+n ), stdev(y i ,y i+n ), stdev(z i , z i+n ) is the moving standard deviation of the acceleration data along the x, y, and z axes, and n is the number of items. The moving standard deviation can be used to smooth the acceleration data along the three axes and reduce data noise.

[0069] Specifically, taking every 20 data, that is, n=20, as an example, the acceleration data of the x, y, and z axes are correlated, that is, the correlation calculation formula is:

[0070] correlation(x i ,y i )=stdev(x i , x i+20 ) / stdev(y i ,y i+20 )

[0071] correlation(x i , z i )=stdev(x i , x i+20 ) / stdev(z i , z i+20 )

[0072] correlation(y i , z i )=stdev(y i ,y i+20 ) / stdev(z i , z i+20 )

[0073] In addition to using the moving standard deviation, the correlation calculation formula can also be calculated using the mean absolute deviation. The mean absolute deviation (MAD) is used to indicate the degree of dispersion of a set of data. It is calculated by taking the absolute value of the difference between each data point and the mean of the data, and then averaging all the differences. Compared to calculating the moving standard deviation of the acceleration data of the x, y, and z axes, the calculation amount of the mean absolute deviation is smaller. The calculation formula is as follows:

[0074]

[0075] That is, the formula for calculating the correlation can also be as follows:

[0076] correlation(x i ,y i )=MAD(x i , x i+n ) / MAD(y i ,y i+n )

[0077] correlation(x i , z i )=MAD(x i , x i+n ) / MAD(z i , z i+n )

[0078] correlation(y i , z i )=MAD(y i ,y i+n ) / MAD(z i , z i+n )

[0079] where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, MAD(x i , x i+n ), MAD(y i ,y i+n ), MAD(z i , z i+n ) is the mean absolute deviation of the acceleration data of the x, y, and z axes, and n is the number of items.

[0080] like Figure 3 As shown, plot correlation(xi,yi), correlation(x i , z i ) and correlation(y i , zi ) waveform, it can be determined that the acceleration data of the x, y, and z axes have a strong correlation.

[0081] In step S103, correlation data of the acceleration data of the three axes x, y, and z are obtained, and a fixed number of the correlation data are selected to calculate the standard deviation.

[0082] In this embodiment, the standard deviation of the correlation data of the acceleration data of the three axes x, y, and z is calculated based on a fixed number of data.

[0083] The greater the fluctuation of the correlation data, the larger the standard deviation. The standard deviation can reflect the amplitude of data changes to a certain extent.

[0084] Specifically, taking the x-axis data, i.e., correlation (xi, yi), as an example, the standard deviation is calculated for every 60 data nodes.

[0085] like Figure 4 As shown, the waveform diagram in the figure includes the waveform diagram of the original data of the x-axis and the data after the standard deviation of the original data of the x-axis is taken, wherein the original data of the x-axis refers to the correlation data correlation (xi, yi) of the acceleration data of the x-axis.

[0086] In step S104, after feature extraction is performed on the standard deviation, secondary feature extraction is performed again on the data obtained by feature extraction.

[0087] In this embodiment, the first feature extraction is performed on the standard deviation data based on the standard deviation.

[0088] Specifically, feature extraction is performed on the standard deviation data, the standard deviation is magnified n times, and the calculation formula for the first feature extraction is rounded to the hundredth place:

[0089] Processed_x_std_1=round(std*n,-2),

[0090] That is, the standard deviation is magnified 10 times and the hundredth place of the magnified data is rounded.

[0091] For the example in step S103, taking the x-axis data, i.e., correlation (xi, yi), as an example, the standard deviation is calculated for every 60 data nodes. At this time, the standard deviation data is magnified 10 times and rounded to the hundredth place. The calculation formula for the feature extraction is:

[0092] Processed_x_std_1=round(std*10,-2).

[0093] Furthermore, a secondary feature extraction is performed on the data after the first feature extraction to determine whether the processed_x_std_1 is greater than C1. If processed_x_std_1 is greater than C1, n2 is taken; if processed_x_std_1 is less than C1, n1 is taken. C1, C2, n1, and n2 are constants, and n1 = -n2. The calculation formula for the secondary feature extraction is:

[0094] Processed_x_std_2=IF(Processed_x_std_1 <C1,n1,n2)-C2。

[0095] Specifically, for the data after the first feature extraction with the standard deviation data magnified 10 times and rounded to the hundredth place, the calculation formula for the second feature extraction can be:

[0096] Processed_x_std_2=IF(Processed_x_std_1<1000,-100,100)-500,

[0097] That is, it is determined whether processed_x_std_1 is greater than 1000. If processed_x_std_1 is greater than 1000, 100 is taken as positive, and if processed_x_std_1 is less than 1000, 100 is taken as negative.

[0098] It should be noted that the specific data values ​​exemplified in this embodiment are intended to make the drawn graphics display intuitive. In actual applications, data values ​​should be determined based on actual conditions. The specific data values ​​exemplified in this embodiment should not be regarded as limiting the features or scope included in the method of this application, but are only for the purpose of more clearly describing the technical solution of this application.

[0099] In step S105 , the data obtained by the secondary feature extraction is visualized and the cleaning stage of the endoscope cleaning machine is determined.

[0100] In this embodiment, the data obtained from the secondary feature extraction is graphed to obtain a waveform diagram of the data, which is then compared and matched with the waveform diagram based on the operating data of the endoscope cleaning machine to identify the duration and characteristics of different cleaning stages of the endoscope cleaning machine and determine the cleaning stage of the endoscope cleaning machine.

[0101] The cleaning process of the endoscope cleaning machine includes a water washing stage, an enzyme solution adding stage, an enzyme washing stage, an enzyme solution draining stage, a disinfectant solution adding stage, a disinfection stage, a disinfection stage, a disinfectant solution draining stage and a drying stage.

[0102] like Figure 5As shown, based on the above example, the standard deviation data is magnified 10 times, and the data after the first feature extraction is rounded to the hundredth place, and the data obtained by the second feature extraction is graphed to obtain a waveform diagram of the data after the second feature extraction.

[0103] Specifically, the cleaning stage is judged based on an actual use example of an endoscope cleaning machine. The operating parameters printed by the endoscope cleaning machine are as follows:

[0104] Decontamination: 21 minutes and 20 seconds; Disinfectant temperature: 23°C; Water washing (1): 1 minute and 00 seconds; Enzyme washing: 2 minutes and 00 seconds; Water washing (2): 1 minute and 00 seconds; Disinfection: 15 minutes and 00 seconds; Water washing (3): 2 minutes and 00 seconds; Blow drying: 0 minutes and 20 seconds.

[0105] Printing parameters also include parameters such as inner diameter code, operator and printing time.

[0106] The above actual operation output data is structured, as shown in Table 1:

[0107]

[0108]

[0109] Table 1: Actual output data table

[0110] Furthermore, based on the actual output data of the endoscope cleaning machine, Figure 5 By comparing and matching the waveform diagram of the data after the secondary feature extraction, the duration and characteristics of the different cleaning stages of the endoscope cleaning machine during this operation can be intuitively identified, thereby determining the different cleaning stages of the endoscope cleaning machine.

[0111] like Figure 6 As shown, the above comparison can clearly identify the characteristics of each cleaning stage of the endoscope cleaning machine. Taking the actual operation output data of the endoscope cleaning machine as the comparison blueprint and the waveform diagram of the data after secondary feature extraction as the comparison source, the duration and corresponding characteristics of each cleaning process corresponding to the operation of the endoscope cleaning machine can be clearly identified.

[0112] According to the general method and application of judging the cleaning process of an endoscope cleaning machine according to the embodiment of the present invention, it can solve the errors caused by manual operation in collecting the report data of the cleaning process of the cleaning machine. By collecting data through algorithms such as correlation calculation and feature extraction and visual processing, automatic data collection is realized, and the errors caused by manual data collection are avoided. It can effectively identify the cleaning stage of the endoscope cleaning machine and the corresponding characteristics of the stage.

[0113] like Figure 7As shown, a general device for determining the cleaning process of an endoscope cleaning machine according to a specific embodiment of the present invention is introduced.

[0114] In an embodiment of the present invention, a general device for determining a cleaning process of an endoscope cleaning machine includes a data acquisition module 701 , a correlation calculation module 702 , a standard deviation calculation module 703 , a data processing module 704 and a data determination module 705 .

[0115] The data acquisition module 701 is used to add a vibration data acquisition device with an acceleration sensor to the endoscope cleaning machine and collect acceleration data of the three axes x, y, and z.

[0116] The correlation calculation module 702 is used to perform correlation calculation on any two axes of the acceleration data of the three axes x, y, and z.

[0117] The standard deviation calculation module 703 is used to obtain the correlation data of the three axes x, y, and z, and select a fixed number of nodes of the correlation data to calculate the standard deviation.

[0118] The data processing module 704 is used to perform feature extraction on the data obtained by the feature extraction after performing feature extraction on the standard deviation, and perform visualization processing on the data obtained by the secondary feature extraction.

[0119] The data judgment module 705 is used to judge the cleaning stage of the endoscope cleaning machine based on the visualized data.

[0120] The correlation calculation module 702 is further used to: the correlation calculation formula is:

[0121] correlation(x i ,y i )=stdev(x i , x i+n ) / stdev(y i ,y i+n )

[0122] correlation(x i , z i )=stdev(x i , x i+n ) / stdev(z i , z i+n )

[0123] correlation(y i , z i )=stdev(y i ,y i+n ) / stdev(z i , zi+n )

[0124] where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, stdev(x i , x i+n ), stdev(y i ,y i+n ), stdev(z i , z i+n ) is the moving standard deviation of the acceleration data of the x, y, and z axes, n is the number of items, and a value of n is 20.

[0125] The standard deviation calculation module 703 is also used to: the calculation formula for the first feature extraction is:

[0126] Processed_x_std_1=round(std*10,-2),

[0127] That is, the standard deviation is magnified 10 times and the hundredth place of the magnified data is rounded.

[0128] The data processing module 704 is also used to: the calculation formula for the secondary feature extraction is:

[0129] Processed_x_std_2=IF(Processed_x_std_1<1000,-100,100)-500,

[0130] That is, it is determined whether processed_x_std_1 is greater than 1000. If processed_x_std_1 is greater than 1000, 100 is taken as positive, and if processed_x_std_1 is less than 1000, 100 is taken as negative.

[0131] The data processing module 704 is further configured to perform graphing on the data obtained by the secondary feature extraction to obtain a waveform graph of the data.

[0132] The data judgment module 705 is further used to compare and match the operating data of the endoscope cleaning machine with the waveform diagram, identify the duration and characteristics of different cleaning stages of the endoscope cleaning machine, and judge the cleaning stage of the endoscope cleaning machine.

[0133] The data judgment module 705 is also used for: the stages of the cleaning process of the endoscope cleaning machine include water washing stage, enzyme solution adding stage, enzyme washing stage, enzyme solution draining stage, disinfectant solution adding stage, disinfection stage, disinfectant solution draining stage and drying stage.

[0134] Figure 8FIG. 8 shows a hardware structure diagram of a computing device 80 for determining a cleaning process of a general endoscope cleaning machine according to an embodiment of the present specification. Figure 8 As shown, the computing device 80 may include at least one processor 801, a memory 802 (e.g., a non-volatile memory), a storage 803, and a communication interface 804, and the at least one processor 801, the memory 802, the storage 803, and the communication interface 804 are connected together via a bus 805. The at least one processor 801 executes at least one computer-readable instruction stored or encoded in the storage 802.

[0135] It should be understood that the computer executable instructions stored in the memory 802, when executed, cause at least one processor 801 to perform the above combined operations in various embodiments of this specification. Figure 1-8 Describes the various operations and functions.

[0136] In the embodiments of the present specification, the computing device 80 may include, but is not limited to, a personal computer, a server computer, a workstation, a desktop computer, a laptop computer, a notebook computer, a mobile computing device, a smart phone, a tablet computer, a cellular phone, a personal digital assistant (PDA), a handheld device, a messaging device, a wearable computing device, a consumer electronic device, and the like.

[0137] According to one embodiment, a program product such as a machine-readable medium is provided. The machine-readable medium may have instructions (i.e., the above-mentioned elements implemented in software form), which, when executed by a machine, causes the machine to perform the above-mentioned combined embodiments of the present specification. Figure 1-8 Specifically, a system or device equipped with a readable storage medium can be provided, on which software program codes for implementing the functions of any of the above-mentioned embodiments are stored, and a computer or processor of the system or device can be enabled to read and execute the instructions stored in the readable storage medium.

[0138] According to the general method and application of judging the cleaning process of an endoscope cleaning machine according to the embodiment of the present invention, it can solve the errors caused by manual operation in collecting the report data of the cleaning process of the cleaning machine. By collecting data through algorithms such as correlation calculation and feature extraction and visual processing, automatic data collection is realized, and the errors caused by manual data collection are avoided. It can effectively identify the cleaning stage of the endoscope cleaning machine and the corresponding characteristics of the stage.

[0139] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0141] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0143] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A general method for determining the cleaning process of an endoscope cleaning machine, characterized in that: The method comprises: Add a vibration data acquisition device with an acceleration sensor to the endoscope cleaning machine and collect acceleration data on the x, y, and z axes; Performing correlation calculation on the acceleration data of any two axes on the acceleration data of the three axes of x, y, and z; Obtain correlation data of the acceleration data of the three axes x, y, and z, and select a fixed number of the correlation data to calculate the standard deviation; After performing feature extraction on the standard deviation, performing secondary feature extraction on the data obtained by feature extraction; and The data obtained by the secondary feature extraction is visualized and the cleaning stage of the endoscope cleaning machine is determined. The data obtained by the secondary feature extraction is visualized, including: graphing the data obtained by the secondary feature extraction to obtain a waveform diagram of the data.

2. The method for determining a cleaning process of a universal endoscope cleaning machine according to claim 1, wherein: The acceleration data of the three axes x, y, and z are correlated with the acceleration data of any two axes. The correlation calculation formula is: correlation(x i ,y i ) = stdev(x i ,x i+n ) / stdev(y i ,y i+n ) correlation(x i ,z i ) = stdev(x i ,x i+n ) / stdev(z i ,z i+n ) correlation(y i ,z i ) = stdev(y i ,y i+n ) / stdev(z i ,z i+n ) where x i 、y i 、z i is the acceleration data of x, y, and z axes at different times, stdev(x i , x i+n ), stdev(y i ,y i+n ), stdev(z i , z i+n ) is the moving standard deviation of the acceleration data of the x, y, and z axes, and n is the number of items.

3. The method for determining a cleaning process of a universal endoscope cleaning machine according to claim 1, wherein: The feature extraction of the standard deviation is performed, and the calculation formula for the first feature extraction is: Processed_x_std_1 = round(std * n, -2), That is, the standard deviation is magnified n times and the hundredth place of the magnified data is rounded off.

4. The method for determining a cleaning process of a universal endoscope cleaning machine according to claim 3, wherein: The data obtained by feature extraction is subjected to secondary feature extraction again, and the calculation formula of the secondary feature extraction is: Processed_x_std_2 = IF(Processed_x_std_1 <C1,n1,n2)-C2, That is, determine whether processed_x_std_1 is greater than C1. If processed_x_std_1 is greater than C1, take n2. If processed_x_std_1 is less than C1, take n1. Among them, C1, C2, n1, and n2 are constants, and n1=-n2.

5. The method for determining a cleaning process of a universal endoscope cleaning machine according to claim 1, wherein: Determining the cleaning stage of the endoscope cleaning machine includes: The operation data of the endoscope cleaning machine is compared and matched with the waveform diagram to identify the duration and characteristics of different cleaning stages of the endoscope cleaning machine and determine the cleaning stage of the endoscope cleaning machine.

6. The method for determining a cleaning process of a universal endoscope cleaning machine according to claim 5, wherein: The cleaning process of the endoscope cleaning machine includes a water washing stage, an enzyme solution adding stage, an enzyme washing stage, an enzyme solution draining stage, a disinfectant solution adding stage, a disinfection stage, a disinfection stage, a disinfectant solution draining stage and a drying stage.

7. A general device for judging the cleaning process of an endoscope cleaning machine, characterized in that: The device comprises: The data acquisition module is used to add a vibration data acquisition device with an acceleration sensor to the endoscope cleaning machine and collect acceleration data on the x, y, and z axes; A correlation calculation module is used to perform correlation calculation on any two axes of the acceleration data of the three axes x, y, and z; A standard deviation calculation module is used to obtain the correlation data of the three axes x, y, and z, and select a fixed number of nodes of the correlation data to calculate the standard deviation; a data processing module, configured to perform feature extraction on the data obtained by the feature extraction after feature extraction on the standard deviation, and perform visualization processing on the data obtained by the secondary feature extraction, wherein the visualization processing on the data obtained by the secondary feature extraction includes: graphing the data obtained by the secondary feature extraction to obtain a waveform graph of the data; and The data judgment module is used to judge the cleaning stage of the endoscope cleaning machine through the visually processed data.

8. An electronic device, characterized in that: include: at least one processor; as well as A memory storing instructions, wherein when the instructions are executed by the at least one processor, the at least one processor executes the universal method for determining a cleaning process of an endoscope cleaning machine according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining a cleaning process of a universal endoscope cleaning machine according to any one of claims 1 to 6 are implemented.

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