Method and device for quickly determining actual service life of cold head in medical equipment

Abstract

The invention discloses a method for quickly determining the actual service life of a cold head in medical equipment, which comprises the following steps: RuO parameter data of the corresponding medical equipment are extracted, and the RuO parameter data comprise RuO parameter values and corresponding timestamps; determining a replacement time node of a cold head of the corresponding medical equipment according to the extracted RuO parameter data; and determining the actual service life of the cold head according to the determined replacement time node of the cold head and the preset shortest service life of the cold head. According to the embodiment of the invention, the replacement time of the cold head on the medical equipment can be rapidly determined without manually counting the installation time and the replacement time, and the method is very rapid and intelligent. Moreover, according to the method provided by the embodiment of the invention, the actual service life of all replaced cold heads on the medical equipment within the corresponding timestamp span range of the RuO parameter data can be determined at one time, and the efficiency is very high.

Description

Technical Field

[0001] The present invention relates to the technical field of big data processing of medical equipment, and in particular to a method and device for quickly determining the actual service life of a cold head in a medical device, as well as an electronic device and a storage medium. Background Art

[0002] Superconducting NMR equipment uses liquid helium as a refrigeration medium. The boiling point of helium is 4.2K. Liquid helium can be used to obtain a low temperature close to absolute zero to establish and maintain a superconducting environment for the superconducting coil. In order to reduce the evaporation of liquid helium, superconducting NMR equipment is generally equipped with a refrigeration system to provide cold capacity to reduce the evaporation of liquid helium. The refrigeration system consists of three parts: a cold head, a helium compressor, and a water cooling unit. The cold head is a key component in the refrigeration system. Its working state will directly affect the volatilization of liquid helium. At the same time, the price of the cold head is relatively expensive. Therefore, timely and reasonable maintenance and care of the cold head to ensure its good working condition and long service life is of great value to ensure the normal operation of the NMR system and thus ensure the economic benefits of the superconducting NMR equipment.

[0003] In order to achieve the above goals, it is necessary to use the actual service life data of the cold head to conduct reasonable predictive analysis on the working status and predicted service life of the cold head. However, at present, the actual service life data of the cold head is generally obtained through manual statistics, such as manually recording the installation time and replacement time of the cold head, and calculating and collecting the actual service life based on the manually recorded installation time and replacement time of the cold head. This method is very inefficient and difficult to record accurately due to the long span of installation time and replacement time. Data loss is serious, and it is difficult to form high-value big data on the actual service life of the cold head. It can be seen that the industry currently lacks a solution that can automatically and quickly obtain the actual service life data of the cold head. Summary of the invention

[0004] The embodiment of the present invention provides a method for quickly determining the actual service life of a cold head in a medical device, so as to at least solve the problem that the actual service life data of the cold head in the prior art is obtained by manual statistics, resulting in low efficiency, easy data loss, and difficulty in forming reliable big data.

[0005] In a first aspect, an embodiment of the present invention provides a method for quickly determining the actual service life of a cold head in a medical device, comprising:

[0006] Extracting RuO parameter data of the corresponding medical device, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps;

[0007] Determine the replacement time node of the cold head of the corresponding medical equipment according to the extracted RuO parameter data;

[0008] The actual service life of the cold head is determined according to the determined replacement time node of the cold head and the preset minimum service life of the cold head.

[0009] In a second aspect, an embodiment of the present invention provides a device for quickly determining the actual service life of a cold head in a medical device, comprising:

[0010] A parameter extraction module, used to extract RuO parameter data, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps;

[0011] A replacement time determination module, used to determine a replacement time node of a cold head of a corresponding medical device according to the extracted RuO parameter data;

[0012] The service life determination module is used to determine the actual service life of the cold head according to the determined replacement time node of the cold head and the preset minimum service life of the cold head.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0014] At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method of the first aspect above.

[0015] In a fourth aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, which implements the steps of the method of the first aspect when executed by a processor.

[0016] The beneficial effect of the embodiment of the present invention is that the method provided by the embodiment of the present invention determines the replacement time node of the cold head of the medical device by extracting the RuO parameter data of the medical device, and obtains the actual service life of the cold head according to the replacement time node of the cold head determined based on the RuO parameter data and the shortest service life of the cold head. It does not need to rely on manual statistics of the installation time and the replacement time, and can quickly determine the replacement time of the cold head on the medical device, which is very fast and intelligent. Moreover, based on the timestamp span of the extracted RuO parameter data of the medical device, the method of the embodiment of the present invention can also determine the actual service life of all replaced cold heads on the medical device within the timestamp span at one time, and the efficiency is very high. In addition, since the method of the embodiment of the present invention can automatically and batch determine the actual service life of all replaced cold heads of the corresponding medical device within the corresponding time span based only on the extracted RuO parameter data of the corresponding medical device, it is possible to quickly collect big data on the actual service life of the cold head, which has a very high utilization value and reference value, and is conducive to real-time monitoring, analysis and maintenance of the working status and service life of the cold head. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0018] Figure 1 A flow chart of a method for quickly determining the actual service life of a cold head in a medical device according to an embodiment of the present invention;

[0019] Figure 2 One embodiment of the present invention Figure 1 Flow chart of the implementation method of step S10;

[0020] Figure 3 One embodiment of the present invention Figure 2 Flow chart of the implementation method of step S102;

[0021] Figure 4 A flowchart of a method for writing a log record determined as a sampling target into a data file corresponding to a corresponding medical device according to an embodiment of the present invention;

[0022] Figure 5 One embodiment of the present invention Figure 1 Flow chart of the implementation method of step S11;

[0023] Figure 6 Another embodiment of the present invention Figure 1 Flow chart of the implementation method of step S11;

[0024] Figure 7 Another embodiment of the present invention Figure 1 Flow chart of the implementation method of step S11;

[0025] Figure 8 One embodiment of the present invention Figure 7 Flow chart of the implementation method of step S73;

[0026] Fig. 9 One embodiment of the present invention Figure 8 Flow chart of the implementation method of step S82;

[0027] Fig.10 One embodiment of the present invention Figure 1 Flow chart of the implementation method of step S12;

[0028] Fig.11 Another embodiment of the present invention Figure 1 Flow chart of the implementation method of step S12;

[0029] Fig.12 This is a principle block diagram of a device for quickly analyzing a medical device log file according to an embodiment of the present invention;

[0030] Fig.13 This is a principle block diagram of a device for quickly determining the actual service life of a cold head in a medical device according to an embodiment of the present invention;

[0031] Fig.14 It is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other.

[0034] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0035] In the present invention, "module", "device", "system" and the like refer to related entities applied to computers, such as hardware, a combination of hardware and software, software or software in execution, etc. In detail, for example, an element can be, but is not limited to, a process, a processor, an object, an executable element, an execution thread, a program and / or a computer running on a processor. In addition, an application or script program running on a server, a server can all be an element. One or more elements can be in an execution process and / or thread, and an element can be localized on a computer and / or distributed between two or more computers, and can be operated by various computer-readable media. An element can also communicate through local and / or remote processes according to a signal with one or more data packets, for example, a signal from a data that interacts with another element in a local system, a distributed system, and / or a network on the Internet through a signal to interact with other systems.

[0036] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include" and "comprise" include not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such processes, methods, articles or equipment. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or equipment that includes the elements.

[0037] After long-term observation, experiment and experience summary, the inventor found that the RuO parameter data of medical equipment can be used to determine the actual service life of the cold head, and the inventor also found the correlation between the RuO parameter data and the actual service life of the cold head. Based on this, the inventor proposed a method for quickly determining the actual service life of the cold head in the medical device. The method can automatically and quickly determine the actual service life of the cold head based on the RuO parameter data of the medical device. Since it is more efficient and does not require manual statistics, it is easier to form big data of the actual service life of the cold head through this method, so that it is possible to perform accurate analysis and prediction such as current service life prediction of the cold head based on the big data of the actual service life of the cold head, thereby achieving more accurate and real-time monitoring and analysis of the cold head, improving the equipment management effect such as maintenance of the cold head, and reducing equipment management and maintenance costs.

[0038] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0039] Figure 1The process of a method for quickly determining the actual service life of a cold head in a medical device according to an embodiment of the present invention is schematically shown. The method can be executed by any computing device, including but not limited to a personal computer, a server, and smart electronic devices such as a tablet computer. Figure 1 As shown, the method comprises the following steps:

[0040] Step S10: extracting RuO parameter data of the corresponding medical device, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps;

[0041] Step S11: determining a replacement time node for a cold head of a corresponding medical device according to the extracted RuO parameter data;

[0042] Step S12: determining the actual service life of the cold head according to the determined replacement time node of the cold head and the preset shortest service life of the cold head.

[0043] In step S10, RuO parameter data can be extracted from the log file of the corresponding medical device. In specific practice, the medical device system where high-value medical device components, such as nuclear magnetic cold heads, CT tubes, etc., are located will provide complete parameter logs, so the RuO parameter data of the medical device can be extracted by obtaining log files recording these parameter logs and analyzing these log files. As a possible implementation, RuO parameter data can be extracted by matching keywords to the log file line by line. In other embodiments, the log file can also be analyzed by machine learning to extract RuO parameter data. In other embodiments, RuO parameter data can also be extracted from other storage units storing historical records of RuO parameter values ​​of medical devices and their corresponding timestamps, such as from databases or other structured data files.

[0044] As a better implementation method, Figure 2 The method flow of extracting RuO parameter data according to one embodiment of the present invention is schematically shown, as shown in FIG. Figure 2 As shown, it can be implemented by including the following steps:

[0045] Step S101: Obtaining a log file of a medical device;

[0046] Step S102: sampling log records of the log file of the medical device according to the log time of each log record in the log file of the medical device;

[0047] Step S103: extracting RuO parameter data of the medical device based on the sampled log records.

[0048] In step S101, obtaining the log file of the medical device can be directly acquiring the required log file from the medical device through a dedicated data acquisition box, or it can be acquired from a storage module storing all the log files of the corresponding medical device. In practical applications, taking the device log acquisition platform of Xushui Company as an example, the log records on the medical device are generally collected through the data acquisition box independently developed by Xushui Company, and the log records are sent to the kafka server cluster in the form of timestamp + parameter data, and then the newly sent log record message is read from the kafka server cluster through the storage program developed by Xushui Company, and all the log records read from the kafka server cluster are persisted to the disk array according to the device number, thereby forming the log file corresponding to each medical device. In an embodiment of the present invention, in step S101, the log file of the corresponding medical device can be read from the disk array. It should be noted that the embodiment of the present invention does not limit the way and method of obtaining the log file of the medical device. In practical applications, any possible prior art solution can be used to obtain the log file of the corresponding medical device from the corresponding storage location, and the method of the embodiment of the present invention can be used to extract RuO parameter data.

[0049] In step S102, the log records of the log file of the medical device can be sampled by using a time-based compression mapping method, wherein the time-based compression mapping method is preferably implemented to ensure that the sampled log records are in all device log time periods where log records are generated, so that the sampled log records can effectively cover all device log time periods where log records are generated, thereby effectively ensuring that log records are extracted from each device log time period, so that the RuO parameter value extracted based on this can also cover the entire device log time period, thereby ensuring that the RuO parameter value can accurately reflect the replacement time node of the cold head and the actual service life of the cold head.

[0050] As a preferred embodiment, sampling the log records of the log file of the medical device based on the time compression mapping method can be implemented based on the log time of each log record in the log file and the set reference time. Among them, the reference time can be preset according to the needs, and each medical device corresponds to a reference time adapted to the medical device, and the reference time corresponding to different medical devices can be the same or different. It should be noted that each log file of the medical device includes at least one log record, and each log record corresponds to a timestamp adapted to the log record, which is used to characterize the time or occurrence time of the log record. Therefore, the timestamp corresponding to each log record can be obtained by scanning the log records of the log file of the medical device one by one, and it is used as the log time of the corresponding log record. Among them, scanning the log records of the log file of the medical device one by one to obtain the timestamp corresponding to each log record and the method of obtaining each log record can be implemented with reference to the existing technology, such as by keyword matching or machine learning, etc., and the embodiment of the present invention does not limit this.

[0051] Taking the sampling of log records in the log file of a medical device based on the log time of each log record in the log file and the set reference time as an example, the following is combined with Figure 3 A possible implementation of step S102 is described in detail. Figure 3 As shown, sampling the log records of the log files of the medical device based on the log time of each log record and the preset reference time can be specifically implemented as follows:

[0052] Step S111: determining the index to which the corresponding log record is mapped according to the log time of each log record in the log file of the corresponding medical device and a preset reference time, wherein the index is used to indicate the storage location of the validity identifier corresponding to the corresponding log record in the partition file corresponding to the medical device, and the partition file is used to store the validity identifier corresponding to the log record of the corresponding medical device;

[0053] Step S112: Obtain the validity identifier corresponding to the corresponding log record from the partition file according to the index of the corresponding log record, and when the validity identifier is judged to be valid, determine the corresponding log record as a sampling target, sample it, and update the validity identifier at the index after sampling it to invalid.

[0054] In step S111, the log record can be mapped to an index based on the log time of the corresponding log record and the reference time of the medical device. The mapped index is the storage location of the validity identifier corresponding to the corresponding log record in the partition file corresponding to the medical device. In a specific implementation, a partition file can be created for each medical device and loaded into the memory, wherein the partition file is not used to store the actual log record content, but is only used to store the validity identifier corresponding to the log record of the corresponding medical device, so the capacity space occupied is very small, and it can be loaded into the memory to achieve fast and frequent access and fast read and write operations. In an embodiment of the present invention, the validity identifier is used to characterize whether the log record mapped to the corresponding index has been sampled. When the validity identifier corresponding to the corresponding index in the partition file is marked as invalid, it indicates that the log record mapped to the index has been sampled. When the validity identifier corresponding to the corresponding index in the partition file is marked as valid, it indicates that the log record mapped to the index has not been sampled. The validity identifier can be marked by the data content of each byte in the partition file. For example, the character "0" can be used to mark the validity identifier as valid and the character "1" can be used to mark the validity identifier as invalid, so that the validity of the validity identifier at the corresponding position can be judged by the byte content at each position in the partition file. As a possible implementation, the size of the created partition file can be determined according to the time length and sampling frequency covered by the log file of the corresponding medical device to be parsed. For example, when the time length covered by the log file of the corresponding medical device to be parsed is ten years and the sampling frequency is one minute and only one log record is sampled, the size of the partition file can be set to 10*365*24*60, so that each byte of the partition file corresponds to the validity mark of the log record of the corresponding minute, such as the 0th byte is the validity mark corresponding to the log record with the log time as the start time, and the 2nd byte is the validity mark corresponding to the log record of the first minute after the log time as the start time, and so on. In the initial stage, the contents of the validity marks corresponding to all bytes of the partition file are initialized to valid. Taking the validity mark as an example of using the character "0" to mark valid and the character "1" to mark invalid, after the partition file is created, the contents of all bytes of the partition file will first be initialized to the character "0". In the embodiment of the present invention, the index determined by step S111 is the index position of each byte in the partition file for storing the validity mark.

[0055] As a preferred implementation, the benchmark time adapted to each medical device can be set to the log time of the earliest log record that needs to be parsed on the medical device (wherein, the time period of the log records that need to be parsed can be selected based on the installation time period of the cold head that needs to determine the actual service life), and the index mapped to the corresponding log record is determined based on the difference between the log time of the log record and the preset benchmark time. Take the example that the reference time of a medical device is set to the log time of the earliest log record that needs to be parsed on the medical device, and the index mapped to the corresponding log record is determined based on the difference between the log time of the log record and the preset reference time, as shown in Table A below. Assuming that the log time of the earliest log record that needs to be parsed on a medical device is 2013-01-01 00:00, the reference time of the medical device will be set to 2013-01-01 00:00. At this time, when sampling log records in the log file of the medical device, first, for the earliest log record that needs to be parsed, the difference between its log time and the reference time is 0. Assuming that the medical device generates ten log records in one minute, the difference between the log time of the nine log records after the earliest log record, i.e., the 2nd to 10th log records and the reference time is also 0, and the difference between the log time of the 11th log record and the reference time is 1. That is, for the log time of 2013-01-01 For the log record with a log time of 2013-01-01 00:01, the difference between its log time and the reference time is 1, for the log record with a log time of 2013-01-01 00:02, the difference between its log time and the reference time is 2, and so on. At this time, as a preferred implementation, the difference between the log time of the log record and the reference time can be directly used as the index mapped to the corresponding log record. In this case, for the log record with a log time of 2013-01-01 00:00, the index mapped to is 0, for the log record with a log time of 2013-01-01 00:01, the index mapped to is 1, and so on. At this time, as shown in the table on the left side of Table A, the characters "0" and "1" can be used in the partition file to identify the content of the validity mark at the corresponding index.Therefore, by recording the validity identifier at the corresponding index in the partition file, it is possible to indicate the validity of the log record, that is, whether a log record has been sampled at the log time, without the need to analyze and read and write the content of the corresponding log record. This allows for fast sampling of log records based on log time. In addition, since the characters "0" and "1" only occupy one byte of storage space, the index corresponds exactly to the position of the corresponding byte in the partition file. For example, an index of 0 corresponds to the position of the 0th byte in the partition file, an index of 1 corresponds to the position of the 1st byte in the partition file, and so on. The partition file is only used to store the validity identifier corresponding to the corresponding index position. Therefore, the size of the partition file will be very small, and the memory space occupied is very small. Fast and frequent read and write access can be achieved, and both time and performance consumption are very small.

[0056]

[0057]

[0058] Table A

[0059] After determining the index to which the corresponding log record is mapped, in step S112, the partition file can be searched according to the index of the corresponding log record to obtain the validity identifier corresponding to the corresponding log record from the partition file. After obtaining the validity identifier corresponding to the corresponding log record, it is first judged. When the validity identifier is valid, such as the character "0", it means that no log record has been sampled at the log time. At this time, the corresponding log record is determined as the sampling target, sampled, and the validity identifier at the index position is updated after sampling, and the byte content at the corresponding position is modified to invalid, such as the character "1". If the validity identifier is judged to be invalid, such as the character "1", it means that a log record has been sampled at the log time. At this time, the current log record will no longer be sampled, that is, it will be discarded. Therefore, the method of the embodiment of the present invention only samples one of the log records at the same log time, that is, only one valid log record is retained at the same log time, and no subsequent analysis and processing will be performed on other log records at the same log time, and RuO parameter data will not be extracted from the unsampled log records, so the parsing efficiency of the log file can be greatly improved, and the execution time of extracting RuO parameter data from the log file can be shortened, which is particularly suitable for the scenario of forming the actual service life big data of the cold head based on the log file. At the same time, since the method of the embodiment of the present invention is sampled based on the log time, it realizes the compression mapping of the log file based on the log time, so the sampled log records can cover all time periods with collected log records, thereby ensuring the validity of the RuO parameter data extracted from the log file, and can greatly reduce the redundancy of the extracted RuO parameter data, and improve the algorithm performance of determining the actual service life of the cold head based on the RuO parameter data.

[0060] As a possible implementation, in step S112, sampling the log records can be specifically implemented as including writing the log records determined as sampling targets into the data file corresponding to the corresponding medical device. The data file specifically refers to a file for storing the log records determined as sampling targets extracted from the log files of the corresponding medical device. Thus, by sampling the log records in the log files based on log time, time-based compression mapping of the log files is achieved, so that only the log records determined as sampling targets in the log files need to be written into the data files, that is, data is written to the data files only once when the log records are determined as sampling targets, which effectively shortens the time for extracting the log records in the log files and the subsequent time for extracting RuO parameter data. Moreover, since only the partition files with small capacity and frequent access are stored in the memory, the memory occupied space can be reduced and the data access speed for determining the sampling targets can be improved, thereby improving the efficiency of extracting RuO parameter data from the log files.

[0061] As a possible implementation, Figure 4 The specific method flow of writing the log records determined as sampling targets into the data files corresponding to the corresponding medical devices is schematically shown, such as Figure 4 As shown, it can be implemented as including:

[0062] Step S31: writing a flag character into the Xth data bit of the data file, wherein X=K*N+1, K is the number of log records written into the data file corresponding to the corresponding medical device, and N is the preset maximum data length;

[0063] Step S32: writing the log record determined as the sampling target after the flag character;

[0064] Step S33: based on the data length M of the log record determined as the sampling target, when it is determined that the data length M does not reach the preset maximum data length N, Y padding characters are written after the written log record, where Y=N-1-M.

[0065] In step S31, the maximum data length N can be set to the maximum length of the log record, wherein the maximum length of the log record can be determined based on prior knowledge or experience or the manual of the medical device. The written flag character can be any fixed character that occupies one bit, such as the fixed character "1", which is used to mark the beginning of a log record. In step S33, the place-filling character can be a preset fixed character different from the flag character, each place-filling character occupies one bit, and is preferably a blank character. Thus, by Figure 4The data file formed by the method shown is formed by a data block with a data length of (N+1) bits, and what remains after removing the flag character and the place-filling character from each data block is the content of a log record. Therefore, in step S103, extracting the RuO parameter data of the medical device based on the sampled log record can be implemented as including reading a data block with a data length of N+1 from the data file of the corresponding medical device each time, removing the flag character and the place-filling character from the read data block, performing keyword extraction, extracting the RuO parameter value and the timestamp in the corresponding data block for structured storage, wherein the storage method can be, for example, storing the extracted RuO parameter value and the timestamp in the corresponding field of the database corresponding to the medical device. Since the data file stores the sampled log records, it is not necessary to write all the log records in the log file into the data file and read them from the data file, so the execution time of extracting the RuO parameter data based on the log records in the data file will also be greatly shortened, so the parsing efficiency of the log file of the medical device is very high. In particular, under the implementation method of directly using the difference between the log time of the log record and the reference time as the index mapped to the corresponding log record, a subtraction operation and a partition file query process can be used to directly and quickly determine whether the current log record is a sampling target. If it is a sampling target, it is written to the data file. If it is not a sampling target, the next log record is analyzed. This processing method reduces the time complexity of writing the sampled log record to the data file to O(1). Combined with the compression of the data file generated by sampling, the analysis and processing efficiency of the log files of medical equipment is very high, especially when extracting RuO parameter data from massive log files, the execution time is greatly shortened.

[0066] In other possible implementations, after obtaining the data file based on the log file, the data file can be converted into a CSV format, and the converted CSV format file can be imported into a database, and then the RuO parameter data can be extracted from the database through a SQL statement to achieve faster RuO parameter data extraction.

[0067] In some other possible implementations, sampling of log records in a log file of a medical device based on time compression mapping can also be implemented based on the log time of the log record and a hash algorithm. For example, the index to which the corresponding log record is mapped can be determined by directly performing a hash algorithm on the log time of the log record in the log file of the corresponding medical device and the unique serial number of the corresponding medical device. Since when the log time and the unique serial number of the device are the same, the index calculated by the hash algorithm is also the same, the log time used is set according to the required sampling frequency. For example, when the sampling frequency is one log record per minute, the corresponding sampling target can be achieved by directly inputting the log time accurate to the minute into the hash algorithm. It should be noted that in this embodiment, the determined index is the hash value of the log time and the unique serial number of the device, so the above-mentioned partition file and data file can be omitted, but the index, that is, the hash value, is used as a unique identifier, and the first log record corresponding to the index is stored as data content in a structural data storage unit such as redis or other high-performance memory databases. Thus, after determining the index of the corresponding log record, by querying whether the corresponding index position of the structural data storage unit such as redis or other high-performance memory databases stores log records, it can be determined whether there is log data sampled at the log time. If data has been sampled at the log time, other log records with the same index are discarded. If no data has been sampled at the log time, the index and its corresponding log record are stored in a structural data storage unit such as redis or other high-performance memory databases. In this case, the RuO parameter data can be extracted from the database through a SQL statement to achieve faster RuO parameter data extraction. Preferably, in order to facilitate the subsequent processing of determining the actual service life of the cold head based on the RuO parameter data, after extracting the RuO parameter data, the stored log records can be further sorted using the index so that they can be stored in the time order shown by the timestamp.

[0068] Of course, it is not difficult to understand that the aforementioned method of sampling based on log files to obtain data files or database contents can also be used as a processing process of the front-end module. In this case, when extracting RuO parameter data, it is only necessary to directly use the data file or database content generated by the front-end module to extract RuO parameter data.

[0069] After a lot of data analysis and verification, the inventor found the correlation between the RuO parameter data and the replacement time node of the cold head, and found that the RuO parameter value has a periodic change based on the warning line, and this periodic change can basically accurately reflect the relationship between the RuO parameter value and the replacement time node of the cold head. Based on this, in step S11, the embodiment of the present invention can specifically determine the replacement time node of the cold head according to the relationship between the extracted RuO parameter data and the preset warning line. Based on this technical concept, Figures 5 to 7 The method flow of determining the replacement time node of the cold head in three different implementations is schematically shown. The method process of the three different implementations will be described in detail below with reference to the accompanying drawings.

[0070] like Figure 5 As shown, in one embodiment of the present invention, the method for determining the replacement time node of the cold head based on RuO parameter data can be implemented as comprising the following steps:

[0071] Step S51: generating at least one group of first time features (T1, T2, S) according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that first exceeds the warning line after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that first falls below the warning line after T1 of the corresponding group of first time features, and S represents the duration of the RuO parameter value corresponding to the corresponding group of first time features exceeding the warning line;

[0072] Step S52: Determine a cold head replacement time node from the at least one group of first time features (T1, T2, S) according to a preset first filtering condition, wherein the cold head replacement time node is a timestamp T2 in the first time feature whose duration S satisfies the first filtering condition.

[0073] As a possible implementation, in step S51, the relationship between each RuO parameter value and the preset warning line can be determined one by one for the extracted RuO parameter data according to the time sequence indicated by its timestamp, and at least one group of first time features (T1, T2, S) can be formed according to the relationship between the RuO parameter value and the preset warning line and the timestamp corresponding to the corresponding RuO parameter value. Specifically, it can be implemented as follows: first, for each RuO parameter data, the relationship between its RuO parameter value and the preset warning line will be judged one by one. When it is judged that the RuO parameter value corresponding to a certain RuO parameter data exceeds the warning line, the timestamp corresponding to the RuO parameter data is recorded as T1, and then the relationship between the RuO parameter value and the preset warning line is judged for other RuO parameter data after the RuO parameter data, and the timestamp of the first appearance of RuO parameter data with a RuO parameter value lower than the warning line after T1 is recorded as T2; then, the duration S that the RuO parameter value of the medical device exceeds the warning line is calculated according to the difference between T1 and T2, and the group of T1, T2 and S is formed into a group of first time features (T1, T2, S). Through step S51, all the extracted RuO parameter data are processed one by one, thereby forming at least one group of first time features (T1, T2, S), wherein it should be noted that T1 in each group of first time features (T1, T2, S) corresponds to the timestamp corresponding to the RuO parameter data whose RuO parameter value exceeds the warning line for the first time after T2 of the previous group of first time features (T1, T2, S). For example, for the first group of first time features (T1, T2, S), T1 is 2013-01-01 00:01 and T2 is 2013-01-02 00:01, then for the second group of first time features (T1, T2, S) thereafter, T1 is the timestamp corresponding to the RuO parameter data whose RuO parameter value exceeds the warning line found for the first time after 2013-01-02 00:01. Among them, the warning line can be used to characterize and reflect the correlation between the RuO parameter value and the replacement of the cold head. When the RuO parameter value exceeds the warning line, it usually indicates that the cold head may have failed and needs to be replaced. After exceeding the warning line, if the RuO parameter value subsequently obtained is lower than the warning line, it usually indicates that the cold head has been replaced. This correlation is determined by the inventor based on the results of big data analysis and empirical data. According to the correlation between the RuO parameter value and the replacement of the cold head discovered by the inventor and the empirical knowledge, the warning line can preferably be set to 300.

[0074] In step S52, the first filtering condition is set to 1≤S≤48. Thus, in step S52, it is possible to judge whether S satisfies the first filtering condition for all the first time features determined in step S51. When it is judged that S of a group of first time features satisfies the first filtering condition, T2 in the group of first time features is recorded as a cold head replacement time node. Thus, at least one cold head replacement time node is obtained through the processing of step S52, that is, when multiple cold head replacement time nodes are determined, a set of cold head replacement time nodes is obtained through the processing of step S52. It should be noted that when more than one cold head replacement time node is obtained, it means that there may be a suspected replacement time node in the cold head replacement time node at this time, or it may be that the medical device has undergone multiple cold head replacements, so there are replacement time nodes for multiple different cold heads. Therefore, it is necessary to perform specific processing in step S12 to finally determine which cold head replacement time nodes are valid and which are invalid, and then exclude the invalid cold head replacement time nodes and determine the actual service life corresponding to each cold head that has been replaced.

[0075] like Figure 6 As shown, in another embodiment of the present invention, the method for determining the replacement time node of the cold head based on RuO parameter data can be implemented as comprising the following steps:

[0076] Step S61: generating at least one group of first time features (T1, T2, S) according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that first exceeds the warning line after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that first falls below the warning line after T1 of the corresponding group of first time features, and S represents the duration of the RuO parameter value corresponding to the corresponding group of first time features exceeding the warning line;

[0077] Step S62: performing a first filtering process on the at least one set of first time features (T1, T2, S) formed, deleting the first time features (T1, T2, S) belonging to the first type of abnormality, and obtaining at least one set of second time features (T1, T2, S), wherein the first time feature of the first type of abnormality refers to the first time feature S that meets the first preset condition;

[0078] Step S63: Determine the cold head replacement time node from the at least one group of second time features (T1, T2, S) according to the preset first filtering condition, wherein the cold head replacement time node is the timestamp T2 in the second time feature whose duration S satisfies the first filtering condition.

[0079] exist Figure 6In the embodiment shown, the processing of step S61 is the same as Figure 5 The processing of step S51 in the implementation mode is the same, that is, at least one set of first time features is determined by comparison with the warning line. Figure 5 The embodiment shown is different in that Figure 6 In the implementation manner, after determining at least one set of first time features, the first time features are first subjected to a first filtering process to exclude obvious abnormal data, such as abnormal parameter data caused by abnormal sensors, etc. In step S62 of the embodiment of the present invention, it is preferred to first delete the first time features belonging to the first category of abnormalities through the first filtering process. Among them, the first time feature of the first type of abnormality refers to the first time feature in which S in the first time feature satisfies the first preset condition. The first preset condition is preferably used to characterize the time node in which the RuO parameter value is continuously higher than the warning line for a long time. Exemplarily, the first preset condition can be S≥72, that is, the first time feature in which the RuO parameter value is higher than the warning line for more than three consecutive days is determined as the first time feature belonging to the first type of abnormality, so as to delete it from the data determined in step S61, thereby obtaining at least one set of second time features (T1, T2, S). It should be noted here that the meanings of T1, T2 and S in the second time feature are the same as those in the first time feature, and the difference between it and the first time feature is that the first time feature belonging to the first type of abnormality is filtered out from the first time feature determined in step S61, thereby in step S63, T2 in the first time feature of the first type of abnormality will no longer be determined as the cold head replacement time node, that is, it can effectively exclude invalid cold head replacement time nodes, reduce the interference of invalid data, and can reduce the data processing amount in step S63 when determining the cold head replacement time and step S12 when determining the actual service life of the cold head, thereby improving the algorithm performance. It should be further explained that the specific implementation process of step S63 is similar to the implementation process of step S52 above, so it will not be repeated here.

[0080] like Figure 7 As shown, in another embodiment of the present invention, the method for determining the replacement time node of the cold head based on RuO parameter data can be implemented as comprising the following steps:

[0081] Step S71: generating at least one group of first time features (T1, T2, S) according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that exceeds the warning line for the first time after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that is lower than the warning line for the first time after T1 of the corresponding group of first time features, and S represents the duration of the RuO parameter value corresponding to the corresponding group of first time features exceeding the warning line;

[0082] Step S72: performing a first filtering process on the at least one set of first time features (T1, T2, S) formed, deleting the first time features (T1, T2, S) belonging to the first type of anomalies, and obtaining at least one set of second time features (T1, T2, S);

[0083] Step S73: performing a second filtering process on the at least one set of second time features (T1, T2, S) formed, deleting the second time features (T1, T2, S) belonging to the second type of anomalies, and obtaining at least one set of third time features (T1, T2, S);

[0084] Step S74: Determine the cold head replacement time node from the at least one group of third time features (T1, T2, S) according to the preset first filtering condition, wherein the cold head replacement time node is the timestamp T2 in the third time feature whose duration S satisfies the first filtering condition.

[0085] exist Figure 7 In the embodiment shown, the processing of step S71 and step S72 is the same as Figure 6 The processing of step S61 and step S62 in the implementation mode is the same, that is, at least one set of first time features is determined by comparison with the warning line, and the first type of abnormal data is filtered out from the at least one set of first time features determined by the first filtering process. Figure 6 The embodiment shown is different in that Figure 7In the implementation manner, after at least one set of second time features are determined, the second time features are further subjected to a second filtering process through step S73 to exclude data with obvious second-type abnormalities. Among them, the second time feature of the second type of abnormality refers to the second time feature that the duration of the corresponding RuO parameter value being higher than the warning line is less than one hour, and the difference in the average value of the RuO parameter value within one hour before the time node T1 and within one hour after the time node T2 does not exceed 5. The second time feature of the second type of abnormality is deleted from the data determined in step S72 through the second filtering process, thereby obtaining at least one set of third time features (T1, T2, S). It should be noted here that the meanings of T1, T2 and S in the third time feature are the same as those in the first time feature, and the difference between the third time feature and the first time feature is that the first time feature belonging to the first type of abnormality and the second time feature belonging to the second type of abnormality are filtered out from the data determined in steps S71 and 72. Therefore, in step S74, T2 in the first time feature of the first type of abnormality and T2 in the second time feature of the second type of abnormality will no longer be determined as the cold head replacement time node, which can effectively exclude invalid cold head replacement time nodes and reduce the interference of invalid data, and can also reduce the data processing amount in step S74 when determining the cold head replacement time and step S12 when determining the actual service life of the cold head, thereby improving the algorithm performance. It should be further explained that the specific implementation process of step S74 is similar to the implementation process of step S52 above, so it will not be repeated here.

[0086] Taking the second time feature of the second type of anomaly as an example, the duration of the corresponding RuO parameter value being higher than the warning line is less than one hour, and the difference in the average value of the RuO parameter value within one hour before the time node T1 and within one hour after the time node T2 does not exceed 5, the following will be combined with Figure 8 The implementation process of the second filtering process in step S73 is described in detail. Figure 8 As shown, its implementation includes:

[0087] Step S81: Filter out the second time feature S that meets the second preset condition from the at least one set of second time features (T1, T2, S);

[0088] Step S82: for each of the screened second time features that S meets the second preset condition, respectively obtain the first RuO parameter average value and the second RuO parameter average value corresponding to each of the second time features;

[0089] Step S83: Determine the second time feature belonging to the second type of anomaly based on the first RuO parameter average value and the second RuO parameter average value in each second time feature, and delete it from the at least one set of second time features (T1, T2, S) formed.

[0090] In step S81, the second preset condition is used to characterize a second time feature that the duration of the corresponding RuO parameter value being higher than the warning line is less than one hour. Exemplarily, it can be set to S≤1.

[0091] In step S82, the first RuO parameter average value is used to represent the average value of the RuO parameter values ​​obtained within one hour before the time node T1, and the second RuO parameter average value is used to represent the average value of the RuO parameter values ​​obtained within one hour after the time node T2. Among them, time node T1 refers to T1 in the second time feature that meets the second preset condition, and time node T2 refers to T2 in the second time feature that meets the second preset condition. Among them, when calculating the first RuO parameter average value and the second RuO parameter average value, T1 and T2 of the same group of second time features that meet the second preset condition are selected, that is, when determining the difference in the average values ​​of the RuO parameter values ​​within one hour before the time node T1 and within one hour after the time node T2, the second time feature corresponding to T1 when calculating the first RuO parameter average value and the second time feature corresponding to T2 when calculating the second RuO parameter average value are the same. Therefore, by calculating the difference between the first RuO parameter average value and the second RuO parameter average value corresponding to the same group of second time features, the second time feature in which the difference in the average values ​​of the RuO parameter values ​​within one hour before the time node T1 and within one hour after T2 does not exceed 5 can characterize the time node when the parameter data is obviously abnormal, and is the second time feature belonging to the second type of abnormality.

[0092] Specifically, in step S82, Fig. 9 The method shown in FIG. 1 is used to determine the first RuO parameter average value and the second RuO parameter average value corresponding to each second time feature, such as Fig. 9 As shown, it can be implemented as including:

[0093] Step S821: for each second time feature that satisfies the second preset condition, T1 of each second time feature is pushed forward one hour and T2 is pushed back one hour, and all RuO parameter data within one hour before T1 and all RuO parameter data within one hour after T2 are obtained;

[0094] Step S822: Calculate the average value of the RuO parameter values ​​in all the RuO parameter data within one hour before T1 as the first RuO parameter average value corresponding to the corresponding second time feature;

[0095] Step S823: Calculate the average value of the RuO parameter values ​​in all the RuO parameter data within one hour after T2 as the second RuO parameter average value corresponding to the corresponding second time feature.

[0096] It should be noted that, in step S821, the expected RuO parameter data can be obtained based on the time period of T1 one hour forward and T2 one hour backward of each second time feature and the timestamp of the RuO parameter data extracted in step S10, and in steps S822 and S823, the first RuO parameter average value and the second RuO parameter average value corresponding to the corresponding second time feature can be calculated based on the RuO parameter value in the obtained RuO parameter data that meets the expectations.

[0097] In step S83, the second time features belonging to the second type of anomaly are determined based on the first RuO parameter average value and the second RuO parameter average value in each second time feature. Specifically, the first RuO parameter average value and the second RuO parameter average value corresponding to the same group of second time features are subtracted, and it is determined whether the difference does not exceed 5. The second time features not exceeding 5 are deleted as second time features belonging to the second type of anomaly, and the second time features exceeding 5 are retained.

[0098] It should be noted that in Figure 6 and Figure 7 In the implementation manner, the definitions and characteristics of the first time characteristics of the first type of anomaly and the second time characteristics of the second type of anomaly are discovered by the inventor based on a large amount of experience and analysis and summary based on big data, that is, the inventor discovered the correlation between the change of RuO parameter value and the warning line based on a large amount of experience and analysis and summary based on big data, and found out the relationship characteristics between the RuO parameter value that is obviously abnormal and the warning line. Therefore, based on this, by filtering and deleting the time characteristic data that meets these characteristics, the parameter data that is obviously abnormal can be effectively removed, and the repeated processing of invalid data can be reduced. In the scenario of massive data processing, the algorithm performance and efficiency can be greatly improved.

[0099] In step S12, the shortest life S of the cold head can be set based on experience or determined based on a dictionary value provided by the cold head manufacturer. In specific applications, the shortest life of cold heads of different manufacturers and models may be different. The shortest life of the cold head is used to characterize the possible shortest service life of the cold head in actual applications. Therefore, it can be used to effectively screen out invalid suspected replacement time nodes from the replacement time nodes of the cold head. Exemplarily, as a preferred implementation, the determined cold head replacement time node can be compared with the preset shortest life of the cold head to distinguish between valid cold head replacement time nodes and invalid suspected replacement time nodes. For example, when the actual service life of the cold head determined based on the cold head replacement time node is lower than the value, it can be indicated that the corresponding cold head replacement time node is only an invalid suspected replacement time node, not a valid cold head replacement time node. Fig.10The method flow of determining the actual service life of a cold head according to a determined replacement time node of the cold head and a preset minimum service life of the cold head according to an embodiment of the present invention is schematically shown, as shown in FIG. Fig.10 As shown, its implementation includes:

[0100] Step S121: subtract the determined replacement time nodes of the cold heads from the cold head installation time nodes that match the corresponding replacement time nodes to obtain the suspected life of the cold heads;

[0101] Step S122: Compare the suspected life of the cold head with the preset shortest life of the cold head, and determine the actual service life of the cold head corresponding to the installation time of each cold head according to the comparison result.

[0102] In step S121, the cold head installation time adapted to the corresponding replacement time node refers to the cold head installation time adapted to the determined cold head replacement time node. As can be seen from the foregoing, the determined cold head replacement time node includes at least one, that is, it may be a set including more than two cold head replacement time nodes. In this case, it means that the medical device may have replaced the cold head multiple times within the corresponding time period. Therefore, for different cold heads, the corresponding cold head installation time is different. Based on this, the cold head installation time adapted to the determined cold head replacement time node in the embodiment of the present invention may also be variable. For example, for the first cold head replacement time node in the determined cold head replacement time node set, the corresponding cold head installation time is the time of the cold head installed before the cold head replacement time node, which can be set according to the actual situation by initialization; and for the second and subsequent cold head replacement time nodes in the determined cold head replacement time node set, the corresponding cold head installation time varies according to the situation, and the specific variation rule can be that when the cold head replacement time node before is valid, it means that the cold head replacement has occurred at the cold head replacement time node before, then the cold head installation time corresponding to the cold head replacement time node at this time is the valid cold head replacement time node before, and when the cold head replacement time node before is invalid, it means that the cold head replacement has not occurred at the cold head replacement time node before, then the cold head installation time corresponding to the cold head replacement time at this time is the same as the cold head installation time corresponding to the cold head replacement time node before. That is to say, the cold head installation time adapted to the current cold head replacement time node may be the initialized cold head installation time or a cold head replacement time node before, and its specific value depends on the validity of the cold head replacement time node before.

[0103] In step S121 and step S122, the embodiment of the present invention mainly conceives to judge the validity of the determined cold head replacement time node by the preset cold head shortest life, so as to determine the effective replacement time node of the cold head and its corresponding actual service life. Based on this technical concept, in step S121, the embodiment of the present invention needs to calculate a preliminary cold head life according to the determined cold head installation time adapted to the corresponding cold head replacement time node, which is called the suspected life of the cold head in the embodiment of the present invention. The life corresponds to the determined specific cold head replacement time node. Afterwards, in step S122, the embodiment of the present invention will verify the suspected life of the cold head according to the preset cold head shortest life S to further determine whether the cold head replacement time node corresponding to the suspected life of the cold head is valid. When it is valid, the suspected life of the cold head can be confirmed as the actual service life of the previous cold head corresponding to the cold head replacement time node (that is, the cold head replaced at the cold head replacement time node), and the effective cold head replacement time node is used as the cold head installation time of the replaced cold head, and then the next determined cold head replacement time node is continued to be verified. The processing of step S121 and step S122 is performed. At this time, for the next determined cold head replacement time node, its corresponding cold head installation time is updated to the last valid cold head replacement time node; and when it is verified that the cold head replacement time node corresponding to the suspected life of the cold head is invalid, it means that the cold head replacement has not actually occurred at this time. Therefore, the cold head installation time will not be updated, but the cold head installation time will be kept as the existing value, and then the processing of step S121 and step S122 will continue for the next determined cold head replacement time node. It should be noted that under this technical concept, the effective cold head replacement time node in the embodiment of the present invention not only represents the end time of the life of the previous cold head, but also represents the installation time of the next cold head. It is both the basis for determining the actual service life of the previous cold head and the basis for updating the cold head installation time, while the invalid cold head replacement time node is invalid data that will be excluded. As a possible implementation method, the judgment of the validity of a certain cold head replacement time node and the calculation of the actual service life corresponding to the cold head replacement time node based on the above technical concept can be implemented by the following formula:

[0104]

[0105] Where f(x,y) is the actual service life of the cold head before replacement corresponding to the yth cold head replacement time node, T x is the cold head installation time adapted to the yth cold head replacement time node, T y is the yth replacement time node among the determined replacement time nodes of the cold head, S is the preset shortest life of the cold head, T x -T yThe absolute value of the result is the suspected life of the cold head. When the suspected life of the cold head is less than the preset minimum life of the cold head S, it means that the time node T for replacing the cold head is now. y is invalid, therefore, the cold head installation time T x No change, and continue to replace the next cold head at time T y+1 Make a judgment (that is, at this time, T x =T x , y=y+1, the next cold head replacement time node T y+1 Installation time of the adapted cold head and T y The installation time of the adapted cold head is the same), and when the suspected life of the cold head is not less than the preset shortest life of the cold head S, it means that the cold head replacement time node T at this time y is valid, then the suspected life of the cold head will be determined as the actual service life of the corresponding cold head, and the installation time of the cold head T x Update to T y , and then continue to replace the next cold head at time T y+1 Make a judgment (that is, at this time T x =T y , y=y+1, the next cold head replacement time node T y+1 The installation time of the adapted cold head is updated to T y , and T y The adapted cold head installation time is different). It should be noted that, in the above formula, the initial value of x is 0, the initial value of y is 1, T0 is the cold head installation time set by initialization, and T1 is the first cold head replacement time node in the determined cold head replacement time node set.

[0106] In other embodiments, in step S12, the determined cold head replacement time node may be subtracted from the initial cold head installation time, and a valid cold head replacement time node and an invalid suspected replacement time node may be distinguished based on the result of the subtraction and the preset minimum life of the cold head. For example, when the difference between the current cold head replacement time node and the initial cold head is lower than the sum of the preset minimum life of the cold head and the cumulative sum of the actual service life of all cold heads before the cold head replacement time node, it can be indicated that the corresponding cold head replacement time node is only an invalid suspected replacement time node. Fig.11 The method flow of determining the actual service life of a cold head according to a determined replacement time node of the cold head and a preset minimum service life of the cold head according to an embodiment of the present invention is schematically shown, as shown in FIG. Fig.11 As shown, its implementation includes:

[0107] Step S123: determining the current cold head for which actual service life needs to be calculated;

[0108] Step S124: determining the starting position of the cold head replacement time node for calculating the actual service life of the current cold head according to the current cold head;

[0109] Step S125: Starting from the starting position, based on the difference between the qualified cold head replacement time node among the determined cold head replacement time nodes and the cold head installation time of the initial cold head, and the cold head life cumulative benchmark, determine the actual service life of the current cold head, wherein the cold head life cumulative benchmark is the sum of the preset shortest life of the cold head and the actual service life of all cold heads before the current cold head.

[0110] As a possible implementation, the technical concept described in step S123 to step S125 can be specifically implemented by the following formula:

[0111]

[0112]

[0113] Wherein, g(n) in the formula is the actual service life of the nth cold head (i.e., the current cold head), wherein n=0 indicates that it corresponds to the initial cold head (i.e., the cold head installation time corresponding to the first cold head replacement time node in the determined cold head replacement time node set, i.e., the time of the cold head installed before this cold head replacement time node), n=1 indicates the cold head replaced for the first time after the initial cold head is put into use, and so on; T0 in the formula is the installation time of the initial cold head, which can be set according to the actual situation by initialization, T x is the x-th cold head replacement time node in the determined cold head replacement time node set, f(x,m) is the cumulative sum of the actual service lives of the first n-1 cold heads, and S in the formula is the preset minimum life of the cold head. In a specific application, the actual service life of the n-th cold head (i.e., the current cold head) can be calculated in combination with all the cold head replacement time nodes in the determined cold head replacement time node set. Exemplarily, the actual service life of the initial cold head can be calculated first. At this time, the value of n is set to 0, and the actual service life of the current cold head is g(0)=f(1,S). Since S in the formula is the preset minimum life of the cold head, when n=0, the cumulative benchmark of the cold head life is m=S. At this time, T x -T0 is the suspected life of the initial cold head. When the suspected life of the initial cold head is less than the preset minimum life of the cold head S, that is, when T x -T0<m, it means the time node T for cold head replacement at this time x is invalid, that is, the initial cold head has not been replaced, so the next cold head replacement time T x+1This judgment is made, that is, the value of x is updated to x=x+1, and when the suspected life of the initial cold head is not less than the preset shortest life of the cold head S, T x - When T0≥m, it indicates the time node T for cold head replacement. x is valid, and the determined g(0)=T x The value of -T0 is determined as the actual service life of the initial cold head (i.e., the current cold head). Afterwards, the value of x when the actual service life g(0) of the initial cold head is calculated and the number of cold head replacement time nodes in the cold head replacement time node set can be used to determine whether there is a next cold head whose actual service life needs to be calculated. Specifically, when the value of x when the actual service life g(0) of the initial cold head is calculated is equal to the number of cold head replacement time nodes in the cold head replacement time node set, it means that there are no more cold head replacement time nodes in the cold head replacement time node set, that is, only the initial cold head has been replaced, and the first cold head after replacement is still in use, and its actual service life cannot be calculated yet. At this time, the update of n and the continued judgment of the cold head replacement time node will be stopped. Of course, it is not difficult to understand that this processing is not only applicable to The same applies to the case where n=0, and the same applies to the case where n is other values; and when the value of x when calculating the actual service life g(0) of the initial cold head is less than the number of cold head replacement time nodes in the cold head replacement time node set, it means that there are still cold head replacement time nodes in the cold head replacement time node set that have not been judged. At this time, the value of n will be updated to n=n+1 (if n=0, it will be updated to 1), that is, the current cold head is determined by updating the value of n, and then the actual service life of the current cold head is calculated using the formula g(n). At this time, according to the determined current cold head, the cold head replacement time node is selected from the cold head replacement time node set for the above judgment starting from the initial position n+1, that is, for the current cold head, it is from T x =T n+1 It is not difficult to understand that this process is not only applicable to the case where n=0, but also to the case where n is other values. When the value of n is updated to be greater than or equal to 1, since the initial cold head has been replaced at least once, for the nth cold head, T x The result of -T0 is actually the sum of the actual service life of the first n-1 cold heads and the suspected service life of the nth cold head. Therefore, when the value of n is updated to be greater than or equal to 1, the value of m will be set to the sum of the shortest service life S of the cold head and the actual service life of the first n-1 cold heads, that is, there is a cold head life accumulation benchmark At this time, when T x -T0≥m can indicate the time node T for cold head replacement. x is effective (i.e. at the cold head replacement time node T xCold head replacement occurred), and when T x -T0<m can indicate the time node T for cold head replacement. x is invalid (i.e. at the cold head replacement time node T x The cold head has not been replaced), so the actual service life of the nth cold head can be calculated based on the xth cold head replacement time node in the cold head replacement time node set and the value of m. It should also be noted that when calculating the actual service life of the cold head, if the value of x is equal to the number of cold head replacement time nodes in the cold head replacement time node set, the value of g(n) is not obtained, that is, if the value of x is equal to the number of cold head replacement time nodes in the cold head replacement time node set, it is not possible to find a value that satisfies T x -T0≥m cold head replacement time node T x , it means that the nth cold head is still in use and has not been replaced. At this time, the update of n and the continued judgment of the cold head replacement time node will be stopped.

[0114] Fig.12 The apparatus for quickly analyzing a medical device log file according to an embodiment of the present invention is schematically shown. Fig.12 As shown, it includes:

[0115] A log acquisition module 50, used to acquire the log file to be parsed of the medical device;

[0116] The compression sampling module 51 is used to sample log records of the log files to be parsed of each medical device according to the log time of each log record in the log files to be parsed of each medical device;

[0117] The parsing module 52 is used to generate and store parsing results of the log files to be parsed of each medical device based on the sampled log records.

[0118] Among them, as a preferred embodiment, Fig.12 As shown, the compression sampling module 51 can be specifically implemented as including

[0119] An index mapping unit 511, used to determine the index to which the corresponding log record is mapped according to the log time of each log record in the log file to be parsed of the corresponding medical device and a preset reference time, wherein the index is used to indicate the storage location of the validity identifier corresponding to the corresponding log record in the partition file corresponding to the medical device, and the partition file is used to store the validity identifier corresponding to the log record of the corresponding medical device;

[0120] The sampling target determination unit 512 is used to obtain the validity identifier corresponding to the corresponding log record from the partition file according to the index of the corresponding log record, and when the validity identifier is determined to be valid, determine the corresponding log record as a sampling target and sample it.

[0121] It should be noted that the specific implementation process and implementation principle of each module and unit of the apparatus for quickly analyzing medical device log files in the embodiment of the present invention can be found in the corresponding description of the above method embodiment, so they will not be repeated here.

[0122] Fig.13 The device for quickly determining the actual service life of a cold head in a medical device according to an embodiment of the present invention is schematically shown. Fig.13 As shown, it includes:

[0123] A parameter extraction module 60, configured to extract RuO parameter data, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps;

[0124] A replacement time determination module 61 is used to determine a replacement time node of a cold head of a corresponding medical device according to the extracted RuO parameter data;

[0125] The service life determination module 62 is used to determine the actual service life of the cold head according to the determined replacement time node of the cold head and the preset minimum service life of the cold head.

[0126] In some embodiments, an embodiment of the present invention provides a non-volatile computer-readable storage medium, which stores one or more programs including execution instructions, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to execute the method for quickly determining the service life of the cold head of a medical device according to any of the above embodiments of the present invention.

[0127] In some embodiments, an embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method for quickly determining the service life of the cold head of a medical device according to any of the above embodiments.

[0128] In some embodiments, an embodiment of the present invention further provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for quickly determining the service life of the cold head of a medical device of any of the above embodiments.

[0129] In some embodiments, the present invention further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quickly determining the service life of a cold head of a medical device according to any of the above embodiments.

[0130] Fig.14 FIG. 1 is a schematic diagram of the hardware structure of an electronic device for a method for quickly determining the service life of a cold head of a medical device provided by another embodiment of the present application. Fig.14 As shown, the device includes:

[0131] One or more processors 610 and memory 620, Fig.14 A processor 610 is taken as an example.

[0132] The device for executing the method for quickly determining the service life of a cold head of a medical device may further include: an input device 630 and an output device 640 .

[0133] The processor 610, the memory 620, the input device 630 and the output device 640 may be connected via a bus or other means. Fig.14 The example of connecting through bus is taken in the following.

[0134] The memory 620, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the method for quickly determining the service life of the cold head of a medical device in the embodiment of the present application. The processor 610 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 620, that is, the method for quickly determining the service life of the cold head of a medical device in the above method embodiment is implemented.

[0135] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of a method for quickly determining the service life of a cold head of a medical device, etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely arranged relative to the processor 610, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0136] The input device 630 may receive input digital or character information and generate signals related to user settings and function control of the image processing device. The output device 640 may include a display device such as a display screen.

[0137] The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, the method for quickly determining the service life of a cold head of a medical device in any of the above method embodiments is executed.

[0138] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0139] The electronic devices of the embodiments of the present application exist in various forms, including but not limited to:

[0140] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0141] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0142] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, as well as smart toys and portable car navigation devices.

[0143] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0144] (5) Other electronic devices with data interaction functions.

[0145] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for quickly determining the actual service life of a cold head in a medical device, characterized in that: include: Extracting RuO parameter data of the corresponding medical device, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps; Determine the replacement time node of the cold head of the corresponding medical equipment according to the extracted RuO parameter data; The actual service life of the cold head is determined according to the determined replacement time node of the cold head and the preset minimum service life of the cold head.

2. The method according to claim 1, characterized in that Determining the replacement time node of the cold head of the corresponding medical device according to the extracted RuO parameter data includes: At least one group of first time features (T1, T2, S) is generated according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that exceeds the warning line for the first time after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that falls below the warning line for the first time after T1 of the corresponding group of first time features, and S represents the duration of time that the RuO parameter value corresponding to the corresponding group of first time features exceeds the warning line; A cold head replacement time node is determined from the at least one group of first time features (T1, T2, S) according to a preset first filtering condition, wherein the cold head replacement time node is a timestamp T2 in the first time feature whose duration S satisfies the first filtering condition.

3. The method according to claim 1, characterized in that Determining the replacement time node of the cold head of the corresponding medical device according to the extracted RuO parameter data includes: At least one group of first time features (T1, T2, S) is generated according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that exceeds the warning line for the first time after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that falls below the warning line for the first time after T1 of the corresponding group of first time features, and S represents the duration of time that the RuO parameter value corresponding to the corresponding group of first time features exceeds the warning line; Performing a first filtering process on the at least one group of first time features (T1, T2, S), deleting the first time features (T1, T2, S) belonging to the first type of anomaly, and obtaining at least one group of second time features (T1, T2, S), wherein the first time feature of the first type of anomaly refers to the first time feature S that satisfies the first preset condition; A cold head replacement time node is determined from the at least one group of second time features (T1, T2, S) according to a preset first filtering condition, wherein the cold head replacement time node is a timestamp T2 in the second time feature whose duration S satisfies the first filtering condition.

4. The method according to claim 1, characterized in that: Determining the replacement time node of the cold head of the corresponding medical device according to the extracted RuO parameter data includes: At least one group of first time features (T1, T2, S) is generated according to the RuO parameter values ​​in the extracted RuO parameter data and their corresponding timestamps, and the preset warning line, wherein T1 represents the timestamp corresponding to the RuO parameter value that exceeds the warning line for the first time after T2 of a group of first time features before the corresponding group of first time features, T2 represents the timestamp corresponding to the RuO parameter value that falls below the warning line for the first time after T1 of the corresponding group of first time features, and S represents the duration of time that the RuO parameter value corresponding to the corresponding group of first time features exceeds the warning line; Performing a first filtering process on the at least one group of first time features (T1, T2, S), deleting the first time features (T1, T2, S) belonging to the first type of anomalies, and obtaining at least one group of second time features (T1, T2, S); Performing a second filtering process on the at least one set of second time features (T1, T2, S) formed, deleting the second time features (T1, T2, S) belonging to the second type of anomalies, and obtaining at least one set of third time features (T1, T2, S); A cold head replacement time node is determined from the at least one group of third time features (T1, T2, S) according to a preset first filtering condition, wherein the cold head replacement time node is a timestamp T2 in the third time feature whose duration S satisfies the first filtering condition.

5. The method according to claim 4, characterized in that Performing a second filtering process on the at least one set of second time features (T1, T2, S) formed, deleting the second time features (T1, T2, S) belonging to the second type of anomaly, and obtaining at least one set of third time features (T1, T2, S), including: Filter out the second time feature S that meets the second preset condition from the at least one set of second time features (T1, T2, S); For each of the second time features that S satisfies the second preset condition, respectively obtain the first RuO parameter average value and the second RuO parameter average value corresponding to each of the second time features; The second time feature belonging to the second type of anomaly is determined based on the first RuO parameter average value and the second RuO parameter average value in each second time feature, and is deleted from the at least one set of second time features (T1, T2, S) formed.

6. The method according to claim 5, characterized in that Obtaining the first RuO parameter average value and the second RuO parameter average value corresponding to each second time feature includes: For each second time feature that S satisfies the second preset condition, T1 of each second time feature is pushed forward one hour and T2 is pushed back one hour, and all RuO parameter data within one hour before T1 and all RuO parameter data within one hour after T2 are obtained; Calculate the average value of the RuO parameter values ​​in all the RuO parameter data within one hour before T1 as the first RuO parameter average value corresponding to the corresponding second time feature; The RuO parameter values ​​in all RuO parameter data within one hour after T2 are averaged as the second RuO parameter average value corresponding to the corresponding second time feature.

7. The method according to any one of claims 2 to 6, characterized in that: The actual service life of the cold head is determined according to the determined replacement time node of the cold head and the preset minimum service life of the cold head, including: The determined replacement time nodes of the cold heads are respectively subtracted from the cold head installation time adapted to the corresponding replacement time nodes to obtain the suspected life of the cold heads; The suspected life of the cold head is compared with the preset shortest life of the cold head, and the actual service life of the cold head corresponding to the installation time of each cold head is determined according to the comparison result.

8. The method according to any one of claims 2 to 6, characterized in that: The actual service life of the cold head is determined according to the determined replacement time node of the cold head and the preset minimum service life of the cold head, including: For the current cold head whose actual service life needs to be calculated, the actual service life of the current cold head is determined based on the difference between the qualified cold head replacement time node among the determined cold head replacement time nodes and the cold head installation time of the initial cold head, and the cold head life cumulative benchmark, wherein the cold head life cumulative benchmark is the sum of the preset shortest life of the cold head and the actual service life of all cold heads before the current cold head.

9. A device for quickly determining the actual service life of a cold head in a medical device, characterized in that: include: A parameter extraction module, used to extract RuO parameter data, wherein the RuO parameter data includes RuO parameter values ​​and their corresponding timestamps; A replacement time determination module, used to determine a replacement time node of a cold head of a corresponding medical device according to the extracted RuO parameter data; The service life determination module is used to determine the actual service life of the cold head according to the determined replacement time node of the cold head and the preset minimum service life of the cold head.

10. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method according to any one of claims 1 to 8.

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