Medical product after-sales record management method and system based on the Internet of Things

Abstract

The present invention relates to the field of record management, and specifically to a medical product after-sales record management method and system based on the Internet of Things, comprising: an after-sales record module, a data storage module, a storage classification module, a timing compression module and a quality assessment module. The after-sales record module is used to record after-sales behavior, the data storage module is used to screen storage records, split storage data packets, and establish indexes in table headers, the storage classification module is used to divide memory levels, establish a retrieval mechanism, the timing compression module is used to compress data into non-binary sequences, and set a compression rate adjustment clock, and the quality assessment module is used to evaluate the quality level of products based on after-sales service retrieval records. The present invention can improve data quality, reduce the workload of database maintenance, improve data management efficiency, better organize data structure, optimize medical product after-sales record management, and improve after-sales service efficiency and customer satisfaction.

Description

Technical Field

[0001] The present invention relates to the field of record management, and in particular to a method and system for post-sale record management of medical products based on the Internet of Things. Background Art

[0002] Medical products refer to devices, equipment, medicines, materials or other related products used for medical and health purposes, mainly used to prevent, diagnose, treat diseases and restore human functions. In the life cycle of medical products from production to use and recycling, product after-sales service is required many times, including product service processes such as component replacement and repair, providing technical support and collecting feedback. In order to ensure the traceability of medical products, each after-sales record needs to be archived in the database.

[0003] Since medical products have a long life cycle and a large number of spare parts and consumables are produced, many invalid after-sales records will accumulate in the database, occupying a large amount of database memory. If compression technology is used to compress records that exceed a certain period, it may cause difficulty in decompressing the after-sales data when retrieving it, and it will take a lot of time to retrieve it after decompression, causing delays in after-sales time.

[0004] In addition, after the after-sales records are archived in the database, it is difficult to evaluate the after-sales quality of the product unless the data is extracted one by one and sorted manually. There is also a lack of effective horizontal comparison methods for different batches of the same product, which is not conducive to medical product manufacturers to adjust product quality and will also hinder the stable operation of the product chain. Summary of the invention

[0005] The purpose of the present invention is to provide a medical product post-sales record management method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A medical product after-sales record management system based on the Internet of Things, comprising: an after-sales record module, a data storage module, a storage classification module, a timing compression module and a quality assessment module;

[0007] The after-sales record module is used to record the medical products when they are shipped out and sold, determine the average after-sales duration and quality stability of the medical products based on the records, calculate the probability of after-sales behavior of medical products shipped out in different time periods, and determine the information entropy range of the medical product records based on the batches of medical products, after-sales processes and historical records;

[0008] The data storage module is used to store the after-sales service records uploaded to the server by each computer into the database, filter out the records whose information entropy of a single record is less than the information entropy range of the medical product record corresponding to the after-sales record, and retrieve the data segment representing the product number in the record as the header, establish an index pointing to other parts of the after-sales record in the header, and merge the other parts of the after-sales record into a bottom data packet;

[0009] The storage classification module is used to classify the server memory. When the after-sales record enters the database, the header is stored in the primary memory, the bottom data packet is stored in the secondary memory, a two-level memory retrieval mechanism is established, and the bottom data packet is expanded into a binary sequence form in the secondary memory;

[0010] The timing compression module is used to calculate the compression rate of the bottom data packet according to the minimum information entropy and the probability of after-sales behavior, compress the bottom data packet into a non-binary sequence according to the compression rate, and then calculate the clock length when the message compression rate reaches the threshold according to the probability of after-sales recycling of the medical product at the current moment. When the clock runs out, the compression rate of the message sequence is adjusted by one base, and the clock is reset;

[0011] The quality assessment module is used to calculate the quality grade of the product when the after-sales record header of the same batch of products is called according to the compression rate of the data packet in the secondary memory and the clock remaining length at the time of the call, obtain the historical call records of the same batch of medical products, and evaluate the production quality of the same batch of medical products according to the historical average quality grade of the medical products in the records.

[0012] Furthermore, the after-sales record module includes: an in-and-out storage unit, a quality recovery unit and an information management unit;

[0013] The in-and-out storage unit is used to record after-sales behavior when a medical product undergoes after-sales behavior, forming an after-sales service record, wherein the after-sales service record includes: the batch number of the product, the type of after-sales service, and the after-sales processing result;

[0014] The quality recovery unit is used to calculate the average after-sales time and after-sales time stability of each product based on historical after-sales records;

[0015] The information management unit is used to record the information length in the after-sales service record and calculate the information entropy range of the effective after-sales record.

[0016] Further, the data storage module includes: a pre-cleaning unit and a record retrieval unit;

[0017] The pre-cleaning unit is used to clean the records whose data volume does not meet the information entropy range before the after-sales records are uploaded to the database;

[0018] The record retrieval unit is used to retrieve the after-sales record, extract the part of the record containing the product number as the header, establish an index pointing to other parts of the after-sales record, and merge the other parts of the after-sales record into a bottom data packet.

[0019] Further, the storage classification module includes: a hierarchical indexing unit and a sequence expansion unit;

[0020] The hierarchical indexing unit is used to divide the memory of the server into two levels;

[0021] The sequence expansion unit is used to store the header and the bottom data packet in two levels of memory respectively, and establish a two-level memory retrieval mechanism.

[0022] Furthermore, the timing compression module includes: a base compression unit, a clock adjustment unit and a record retrieval unit;

[0023] The binary compression unit is used to calculate the compression rate of the bottom data packet, and compress the bottom data packet into a non-binary sequence according to the compression rate;

[0024] The clock adjustment unit is used to set a clock with a duration numbered according to the recovery probability for the bottom data packet according to the after-sales recovery probability, and change the compression rate of the bottom data packet when the clock runs out;

[0025] The record retrieval unit is used to decompress the header and the corresponding bottom data packet and merge them into an after-sales record when a call request is received in the primary memory.

[0026] Further, the quality assessment module includes: an after-sales grade unit and a batch evaluation unit;

[0027] The after-sales level unit is used to record the current compression level and the remaining time of the clock of the bottom data packet when the after-sales record is called;

[0028] The batch evaluation unit evaluates the after-sales quality of the products according to the call records of the same batch of products.

[0029] The post-sale record management method of medical products based on the Internet of Things includes the following steps:

[0030] Step S1. Record the after-sales records of medical products when they have after-sales services. According to the time between the after-sales records and the sales time of the same type of medical products, the average after-sales time and the stability of the time are used as normal parameters to obtain the function of the probability of after-sales behavior of the product changing over time;

[0031] Step S2. After screening the after-sales records, upload the after-sales records to the database, extract the product number field in the after-sales records as the header, and merge the after-sales record fields after removing the header into a bottom data packet;

[0032] Step S3. Divide the database memory into primary memory and secondary memory, store the header of the after-sales record into the primary memory, store the bottom data packet into the secondary memory, and establish an index for the header to point to the corresponding bottom data packet, and determine the compression rate of the bottom data packet according to the probability of after-sales behavior of the product at the current moment, and compress the bottom data packet into a non-binary sequence;

[0033] Step S4. Set a clock for the bottom data packet stored in the secondary memory, and use the time when the probability of after-sales behavior of the product reaches the threshold as the clock length. When the clock runs out, adjust the compression rate of the bottom data packet by one base, and reset the clock until the compression rate of the data packet reaches the maximum compression rate.

[0034] Step S5. When the database receives an after-sales record call request, it retrieves the header in the primary memory according to the product number in the call request, obtains the bottom data packet in the secondary memory by the header index, outputs the after-sales record after merging and restoring, and weightedly adds the compression rate of the bottom data packet and the remaining clock duration when the record is called to obtain the single after-sales quality of the product.

[0035] Further, step S1 includes:

[0036] Step S11. When a medical product has an after-sales behavior, the after-sales behavior is recorded to form an after-sales record, which includes: product type, product number, after-sales type, after-sales occurrence time and after-sales processing results;

[0037] Step S12. After the after-sales record is generated, query the time when the product is shipped out of the factory according to the product number to obtain the time from product sales to after-sales behavior;

[0038] Step S13. According to the product type in the after-sales record, obtain the time from product sales to after-sales behavior in the after-sales records of similar products in history, and form an after-sales time set {W1, W2, ..., W n}, where n represents the number of historical after-sales records of similar products, W n Represents the time from product sales to after-sales behavior in the nth after-sales record of the same product;

[0039] Step S14. Calculate the stability of the product after-sales service time according to the following formula:

[0040]

[0041] Among them, a represents the stability of the product after-sales time, W0 represents the average value of all elements in the after-sales time set, i is the position number, and W i Represents the i-th element of the after-sales duration set;

[0042] Step S15. Based on the average after-sales duration of the product and the stability of the duration, a normal function is used to fit the function of the probability of after-sales behavior of the product changing over time:

[0043]

[0044] Among them, F(t) represents the function of the probability of after-sales behavior of the product changing with time, t represents time, and e is the base of the natural logarithm.

[0045] Further, step S2 includes:

[0046] Step S21. Upload the after-sales record to the database, and the database analyzes the data volume of the after-sales record. If the data volume of the after-sales record is not within the preset range, the after-sales record is filtered out and does not enter the database memory. If it is within the preset range of the data volume of the after-sales record, go to step S22;

[0047] Step S22. Before data archiving, retrieve the product number in the after-sales record, extract the field containing the product number in the after-sales record as the header, and merge the other fields in the after-sales record into the bottom data packet.

[0048] Further, step S3 includes:

[0049] Step S31. Create a virtual memory in the database, isolate all storage space into two different memory spaces, as primary memory and secondary memory, store the header in the same after-sales record into the primary memory, store the bottom data packet into the secondary memory, and create an index for the header to point to the corresponding bottom data packet;

[0050] Step S32. Calculate the compression rate G of the after-sales record bottom data packet, where G=U-MOD[F(t0)·U / F(W0)], where U is the preset maximum compression rate, U is an integer and U>2, t0 is the current time, and MOD is a rounding function;

[0051] Step S33: Convert the data in the bottom data packet from binary data to a G-binary compressed file, and store the G-binary compressed file in the secondary memory instead of the bottom data packet.

[0052] Further, step S4 includes:

[0053] Step S41. Set a clock for the G-binary compressed file stored in the secondary memory, and the clock length T satisfies: |F(T+t0)-F(t0)|=1 / U;

[0054] If F(T+t0)-F(t0)>0, then when the clock runs out, the G-binary compressed file is decompressed into the G-1-binary system; if F(T+t0)-F(t0)≤0, then when the clock runs out, the G-binary compressed file is further compressed into the G+1-binary system;

[0055] Step S42. Detect the compression ratio of the current file. If the compression ratio of the file G=U, the clock is not reset. If G≠U, the clock is reset and go to step S41.

[0056] Further, step S5 includes:

[0057] Step S51. When the after-sales record is retrieved due to another after-sales behavior, after receiving the product number, the database retrieves the header corresponding to the product number in the primary memory, extracts the bottom data packet from the secondary memory according to the index pointed to by the header, decompresses the bottom data packet, adds the header, and downloads the restored file to the requesting device;

[0058] Step S52. Record the compression rate of the bottom data packet when the after-sales record is retrieved and the remaining time of the clock, and calculate the single after-sales quality Q of the product. When F(T+t0)-F(t0)≤0, Q=(U-G0)+TS / T, where G0 represents the compression rate of the bottom data packet when the after-sales record is retrieved, TS represents the remaining time of the clock, and T represents the clock length. When F(T+t0)-F(t0)>0, Q=G0+(T-TS) / T;

[0059] Step S53: Summarize the after-sales qualities of the same type of products, calculate the average value, and send the calculated average value as the single after-sales quality of the product to the after-sales center.

[0060] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0061] 1. The present invention can record when medical products are shipped out and when after-sales service occurs, calculate the probability of after-sales recovery of medical products shipped out in different time periods, and determine the information entropy range of medical product records. It can determine the importance of different records and avoid warehousing redundant records, which can improve data quality, reduce the workload of database maintenance, and improve data management efficiency.

[0062] 2. When the after-sales record enters the database, the present invention can store the header into the primary memory and create an index, and the remaining messages constitute a message sequence and store it in the secondary memory, compressing the message sequence into a non-binary sequence, thereby maximizing the savings of the database's memory capacity, while ensuring the flexibility of data retrieval, improving the efficiency of data query and analysis, and better organizing the data structure, making the data clearer, more orderly, and easier to manage and use.

[0063] 3. The present invention can preset the compression clock length of the message. When the clock runs out, the message sequence is decompressed one base at a time, and the message compression rate is adjusted according to the call probability of the message. The quality level of the product is evaluated based on the message sequence compression rate and the remaining clock length, which can optimize the after-sales record management of pharmaceutical products and improve after-sales service efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0065] Figure 1 It is a structural schematic diagram of the medical product after-sales record management system based on the Internet of Things of the present invention;

[0066] Figure 2 It is a schematic diagram of the steps of the medical product after-sales record management method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0068] See also Figure 1 , the present invention provides a technical solution: a medical product after-sales record management system based on the Internet of Things, including: an after-sales record module, a data storage module, a storage classification module, a timing compression module and a quality assessment module;

[0069] The after-sales record module is used to record the medical products when they are shipped out and sold, determine the average after-sales duration and quality stability of the medical products based on the records, calculate the probability of after-sales behavior of medical products shipped out in different time periods, and determine the information entropy range of the medical product records based on the batches of medical products, after-sales processes and historical records;

[0070] The after-sales record module includes: an in-and-out storage unit, a quality recovery unit and an information management unit;

[0071] The in-and-out storage unit is used to record after-sales behavior when a medical product undergoes after-sales behavior, forming an after-sales service record, wherein the after-sales service record includes: the batch number of the product, the type of after-sales service, and the after-sales processing result;

[0072] The quality recovery unit is used to calculate the average after-sales time and after-sales time stability of each product based on historical after-sales records;

[0073] The information management unit is used to record the information length in the after-sales service record and calculate the information entropy range of the effective after-sales record.

[0074] The data storage module is used to store the after-sales service records uploaded to the server by each computer into the database, filter out the records whose information entropy of a single record is less than the information entropy range of the medical product record corresponding to the after-sales record, and retrieve the data segment representing the product number in the record as the header, establish an index pointing to other parts of the after-sales record in the header, and merge the other parts of the after-sales record into a bottom data packet;

[0075] The data storage module includes: a pre-cleaning unit and a record retrieval unit;

[0076] The pre-cleaning unit is used to clean the records whose data volume does not meet the information entropy range before the after-sales records are uploaded to the database;

[0077] The record retrieval unit is used to retrieve the after-sales record, extract the part of the record containing the product number as the header, establish an index pointing to other parts of the after-sales record, and merge the other parts of the after-sales record into a bottom data packet.

[0078] The storage classification module is used to classify the server memory. When the after-sales record enters the database, the header is stored in the primary memory, the bottom data packet is stored in the secondary memory, a two-level memory retrieval mechanism is established, and the bottom data packet is expanded into a binary sequence form in the secondary memory;

[0079] The storage classification module includes: a hierarchical index unit and a sequence expansion unit;

[0080] The hierarchical indexing unit is used to divide the memory of the server into two levels;

[0081] The sequence expansion unit is used to store the header and the bottom data packet in two levels of memory respectively, and establish a two-level memory retrieval mechanism.

[0082] The timing compression module is used to calculate the compression rate of the bottom data packet according to the minimum information entropy and the probability of after-sales behavior, compress the bottom data packet into a non-binary sequence according to the compression rate, and then calculate the clock length when the message compression rate reaches the threshold according to the probability of after-sales recycling of the medical product at the current moment. When the clock runs out, the compression rate of the message sequence is adjusted by one base, and the clock is reset;

[0083] The timing compression module includes: a base compression unit, a clock adjustment unit and a record retrieval unit;

[0084] The binary compression unit is used to calculate the compression rate of the bottom data packet, and compress the bottom data packet into a non-binary sequence according to the compression rate;

[0085] The clock adjustment unit is used to set a clock with a duration numbered according to the recovery probability for the bottom data packet according to the after-sales recovery probability, and change the compression rate of the bottom data packet when the clock runs out;

[0086] The record retrieval unit is used to decompress the header and the corresponding bottom data packet and merge them into an after-sales record when a call request is received in the primary memory.

[0087] The quality assessment module is used to calculate the quality grade of the product when the after-sales record header of the same batch of products is called according to the compression rate of the data packet in the secondary memory and the clock remaining length at the time of the call, obtain the historical call records of the same batch of medical products, and evaluate the production quality of the same batch of medical products according to the historical average quality grade of the medical products in the records.

[0088] The quality assessment module includes: an after-sales grade unit and a batch evaluation unit;

[0089] The after-sales level unit is used to record the current compression level and the remaining time of the clock of the bottom data packet when the after-sales record is called;

[0090] The batch evaluation unit evaluates the after-sales quality of the products according to the call records of the same batch of products.

[0091] like Figure 2 As shown, the post-sale record management method of medical products based on the Internet of Things includes the following steps:

[0092] Step S1. Record the after-sales records of medical products when they have after-sales services. According to the time between the after-sales records and the sales time of the same type of medical products, the average after-sales time and the stability of the time are used as normal parameters to obtain the function of the probability of after-sales behavior of the product changing over time;

[0093] Step S1 includes:

[0094] Step S11. When a medical product has an after-sales behavior, the after-sales behavior is recorded to form an after-sales record, which includes: product type, product number, after-sales type, after-sales occurrence time and after-sales processing results;

[0095] Step S12. After the after-sales record is generated, query the time when the product is shipped out of the factory according to the product number to obtain the time from product sales to after-sales behavior;

[0096] Step S13. According to the product type in the after-sales record, obtain the time from product sales to after-sales behavior in the after-sales records of similar products in history, and form an after-sales time set {W1, W2, ..., W n}, where n represents the number of historical after-sales records of similar products, W n Represents the time from product sales to after-sales behavior in the nth after-sales record of the same product;

[0097] Step S14. Calculate the stability of the product after-sales service time according to the following formula:

[0098]

[0099] Among them, a represents the stability of the product after-sales time, W0 represents the average value of all elements in the after-sales time set, i is the position number, and W i Represents the i-th element of the after-sales duration set;

[0100] Step S15. Based on the average after-sales duration of the product and the stability of the duration, a normal function is used to fit the function of the probability of after-sales behavior of the product changing over time:

[0101]

[0102] Among them, F(t) represents the function of the probability of after-sales behavior of the product changing with time, t represents time, and e is the base of the natural logarithm.

[0103] Step S2. After screening the after-sales records, upload the after-sales records to the database, extract the product number field in the after-sales records as the header, and merge the after-sales record fields after removing the header into a bottom data packet;

[0104] Step S2 includes:

[0105] Step S21. Upload the after-sales record to the database, and the database analyzes the data volume of the after-sales record. If the data volume of the after-sales record is not within the preset range, the after-sales record is filtered out and does not enter the database memory. If it is within the preset range of the data volume of the after-sales record, go to step S22;

[0106] Step S22. Before data archiving, retrieve the product number in the after-sales record, extract the field containing the product number in the after-sales record as the header, and merge the other fields in the after-sales record into the bottom data packet.

[0107] Step S3. Divide the database memory into primary memory and secondary memory, store the header of the after-sales record into the primary memory, store the bottom data packet into the secondary memory, and establish an index for the header to point to the corresponding bottom data packet, and determine the compression rate of the bottom data packet according to the probability of after-sales behavior of the product at the current moment, and compress the bottom data packet into a non-binary sequence;

[0108] Step S3 includes:

[0109] Step S31. Create a virtual memory in the database, isolate all storage space into two different memory spaces, as primary memory and secondary memory, store the header in the same after-sales record into the primary memory, store the bottom data packet into the secondary memory, and create an index for the header to point to the corresponding bottom data packet;

[0110] Step S32. Calculate the compression rate G of the after-sales record bottom data packet, where G=U-MOD[F(t0)·U / F(W0)], where U is the preset maximum compression rate, U is an integer and U>2, t0 is the current time, and MOD is a rounding function;

[0111] Step S33: Convert the data in the bottom data packet from binary data to a G-binary compressed file, and store the G-binary compressed file in the secondary memory instead of the bottom data packet.

[0112] Step S4. Set a clock for the bottom data packet stored in the secondary memory, and use the time when the probability of after-sales behavior of the product reaches the threshold as the clock length. When the clock runs out, adjust the compression rate of the bottom data packet by one base, and reset the clock until the compression rate of the data packet reaches the maximum compression rate.

[0113] Step S4 includes:

[0114] Step S41. Set a clock for the G-binary compressed file stored in the secondary memory, and the clock length T satisfies: |F(T+t0)-F(t0)|=1 / U;

[0115] If F(T+t0)-F(t0)>0, then when the clock runs out, the G-binary compressed file is decompressed into the G-1-binary system; if F(T+t0)-F(t0)≤0, then when the clock runs out, the G-binary compressed file is further compressed into the G+1-binary system;

[0116] Step S42. Detect the compression ratio of the current file. If the compression ratio of the file G=U, the clock is not reset. If G≠U, the clock is reset and go to step S41.

[0117] Step S5. When the database receives an after-sales record call request, it retrieves the header in the primary memory according to the product number in the call request, obtains the bottom data packet in the secondary memory by the header index, outputs the after-sales record after merging and restoring, and weightedly adds the compression rate of the bottom data packet and the remaining clock duration when the record is called to obtain the single after-sales quality of the product.

[0118] Step S5 includes:

[0119] Step S51. When the after-sales record is retrieved due to another after-sales behavior, after receiving the product number, the database retrieves the header corresponding to the product number in the primary memory, extracts the bottom data packet from the secondary memory according to the index pointed to by the header, decompresses the bottom data packet, adds the header, and downloads the restored file to the requesting device;

[0120] Step S52. Record the compression rate of the bottom data packet when the after-sales record is retrieved and the remaining time of the clock, and calculate the single after-sales quality Q of the product. When F(T+t0)-F(t0)≤0, Q=(U-G0)+TS / T, where G0 represents the compression rate of the bottom data packet when the after-sales record is retrieved, TS represents the remaining time of the clock, and T represents the clock length. When F(T+t0)-F(t0)>0, Q=G0+(T-TS) / T;

[0121] Step S53: Summarize the after-sales qualities of the same type of products, calculate the average value, and send the calculated average value as the single after-sales quality of the product to the after-sales center.

[0122] Embodiment: After a pharmaceutical product undergoes after-sales service, the after-sales information of the pharmaceutical product is recorded, and the after-sales records are packaged and sent to a database. The database extracts the product number from the after-sales information and stores it in a primary memory. The average after-sales service of similar pharmaceutical products is 10 days, and the stability is 0.5. The probability function of the product is F(t)=e^[-2(t-10)^2] / √(Π / 2). If the current moment is 9 days away from the sale, the probability of the product undergoing after-sales service is 0.11. The preset maximum base U=10, the compression rate at the current moment is 4, and the after-sales record is compressed into a 4-base file. The clock length is 0.4 days, and after 0.4 days, the after-sales record is converted into a 3-base file.

[0123] 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", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0124] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A medical product post-sale record management method based on the Internet of Things, characterized in that: The method comprises the following steps: Step S1. Record the after-sales records of medical products when they have after-sales services. According to the time between the after-sales records and the sales time of the same type of medical products, the average after-sales time and the stability of the time are used as normal parameters to obtain the function of the probability of after-sales behavior of the product changing over time; Step S2. After screening the after-sales records, upload the after-sales records to the database, extract the product number field in the after-sales records as the header, and merge the after-sales record fields after removing the header into a bottom data packet; Step S3. Divide the database memory into primary memory and secondary memory, store the header of the after-sales record into the primary memory, store the bottom data packet into the secondary memory, and establish an index for the header to point to the corresponding bottom data packet, and determine the compression rate of the bottom data packet according to the probability of after-sales behavior of the product at the current moment, and compress the bottom data packet into a non-binary sequence; Step S4. Set a clock for the bottom data packet stored in the secondary memory, and use the time length when the probability of after-sales behavior of the product reaches the threshold as the clock length. When the clock runs out, perform a binary compression rate adjustment on the bottom data packet and reset the clock until the compression rate of the data packet reaches the maximum compression rate. Step S5. When the database receives a request to call an after-sales record, it retrieves the header in the primary memory according to the product number in the call request, obtains the bottom data packet in the secondary memory by the header index, outputs the after-sales record after merging and restoring, and weightedly adds the compression rate of the bottom data packet and the remaining clock duration when the record is called to obtain the single after-sales quality of the product; Step S1 includes: Step S11. When a medical product has an after-sales behavior, the after-sales behavior is recorded to form an after-sales record, which includes: product type, product number, after-sales type, after-sales occurrence time and after-sales processing results; Step S12. After the after-sales record is generated, query the time when the product is shipped out of the factory according to the product number to obtain the time from product sales to after-sales behavior; Step S13. According to the product type in the after-sales record, obtain the time from product sales to after-sales behavior in the after-sales records of similar products in history, and form an after-sales time set {W1, W2, ..., W n }, where n represents the number of historical after-sales records of similar products, W n Represents the time from product sales to after-sales behavior in the nth after-sales record of the same product; Step S14. Calculate the stability of the product after-sales service time according to the following formula: Among them, a represents the stability of the product after-sales time, W0 represents the average value of all elements in the after-sales time set, i is the position number, and W i Represents the i-th element of the after-sales duration set; Step S15. Based on the average after-sales duration of the product and the stability of the duration, a normal function is used to fit the function of the probability of after-sales behavior of the product changing over time: Among them, F(t) represents the function of the probability of a product undergoing after-sales behavior changing over time, t represents time, and e is the base of the natural logarithm; Step S2 includes: Step S21. Upload the after-sales record to the database, and the database analyzes the data volume of the after-sales record. If the data volume of the after-sales record is not within the preset range, the after-sales record is filtered out and does not enter the database memory. If it is within the preset range of the data volume of the after-sales record, go to step S22; Step S22. Before data archiving, retrieve the product number in the after-sales record, extract the field containing the product number in the after-sales record as the header, and merge the other fields in the after-sales record into the bottom data packet; Step S3 includes: Step S31. Create a virtual memory in the database, isolate all storage space into two different memory spaces, as primary memory and secondary memory, store the header in the same after-sales record into the primary memory, store the bottom data packet into the secondary memory, and create an index for the header to point to the corresponding bottom data packet; Step S32. Calculate the compression rate G of the after-sales record bottom data packet, where G=U-MOD[F(t0)·U / F(W0)], where U is the preset maximum compression rate, U is an integer and U>2, t0 is the current time, and MOD is a rounding function; Step S33: Convert the data in the bottom data packet from binary data to a G-binary compressed file, and store the G-binary compressed file in the secondary memory instead of the bottom data packet.

2. The method for post-sale record management of medical products based on the Internet of Things according to claim 1 is characterized in that: Step S4 includes: Step S41. Set a clock for the G-binary compressed file stored in the secondary memory, and the clock length T satisfies: |F(T+t0)-F(t0)|=1 / U; If F(T+t0)-F(t0)>0, then when the clock runs out, the G-binary compressed file is decompressed into the G-1-binary system; if F(T+t0)-F(t0)≤0, then when the clock runs out, the G-binary compressed file is further compressed into the G+1-binary system; Step S42. Detect the compression ratio of the current file. If the compression ratio of the file G=U, the clock is not reset. If G≠U, the clock is reset and go to step S41.

3. The method for post-sale record management of medical products based on the Internet of Things according to claim 2 is characterized in that: Step S5 includes: Step S51. When the after-sales record is retrieved due to another after-sales behavior, after receiving the product number, the database retrieves the header corresponding to the product number in the primary memory, extracts the bottom data packet from the secondary memory according to the index pointed to by the header, decompresses the bottom data packet, adds the header, and downloads the restored file to the requesting device; Step S52. Record the compression rate of the bottom data packet when the after-sales record is retrieved and the remaining time of the clock, and calculate the single after-sales quality Q of the product. When F(T+t0)-F(t0)≤0, Q=(U-G0)+TS / T, where G0 represents the compression rate of the bottom data packet when the after-sales record is retrieved, TS represents the remaining time of the clock, and T represents the clock length. When F(T+t0)-F(t0)>0, Q=G0+(T-TS) / T; Step S53: Summarize the after-sales qualities of the same type of products, calculate the average value, and send the calculated average value as the single after-sales quality of the product to the after-sales center.

4. A medical product post-sale record management system based on the Internet of Things, the system executing the medical product post-sale record management method based on the Internet of Things as described in claim 1, characterized in that: The system includes the following modules: after-sales record module, data storage module, storage classification module, timing compression module and quality assessment module; The after-sales record module is used to record the medical products when they are shipped out and sold, determine the average after-sales duration and quality stability of the medical products based on the records, calculate the probability of after-sales behavior of medical products shipped out in different time periods, and determine the information entropy range of the medical product records based on the batches of medical products, after-sales processes and historical records; The data storage module is used to store the after-sales service records uploaded to the server by each computer into the database, filter out the records whose information entropy of a single record is less than the information entropy range of the medical product record corresponding to the after-sales record, and retrieve the data segment representing the product number in the record as the header, establish an index pointing to other parts of the after-sales record in the header, and merge the other parts of the after-sales record into a bottom data packet; The storage classification module is used to classify the server memory. When the after-sales record enters the database, the header is stored in the primary memory, the bottom data packet is stored in the secondary memory, a two-level memory retrieval mechanism is established, and the bottom data packet is expanded into a binary sequence form in the secondary memory; The timing compression module is used to calculate the compression rate of the bottom data packet according to the minimum information entropy and the probability of after-sales behavior, compress the bottom data packet into a non-binary sequence according to the compression rate, and then calculate the clock length when the message compression rate reaches the threshold according to the probability of after-sales recycling of the medical product at the current moment. When the clock runs out, a binary compression rate adjustment is performed on the message sequence, and the clock is reset; The quality assessment module is used to calculate the quality grade of the product when the after-sales record header of the same batch of products is called according to the compression rate of the data packet in the secondary memory and the clock remaining length at the time of the call, obtain the historical call records of the same batch of medical products, and evaluate the production quality of the same batch of medical products according to the historical average quality grade of the medical products in the records.

5. The medical product after-sales record management system based on the Internet of Things according to claim 4 is characterized in that: The after-sales record module includes: an in-and-out storage unit, a quality recovery unit and an information management unit; The in-and-out storage unit is used to record after-sales behavior when a medical product undergoes after-sales behavior, forming an after-sales service record, wherein the after-sales service record includes: the batch number of the product, the type of after-sales service, and the after-sales processing result; The quality recovery unit is used to calculate the average after-sales time and after-sales time stability of each product based on historical after-sales records; The information management unit is used to record the information length in the after-sales service record and calculate the information entropy range of the effective after-sales record.

6. The medical product after-sales record management system based on the Internet of Things according to claim 5 is characterized in that: The data storage module includes: a pre-cleaning unit and a record retrieval unit; The pre-cleaning unit is used to clean the records whose data volume does not meet the information entropy range before the after-sales records are uploaded to the database; The record retrieval unit is used to retrieve the after-sales record, extract the part of the record containing the product number as the header, establish an index pointing to other parts of the after-sales record, and merge the other parts of the after-sales record into a bottom data packet; The storage classification module includes: a hierarchical index unit and a sequence expansion unit; The hierarchical indexing unit is used to divide the memory of the server into two levels; The sequence expansion unit is used to store the header and the bottom data packet in two levels of memory respectively, and establish a two-level memory retrieval mechanism.

7. The medical product after-sales record management system based on the Internet of Things according to claim 6 is characterized in that: The timing compression module includes: a base compression unit, a clock adjustment unit and a record retrieval unit; The binary compression unit is used to calculate the compression rate of the bottom data packet, and compress the bottom data packet into a non-binary sequence according to the compression rate; The clock adjustment unit is used to set a clock with a duration numbered according to the recovery probability for the bottom data packet according to the after-sales recovery probability, and change the compression rate of the bottom data packet when the clock runs out; The record retrieval unit is used to decompress the header and the corresponding bottom data packet and merge them into an after-sales record when a call request is received in the primary memory.

8. The medical product after-sales record management system based on the Internet of Things according to claim 7 is characterized in that: The quality assessment module includes: an after-sales grade unit and a batch evaluation unit; The after-sales level unit is used to record the current compression level and the remaining time of the clock of the bottom data packet when the after-sales record is called; The batch evaluation unit evaluates the after-sales quality of the products according to the call records of the same batch of products.

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