Data storage method, system and application of medical detection equipment

Abstract

The invention provides a data storage method and system for medical detection equipment and application, and the method comprises the steps: obtaining equipment data, and dividing the equipment data into medical data items and non-medical data items; searching an associated data group; obtaining a storage template of a special storage server; dividing the associated data group and the storage template which have the same logical relationship into a first template group; the storage templates and the associated data sets with the coincidence degree higher than the preset high coincidence degree are divided into second template sets; and constructing and determining a template storage scheme covering all the medical data items based on the first template and the second template, and performing storage according to the template storage scheme. According to the method, the data storage amount is effectively reduced, meanwhile, the integrity and availability of the medical data are kept, storage resources are utilized more efficiently, unnecessary redundant storage is reduced, and the storage efficiency, storage integrity and storage safety of the data of the medical detection equipment are improved.

Description

Technical Field

[0001] The present application relates to the field of role simulation, and more specifically, to a data storage method, system, and application for medical detection equipment. Background Art

[0002] With the advancement of modern medical technology, medical testing equipment has become an indispensable component of healthcare institutions. These devices can quickly and accurately collect patients' physiological data, providing doctors with crucial diagnostic evidence. However, the widespread use of medical testing equipment has also generated a large amount of data. This data includes not only patients' personal information and health records, but also non-medical data such as the device's own operating status.

[0003] Traditional data storage methods often lack effective data classification and management methods, leading to high data storage costs, inefficient data retrieval, and difficulties in ensuring data security. In the medical field, in particular, due to the large amount of sensitive information involved, ensuring data security while improving data management and utilization efficiency has become a pressing issue. Summary of the Invention

[0004] To address the problems in the existing technology, this application provides a data storage method, system, and application for medical testing equipment. The specific solution is as follows:

[0005] In the first part, this application proposes a data storage method for medical detection equipment, including the following:

[0006] Obtaining device data of a preset medical detection device to be stored in a server, and dividing the device data into medical data items and non-medical data items based on whether the data involves patient information;

[0007] Searching for associated data items that have a logical relationship with one or more medical data items in each non-medical data item, and classifying the associated data items and the medical data items involved in the logical relationship into an associated data group;

[0008] Acquire storage templates for multiple specialized storage servers, each storage template recording the specialized data stored by the corresponding storage server, and some storage templates mapping one or more logical relationships between medical data items and non-medical data items;

[0009] Analyzing the logical relationship mapped by the storage template and the logical relationship in the associated data group, and dividing the associated data group and the storage template having the same logical relationship into a first template group;

[0010] Among the remaining stored templates and associated data groups, analyzing the degree of overlap between the data items in each stored template and the medical data items in each associated data group, and classifying the stored templates and associated data groups with an overlap degree higher than a preset high overlap into a second template group;

[0011] Based on the first template and the second template, a template storage scheme covering all medical data items is constructed and determined, and each associated data group is converted into the format of the corresponding storage template according to the template storage scheme, and uploaded to the corresponding dedicated storage server for storage.

[0012] In some specific embodiments, if there is an associated data group that is not classified into the first template group and the second template group, it is used as the third associated data group:

[0013] Incorporate the third associated data group into the template conversion scheme; or, preferentially select a special server that has no recorded logical relationship and whose overlap degree is not 0 as a supplementary server, adjust the data specialization of the supplementary server and update its storage template so that the updated storage template covers all medical data items in the associated data group and maps the logical relationship in the associated data group.

[0014] In some specific embodiments, the medical data items in the associated data group under the same first template group or second template group are formatted according to the corresponding storage template, and the associated data items in the associated data group are formatted according to the logical relationship, so as to obtain a storage data packet with the same format as the storage template, filled with medical data items, and capable of deducing corresponding non-medical data items based on the medical data items, and the storage data packet is uploaded to the corresponding special storage server.

[0015] In some specific embodiments, the process of obtaining the template storage solution specifically includes:

[0016] Based on each first template group and in combination with the second template group, at least one quasi-template solution is constructed;

[0017] Evaluate the complexity of the storage process based on the number of storage templates involved in each quasi-template solution;

[0018] evaluating the storage completeness of the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template solution;

[0019] A recommendation value is calculated according to the complexity and the storage completeness and respective preset weights, and the quasi-template solution with the highest recommendation value is selected as the storage template solution.

[0020] In some specific embodiments, if there are medical data items that are not classified into the associated data group, such medical data items are treated as non-associated medical data items and added to each quasi-template solution;

[0021] When evaluating the storage completeness of each quasi-template solution, it is also based on the coverage degree of non-associated medical data items by all stored templates in each quasi-template solution.

[0022] In some specific embodiments, the storage template further includes coding rules, wherein the coding rules involve patient information and time information of data collection;

[0023] Before performing data format conversion according to the storage template, encoding the patient information and time information in the medical data item according to the encoding rule to obtain identification information;

[0024] The identification information and the corresponding medical data item data are converted together.

[0025] In some specific embodiments, before performing data format conversion according to the storage template, it is determined whether the identification information exists in a dedicated storage server corresponding to the storage template;

[0026] If so, compare whether the medical data item corresponding to the identification information has been recorded in the dedicated storage server corresponding to the storage template;

[0027] If it has been recorded, the format conversion of the identification information and its medical data item is canceled, and the associated data items that have a logical relationship with the medical data item are annotated.

[0028] In the second part, this application proposes a data storage system for medical detection equipment, including the following:

[0029] An input unit, configured to obtain device data of a preset medical detection device to be stored in a server, and to divide the device data into medical data items and non-medical data items according to whether the data involves patient information;

[0030] an association unit for searching, among various non-medical data items, associated data items that have a logical relationship with one or more medical data items, and classifying the associated data items and the medical data items involved in the logical relationship into an associated data group;

[0031] A template unit is used to obtain storage templates of multiple specialized storage servers, each storage template recording the specialized data stored by the corresponding storage server, and some storage templates are mapped with one or more logical relationships between medical data items and non-medical data items;

[0032] A first template unit is configured to analyze the logical relationship mapped by the storage template and the logical relationship in the associated data group, and to group the associated data groups and storage templates having the same logical relationship into a first template group;

[0033] The second template unit is configured to analyze, among the remaining stored templates and associated data groups, the degree of overlap between the data items in each of the stored templates and the medical data items in each of the associated data groups, and to classify the stored templates and associated data groups having an overlap higher than a preset high overlap into a second template group;

[0034] The storage unit is used to construct and determine a template storage scheme covering all medical data items based on the first template and the second template, convert each associated data group into the format of the corresponding storage template according to the template storage scheme, and upload it to the corresponding dedicated storage server for storage.

[0035] In the third part, the present application proposes a computer device, the computer device comprising:

[0036] one or more processors;

[0037] a memory for storing one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement the data storage method for the medical detection device as described in any one of the first parts.

[0039] In the fourth part, the present application proposes a computer program product, comprising executable instructions for implementing the data storage method for a medical detection device as described in any one of the first parts when executed by a processor.

[0040] Beneficial effects: This application proposes a data storage method, system and application for medical testing equipment. By carefully classifying and associating the data of medical testing equipment, combined with special storage servers and storage templates, the storage volume is effectively reduced while maintaining the integrity and availability of the data, more efficiently utilizing storage resources, reducing unnecessary redundant storage, and improving the storage efficiency, storage integrity and storage security of medical testing equipment data. By processing medical data items separately from non-medical data items, the patient's privacy information can be better protected. Patient information is encrypted using coding rules to improve data security. By analyzing the logical relationship between storage templates and associated data groups, storage resources can be used more efficiently and unnecessary data redundancy can be reduced. By constructing template storage solutions based on different data items and logical relationships, the storage strategy can be flexibly adjusted according to actual needs. By evaluating the storage integrity and complexity and calculating the recommended value, a comprehensive and efficient storage solution can be found.

[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a flowchart of the work role simulation method of this application;

[0044] Figure 2 It is a schematic diagram of the principle of this application;

[0045] Figure 3 This is a schematic diagram of the work role simulation system module of this application.

[0046] Reference numerals: 1 - input unit; 2 - association unit; 3 - template unit; 4 - first template unit; 5 - second template unit; 6 - storage unit. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0048] This application proposes a data storage method for medical detection equipment, which effectively reduces the storage capacity while maintaining the integrity and availability of data, more efficiently utilizes storage resources, reduces unnecessary redundant storage, and improves the storage efficiency, storage integrity and storage security of medical detection equipment data. Figure 1 As shown in the attached Figure 2 The specific plan is as follows:

[0049] A data storage method for medical detection equipment, comprising the following steps:

[0050] 101. Obtain device data of a preset medical detection device to be stored in a server, and classify the device data into medical data items and non-medical data items based on whether the data involves patient information;

[0051] 102. Searching for associated data items in each non-medical data item that have a logical relationship with one or more medical data items, and grouping the associated data items and the medical data items involved in the logical relationship into an associated data group;

[0052] 103. Obtain storage templates for multiple specialized storage servers, each storage template recording the specialized data stored by the corresponding storage server, and some storage templates mapping one or more logical relationships between medical data items and non-medical data items;

[0053] 104. Analyze the logical relationship mapped by the storage template and the logical relationship in the associated data group, and group the associated data groups and storage templates with the same logical relationship into a first template group;

[0054] 105. Among the remaining stored templates and associated data groups, analyze the degree of overlap between the data items in each stored template and the medical data items in each associated data group, and classify the stored templates and associated data groups with an overlap degree higher than a preset high overlap into a second template group;

[0055] 106. Construct and determine a template storage solution covering all medical data items based on the first template and the second template, convert each associated data group into a format corresponding to the storage template according to the template storage solution, and upload it to the corresponding dedicated storage server for storage.

[0056] In response to the ever-growing amount of medical data, this application achieves effective data management and efficient storage by carefully classifying and associating the data of medical testing equipment. This not only improves the efficiency of data management, but also reduces storage costs, while ensuring data security and compliance, which helps promote the digital transformation and development of the medical industry.

[0057] In step 101, all data to be stored is collected from medical testing equipment and categorized into medical data items and non-medical data items based on whether the data involves patient information. Medical testing equipment refers to specialized equipment used to collect patient physiological data or other related information, such as blood analyzers, X-ray machines, and electrocardiograms. The various data generated by medical testing equipment during use include, but are not limited to, patient examination results and equipment operating status information.

[0058] In this application, based on the unique nature of the data generated by medical testing equipment, device data is further divided directly based on whether it involves personal information. This simplifies classification and avoids errors caused by inconsistent classification rules. Medical data items: Data related to a patient's personal information or directly related to their health status, such as name, age, gender, medical history, and test results. Non-medical data items: Data that does not contain sensitive information, such as device model, serial number, software version, and maintenance records. Patient information often contains sensitive personal data such as name, ID number, and medical history, which requires strict protection to prevent privacy violations caused by leakage. By storing data involving patient information separately from other data, a higher level of protection for sensitive data can be ensured, reducing the risk of data leakage. Non-medical data items generally do not require the same stringent security measures as medical data, allowing for simpler management and storage methods, thereby streamlining data management processes. Specifically, non-medical data items can be stored on less secure servers, reducing the need for expensive encryption technology and advanced access controls. By optimizing storage strategies, hospitals can reduce storage costs while ensuring data security.

[0059] Specifically, the classification process includes: First, defining data classification standards to clearly identify data items that are considered to involve patient information. Medical data items include at least name, age, gender, medical history, and test results, with test results set according to the specific medical testing equipment. Non-medical data items include device model, serial number, software version, maintenance records, usage time, number of uses, and abnormal conditions. Data from the device can be read through the API (application programming interface) or SDK (software development kit) provided by the medical testing equipment. If it is through a physical interface (such as a USB or serial port), a program must be written to communicate with this interface. Use a programming language to parse the data file and determine which data items involve patient information based on the data content. For example, use the Python pandas library to read CSV files, classify the data through conditional statements, and organize the classified data into JSON objects or structured database tables.

[0060] Step 102 mainly searches for associated data items and forms an associated data group. Search for associated data items that have a logical relationship with medical data items in non-medical data items, and combine the associated data items and the medical data items involved in their logical relationship to form an associated data group. Medical data items are divided into associated medical data items and non-associated medical data items. Non-medical data items are divided into associated data items and non-associated data items. The connection between associated medical data items and medical data items is usually established through a common identifier. Assuming that the non-medical data items contain equipment maintenance cycles, equipment usage frequency, etc., and the medical data items also contain the same equipment ID, the non-medical data items can be derived based on the timestamp of each examination, the number of operating hours of the equipment or the number of times it is used, etc., thereby reducing the amount of non-medical data items that need to be stored independently.

[0061] Exemplarily, non-medical data items derived from medical data items include the following:

[0062] 1. Equipment failure warning:

[0063] Medical data items: Equipment operation logs, including error codes, warning messages, etc.

[0064] Derived non-medical data items: By analyzing the frequency of error codes and warning messages, it is possible to predict future equipment failures and issue early warnings.

[0065] Only error codes and warning information in the operation log are stored, and fault warnings are not stored separately.

[0066] 2. Patient waiting time:

[0067] Medical data items: appointment time, actual arrival time, and examination start time.

[0068] Derived non-medical data items: The patient's waiting time can be obtained by calculating the difference between the actual arrival time and the start time of the examination.

[0069] Only the appointment time, actual arrival time and start time of the examination are stored, and the waiting time is not stored separately.

[0070] 3. Equipment maintenance requirements:

[0071] Medical data items: equipment operating time, equipment failure records.

[0072] Derived non-medical data items: By analyzing equipment operating hours and failure records, it is possible to predict when equipment maintenance is required.

[0073] Only equipment operating time and fault records are stored, without separate storage of maintenance requirements.

[0074] 4. Patient flow analysis:

[0075] Medical data items: patient consultation time, patient ID.

[0076] Derived non-medical data items: By counting the number of patient IDs per day or week, peak periods of patient traffic can be analyzed.

[0077] Only the patient's consultation time and patient ID are stored, and traffic analysis data is not stored separately.

[0078] 5. Equipment efficiency analysis:

[0079] Medical data items: equipment operating time, number of completed examinations.

[0080] Derived non-medical data items: By calculating the number of examinations completed per unit time, the efficiency of the equipment can be analyzed.

[0081] Only the equipment operating time and the number of completed inspections are stored, without separate efficiency analysis data.

[0082] 6. Patient satisfaction survey:

[0083] Medical data items: patient consultation feedback, consultation time, and examination results.

[0084] Deduced non-medical data items: By analyzing the relationship between patients' consultation feedback and examination results, patient satisfaction can be inferred.

[0085] Only the patient's consultation feedback, consultation time and examination results are stored, and the satisfaction survey results are not stored separately.

[0086] 7. Frequency of device use:

[0087] Medical data item: timestamp of each examination.

[0088] Derived non-medical data items: By counting the timestamps within a certain period of time, the frequency of device usage can be calculated.

[0089] Only the timestamp is stored, not the usage frequency separately.

[0090] 8. Equipment failure probability:

[0091] Medical data item: Equipment operation log, including records of each successful and failed operation.

[0092] Derived non-medical data items: By analyzing the ratio of successes to failures in the operation log, the failure probability of the equipment can be calculated.

[0093] Only the result of each operation is stored, not the probability of failure.

[0094] 9. Equipment maintenance cycle:

[0095] Medical data items: operating hours or usage of the equipment.

[0096] Derived non-medical data items: Based on the equipment manufacturer's recommended maintenance interval and the actual use of the equipment, the time for the next maintenance can be calculated.

[0097] Only the operating hours or usage times are stored, not the maintenance intervals separately.

[0098] 10. Number of patient visits:

[0099] Medical data items: time of each visit and patient ID.

[0100] Derived non-medical data items: By counting the number of times the patient ID appears, the number of visits for each patient can be obtained.

[0101] Only the time of the visit and the patient ID are stored, not the number of visits separately.

[0102] 11. Equipment utilization rate:

[0103] Medical data items: device power-on time, power-off time, and length of each examination.

[0104] Derived non-medical data items: Equipment utilization can be derived by calculating the ratio of the total time the equipment is on to the total time it is off in a day or week.

[0105] Only the power-on time, power-off time, and the length of each check are stored, not the utilization rate.

[0106] 12. Average equipment inspection time:

[0107] Medical data items: start time, end time and patient ID of each examination.

[0108] Derived non-medical data items: By calculating the duration of each examination and averaging it, the average examination time of the device can be obtained.

[0109] Only the start time, end time, and patient ID of the examination are stored, and the average examination time is not stored separately.

[0110] Medical data items and non-medical data items that have logical relationships need to be pre-selected and set based on the specific medical testing equipment. A specific identifier can be used to establish the logical relationship between non-medical and medical data items. For example, the identifier can be a calculation formula between data items in an associated data group. For another example, each dedicated storage server pre-sets the logical operation relationship between specific columns of data. When the dedicated storage server receives a request for a corresponding non-medical data item, it simply processes the corresponding columns of data based on the logical relationship, eliminating the need for separate storage.

[0111] In this application, not all non-medical data items can be included in the associated data group as associated data items. These data may include basic information of the device, software version, etc., which are not directly associated with specific medical data items. For non-medical data items that are not associated data items, an independent storage area or database table can be created for them to store information unrelated to the medical data items, which simplifies the storage and management of the medical data items and avoids mixing with the medical data items. All non-medical data items that are not included in the associated data group can also be aggregated in one or more storage templates to facilitate unified query and analysis of non-medical data items. In practical applications, non-medical data items that are not included in the associated data group are compressed to reduce storage space usage, reduce storage requirements, and reduce storage costs. Preferably, low-cost cloud storage services are used to store these non-medical data items that are not included in the associated data group, such as AWS S3 Glacier DeepArchive, Google Cloud Storage Nearline, etc., which have low storage costs, strong scalability, and are easy to manage. Furthermore, it is preferable to obtain non-medical data items that are not included in the associated data group, regularly assess the importance of the data and adjust the storage strategy. By analyzing the historical access records of such data to determine the importance and access frequency of the data, different storage strategies can be adopted to optimize storage resource utilization and reduce storage costs. For example, important data with recent high access frequency can be stored on high-performance storage media, while historical data can be stored on lower-cost storage media.

[0112] Step 103 is to obtain a storage template from multiple special storage servers. In this application, a special storage server is pre-configured, which is a server specifically used to store specific types of data, such as a server for storing medical images, a server for storing patient records, etc. Special data refers to a specific type of medical data item, such as basic patient information, examination results, imaging data, etc. The purpose of building a storage template is to define the structure and rules of data storage to ensure the consistency and integrity of the data. The storage template can be used to clarify the name, type, storage location and relationship with other data items of the data item. The storage template usually exists in a structured format, such as JSON, XML or CSV file, and the storage template can be obtained through API calls or reading configuration files.

[0113] In some embodiments: first determine the data items that need to be stored based on the function and purpose of the storage server. If the server is used to store the patient's examination results, the data items may include the examination date, examination type, examination results, device ID, etc. Then define the data structure for each data item, including data type, field name, etc. For example, the examination date is of date type, the examination type is of string type, etc. Metadata fields can also be defined, such as the creation time and modification time of the data, such as each record should have a creation time field and a modification time field. The definition of the storage template is recorded in the form of a document for subsequent reference and maintenance. The storage template can be obtained through API calls or reading configuration files.

[0114] In this application, not all specialized storage servers must involve the logical relationships of associated data groups. New logical relationships can be continuously expanded through subsequent additions and supplements. Some storage templates map one or more logical relationships between medical data items and non-medical data items. The logical relationships here are the logical relationships in the associated data groups. Some specialized storage servers are configured with specific logical relationships that can automatically derive new non-medical data items that are not recorded in the server based on certain medical data items.

[0115] In step 104, the logical relationship mapped by the storage template and the logical relationship in the associated data group are analyzed, and the associated data groups and storage templates with the same logical relationship are divided into a first template group. The associated data groups in the first template group can be directly uploaded to the relevant storage template in the future without the need for selection. The existence of corresponding logical relationships in the storage template means that the dedicated storage server corresponding to the template is specifically responsible for storing this group of medical data items and associated data items. In some cases, a storage template may have multiple logical relationships. If they are matched at the same time, the storage template can be divided into the same first template group in multiple associated data groups. In actual operation, various abnormal situations may be encountered, such as missing data, incorrect data format, etc. Therefore, it is necessary to add an appropriate error handling mechanism to deal with these problems.

[0116] Step 105, based on step 104, further compares the associated data groups and stored templates with inconsistent logical relationships, analyzes the degree of overlap between the remaining stored templates and associated data groups, and classifies the stored templates and associated data groups with a degree of overlap exceeding a preset high overlap into a second template group. The degree of overlap measures the degree of shared data items between two data sets, specifically the number of data types with the same data type. The preset high overlap is a pre-set threshold used to determine whether the degree of overlap between two data sets is sufficiently high and can be flexibly set based on actual circumstances. The second template group consists of stored templates and associated data groups with a degree of overlap exceeding the preset high overlap. Each associated data group and each stored template is traversed to calculate the degree of overlap between them. The degree of overlap can be measured by calculating the intersection of the two sets (the data items in the stored template and the medical data items in the associated data group). If a particular associated data group has sufficient overlap with a particular data item in a stored template, then this pair of associated data groups and stored templates belongs to the second template group. A reasonable threshold is set as the preset high overlap based on actual circumstances. For example, if there are 3 medical data items in an associated data group and there are 3 data items in the storage template, the preset high overlap can be set to 2 or 3, which means that at least 2 or 3 data items must overlap between the two to meet the conditions.

[0117] For data not included in the first or second template groups, alternative processing methods should be considered to ensure that all data is properly stored. This data can be included as a third template group in the quasi-template solution, or the storage template can be modified separately. In some specific embodiments, if there is an associated data group that is not classified into the first or second template groups, a dedicated server with no documented logical relationships and a non-zero overlap is preferentially selected as a supplementary server. The data specification of the supplementary server is adjusted and its storage template is updated so that the updated storage template covers all medical data items in the associated data group and maps the logical relationships in the associated data group. Specifically, the updated storage template is sorted from highest to lowest based on the degree of overlap, and a dedicated server with no documented logical relationships but the highest degree of overlap with the medical data items in the associated data group is preferentially selected as the supplementary server. The supplementary server is the dedicated storage server selected to store the unclassified associated data group. The remaining dedicated storage servers are traversed, and a server with no documented logical relationships and a non-zero overlap is selected as the supplementary server. The data specification of the supplementary server is adjusted based on the medical data items in the unclassified associated data group, and the data in the supplementary server is added to the data specification of the supplementary server. Afterwards, the storage template of the supplementary server is updated to ensure that it covers all medical data items in the unclassified related data groups and maps the logical relationships in the related data groups. The updated storage template is used to convert the format of the unclassified related data groups and upload them to the supplementary server.

[0118] In step 106, a template storage scheme covering all medical data items is constructed and determined based on the first template group and the second template group, and each associated data group is converted into the format of the corresponding storage template according to the scheme, and then uploaded to the corresponding special storage server for storage. The template storage scheme specifies a specific plan for how to store data according to the storage template. All data items in the first template group and the second template group are summarized to ensure that all medical data items are covered. All logical relationships involved are sorted out to ensure that all necessary logical relationships are included in the storage scheme. Based on the storage templates in the first template group and the second template group, determine to which special storage server each associated data group should be uploaded. Based on the template storage scheme, the data in the associated data group is converted into a format that matches the storage template, and determine to which special storage server each converted data should be uploaded. Finally, the data is uploaded to the corresponding special storage server using API calls or file transfer protocols.

[0119] In some specific embodiments, the process of obtaining a template storage solution specifically includes: constructing at least one quasi-template solution based on each first template group and the second template group; selecting one quasi-template solution as the storage template solution based on the complexity of the storage process and the completeness of device data storage for each quasi-template solution. Merging all data items in the first template group with those in the second template group, integrating logical relationships, and sorting out all logical relationships in the first and second template groups to ensure consistency and completeness of the logical relationships. If the first template group specifies an association between A and B, and the second template group specifies an association between A and C, the logical relationships in the merged solution should include these two association rules. A complexity analysis is performed on each quasi-template solution, taking into account the resources and time required for the storage process. If a quasi-template solution involves a large number of servers, it has a higher complexity. Each quasi-template solution is evaluated to see if it covers all required data items. If a quasi-template solution omits the data item "gender," it has a lower completeness. Quasi-template solutions with lower complexity and higher completeness are selected as the final storage template solution.

[0120] In some specific embodiments, the complexity of the storage process is evaluated based on the number of storage templates involved in each quasi-template solution; the storage completeness for device data is evaluated based on the number of medical data items covered and the number of associated data items involved in each quasi-template solution; a recommendation value is calculated based on the complexity and storage completeness, and their respective pre-set weights, and the quasi-template solution with the highest recommendation value is selected as the storage template solution. In some specific embodiments, the complexity of the storage process is evaluated based on the number of storage templates involved in each quasi-template solution; the storage completeness for device data is evaluated based on the number of medical data items covered and the number of associated data items involved in each quasi-template solution; a recommendation value is calculated based on the complexity and storage completeness, and their respective pre-set weights, and the quasi-template solution with the highest recommendation value is selected as the storage template solution. The complexity of the storage process refers to the computing resources, time, and number of steps required to implement the storage solution. Storage completeness refers to the degree to which the storage solution can fully cover all data items that need to be stored. The recommendation value is a numerical value calculated based on the complexity and storage completeness, and their respective pre-set weights, and is used to evaluate and select the optimal storage template solution. For each quasi-template solution, the number of storage templates involved is counted. The greater the number of storage templates, the more complex the storage process, and vice versa. This relationship can be quantified using a simple linear scoring system. For each quasi-template solution, count the number of covered medical data items and the number of associated data items involved. The more comprehensive the coverage of medical and associated data items, the higher the storage completeness. This relationship can also be quantified using a linear scoring system. Preset weights for complexity and storage completeness. For example, if storage completeness is more important, give it a higher weight.

[0121] In some specific embodiments, if there are medical data items that are not classified into the associated data group, such medical data items are regarded as non-associated medical data items and are added to each quasi-template scheme; when evaluating the storage completeness of each quasi-template scheme, the coverage of non-associated medical data items by all stored templates in each quasi-template scheme is also based on it. Non-associated medical data items refer to those medical data items that are not classified into the associated data group. Data items that are not classified into any associated data group are filtered out from all medical data items. These non-associated medical data items are added to each quasi-template scheme to ensure that each scheme covers all medical data items. In addition to evaluating the number of medical data items covered in each quasi-template scheme and the number of associated data items involved, the coverage of non-associated medical data items by all stored templates must also be considered.

[0122] In some specific embodiments, the storage template also includes encoding rules that relate to patient information and the time information of data collection. Before performing data format conversion according to the storage template, the patient information and time information in the medical data item are encoded according to the encoding rules to obtain identification information; the identification information and its corresponding medical data item data are converted together. The time information is the time when the medical detection equipment generated the data, which can be a time period or a time point. Before the actual data is imported, the original data is encoded according to the defined encoding rules, the encoded data is converted according to the format defined by the storage template, and finally the converted data is stored in a designated database or file system. By encoding sensitive information (such as patient name, ID number, etc.), patient privacy can be protected while ensuring data consistency and comparability, facilitating data analysis and processing. Exemplarily, the patient's name and ID number are hashed using the SHA-256 hash algorithm, all time information is converted to Unix timestamp format, and the encoded patient information and time information are merged with other non-sensitive data into a single record.

[0123] To ensure data consistency and security and avoid duplicate data storage, identification information can be used to determine whether data for a patient at a specific time has already been stored. In some specific embodiments, before performing data format conversion according to a storage template, a determination is made as to whether the identification information exists in the dedicated storage server corresponding to the storage template. If so, a comparison is performed to determine whether the medical data item corresponding to the identification information has already been recorded in the dedicated storage server corresponding to the storage template. If so, the format conversion of the identification information and its associated medical data item is canceled, and a note is added to any associated data items that have a logical relationship with the medical data item. Identification information is information obtained through encoding rules and used to uniquely identify a specific data item. It includes identity information and time information. The identity information is used to identify the specific person, and the time information is used to determine data consistency. A query request is sent to the dedicated storage server to determine whether the identification information already exists. If the identification information does not exist, it indicates that the dedicated storage server does not store the medical data item for the patient at the specific time, and subsequent storage procedures can be performed. If the identification information exists, a request is sent to obtain the medical data item associated with the identification information and compare it with the medical data item to be stored. By comparing the data associated with the same identification information to determine whether they are consistent, a duplicate is determined. By determining whether the identification information already exists in a dedicated storage server and taking appropriate measures based on the determination result (such as canceling format conversion and adding remarks), duplicate data storage can be avoided while ensuring data consistency and standardization.

[0124] In some specific embodiments, the medical data items in the associated data groups under the same first template group or second template group are formatted according to the corresponding storage template, and the associated data items in the associated data groups are formatted according to the logical relationships, resulting in a storage data packet that is the same format as the storage template, filled with medical data items, and capable of deducing corresponding non-medical data items based on the medical data items. The storage data packet is then uploaded to a corresponding dedicated storage server. After the format conversion, the storage data packet is consistent with the storage template format, containing medical data items and data packets that can derive corresponding non-medical data items based on the medical data items. The medical data items in the associated data group are converted according to the data format defined in the storage template. According to the logical relationships in the associated data group, the non-medical data items are converted into a format that meets the requirements of the storage template. The logical relationships defined in the storage template are applied to ensure that the data items in the storage data packet are logically consistent. By converting the associated data group into a storage data packet consistent with the storage template format and uploading the storage data packet to the corresponding dedicated storage server, it helps to ensure data consistency and standardization, and also facilitates subsequent data storage and management.

[0125] This application provides a data storage system for medical detection equipment, including the following:

[0126] Input unit 1, used to obtain device data of a preset medical detection device to be stored in the server, and divide the device data into medical data items and non-medical data items according to whether the data involves patient information;

[0127] An association unit 2 is configured to search for associated data items that have a logical relationship with one or more medical data items in each non-medical data item, and group the associated data items and the medical data items involved in the logical relationship into an associated data group;

[0128] Template unit 3 is used to obtain storage templates of multiple dedicated storage servers, each storage template records the data dedicated to the corresponding storage server, and some storage templates map one or more logical relationships between medical data items and non-medical data items;

[0129] A first template unit 4 is configured to analyze the logical relationship mapped by the storage template and the logical relationship in the associated data group, and to divide the associated data group and storage template having the same logical relationship into a first template group;

[0130] The second template unit 5 is used to analyze the degree of overlap between the data items in each of the stored templates and the medical data items in each of the associated data groups in the remaining stored templates and associated data groups, and classify the stored templates and associated data groups with an overlap degree higher than a preset high overlap into the second template group;

[0131] The storage unit 6 is used to construct and determine a template storage scheme covering all medical data items based on the first template and the second template, convert each associated data group into the format of the corresponding storage template according to the template storage scheme, and upload it to the corresponding dedicated storage server for storage.

[0132] The present application provides a computer program product comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to implement a data storage method for medical testing equipment. The data storage method for medical testing equipment is applied to a computer program product to facilitate execution.

[0133] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the data storage method for a medical detection device as described above.

[0134] The computer storage medium of this application may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. This application applies a data storage method for medical testing equipment to a computer-readable storage medium, on which a computer program is stored. When executed by a processor, the program implements the steps of the clothing simulation method provided in this application, which is simple, fast, easy to store, and not easily lost.

[0135] This application proposes a data storage method, system, and application for medical testing equipment. By carefully classifying and associating the data of medical testing equipment, combined with dedicated storage servers and storage templates, the storage volume is effectively reduced while maintaining data integrity and availability, more efficiently utilizing storage resources, reducing unnecessary redundant storage, and improving the storage efficiency, storage integrity, and storage security of medical testing equipment data. By processing medical data items separately from non-medical data items, the patient's privacy information can be better protected. Patient information is encrypted using coding rules to improve data security. By analyzing the logical relationship between storage templates and associated data groups, storage resources can be used more efficiently and unnecessary data redundancy can be reduced. By constructing template storage solutions based on different data items and logical relationships, storage strategies can be flexibly adjusted according to actual needs. By evaluating storage integrity and complexity and calculating recommended values, a comprehensive and efficient storage solution can be found.

[0136] Those skilled in the art will appreciate that the modules of the present application described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computer system, so that they can be stored in a storage system and executed by the computing system. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0137] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

[0138] The above disclosure only describes several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the scope of protection of the present application.

Claims

1. A data storage method for medical detection equipment, characterized in that: These include: Acquire device data of a preset medical detection device to be stored in a server, and divide the device data into medical data items and non-medical data items according to whether the patient information is involved; Searching for associated data items that have a logical relationship with one or more medical data items in each non-medical data item, and classifying the associated data items and the medical data items involved in the logical relationship into an associated data group; Acquire storage templates of multiple special storage servers, each storage template records the special data stored by the corresponding storage server, and some storage templates map one or more logical relationships between medical data items and non-medical data items; Analyzing the logical relationship mapped by the storage template and the logical relationship in the associated data group, and dividing the associated data group and the storage template having the same logical relationship into a first template group; Among the remaining storage templates and associated data groups, the overlap between the data items in each storage template and the medical data items in each associated data group is analyzed, and the storage templates and associated data groups with overlaps higher than a preset high overlap are divided into a second template group; A template storage scheme covering all medical data items is constructed and determined based on the first template and the second template, and each associated data group is converted into a format corresponding to the storage template according to the template storage scheme, and uploaded to the corresponding dedicated storage server for storage.

2. The data storage method according to claim 1, characterized in that: If there is an associated data group that is not classified into the first template group and the second template group, it is used as the third associated data group: Incorporate the third associated data group into the template conversion scheme; or, preferentially select a dedicated server with no recorded logical relationship and a non-zero overlap degree as a supplementary server, adjust the data specialization of the supplementary server and update its storage template, so that the updated storage template covers all medical data items in the associated data group and maps the logical relationship in the associated data group.

3. The data storage method according to claim 1, characterized in that: The medical data items in the associated data group under the same first template group or second template group are formatted according to the corresponding storage template, and the associated data items in the associated data group are formatted according to the logical relationship to obtain a storage data packet that has the same format as the storage template and is filled with medical data items and can derive corresponding non-medical data items based on the medical data items, and the storage data packet is uploaded to the corresponding special storage server.

4. The data storage method according to claim 1, characterized in that: The process of acquiring the template storage solution specifically includes: Based on each first template group and in combination with the second template group, at least one quasi-template scheme is constructed; Evaluate the complexity of the storage process based on the number of storage templates involved in each quasi-template solution; Evaluating the storage completeness of the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template solution; A recommendation value is calculated according to the complexity and the storage completeness and respective preset weights, and a quasi-template solution with the highest recommendation value is selected as the storage template solution.

5. The data storage method according to claim 4, characterized in that: If there are medical data items that are not classified into the associated data group, such medical data items are regarded as non-associated medical data items and added to each quasi-template solution; When evaluating the storage completeness of each quasi-template solution, it is also based on the coverage of non-associated medical data items by all stored templates in each quasi-template solution.

6. The data storage method according to claim 1, characterized in that: The storage template also includes coding rules, which involve patient information and time information of data collection; Before performing data format conversion according to the storage template, encoding the patient information and time information in the medical data item according to the encoding rule to obtain identification information; The identification information and the medical data item data corresponding thereto are converted together.

7. The data storage method according to claim 6, characterized in that: Before performing data format conversion according to the storage template, determining whether the identification information exists in the dedicated storage server corresponding to the storage template; If so, compare whether the medical data item corresponding to the identification information has been recorded in the special storage server corresponding to the storage template; If it has been recorded, the format conversion of the identification information and its medical data items is canceled, and the associated data items that have a logical relationship with the medical data items are annotated.

8. A data storage system for medical testing equipment, characterized in that: These include: An input unit, used to obtain device data of a preset medical detection device to be stored in a server, and to divide the device data into medical data items and non-medical data items according to whether the patient information is involved; An association unit, used to search for associated data items that have a logical relationship with one or more medical data items in various non-medical data items, and classify the associated data items and the medical data items involved in the logical relationship into an associated data group; A template unit, used to obtain storage templates of multiple special storage servers, each storage template records the data special items stored by the corresponding storage server, and some storage templates are mapped with one or more logical relationships between medical data items and non-medical data items; A first template unit, configured to analyze the logical relationship mapped by the storage template and the logical relationship in the associated data group, and to divide the associated data group and the storage template having the same logical relationship into a first template group; The second template unit is used to analyze the overlap between the data items in each of the storage templates and the medical data items in each of the associated data groups in the remaining storage templates and associated data groups, and classify the storage templates and associated data groups with overlaps higher than a preset high overlap into the second template group; The storage unit is used to construct and determine a template storage scheme covering all medical data items based on the first template and the second template, convert each associated data group into a format corresponding to the storage template according to the template storage scheme, and upload it to the corresponding dedicated storage server for storage.

9. A computer device, characterized in that: The computer device comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the data storage method for the medical detection device as described in any one of claims 1-7.

10. A computer program product, characterized in that It includes executable instructions for implementing the data storage method of the medical detection device as described in any one of claims 1-7 when executed by a processor.

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