Data Storage Method, System and Application of Medical Detection Equipment

Abstract

The present application proposes a data storage method, system and application for medical detection equipment. The method includes: obtaining device data and dividing it into medical data items and non-medical data items; searching for associated data groups; obtaining a storage template of a special storage server; dividing the associated data groups and storage templates with the same logical relationship into a first template group; dividing the storage templates and associated data groups with a coincidence degree higher than a preset high coincidence degree into a second template group; constructing and determining a template storage scheme covering all medical data items based on the first template and the second template, and performing storage according to the template storage scheme. The present application effectively reduces the data storage volume, while maintaining the integrity and availability of medical 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.

Description

Technical Field

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

[0002] With the development of modern medical technology, medical detection equipment has become an indispensable part of medical institutions. These devices can quickly and accurately collect patients' physiological data, providing important diagnostic basis for doctors. However, with the wide use of medical detection equipment, a large amount of data is generated. These data include not only patients' personal information and health status records, but also non-medical data items such as the operating status of the equipment itself.

[0003] Traditional data storage methods usually lack effective data classification and management means, resulting in problems such as high data storage costs, low data retrieval efficiency, and difficult to ensure data security. Especially in the medical field, due to the involvement of a large amount of sensitive information, how to improve the management and use efficiency of data while ensuring data security has become an urgent problem to be solved. Summary of the Invention

[0004] Based on the problems existing in the prior art, the present application provides a data storage method, system and application for medical detection equipment. The specific solutions are as follows:

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

[0006] Obtain the device data of a preset medical detection equipment to be stored in the server, and divide the device data into medical data items and non-medical data items according to whether it involves patient information;

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

[0008] Obtain the storage templates of multiple dedicated storage servers. Each storage template records the data special items stored in the corresponding storage server, and there are some storage templates that map one or more logical relationships regarding medical data items and non-medical data items;

[0009] Analyze the logical relationships mapped by the storage templates and the logical relationships in the associated data group, and classify the associated data groups and storage templates with the same logical relationship into the first template group;

[0010] Among the remaining storage templates and associated data groups, analyze the overlap degree between the data specializations in each storage template and the medical data items in each associated data group, and classify the storage templates and associated data groups with an overlap degree higher than the preset high overlap degree into a second template group;

[0011] Based on the first template and the second template, construct and determine a template storage scheme that covers all medical data items, 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 special storage server for storage.

[0012] In some specific embodiments, if there are associated data groups that are not classified into the first template group and the second template group, they are regarded 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 does not record logical relationships and has 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 relationships in the associated data group.

[0014] In some specific embodiments, convert the medical data items in the associated data groups under the same first template group or second template group according to the corresponding storage template, and convert the associated data items in the associated data groups according to the logical relationships to obtain a storage data packet with the same format as the storage template, filled with medical data items and capable of deriving corresponding non-medical data items from the medical data items, and upload the storage data packet to the corresponding special storage server.

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

[0016] Based on each first template group and combined with the second template group, construct at least one quasi-template scheme;

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

[0018] Evaluate the storage integrity for the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template scheme;

[0019] Calculate the recommendation value according to the complexity, the storage integrity and their respective preset weights, and select the quasi-template scheme with the highest recommendation value as the storage template scheme.

[0020] In some specific embodiments, if there are medical data items that are not assigned to the associated data group, such medical data items are regarded as non-associated medical data items and supplemented to each quasi-template scheme;

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

[0022] In some specific embodiments, the storage template further includes a coding rule, and the coding rule involves patient information and time information of data collection;

[0023] 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 coding 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 the dedicated storage server corresponding to the storage template;

[0026] If it exists, it is compared 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 cancelled, and a note is made for the associated data item having a logical relationship with the medical data item.

[0028] Part two, the present application proposes a data storage system for a medical detection device, 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 divide the device data into medical data items and non-medical data items according to whether it involves patient information;

[0030] An association unit, configured to find associated data items having a logical relationship with one or more medical data items in each non-medical data item, and classify the associated data items and the medical data items involved in their logical relationship into an associated data group;

[0031] A template unit, configured to obtain storage templates of multiple dedicated storage servers, each storage template records the data special item stored in the corresponding storage server, and there are some storage templates mapping one or more logical relationships regarding medical data items and non-medical data items;

[0032] The first template unit is used to analyze the logical relationships mapped by the storage templates and the logical relationships in the associated data groups, and divide the associated data groups and storage templates with the same logical relationships into the first template group;

[0033] The second template unit is used to analyze the degree of coincidence between the data specializations in each of the remaining storage templates and the medical data items in each of the associated data groups, and divide the storage templates and associated data groups with a coincidence degree higher than the preset high coincidence degree into the 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 special storage server for storage.

[0035] Part three, this application proposes a computer device, and the computer device includes:

[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 of the medical detection device as described in any one of the first part.

[0039] Part four, this application proposes a computer program product, including executable instructions, which are used to implement the data storage method of the medical detection device as described in any one of the first part when executed by a processor.

[0040] Beneficial effects: This application proposes a data storage method, system and application for a medical detection device. By classifying and associating the data of the medical detection device in detail, combining a special storage server and storage templates, the storage volume is effectively reduced, while maintaining the integrity and availability of the data, making more efficient use of storage resources, reducing unnecessary redundant storage, and improving the storage efficiency, storage integrity and storage security of the data of the medical detection device. By separately processing medical data items and non-medical data items, the privacy information of patients can be better protected. Using coding rules to encrypt patient information improves the security of the data. By analyzing the logical relationships between storage templates and associated data groups, storage resources can be utilized more efficiently, reducing unnecessary data redundancy. A template storage scheme can be constructed according to different data items and logical relationships, and the storage strategy can be flexibly adjusted according to actual needs. By evaluating and calculating the recommended value of the storage integrity and complexity, a storage scheme that is both comprehensive and efficient can be found.

[0041] To make the above objects, features, and advantages of the present application more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and detailed descriptions are as follows. Description of the Drawings

[0042] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces 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 should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0043] Figure 1 is a schematic flowchart of the working role simulation method of the present application;

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

[0045] Figure 3 is a schematic diagram of the working role simulation system module of the present 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 Embodiments

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0048] The present application proposes a data storage method for medical detection devices, which effectively reduces the storage volume, while maintaining the integrity and availability of the data, more efficiently utilizes storage resources, reduces unnecessary redundant storage, and improves the storage efficiency, storage integrity, and storage security of medical detection device data. The flowchart of the data storage method for medical detection devices is as shown in the appendix Figure 1 as shown, and the principle is as shown in the appendix Figure 2 as shown, and the specific solution is as follows:

[0049] A data storage method for medical detection devices includes the following:

[0050] 101. Obtain the 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 it involves patient information;

[0051] 102. Search for associated data items that have a logical relationship with one or more medical data items among various non-medical data items, and classify the associated data items and the medical data items involved in their logical relationships into an associated data group;

[0052] 103. Obtain the storage templates of multiple dedicated storage servers. Each storage template records the data specializations stored in the corresponding storage server, and there are some storage templates that map one or more logical relationships regarding medical data items and non-medical data items;

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

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

[0055] 106. Based on the first template and the second template, construct and determine a template storage scheme that covers all medical data items, 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.

[0056] For the increasing volume of medical data, the present application realizes the effective management and efficient storage of data by carefully classifying and associating the data of medical detection devices. It can not only improve the efficiency of data management, but also reduce the storage cost, while ensuring the security and compliance of the data, which helps to promote the digital transformation and development of the medical industry.

[0057] In step 101, collect all the data that needs to be stored from the medical detection devices, and divide the data into medical data items and non-medical data items according to whether the data involves patient information. Medical detection devices refer to professional devices used to collect patients' physiological data or other relevant information, such as blood analyzers, X-ray machines, electrocardiographs, etc. Various data generated during the use of medical detection devices include, but are not limited to, patients' examination results, device working status information, etc.

[0058] In this application, based on the particularity of the data generated by medical detection equipment, the equipment data is further classified directly according to whether it involves personal information, reducing the classification difficulty and avoiding various errors caused by inconsistent classification rules. Medical data items: data that involves patient personal information or is directly related to the patient's health status, such as name, age, gender, medical history, examination results, etc. Non-medical data items: data that does not contain sensitive information, such as equipment model, serial number, software version, maintenance records, etc. Patient information usually contains sensitive personal data, such as name, ID number, medical history, etc., and this data needs to be strictly protected to avoid privacy violations caused by leakage. By storing the data involving patient information separately from other data, it can ensure that sensitive data is protected at a higher level and reduce the risk of data leakage. Non-medical data items usually do not require as strict security measures as medical data items, so simpler management and storage methods can be adopted, thus simplifying the data management process. Specifically, non-medical data items can be stored on a server with lower security, reducing the need for expensive encryption technologies and advanced access controls. By optimizing the storage strategy, the hospital can reduce the storage cost while ensuring data security.

[0059] Specifically, the classification process includes: First, define the data classification criteria and clarify the data items considered to involve patient information. Medical data items at least include name, age, gender, medical history, examination results, and the examination results are set according to specific medical detection equipment. Non-medical data items include equipment model, serial number, software version, maintenance records, usage time, usage frequency, abnormal status. The data in the equipment can be read through the API (Application Programming Interface) or SDK (Software Development Kit) provided by the medical detection equipment. If it is through a physical interface (such as USB or serial port), a program needs to be written to communicate with these interfaces. Use a programming language to parse the data file, and judge which data items involve patient information according to the data content. For example, use the pandas library in Python to read a CSV file, and classify the data through conditional statements, and organize the classified data into a JSON object or a structured database form.

[0060] Step 102 mainly involves finding associated data items and forming an associated data group. Search for associated data items in non-medical data items that have a logical relationship with medical data items, and combine the associated data items and the medical data items involved in their logical relationships 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. Suppose non-medical data items include equipment maintenance cycles, equipment usage frequencies, etc., and the same equipment ID is also included in medical data items. Non-medical data items can be derived based on the timestamp of each inspection, the running hours of the equipment, or the number of uses, etc., reducing the amount of non-medical data items that need to be stored independently.

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

[0062] 1. Equipment failure warning:

[0063] Medical data item: The operation log of the equipment, including error codes, warning messages, etc.

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

[0065] Only store the error codes and warning messages in the operation log, without storing the failure warning separately.

[0066] 2. Patient waiting time:

[0067] Medical data items: Appointment time, actual arrival time, start inspection time.

[0068] Derived non-medical data item: By calculating the difference between the actual arrival time and the start inspection time, the patient's waiting time can be obtained.

[0069] Only store the appointment time, actual arrival time, and start inspection time, without storing the waiting time separately.

[0070] 3. Equipment maintenance requirement:

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

[0072] Derived non-medical data item: By analyzing the equipment running time and failure records, it is possible to predict when the equipment needs maintenance.

[0073] Only store the equipment running time and failure records, without storing the maintenance requirement separately.

[0074] 4. Patient flow analysis:

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

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

[0077] Only store the patient visit time and patient ID, without storing the flow analysis data separately.

[0078] 5. Equipment efficiency analysis:

[0079] Medical data items: Equipment operation time, number of examinations completed.

[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 store the equipment operation time and the number of examinations completed, without storing the efficiency analysis data separately.

[0082] 6. Patient satisfaction survey:

[0083] Medical data items: Patient visit feedback, visit time, examination results.

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

[0085] Only store the patient visit feedback, visit time and examination results, without storing the satisfaction survey results separately.

[0086] 7. Equipment usage frequency:

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

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

[0089] Only store the timestamps, without storing 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 the number of successful and failed operations in the operation log, the equipment failure probability can be calculated.

[0093] Only store the result of each operation, rather than the failure probability.

[0094] 9. Equipment maintenance cycle:

[0095] Medical data item: The running hours or usage times of the equipment.

[0096] Derived non-medical data item: Based on the recommended maintenance intervals from the equipment manufacturer and the actual usage of the equipment, the time for the next maintenance can be calculated.

[0097] Only store the running hours or usage times, and do not store the maintenance cycle separately.

[0098] 10. Number of patient visits:

[0099] Medical data item: The time of each visit and the patient ID.

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

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

[0102] 11. Equipment utilization rate:

[0103] Medical data item: The power-on time, power-off time of the equipment, and the duration of each examination.

[0104] Derived non-medical data item: By calculating the ratio of the total power-on time to the total power-off time of the equipment in a day or a week, the equipment utilization rate can be obtained.

[0105] Only store the power-on time, power-off time, and the duration of each examination, and do not store the utilization rate separately.

[0106] 12. Average examination time of the equipment:

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

[0108] Derived non-medical data item: By calculating the duration of each examination and taking the average, the average examination time of the equipment can be obtained.

[0109] Only store the start time, end time of the examination, and the patient ID, and do not store the average examination time separately.

[0110] Medical data items and non-medical data items with logical relationships need to be pre-selected and set according to specific medical detection devices. The logical relationship between non-medical data items and medical data items can be established through specific identifiers. For example, the identifier is the calculation formula between data items in associated data groups. Another example is that in each dedicated storage server, the logical operation relationships of specific columns of data are pre-set. When the dedicated storage server receives a request to obtain corresponding non-medical data items, it only needs to perform arithmetic processing on the corresponding columns of data according to the logical relationship, without separate storage.

[0111] In this application, not all non-medical data items can be included as associated data items in the associated data group. These data may include the 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 medical data items, simplifying the storage and management of medical data items and avoiding mixing with medical data items. All non-medical data items 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, the non-medical data items not included in the associated data group are compressed to reduce the occupancy of storage space, reduce storage requirements, and lower storage costs. Preferably, low-cost cloud storage services are used to store these non-medical data items 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. Further preferably, the non-medical data items not included in the associated data group are obtained, the importance of the data is regularly evaluated and the storage strategy is adjusted. By analyzing the historical access records of such data to judge the importance and access frequency of the data, different storage strategies are then adopted to optimize the use of storage resources and reduce storage costs. For example, important data with a high access frequency recently can be stored on high-performance storage media, while historical data can be stored on storage media with lower costs.

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

[0113] In some embodiments: First, determine the data specialization to be stored according to the function and use of the storage server. If the server is used to store the examination results of patients, the data specialization may include examination date, examination type, examination results, device ID, etc. Then define the data structure for each data specialization, 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. For example, each record should have a creation time field and a modification time field. Record the definition of the storage template in document form for subsequent reference and maintenance. The storage template can be obtained by API calls or reading configuration files, etc.

[0114] In this application, not all dedicated storage servers must involve the logical relationships of associated data groups, and new logical relationships can be continuously added and expanded through subsequent supplementation. There are some storage templates that map one or more logical relationships between medical data items and non-medical data items, and the logical relationships here are the logical relationships in the associated data group. Some dedicated storage servers are configured with specific logical relationships and 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, analyze the logical relationships mapped by the storage templates and the logical relationships in the associated data groups, and divide the associated data groups and storage templates with the same logical relationships into the first template group. The associated data groups in the first template group can be directly uploaded to the relevant storage templates in the subsequent process without the need for selection. The existence of corresponding logical relationships in the storage templates 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 simultaneously, the storage template and multiple associated data groups can be divided into the same first template group. In actual operations, various abnormal situations may be encountered, such as data loss, incorrect data formats, etc. Therefore, an appropriate error handling mechanism needs to be added to address these issues.

[0116] Step 105 is based on step 104. Continue to compare the associated data groups and storage templates with inconsistent logical relationships, analyze the overlap degree between the remaining storage templates and associated data groups, and divide the storage templates and associated data groups with an overlap degree higher than the preset high overlap degree into the second template group. Among them, the overlap degree is a measure of the degree of shared data items between two data sets, that is, the number of data items with the same data type. The preset high overlap degree is a threshold set in advance, used to determine whether the overlap degree between two data sets is high enough, and can be flexibly set according to the actual situation. The second template group is a set composed of storage templates and associated data groups with an overlap degree higher than the preset high overlap degree. Traverse each associated data group and each storage template, and calculate the overlap degree between them. The overlap degree can be measured by calculating the size of the intersection of two sets (the data specializations of the storage template and the medical data items in the associated data group). If there is a sufficient intersection between a certain associated data group and the data specialization of a certain storage template, then this pair of associated data group and storage template belongs to the second template group. Set a reasonable threshold as the preset high overlap degree according to the actual situation. For example, if there are 3 medical data items in an associated data group and there are also 3 data specializations in the storage template, then the preset high overlap degree can be set to 2 or 3, indicating that at least 2 or 3 data items need to overlap between the two to meet the conditions.

[0117] For data not included in the first template group or the second template group, other processing methods need to be considered to ensure that all data can be properly stored. It can be used as the third template group and incorporated into the quasi-template solution, or the storage template can be modified separately for it. In some specific embodiments, if there is an associated data group that has not been assigned to the first template group and the second template group, then: preferentially select a special-purpose server that does not record the logical relationship and has a non-zero coincidence degree as the 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. Specifically, select according to the coincidence degree from the highest to the lowest, and preferentially select a special-purpose server that does not record the logical relationship but has the highest coincidence degree with the medical data items in the associated data group as the supplementary server. Among them, the supplementary server is a special-purpose storage server selected to store the unassigned associated data group. Traverse the remaining special-purpose storage servers and select a server that does not record the logical relationship and has a non-zero coincidence degree as the supplementary server. Adjust the data specialization of the supplementary server according to the medical data items in the unassigned associated data group and add the data therein to the data specialization of the supplementary server. Then, update the storage template of the supplementary server to ensure that it covers all medical data items in the unassigned associated data group and maps the logical relationship in the associated data group. Use the updated storage template to perform format conversion on the unassigned associated data group and upload it to the supplementary server.

[0118] In step 106, construct and determine a template storage solution that covers all medical data items based on the first template group and the second template group, convert each associated data group into the format of the corresponding storage template according to this solution, and then upload it to the corresponding special-purpose storage server for storage. The template storage solution specifies the specific plan for how to store data according to the storage template. Summarize all data specializations in the first template group and the second template group to ensure that all medical data items are covered. Sort out all the involved logical relationships to ensure that all necessary logical relationships are included in the storage solution. According to the storage templates in the first template group and the second template group, determine which special-purpose storage server each associated data group should be uploaded to. According to the template storage solution, convert the data in the associated data group into a format that matches the storage template, and determine which special-purpose storage server each converted data should be uploaded to. Finally, use methods such as API calls or file transfer protocols to upload the data to the corresponding special-purpose storage server.

[0119] In some specific embodiments, the process of obtaining the template storage scheme specifically includes: combining the second template group based on each first template group to construct at least one quasi-template scheme; selecting one as the storage template scheme according to the complexity of each quasi-template scheme in the storage process and the storage integrity of the device data. Merge all the data items in the first template group with the data items in the second template group, integrate the logical relationships, sort out all the logical relationships in the first template group and the second template group, and ensure the consistency and integrity of the logical relationships. If the association between A and B is specified in the first template group and the association between A and C is specified in the second template group, the logical relationships in the integrated scheme should include these two association rules. Conduct a complexity analysis on each quasi-template scheme, considering the resources and time required for the storage process. If a quasi-template scheme involves more servers, its complexity is higher. Evaluate whether each quasi-template scheme can cover all the data items that need to be stored. If a certain quasi-template scheme misses the data item "gender", its integrity is lower. Select those quasi-template schemes with lower complexity and higher integrity as the final storage template scheme.

[0120] In some specific embodiments, evaluate the complexity of the storage process based on the number of storage templates involved in each quasi-template scheme; evaluate the storage integrity for the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template scheme; calculate the recommended value according to the complexity, storage integrity and their respective pre-set weights, and select the quasi-template scheme with the highest recommended value as the storage template scheme. In some specific embodiments, evaluate the complexity of the storage process based on the number of storage templates involved in each quasi-template scheme; evaluate the storage integrity for the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template scheme; calculate the recommended value according to the complexity, storage integrity and their respective pre-set weights, and select the quasi-template scheme with the highest recommended value as the storage template scheme. The complexity of the storage process refers to the number of computing resources, time and steps required to implement the storage scheme. The storage integrity refers to the degree to which the storage scheme can completely cover all the data items that need to be stored. The recommended value is a numerical value calculated according to the complexity, storage integrity and their respective pre-set weights, and is used to evaluate and select the best storage template scheme. For each quasi-template scheme, count the number of storage templates involved. The more storage templates involved, the higher the complexity of the storage process, and vice versa. A simple linear scoring system can be used to quantify this relationship. For each quasi-template scheme, count the number of medical data items covered and count the number of associated data items involved. The more comprehensive the coverage of medical data items and associated data items, the higher the storage integrity, and a linear scoring system can also be used to quantify this relationship. Pre-set the weights of complexity and storage integrity. For example, if storage integrity is more important, a higher weight can be given to storage integrity.

[0121] In some specific embodiments, if there are medical data items that are not assigned to the associated data group, such medical data items are regarded as non-associated medical data items and supplemented to each quasi-template scheme; when evaluating the storage integrity of each quasi-template scheme, it is also based on the coverage of all storage templates in each quasi-template scheme for non-associated medical data items. Non-associated medical data items refer to those medical data items that are not assigned to the associated data group. Data items that are not assigned to any associated data group are screened out from all medical data items. These non-associated medical data items are supplemented 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, it is also necessary to consider the coverage of all storage templates for non-associated medical data items.

[0122] In some specific embodiments, the storage template further includes a coding rule, and the coding rule involves patient information and 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 coding rule to obtain identification information; the identification information and the corresponding medical data item data are converted together. The time information therein is the time when the medical detection device generates the data, which can be a time period or a time point. Before actual data import, the original data is encoded according to the defined coding rule, the encoded data is converted according to the format defined by the storage template, and finally the converted data is stored in the specified 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, which is convenient for data analysis and processing. Exemplarily, the SHA-256 hash algorithm is used to hash the patient's name and ID number, all time information is converted into Unix timestamp format, and the encoded patient information and time information are combined with other non-sensitive data into one record.

[0123] To ensure data consistency and security and avoid duplicate data storage, it is possible to determine whether the data of a patient at a specific time has been stored based on the identification information. In some specific embodiments, before converting the data format according to the storage template, it is determined whether the identification information exists in the dedicated storage server corresponding to the storage template; if it exists, it is compared whether the medical data items corresponding to the identification information have been recorded in the dedicated storage server corresponding to the storage template; if they have been recorded, the format conversion of the identification information and its medical data items is cancelled, and the associated data items logically related to the medical data items are noted. The identification information is the information obtained through encoding rules for uniquely identifying specific data items, including identity information and time information. The identity information is used to determine a specific person, and the time information is used to determine whether the data is consistent. A query request is sent to the dedicated storage server to check whether the identification information already exists. If the identification information does not exist, it proves that the dedicated storage server has not stored the medical data items of the patient at a specific moment, and the subsequent storage process can be carried out. If the identification information exists, a further request is sent to obtain the medical data items under the identification information and compared with the medical data items to be stored. By comparing whether the data under the same identification information is consistent, it is further determined whether there is duplication. By determining whether the identification information already exists in the dedicated storage server and taking corresponding measures according to the determination result (such as cancelling format conversion and adding notes), 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 converted in format according to the corresponding storage template, and the associated data items in the associated data groups are converted in format according to the logical relationship to obtain a storage data packet with the same format as the storage template, filled with medical data items and capable of deriving corresponding non-medical data items based on the medical data items. The storage data packet is uploaded to the corresponding dedicated storage server. After format conversion, the storage data packet is in the same format as the storage template, including medical data items and a data packet capable of deriving corresponding non-medical data items based on the medical data items. For the medical data items in the associated data groups, the conversion is carried out according to the data format defined in the storage template. According to the logical relationship in the associated data groups, the non-medical data items are converted into a format that meets the requirements of the storage template. The logical relationship defined in the storage template is applied to ensure that the data items in the storage data packet are logically consistent. By converting the associated data groups into storage data packets with the same format as the storage template and uploading the storage data packets to the corresponding dedicated storage servers, 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 a medical detection device, including the following:

[0126] An input unit 1, configured to obtain 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 patient information is involved;

[0127] An association unit 2, 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 classify the associated data items and the medical data items involved in the logical relationship into an associated data group;

[0128] A template unit 3, configured to obtain storage templates of multiple special storage servers. Each storage template records the data special items stored in the corresponding storage server, and there are some storage templates that map one or more logical relationships regarding medical data items and non-medical data items;

[0129] A first template unit 4, configured to analyze the logical relationships mapped by the storage templates and the logical relationships in the associated data group, and classify the associated data groups and storage templates with the same logical relationship into a first template group;

[0130] A second template unit 5, configured to analyze the coincidence degree between the data special items in each storage template and the medical data items in each associated data group among the remaining storage templates and associated data groups, and classify the storage templates and associated data groups with a coincidence degree higher than a preset high coincidence degree into a second template group;

[0131] A storage unit 6, configured 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 special storage server for storage.

[0132] The present application provides a computer program product, which includes 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 the processor executes the computer instructions, so that the computer device executes a data storage method for a medical detection device. Applying a data storage method for a medical detection device to a computer program product facilitates execution.

[0133] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of a data storage method for a medical detection device as described above are implemented.

[0134] The computer storage medium of the present application may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: an electrical connection with 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. The present application applies a data storage method of a medical detection device to a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the clothing simulation method provided by the present application, which is simple, fast, easy to store, and not easy to lose.

[0135] The present application proposes a data storage method, system, and application for a medical detection device. By carefully classifying and associating the data of the medical detection device, combining a special storage server and a storage template, the storage amount is effectively reduced, while maintaining the integrity and availability of the data, more efficiently utilizing the storage resources, reducing unnecessary redundant storage, and improving the storage efficiency, storage integrity, and storage security of the medical detection device data. By separately processing medical data items and non-medical data items, the privacy information of patients can be better protected. Using coding rules to encrypt patient information improves the security of the data. By analyzing the logical relationship between the storage template and the associated data group, the storage resources can be utilized more efficiently, and unnecessary data redundancy can be reduced. According to different data items and logical relationships, a template storage scheme is constructed, and the storage strategy can be flexibly adjusted according to actual needs. By evaluating the storage integrity and complexity and calculating the recommended value, a storage scheme that is both comprehensive and efficient can be found.

[0136] Those of ordinary skill in the art should understand that the above-mentioned modules of the present application can be implemented by a general computing system. They can be concentrated on a single computing system or distributed on a network composed of multiple computing systems. Optionally, they can be implemented by program codes executable by the computer system, so that they can be stored in the storage system and executed by the computing system, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0137] Note that the above is only a preferred embodiment of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments here, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope 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 only. Without departing from the concept of the present application, more other equivalent embodiments can be included, and the scope of the present application is determined by the scope of the appended claims.

[0138] The above discloses only 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 protection scope of the present application.

Claims

1. A data storage method for a medical detection device, characterized in that, It includes the following: Obtain the 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 patient information is involved; Search for associated data items in each non-medical data item that have a logical relationship with one or more medical data items, and classify the associated data items and the medical data items involved in their logical relationships into an associated data group; Obtain storage templates of multiple special storage servers. Each storage template records the data specializations stored in the corresponding storage server, and there are some storage templates that map one or more logical relationships regarding medical data items and non-medical data items; Analyze the logical relationships mapped by the storage templates and the logical relationships in the associated data group, and classify the associated data groups and storage templates with the same logical relationship into a first template group; Among the remaining storage templates and associated data groups, analyze the degree of overlap between the data specializations in each storage template and the medical data items in each associated data group, and classify the storage templates and associated data groups with an overlap degree higher than a preset high overlap degree into a second template group; Based on the first template and the second template, construct and determine a template storage scheme that covers all medical data items. 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 special storage server for storage; Convert the medical data items in the associated data groups under the same first template group or second template group into the corresponding storage template format, and convert the associated data items in the associated data groups according to the logical relationship, to obtain a storage data packet with the same format as the storage template, filled with medical data items, and capable of deriving corresponding non-medical data items based on the medical data items. Upload the storage data packet to the corresponding special storage server.

2. The data storage method according to claim 1, wherein If there are associated data groups that are not classified into the first template group and the second template group, then regard them as the third associated data group: Incorporate the third associated data group into the quasi-template scheme; or, preferentially select a special server that does not record logical relationships and has 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, wherein The process of obtaining the template storage scheme specifically includes: Based on each first template group and combined with the second template group, construct at least one quasi-template scheme; Evaluate the complexity of the storage process based on the number of storage templates involved in each quasi-template scheme; Evaluate the storage integrity for the device data based on the number of medical data items covered and the number of associated data items involved in each quasi-template scheme; Calculate a recommendation value according to the complexity, the storage integrity, and their respective preset weights, and select the quasi-template scheme with the highest recommendation value as the storage template scheme.

4. The data storage method according to claim 3, characterized in that If there are medical data items that are not classified into the associated data group, then regard the medical data items that are not classified into the associated data group as non-associated medical data items, and supplement them to each quasi-template scheme; When evaluating the storage integrity of each quasi-template solution, it is also based on the coverage of all storage templates in each quasi-template solution for non-associated medical data items.

5. The data storage method according to claim 1, wherein The storage template also includes a coding rule, and the coding rule involves patient information and time information of data collection; Before performing data format conversion according to the storage template, encode the patient information and time information in the medical data item according to the coding rule to obtain identification information; Convert the identification information and the corresponding medical data item data together.

6. The data storage method according to claim 5, wherein Before performing data format conversion according to the storage template, determine whether the identification information exists in the dedicated storage server corresponding to the storage template; If it exists, compare whether the medical data item corresponding to the identification information has been recorded in the dedicated storage server corresponding to the storage template; If it has been recorded, cancel the format conversion of the identification information and its medical data item, and make a note of the associated data item that has a logical relationship with the medical data item.

7. A data storage system for a medical detection device, characterized in that, It includes the following: An input unit, configured to obtain 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 it involves patient information; An association unit, 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 classify the associated data items and the medical data items involved in the logical relationship into an associated data group; A template unit, configured to obtain storage templates of multiple dedicated storage servers, each storage template records the data special items stored in the corresponding storage server, and there are some storage templates that map one or more logical relationships regarding 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 classify the associated data group and the storage template with the same logical relationship into a first template group; A second template unit, configured to analyze the coincidence degree between the data special items in each storage template and the medical data items in each associated data group in the remaining storage templates and associated data groups, and classify the storage templates and associated data groups with a coincidence degree higher than a preset high coincidence degree into a second template group; A storage unit, configured to construct and determine a template storage solution that covers 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 solution, and upload it to the corresponding dedicated storage server for storage; Among them, convert the medical data items in the associated data group under the same first template group or second template group according to the corresponding storage template, convert the associated data items in the associated data group according to the logical relationship, obtain a storage data packet with the same format as the storage template, filled with medical data items and capable of deriving corresponding non-medical data items based on the medical data items, and upload the storage data packet to the corresponding dedicated storage server.

8. A computer device, characterized in that, The computer device includes: One or more processors; A memory, configured to store 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 of the medical detection device according to any one of claims 1-6.

9. A computer program product, characterized in that, It includes executable instructions that, when executed by a processor, implement the data storage method of the medical detection device according to any one of claims 1-6.

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