A hospital infection prevention and control management data archiving system based on multi-category key indicators

Abstract

This invention discloses a hospital infection control management data archiving system based on multiple categories of key indicators, belonging to the field of hospital infection control data management technology. It aims to solve the problems existing in hospital infection control data archiving, such as disorganized multi-category data, disconnect between archived data and key infection control indicators, difficulty in data traceability, high redundancy, and inability to adapt to dynamic adjustments in hospital infection control. This invention includes a data access unit, a multi-category key indicator dynamic matching and archiving module, an archived data traceability verification and redundancy cleanup module, a data storage unit, and a data retrieval unit. The multi-category key indicator dynamic matching and archiving module achieves accurate binding and hierarchical archiving of multi-dimensional hospital infection control data with key prevention and control indicators. The archived data traceability verification and redundancy cleanup module achieves authenticity verification of archived data, full-process traceability, and accurate cleanup of redundant data, solving the core pain points of poor usability, difficulty in traceability, and redundancy in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of hospital infection control data management, specifically a hospital infection control management data archiving system based on multiple categories of key indicators. Background Technology

[0002] Infection control in hospitals is a core aspect of hospital quality management. Its data encompasses multiple categories, including patient infection information, disinfection operation records, healthcare worker hand hygiene, surgical infection monitoring, antibiotic use, and environmental monitoring. The standardized archiving of this data is a crucial foundation for infection control supervision, risk warning, and decision optimization. Currently, infection control data archiving systems generally suffer from the following problems: The current system uses a "one-size-fits-all" approach to archiving data, storing all infection-related data in a mixed manner without classifying and archiving it according to the core indicators of infection control. This results in a significant amount of time being spent filtering data related to specific control indicators when retrieving data later. For example, when retrieving data related to "ventilator-associated pneumonia (VAP) incidence," it is necessary to manually filter mechanically ventilated patient information, pneumonia diagnosis information, etc. from a massive amount of mixed data. This is extremely inefficient and prone to data omissions, making it impossible to quickly support infection control decision-making.

[0003] The traceability of archived data is difficult, and its authenticity cannot be guaranteed: Hospital infection control data comes from multiple terminals (nurse stations, disinfection equipment, testing instruments, etc.). The existing system has not established a sound traceability mechanism. Information such as data collection personnel, collection equipment, and transmission paths are not linked to the archived data. When data anomalies occur (such as false reporting of disinfection pass rates), it is impossible to trace the source and flow of the data, making it difficult to verify the authenticity of the data. At the same time, although some systems have simple traceability functions, they only record basic information and cannot form a complete traceability chain, which cannot meet the traceability needs of hospital infection control supervision.

[0004] The archived data has high redundancy, consuming a lot of storage resources and affecting data use: the existing system only stores the data in a simple way and does not clean up duplicate or invalid data. For example, the disinfection qualification rate data collected multiple times from the same disinfection equipment is completely identical, or the infection information of the same patient is repeatedly entered, resulting in redundant data occupying a lot of storage resources. At the same time, it increases the difficulty of data retrieval and analysis and reduces the efficiency of data use.

[0005] In existing technologies, hospital infection control data archiving systems mostly rely on the common "data acquisition-storage-display" module architecture, without designing solutions to the aforementioned problems. Some systems attempt to introduce complex algorithms for data processing, but these algorithms are highly generalized and not designed to address the specific characteristics of key hospital infection control indicators, failing to accurately solve the pain points and increasing system complexity and maintenance costs. Therefore, there is an urgent need for a hospital infection control management data archiving system that can specifically address the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a hospital infection control management data archiving system based on multiple categories of key indicators to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a hospital infection control management data archiving system based on multiple categories of key indicators, comprising a data access unit, a dynamic matching and archiving module for multiple categories of key indicators, an archived data traceability verification and redundancy purification module, a data storage unit, and a data retrieval unit; the data access unit is used to collect multiple categories of original data throughout the entire hospital infection control process; The multi-category key indicator dynamic matching and archiving module is used to extract multi-category key indicators for hospital infection control from the raw data collected by the data access unit, establish dynamic mapping rules between indicators and data, and perform category identification and indicator matching on the raw data collected by the data access unit to achieve hierarchical archiving. The archived data traceability verification and redundancy cleanup module is used to verify the authenticity of the data after it is archived by the dynamic matching archiving module for multiple categories of key indicators, mark the traceability of the entire process, and identify and clean up redundant data to ensure the accuracy and simplicity of the archived data. The data storage unit is used to store archived data after it has been processed by the archived data traceability verification and redundancy cleanup module. The data retrieval unit is used by users to retrieve archived data from the data storage unit.

[0008] Preferably, the data access unit is used to collect multiple categories of raw data throughout the entire process of hospital infection control. It adopts a multi-interface adaptation design and connects to various data sources, including but not limited to hospital HIS system, LIS system, disinfection equipment terminal, and hand hygiene monitoring equipment. It supports both real-time collection and batch collection modes. The collected data includes, but is not limited to, patient basic information, infection diagnosis information, disinfection operation records, hand hygiene compliance data, surgical infection data, antimicrobial drug use data, and environmental microbial monitoring data.

[0009] Preferably, the specific implementation steps of the multi-category key indicator dynamic matching and archiving module are as follows: Step A1, Indicator Extraction and Standardization: First, extract the six major categories of key indicators and corresponding sub-indicators for hospital infection control. Combine the National Health Commission's "Hospital Infection Management Medical Quality Control Indicators (2024 Edition)" and key points of clinical practice to clarify the definition, data format requirements, collection frequency and corresponding prevention and control scenarios of each sub-indicator. All indicators are standardized and coded to form an indicator coding library. The coding rule is "major category code - sub-category code - indicator sequence number". For example, if the major category code of the basic prevention and control indicator is 01, the sub-category code of the staff hand hygiene compliance rate is 01, and the indicator sequence number is 01, then its code is 01-01-01. This ensures that each indicator has a unique identifier, which facilitates subsequent data matching and retrieval. Step A2: Establishing Mapping Rules: Combining hospital infection control guidelines and clinical experience, establish dynamic mapping rules between indicators and data, clarifying the key indicators and matching thresholds corresponding to each type of raw data. The mapping rules are dynamically iterated according to indicator updates and adjustments based on control needs. The rules are stored in a mapping rule library, including four core elements: data type, data field, matching indicator code, and matching condition. For example, the data of "disinfection time, disinfection device type, and disinfection qualification status" collected by disinfection equipment corresponds to the "medical device disinfection and sterilization qualification rate" (code 01-02-01) in the basic control indicators, and the matching condition is "disinfection qualification status = qualified / unqualified, and the disinfection device type belongs to the category of medical devices". The data of "infection site, infection type, and length of hospital stay" in the patient infection diagnosis data corresponds to the "morbidity rate of hospital-acquired infections per thousand days" (code 02-01-01) in the infection occurrence indicators, and the matching condition is "infection type is hospital-acquired infection, and length of hospital stay ≥ 1 day". Step A3, Raw Data Preprocessing: Receive the raw data collected by the data access unit, standardize the data format, remove data with format errors (such as incorrect date formats or abnormal numerical values), and supplement missing key fields (such as data collection personnel and collection device numbers) to form standardized raw data, ensuring the accuracy of subsequent data matching; for example, unify the format of "disinfection time" collected from different terminals to "YYYY-MM-DD HH:MM:SS", and supplement the missing "collection device number" data by retrieving terminal information through the data access interface. Step A4, Dynamic Matching and Hierarchical Archiving: An indicator code matching algorithm is used to compare fields in the standardized raw data with data fields in the mapping rule base. Combined with matching thresholds, the key indicator code corresponding to each piece of raw data is determined. For example, a piece of raw data is "Disinfection Time: 2026-04-10 09:30:00, Disinfection Device Type: Infusion Set, Disinfection Qualification Status: Qualified, Collection Device Number: XDS-001". By comparing with the mapping rule base, its matching indicator code is determined to be 01-02-01 (Medical Device Disinfection and Sterilization Qualification Rate). Subsequently, according to the indicator category (basic prevention and control indicator, code 01) and subcategory (disinfection and sterilization related, code 02), the data is archived to the "Basic Prevention and Control - Disinfection and Sterilization" storage partition, generating an archive index. The index includes the indicator code, data source (collection device number XDS-001), and collection time (2026-04-10). The four core pieces of information—09:30:00, archiving time, etc.—are used to synchronously transmit the layered archived data and indexes to the data storage unit and the archived data traceability verification and redundancy cleanup module, thus completing the initial archiving.

[0010] Preferably, the six major categories of indicators and core sub-indicators in step A1 are as follows: Basic prevention and control indicators include staff hand hygiene compliance rate, medical device disinfection and sterilization qualification rate, and medical waste classification and collection rate. Infection incidence indicators: including the incidence rate of hospital-acquired infections per thousand days, the incidence rate of neonatal hospital-acquired infections per thousand days, and the incidence rate of specific multidrug-resistant organism (MDRO) infections per thousand days; High-risk device-related infection indicators include the incidence of catheter-associated urinary tract infection (CA-UTI), ventilator-associated pneumonia (VAP), and vascular catheter-related bloodstream infection (CRBSI). Surgical-related infection indicators include: infection rate of Class I surgical sites and antibiotic prophylaxis rate for Class I surgical sites; Indicators for the rational use of antimicrobial drugs include the rate of etiological testing before hospitalized patients are treated with a combination of key antimicrobial drugs and the rate of rationality of the course of antimicrobial drug treatment. Infection control management and training indicators include the ratio of dedicated infection control personnel to beds, annual infection prevention and control training coverage rate, and awareness rate of emergency response plans for infection outbreaks.

[0011] Preferably, the specific implementation steps for establishing the mapping rules in step A2 are as follows: Step A2.1: Preliminary matching of data fields and indicators: Extract all data fields (such as disinfection time, infection site, duration of antimicrobial drug use, etc.) from the standardized raw data, perform preliminary association with each sub-indicator in the indicator coding library, filter out candidate data fields related to the indicator, and remove completely irrelevant fields (such as patient name, which is irrelevant to the disinfection and sterilization pass rate indicator and is directly removed), and form an "indicator-candidate field" correspondence table; Step A2.2, Quantitative Calculation of Association: The association degree between candidate data fields and corresponding sub-indicators is quantified using the association degree calculation formula, which is as follows: ,in : No. Sub-indicators and the first The correlation of each candidate data field, with a value range of [value range missing]. , The closer it is to 1, the higher the correlation between the data field and the sub-indicator, and the more suitable it is as a matching field for the indicator. : No. Sub-indicators and the first The semantic matching degree of each candidate data field is calculated by the system using a pre-set semantic dictionary based on hospital infection control guidelines, based on the similarity between the field's semantics and the indicator definition. The value range is [value range missing]. For example, the semantic matching degree between the "disinfection compliance status" field and the "medical device disinfection and sterilization compliance rate" indicator. The semantic match between the "disinfection time" field and this indicator. ; : No. Sub-indicators and the first The practical relevance of each candidate data field is determined based on its contribution to indicator statistics and prevention and control analysis in clinical practice, with a value range of [value range missing]. For example, the practical correlation between the "Infection Diagnosis Result" field and the "Incidence Rate of Hospital Infection Cases per Thousand Days" indicator. The practical relevance of the "Data Collector" field to this indicator. ; The semantic matching degree weight coefficient has a value of 0.6, which prioritizes ensuring the semantic consistency between the field and the indicator and avoids mismatches of irrelevant fields. The practical relevance weighting coefficient is set to 0.4 to balance clinical practice needs and ensure that the matched fields can support the practical application of the indicators and meet the requirements. ; Step A2.3 Determining the correlation threshold: Set the correlation threshold (Can be dynamically adjusted according to the precision requirements of hospital infection control), when When, the candidate data field is determined to be the first... Matching fields for sub-indicators; when When this happens, the candidate field is removed to ensure the validity of the matching field; Step A2.4, Matching Threshold Setting: For each "indicator-matching field" pair, set a matching threshold based on the hospital infection control guidelines. The matching threshold is the critical value that the field supports for indicator statistics and prevention and control analysis. For example, when the "disinfection compliance status" field matches the "medical device disinfection and sterilization compliance rate" indicator, set the matching threshold to "qualified / unqualified" (text threshold); when the "length of hospital stay" field matches the "morbidity rate of hospital infection cases per thousand days" indicator, set the matching threshold to "≥1 day" (numerical threshold). Step A2.5, Rule Generation and Storage: Integrate the matching fields, correlation, and matching thresholds corresponding to each sub-indicator to generate complete indicator-data mapping rules. Each rule contains four core elements: data type, data field, matching indicator code, and matching conditions (correlation ≥ 0.5 + matching threshold). All rules are stored in the mapping rule library, and an association index between rules and indicator codes is established to facilitate quick subsequent calls and updates. Step A2.6, Dynamic Iterative Optimization: Establish a rule iteration mechanism to recalculate the correlation degree when infection control indicators are updated (e.g., new sub-indicators are added, indicator definitions are adjusted) or clinical practice needs change. Adjust matching fields, matching thresholds, and weighting coefficients. , This enables dynamic iteration of mapping rules, ensuring that the rules always adapt to the actual needs of hospital infection control.

[0012] Preferably, in step A2.2 The specific content is calculated by the system using a pre-set semantic dictionary based on the hospital infection control guidelines, and by comparing the similarity between the semantics of the fields and the definitions of the indicators. S1. Construction of a Pre-defined Hospital Infection Control Semantic Dictionary: First, based on the National Health Commission's "Hospital Infection Management Medical Quality Control Indicators (2024 Edition)" and clinical hospital infection control practice guidelines, a dedicated hospital infection semantic dictionary is constructed as the basis for semantic matching calculation. The hospital infection semantic dictionary contains three core modules, all of which directly correspond to multiple categories of key indicators and candidate data fields: Indicator Core Thesaurus: Includes core terms, synonyms, and related terms for six categories of key indicators and sub-indicators. For example, the core terms for "medical device disinfection and sterilization qualification rate" are "disinfection, sterilization, qualified, medical device," the synonyms are "device disinfection qualified, sterilization up to standard," and the related terms are "disinfection effect, sterilization quality." Data Field Thesaurus: Includes core terms and synonyms for all collected original hospital infection data fields. For example, the core terms for "disinfection qualification status" are "disinfection, qualified," and the synonyms are "disinfection up to standard status, sterilization qualified result." Weight Thesaurus: Based on the importance of hospital infection control, semantic weights are assigned to each term in the dictionary. (range of values) ), where the weights of core terms (such as "disinfection, sterilization, infection, qualified") are set to 0.8 - 1.0, and the weights of auxiliary terms (such as "situation, result, record") are set to 0.1 - 0.3 to ensure that the core semantics are preferentially matched; S2. Semantic word segmentation and denoising: Respectively perform semantic word segmentation on the definition text of the th sub-index and the name / description text of the th candidate data field, remove stop words without actual semantics (such as "of, and, with, record", etc., and the stop word list is preset in combination with the nosocomial infection scenario to avoid misdeleting domain-related vocabulary), and extract effective semantic words. For example, the th sub-index is "qualified rate of medical device disinfection and sterilization" (defined as "the proportion of the number of qualified devices to the total number of devices after disinfection and sterilization treatment of medical devices used in medical institutions"), and the effective words extracted after word segmentation are "medical device, disinfection, sterilization, qualified, proportion"; the th candidate data field is "disinfection qualification situation", and the effective words extracted after word segmentation are "disinfection, qualified, situation". After removing the stop word "situation", the effective core words are "disinfection, qualified"; S3. Preliminary calculation of semantic similarity: Use an improved cosine similarity algorithm to calculate the preliminary semantic similarity between the set of effective words of the sub-index and the set of effective words of the candidate data field. The formula is: , where each parameter is described as follows in combination with the content of the present invention: : The preliminary semantic similarity between the sub-index and the candidate data field, with the value range ; : The number of common effective words between the sub-index and the candidate data field. If there are no common words, , and the preliminary similarity is 0 at this time; : The semantic weight of the th effective word of the sub-index in the nosocomial infection semantic dictionary. For example, the weight of "disinfection" in the sub-index , and the weight of "qualified" ; : The semantic weight of the th effective word of the candidate data field in the nosocomial infection semantic dictionary. For example, the weight of "disinfection" in the field , and the weight of "qualified" ; : The semantic correlation degree of the th common word between the sub-index and the candidate data field. If it is the same word (such as "disinfection - disinfection"), ; if it is a synonymous word (such as "sterilization - disinfection"), If it is a related term (such as "disinfection-sterilization quality"), If it is an irrelevant word, The correlation threshold is preset by the hospital infection semantic dictionary and can be dynamically updated; : Total number of effective words extracted from sub-indicators : Total number of valid words extracted from the candidate data field; Example: The effective keywords for the sub-index "Medical Device Disinfection and Sterilization Qualification Rate" are {medical device (0.8), disinfection (0.9), sterilization (0.9), qualified (0.8)}, and the effective keywords for the candidate data field "Disinfection Qualification Status" are {disinfection (0.9), qualified (0.8)}. The common keywords are "disinfection, qualified". Therefore, the preliminary similarity calculation is as follows: ; Preliminary similarity ; S4. Infection Control Scenario Correction: Based on practical scenarios of infection control in hospitals, the initial semantic similarity is corrected to obtain the final semantic matching degree. The corrected formula is: ,in This is a scene correction factor, with a value range of... The specific value is determined based on the scenario adaptability of the sub-indicator and the candidate data field: If the candidate data field is a core required field for sub-indicator statistics and prevention and control analysis (e.g., "Disinfection compliance status" is a core required field for "Medical device disinfection and sterilization compliance rate"). The revised version improves semantic matching accuracy and highlights the importance of core fields. If the candidate data field is an auxiliary field of the sub-indicator (e.g., "disinfection time" is an auxiliary field of "medical device disinfection and sterilization qualification rate"). This slightly improves semantic matching accuracy while also taking into account the relevance of auxiliary fields; If the candidate data field matches the sub-indicator scenario but is not a core field (such as "disinfection device type" and "medical device disinfection and sterilization pass rate"), This moderately improves semantic matching accuracy. If the candidate data fields and sub-indicator scenarios are not directly compatible (e.g., "data collection personnel" and "medical device disinfection and sterilization pass rate"), No improvements or corrections will be made.

[0013] Preferably, the specific implementation steps of the archived data traceability verification and redundancy cleanup module are as follows: Step B1, Traceability Information Tagging: Receive hierarchical archived data and archived indexes transmitted by the multi-category key indicator dynamic matching and archiving module. Add a traceability tag to each piece of archived data. The traceability tag uses a simplified blockchain hash encoding method, and the encoding formula is as follows: ,in The traceability hash value is a unique traceability identifier for each archived data entry. It is 18 characters long and consists of a combination of numbers and letters. The underlying hash value is used to represent the data source; each data source corresponds to a unique hash value. For example, data collected by the nurse station terminal Data collected by disinfection equipment Data collected by testing instruments This ensures that the data source can be quickly identified; For the core field values ​​in the archived data, the text information of the core fields is converted into corresponding numerical values ​​(e.g., "qualified" is converted to 1, "unqualified" is converted to 0, date is converted to timestamp, and personnel number is converted to corresponding number). For example, in the disinfection data, "disinfection qualification status = qualified" is converted to... Convert "Disinfection time = 2026-04-10 09:30:00" to a timestamp. ; The weighting coefficients for the corresponding core fields are set according to the importance of the fields in hospital infection control, with values ​​ranging from 0.1 to 0.9. The weighting coefficients for core fields (such as infection diagnosis results and disinfection compliance status) are set to 0.8-0.9, and the weighting coefficients for auxiliary fields (such as remarks and contact information of the data collectors) are set to 0.1-0.2. For example, the weighting coefficient for disinfection compliance status is... Weight of disinfection time ; The number of core fields for each archived data entry is determined based on the corresponding indicator type, generally 3-5, to ensure the uniqueness and relevance of the traceability hash value; In addition to the traceability hash value, the traceability marker also includes the name and employee number of the data collection personnel, the number and operating status of the collection equipment, the data transmission path (such as "disinfection equipment → data access unit → matching archiving module"), and data modification records (if any), forming a complete traceability chain to ensure that the entire process of each archived data is traceable. Step B2, Authenticity Verification: Based on the traceability chain, perform dual authenticity verification on the archived data to ensure the data is authentic and valid, and to prevent the archiving of false data. The first layer of verification is the legality verification. This is done by comparing the operation logs of the data collection equipment (such as whether the power-on time and collection time of the disinfection equipment match) and the operation records of the data collection personnel (such as the working hours and operation permissions of the data collection personnel). If the collection time exceeds the equipment operation time or the data collection personnel do not have operation permissions, the data is marked as suspicious. The second layer of verification is consistency verification. By comparing data from different sources under the same indicator, the consistency of the data is verified. For example, the data on "medical device disinfection and sterilization qualification rate" is compared with the qualification records collected by disinfection equipment and the qualification records of manual supervision. If the deviation between the two exceeds the preset threshold (set according to the type of indicator, the deviation threshold for disinfection qualification rate is ±5%, and the deviation threshold for infection incidence rate is ±3%), it is marked as suspicious data. All suspicious data is sent to the infection control management personnel's terminal for manual review. If the review is successful, the verification status is updated to "qualified" and the data is retained in the archive. If the review fails, the data is marked as "invalid data", deleted from the archive, and the reason for deletion is recorded. The data is also linked to a traceability tag to ensure that the deletion is traceable. Step B3, Redundant Data Identification: Establish rules for judging redundant data, identify redundant data based on key indicator dimensions, avoid the traditional "one-size-fits-all" redundancy judgment method, and ensure the accuracy of redundant data identification. Specifically, two types of redundant data are identified: Type 1: Completely duplicated data, which means that the core fields, indicator codes, and collection times of two data sets are completely identical. For example, two disinfection qualification records collected by the same disinfection equipment at the same time have completely identical core fields and are judged as completely duplicated data. Type 2: Invalid and redundant data, which means that the data content does not exceed the normal range of the corresponding indicator but has no actual reference value for prevention and control. For example, the disinfection qualification rate of a certain disinfection equipment is 100% for 7 consecutive days and there are no abnormal fluctuations. These repeated records have no additional reference value for hospital infection prevention and control decisions and are judged as invalid and redundant data. To accurately identify invalid and redundant data, a redundancy judgment formula is used. ,in This is a redundancy coefficient used to measure the degree of fluctuation in continuous data under the same indicator. The smaller the value, the smaller the data fluctuation and the higher the redundancy. For the first under the same indicator Each data point (core field value), for example, the disinfection compliance rate data for 7 consecutive days (all 100%, converted to a value of 1), then... ; The average data value under the same indicator, i.e. For example, the average disinfection pass rate over the above 7 consecutive days ; Set the number of consecutive data entries under the same indicator. (To avoid misjudging occasional fluctuations in a small amount of data as redundancy), when When the data is deemed invalid and redundant, it is considered to have minimal fluctuations and no practical reference value for prevention and control. Step B4, Redundant Data Cleaning and Update: For completely duplicated data, retain the most recently collected data and delete the rest of the duplicate data; for invalid redundant data, retain the first and last data entries and delete the middle duplicate invalid data. At the same time, record the deletion record of the redundant data and associate it with the traceability mark to ensure that the deletion is traceable; update the cleaned archived data to the data storage unit and update the archived index synchronously to complete the final processing of data archiving.

[0014] Preferably, the data storage unit adopts a hierarchical storage structure, corresponding to hierarchical archived data of multiple categories of key indicators. Each major category of indicators corresponds to an independent storage partition, and the data is stored in an orderly manner according to the collection time within the partition. It supports encrypted storage of data to ensure data privacy and security.

[0015] Preferably, the data retrieval unit supports multi-dimensional retrieval, including but not limited to key indicator categories, sub-indicators, collection time, and data source, and can export archived data and traceability records to meet the needs of hospital infection control supervision, data statistics, and emergency traceability.

[0016] Compared with the prior art, the beneficial effects of the present invention are: the present invention, through This invention, through the collaborative work of a multi-category key indicator dynamic matching archiving module and an archived data traceability verification and redundancy cleanup module, completely solves the problems of "disorganized data and disconnection from prevention and control indicators," "difficult traceability," and "high redundancy" in existing hospital infection control data archiving. Compared with existing systems that use mixed archiving, lack precise traceability, and lack targeted redundancy cleanup, its practicality is greatly improved.

[0017] This invention incorporates the key indicators for hospital infection control issued by the National Health Commission. The modular working steps are aligned with clinical practice, data archiving is standardized and accurate, traceability is available, and redundant data is effectively cleaned up. This saves storage resources and improves the efficiency of data retrieval and analysis. It can quickly support hospital infection control supervision, risk warning, decision optimization, and other tasks, and is suitable for the infection control needs of hospitals of different sizes.

[0018] No complex algorithms required, easy to implement: Neither of the two core modules uses common complex algorithms. Instead, they achieve accurate and efficient processing through targeted rule design and simple formula calculations. This avoids the increased system complexity and maintenance difficulties caused by complex algorithms, making it easy to implement in the hospital's existing information system and highly scalable. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the workflow of the multi-category key indicator dynamic matching and archiving module of the present invention; Figure 3A schematic diagram illustrating the workflow for establishing the mapping rules of this invention; Figure 4 This is a schematic diagram of the workflow of the archived data traceability verification and redundancy purification module of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1-4 This invention provides a technical solution: a hospital infection control management data archiving system based on multiple categories of key indicators, including a data access unit, a multi-category key indicator dynamic matching and archiving module, an archived data traceability verification and redundancy purification module, a data storage unit, and a data retrieval unit; the data access unit is used to collect multi-category raw data from the entire hospital infection control process; the specific implementation steps of the data access unit are as follows: Step 1: Interface Adaptation and Data Source Connection: The data access unit adopts a multi-interface adaptation design, with multiple pre-set standardized interfaces (such as HL7 interface, RS485 interface, TCP / IP interface) to connect to the hospital's existing information systems and various infection control data collection terminals, ensuring full coverage of data sources throughout the entire process. Specific interfaces include: core hospital business systems, such as the HIS (Hospital Information System) and LIS (Laboratory Information System), used to collect basic patient information, length of hospital stay, infection diagnosis results, and antibiotic usage records; dedicated infection control collection terminals, such as disinfection equipment terminals, hand hygiene monitoring equipment, and environmental microbial monitoring instruments, used to collect disinfection operation records, hand hygiene compliance data, and environmental colony counts; and manual data entry terminals used to collect infection control-related data that cannot be automatically collected, such as infection control training records and infection outbreak emergency response records. All interfaces support data format compatibility to avoid data collection failures due to interface incompatibility.

[0022] Step 2, Data Acquisition Mode Selection and Parameter Configuration: Based on the acquisition needs of different data sources, two acquisition modes are configured, which can be manually switched or automatically adapted to ensure acquisition efficiency and data timeliness: Real-time acquisition mode: For dynamically changing data that needs to be archived promptly (such as disinfection operation records and hand hygiene monitoring data), real-time acquisition parameters are configured, setting the acquisition frequency to be completed within 10 seconds of data generation. Each data point generated by the acquisition terminal is immediately transmitted to the data access unit via the adaptation interface to ensure data real-time performance and meet the real-time monitoring needs of hospital infection control. Batch acquisition mode: For data with low change frequency and large data volume (such as daily summary data of antimicrobial drug use and weekly environmental monitoring data), batch acquisition parameters are configured, setting the acquisition time to 00:00-00:30 daily (avoiding peak hospital business hours). Relevant data from the previous day or period is retrieved in batches, reducing resource consumption on the hospital's existing systems.

[0023] Step 3: Collection of Multiple Categories of Raw Data: Following the classification of the entire hospital infection control process, six categories of core raw data will be collected to ensure data coverage of all data sources required for archiving in the invention. Specific collection content includes: Patient infection-related data (collecting basic patient information, infection site, infection type, length of hospital stay, and pathogen detection results from the HIS and LIS systems); Disinfection control-related data (collecting disinfection time, type of disinfection equipment, disinfection compliance status, and disinfection personnel from disinfection equipment terminals); Hand hygiene-related data (collecting monitoring time, monitoring location, compliance status, and monitoring personnel from hand hygiene monitoring equipment); Surgical infection-related data (collecting surgical type, surgical duration, postoperative infection status, and antibiotic prophylaxis usage from the HIS and surgical management systems); Antibiotic usage data (collecting antibiotic names, dosages, treatment courses, and pathogen testing information from the HIS and pharmacy management systems); Infection control management and training data (collecting training time, number of trainees, assessment results, and emergency plan awareness rate from manual data entry terminals and the training management system).

[0024] Step 4: Preliminary Data Screening and Noise Reduction: The collected raw data undergoes preliminary processing to remove obviously invalid data and prevent it from entering subsequent processing stages. Specific operations include: format screening, removing data with incorrect date formats, abnormal values ​​(such as invalid disinfection dates or negative colony counts), and missing fields (such as no collection time or no data source); redundancy screening, removing completely blank or repeatedly transmitted junk data (such as the same data sent repeatedly by the same terminal); and preliminary legality assessment, verifying the legality of the data collection terminal and removing data sent by unauthorized terminals to ensure the compliance of the source of the collected data.

[0025] Step 5: Data Collection, Temporary Storage, and Transmission: The initially screened raw data from multiple categories is temporarily stored in the temporary storage module of the data access unit (using a caching mechanism to avoid data loss). Simultaneously, according to the preset transmission protocol, the temporarily stored raw data is synchronously transmitted to the raw data preprocessing stage of the multi-category key indicator dynamic matching and archiving module. During transmission, the data is simply encrypted (using symmetric encryption) to prevent leakage during data transmission, ensuring the security and integrity of the raw data, and laying the foundation for subsequent data standardization processing and indicator matching.

[0026] The multi-category key indicator dynamic matching and archiving module is used to extract multi-category key indicators for hospital infection control from the raw data collected by the data access unit, establish dynamic mapping rules between indicators and data, and simultaneously perform category identification and indicator matching on the raw data collected by the data access unit to achieve hierarchical archiving. The specific implementation steps of the multi-category key indicator dynamic matching and archiving module are as follows: Step A1, Indicator Extraction and Standardization: First, extract the six major categories of key indicators and their corresponding sub-indicators for hospital infection control. Based on the National Health Commission's "Hospital Infection Management Medical Quality Control Indicators (2024 Edition)" and key clinical practices, clarify the definition, data format requirements, collection frequency, and corresponding prevention and control scenarios for each sub-indicator. The specific six categories of indicators and core sub-indicators are as follows: Basic prevention and control indicators include staff hand hygiene compliance rate, medical device disinfection and sterilization qualification rate, and medical waste classification and collection rate. Infection incidence indicators: including the incidence rate of hospital-acquired infections per thousand days, the incidence rate of neonatal hospital-acquired infections per thousand days, and the incidence rate of specific multidrug-resistant organism (MDRO) infections per thousand days; High-risk device-related infection indicators include the incidence of catheter-associated urinary tract infection (CA-UTI), ventilator-associated pneumonia (VAP), and vascular catheter-related bloodstream infection (CRBSI). Surgical-related infection indicators include: infection rate of Class I surgical sites and antibiotic prophylaxis rate for Class I surgical sites; Indicators for the rational use of antimicrobial drugs include the rate of etiological testing before hospitalized patients are treated with a combination of key antimicrobial drugs and the rate of rationality of the course of antimicrobial drug treatment. Infection control management and training indicators include: the ratio of dedicated infection control personnel to available beds, annual infection prevention and control training coverage rate, and awareness rate of emergency response plans for infection outbreaks. All indicators are standardized and coded to form an indicator coding library. The coding rule is "major category code - sub-category code - indicator sequence number". For example, if the major category code of the basic prevention and control indicator is 01, the sub-category code of the staff hand hygiene compliance rate is 01, and the indicator sequence number is 01, then its code is 01-01-01. This ensures that each indicator has a unique identifier, which facilitates subsequent data matching and retrieval. Step A2, Mapping Rule Establishment: Combining hospital infection control guidelines and clinical experience, establish dynamic index-data mapping rules, clarifying the key indicators and matching thresholds corresponding to each type of raw data. These mapping rules are dynamically iterated based on indicator updates and adjustments to control needs. The rules are stored in a mapping rule library, including four core elements: data type, data field, matching indicator code, and matching condition. For example, the data collected by disinfection equipment, including "disinfection time, disinfection device type, and disinfection qualification status," corresponds to the basic control indicator "medical device disinfection and sterilization qualification rate" (code 01-02-01), with the matching condition "disinfection qualification status = qualified / unqualified, disinfection device type belongs to the medical device category." Similarly, the data in patient infection diagnosis data, including "infection site, infection type, and length of hospital stay," corresponds to the infection occurrence indicator "morbidity rate of hospital-acquired infections per thousand days" (code 02-01-01), with the matching condition "infection type is hospital-acquired infection, length of hospital stay ≥ 1 day." The specific implementation steps for establishing the mapping rules in Step A2 are as follows: Step A2.1: Preliminary matching of data fields and indicators: Extract all data fields (such as disinfection time, infection site, duration of antimicrobial drug use, etc.) from the standardized raw data, perform preliminary association with each sub-indicator in the indicator coding library, filter out candidate data fields related to the indicator, and remove completely irrelevant fields (such as patient name, which is irrelevant to the disinfection and sterilization pass rate indicator and is directly removed), and form an "indicator-candidate field" correspondence table; Step A2.2, Quantitative Calculation of Association: The association degree between candidate data fields and corresponding sub-indicators is quantified using the association degree calculation formula, which is as follows: ,in : No. Sub-indicators and the first The correlation of each candidate data field, with a value range of [value range missing]. , The closer it is to 1, the higher the correlation between the data field and the sub-indicator, and the more suitable it is as a matching field for the indicator. : No. Sub-indicators and the first The semantic matching degree of each candidate data field is calculated by the system using a pre-set semantic dictionary based on hospital infection control guidelines, based on the similarity between the field's semantics and the indicator definition. The value range is [value range missing]. For example, the semantic matching degree between the "disinfection compliance status" field and the "medical device disinfection and sterilization compliance rate" indicator. The semantic match between the "disinfection time" field and this indicator. ; : No. Sub-indicators and the first The practical relevance of each candidate data field is set according to the contribution of the field to index statistics and prevention and control analysis in clinical practice, and the value range is , for example, the practical relevance of the "infection diagnosis result" field to the "incidence rate of hospital infection cases per 1000 patient-days" index , and the practical relevance of the "collector" field to this index ; : Semantic matching degree weight coefficient, with a value of 0.6, to ensure the semantic consistency between the field and the index first, and avoid mis-matching of irrelevant fields; : Practical relevance weight coefficient, with a value of 0.4, taking into account the needs of clinical practice, ensuring that the matching field can support the actual application of the index, and meeting ; Among them The specific content calculated by the system through the similarity between the field semantics and the index definition by combining the preset semantic dictionary of nosocomial infection prevention and control norms is as follows: S1. Construction of the preset nosocomial infection prevention and control semantic dictionary: First, combine the "Medical Quality Control Indicators for Nosocomial Infection Management (2024 Edition)" of the National Health Commission and the clinical nosocomial infection prevention and control practice norms to construct a dedicated nosocomial infection semantic dictionary as the basis for semantic matching degree calculation; the nosocomial infection semantic dictionary contains three core modules, all of which directly correspond to multiple categories of key indicators and candidate data fields: Index core term library:收录六大类关键指标及子指标的核心术语、同义术语、相关术语,例如“医疗器械消毒灭菌合格率”的核心词为“消毒、灭菌、合格、医疗器械”,同义术语为“器械消毒合格、灭菌达标”,相关术语为“消毒效果、灭菌质量”;Data field term library:收录所有采集的院感原始数据字段的核心词、同义术语,例如“消毒合格情况”的核心词为“消毒、合格”,同义术语为“消毒达标情况、灭菌合格结果”;Weight term library: According to the importance of nosocomial infection prevention and control, assign semantic weights to each term in the dictionary (value range ), where the weights of core terms (such as "disinfection, sterilization, infection, qualified") are set to 0.8 - 1.0, and the weights of auxiliary terms (such as "situation, result, record") are set to 0.1 - 0.3 to ensure that core semantics are preferentially matched; S2. Semantic word segmentation and noise removal: Perform semantic word segmentation on the definition text of the th sub-index and the name / description text of the th candidate data field respectively, remove stop words without actual semantics (such as "of, and, with, record", etc., and the stop word list is preset in combination with the nosocomial infection scenario to avoid mis-deleting domain-related vocabulary), and extract effective semantic words. For example, the The sub-indicator is "medical device disinfection and sterilization pass rate" (defined as "the proportion of qualified medical devices used in medical institutions after disinfection and sterilization to the total number of medical devices"). After word segmentation, the effective words extracted are "medical device, disinfection, sterilization, qualified, proportion"; The candidate data field is "disinfection compliance status". After word segmentation, the effective words extracted are "disinfection, compliance, status". After removing the stop word "status", the effective core words are "disinfection, compliance". S3. Preliminary Semantic Similarity Calculation: An improved cosine similarity algorithm is used to calculate the preliminary semantic similarity between the effective vocabulary set of the sub-indicators and the effective vocabulary set of the candidate data fields. The formula is as follows: The parameters are explained in conjunction with the content of this invention as follows: Preliminary semantic similarity between sub-indicators and candidate data fields, with a range of values. ; The number of valid words shared between the sub-indicator and the candidate data field. If there are no shared words, At this point, the initial similarity is 0; Sub-indicator The semantic weight of each effective word in the hospital infection control semantic dictionary, such as the weight of "disinfection" among sub-indicators. The weight of "qualified" ; Candidate data field number The semantic weight of each effective word in the hospital infection semantic dictionary, such as the weight of the field "disinfection". The weight of "qualified" ; : Sub-indicators and candidate data fields The semantic relevance of shared words; if they are the same word (e.g., "disinfection-disinfection"), If they are synonyms (such as "sterilization - disinfection"), If it is a related term (such as "disinfection-sterilization quality"), If it is an irrelevant word, The correlation threshold is preset by the hospital infection semantic dictionary and can be dynamically updated; : Total number of effective words extracted from sub-indicators : Total number of valid words extracted from the candidate data field; Example: The effective keywords for the sub-index "Medical Device Disinfection and Sterilization Qualification Rate" are {medical device (0.8), disinfection (0.9), sterilization (0.9), qualified (0.8)}, and the effective keywords for the candidate data field "Disinfection Qualification Status" are {disinfection (0.9), qualified (0.8)}. The common keywords are "disinfection, qualified". Therefore, the preliminary similarity calculation is as follows: ; Preliminary similarity ; S4. Infection Control Scenario Correction: Based on practical scenarios of infection control in hospitals, the initial semantic similarity is corrected to obtain the final semantic matching degree. The corrected formula is: ,in This is a scene correction factor, with a value range of... The specific value is determined based on the scenario adaptability of the sub-indicator and the candidate data field: If the candidate data field is a core required field for sub-indicator statistics and prevention and control analysis (e.g., "Disinfection compliance status" is a core required field for "Medical device disinfection and sterilization compliance rate"). The revised version improves semantic matching accuracy and highlights the importance of core fields. If the candidate data field is an auxiliary field of the sub-indicator (e.g., "disinfection time" is an auxiliary field of "medical device disinfection and sterilization qualification rate"). This slightly improves semantic matching accuracy while also taking into account the relevance of auxiliary fields; If the candidate data field matches the sub-indicator scenario but is not a core field (such as "disinfection device type" and "medical device disinfection and sterilization pass rate"), This moderately improves semantic matching accuracy. If the candidate data fields and sub-indicator scenarios are not directly compatible (e.g., "data collection personnel" and "medical device disinfection and sterilization pass rate"), No improvements or corrections will be made. Step A2.3 Determining the correlation threshold: Set the correlation threshold (Can be dynamically adjusted according to the precision requirements of hospital infection control), when When, the candidate data field is determined to be the first... Matching fields for sub-indicators; when When this happens, the candidate field is removed to ensure the validity of the matching field; Step A2.4, Matching Threshold Setting: For each "indicator-matching field" pair, set a matching threshold based on the hospital infection control guidelines. The matching threshold is the critical value that the field supports for indicator statistics and prevention and control analysis. For example, when the "disinfection compliance status" field matches the "medical device disinfection and sterilization compliance rate" indicator, set the matching threshold to "qualified / unqualified" (text threshold); when the "length of hospital stay" field matches the "morbidity rate of hospital infection cases per thousand days" indicator, set the matching threshold to "≥1 day" (numerical threshold). Step A2.5, Rule Generation and Storage: Integrate the matching fields, correlation, and matching thresholds corresponding to each sub-indicator to generate complete indicator-data mapping rules. Each rule contains four core elements: data type, data field, matching indicator code, and matching conditions (correlation ≥ 0.5 + matching threshold). All rules are stored in the mapping rule library, and an association index between rules and indicator codes is established to facilitate quick subsequent calls and updates. Step A2.6, Dynamic Iterative Optimization: Establish a rule iteration mechanism to recalculate the correlation degree when infection control indicators are updated (e.g., new sub-indicators are added, indicator definitions are adjusted) or clinical practice needs change. Adjust matching fields, matching thresholds, and weighting coefficients. , This enables dynamic iteration of mapping rules, ensuring that the rules always adapt to the actual needs of hospital infection control. Step A3, Raw Data Preprocessing: Receive the raw data collected by the data access unit, standardize the data format, remove data with format errors (such as incorrect date formats or abnormal numerical values), and supplement missing key fields (such as data collection personnel and collection device numbers) to form standardized raw data, ensuring the accuracy of subsequent data matching; for example, unify the format of "disinfection time" collected from different terminals to "YYYY-MM-DD HH:MM:SS", and supplement the missing "collection device number" data by retrieving terminal information through the data access interface. Step A4, Dynamic Matching and Hierarchical Archiving: An indicator code matching algorithm is used to compare fields in the standardized raw data with data fields in the mapping rule base. Combined with matching thresholds, the key indicator code corresponding to each piece of raw data is determined. For example, a piece of raw data is "Disinfection Time: 2026-04-10 09:30:00, Disinfection Device Type: Infusion Set, Disinfection Qualification Status: Qualified, Collection Device Number: XDS-001". By comparing with the mapping rule base, its matching indicator code is determined to be 01-02-01 (Medical Device Disinfection and Sterilization Qualification Rate). Subsequently, according to the indicator category (basic prevention and control indicator, code 01) and subcategory (disinfection and sterilization related, code 02), the data is archived to the "Basic Prevention and Control - Disinfection and Sterilization" storage partition, generating an archive index. The index includes the indicator code, data source (collection device number XDS-001), and collection time (2026-04-10). The four core pieces of information—09:30:00, archiving time, etc.—are used to synchronously transmit the layered archived data and indexes to the data storage unit and the archived data traceability verification and redundancy cleanup module, thus completing the initial archiving.

[0027] This section provides further details on the steps involved to ensure accurate matching and standardized archiving: A4.1 Standardized Data Field Extraction and Filtering: From the standardized raw dataset generated in step A3, extract all valid fields for each raw data entry (after removing format errors and supplementing missing key fields). Filter out fields that correspond to the "data fields" in the mapping rule base, and remove irrelevant fields (such as patient name, contact information, etc., which do not participate in indicator matching). For example, if a standardized raw data entry is "Disinfection Time: 2026-04-10 09:30:00, Disinfection Device Type: Infusion Set, Disinfection Qualification Status: Qualified, Collection Device Number: XDS-001", the valid matching fields extracted are "Disinfection Device Type, Disinfection Qualification Status", and "Collection Device Number" is removed (it is only used for traceability and does not participate in indicator matching).

[0028] A4.2 Targeted Matching of Mapping Rules: The extracted valid fields are compared with all mapping rules in the mapping rule library. The comparison revolves around two core elements: "data fields" and "matching conditions," and is divided into two steps: Field Matching: Finding rules in the mapping rule library where the data fields are completely consistent with or semantically match the valid fields of the current original data (based on the semantic matching degree calculation mentioned above, ensuring field relevance); Threshold Verification: For the matched rules, verifying whether the content of the original data fields meets the "matching threshold" of the rule (i.e., relevance ≥ 0.5 + field matching threshold). For example, the fields "disinfection device type, disinfection qualification status" of the above original data match the rule corresponding to "medical device disinfection and sterilization qualification rate (code 01-02-01)" in the mapping rule library. The matching threshold of this rule is "disinfection qualification status = qualified / unqualified, disinfection device type belongs to the category of medical devices." Verification confirms that "disinfection qualification status = qualified" and "infusion set belongs to medical devices," thus meeting the matching threshold requirements.

[0029] A4.3 Key Indicator Coding Determination: For mapping rules that pass threshold validation, extract the corresponding "matching indicator code" as the association indicator code for the current original data; if the same original data matches multiple mapping rules (i.e., multiple indicator codes), then select the correlation degree. The highest-ranking rule corresponds to the primary indicator code, while the remaining codes serve as auxiliary codes (for subsequent multi-dimensional searches), ensuring that each piece of data corresponds to a unique primary indicator code. For example, if a piece of original data matches both "medical device disinfection and sterilization pass rate (01-02-01)" and "disinfection equipment operation compliance rate (01-02-03)", and the former has a correlation of 0.85 while the latter has 0.62, then the primary indicator code is determined to be 01-02-01, and the auxiliary code is 01-02-03.

[0030] A4.4 Matching Result Verification and Anomaly Handling: Establish a simple anomaly verification mechanism to avoid mismatches and missed matches: If a piece of original data does not match any mapping rule, it is marked as "unmatched data" and sent to the management terminal. The management personnel will then supplement the mapping rule or determine whether the data is invalid based on the actual needs of hospital infection control. If the correlation degree of the rules matched by a piece of original data is all <0.5, it is determined as "invalid match" and is also sent to the management terminal for review. If the matching result does not match the actual prevention and control scenario (such as matching surgical infection data to basic prevention and control indicators), the indicator code is manually corrected, and the matching conditions in the mapping rule base are updated to achieve dynamic optimization of the rules.

[0031] A4.5 Hierarchical Archiving and Classification: Based on the determined main indicator code and combined with the indicator coding rules mentioned above ("Major Category Code - Sub-Category Code - Indicator Sequence Number"), the raw data is hierarchically classified: It is divided into six major archiving partitions according to the major category code (basic prevention and control, infection occurrence, high-risk device-related infections, etc.); within each major category partition, it is further divided into several sub-partitions according to the sub-category code; within each sub-partition, specific sub-indicators are identified according to the indicator sequence number, thus completing the hierarchical storage of the data. For example, the raw data with the main indicator code 01-02-01 (Basic Prevention and Control Major Category 01, Disinfection and Sterilization Sub-Category 02, Medical Device Disinfection and Sterilization Pass Rate Indicator 01) is archived to the sub-partition "Basic Prevention and Control - Disinfection and Sterilization - Medical Device Disinfection and Sterilization Pass Rate".

[0032] A4.6 Archive Index Generation and Data Transmission: For each piece of data that has completed hierarchical archiving, a dedicated archive index is generated. The index strictly includes four core pieces of information: indicator code (primary code + auxiliary code), data source (collection device number, data source type), collection time, and archiving time, ensuring that the index and data correspond one-to-one, facilitating subsequent traceability and retrieval. Subsequently, the hierarchically archived data and corresponding indexes are synchronously transmitted to the data storage unit (for long-term storage) and the archive data traceability verification and redundancy cleanup module (for subsequent traceability, verification, and redundancy cleanup), completing the initial archiving process and laying the foundation for subsequent data processing.

[0033] The archived data traceability verification and redundancy cleanup module is used to verify the authenticity of data matched and archived by the dynamic matching archiving module for multiple categories of key indicators, perform full-process traceability marking, and identify and clean up redundant data to ensure the accuracy and simplicity of the archived data. The specific implementation steps of the archived data traceability verification and redundancy cleanup module are as follows: Step B1, Traceability Information Tagging: Receive hierarchical archived data and archived indexes transmitted by the multi-category key indicator dynamic matching and archiving module. Add a traceability tag to each piece of archived data. The traceability tag uses a simplified blockchain hash encoding method, and the encoding formula is as follows: ,in The traceability hash value is a unique traceability identifier for each archived data entry. It is 18 characters long and consists of a combination of numbers and letters. The underlying hash value is used to represent the data source; each data source corresponds to a unique hash value. For example, data collected by the nurse station terminal Data collected by disinfection equipment Data collected by testing instruments This ensures that the data source can be quickly identified; For the core field values ​​in the archived data, the text information of the core fields is converted into corresponding numerical values ​​(e.g., "qualified" is converted to 1, "unqualified" is converted to 0, date is converted to timestamp, and personnel number is converted to corresponding number). For example, in the disinfection data, "disinfection qualification status = qualified" is converted to... Convert "Disinfection time = 2026-04-10 09:30:00" to a timestamp. ; The weighting coefficients for the corresponding core fields are set according to the importance of the fields in hospital infection control, with values ​​ranging from 0.1 to 0.9. The weighting coefficients for core fields (such as infection diagnosis results and disinfection compliance status) are set to 0.8-0.9, and the weighting coefficients for auxiliary fields (such as remarks and contact information of the data collectors) are set to 0.1-0.2. For example, the weighting coefficient for disinfection compliance status is... Weight of disinfection time ; The number of core fields for each archived data entry is determined based on the corresponding indicator type, generally 3-5, to ensure the uniqueness and relevance of the traceability hash value; In addition to the traceability hash value, the traceability marker also includes the name and employee number of the data collection personnel, the number and operating status of the collection equipment, the data transmission path (such as "disinfection equipment → data access unit → matching archiving module"), and data modification records (if any), forming a complete traceability chain to ensure that the entire process of each archived data is traceable. Step B2, Authenticity Verification: Based on the traceability chain, perform dual authenticity verification on the archived data to ensure the data is authentic and valid, and to prevent the archiving of false data. The first layer of verification is the legality verification. This is done by comparing the operation logs of the data collection equipment (such as whether the power-on time and collection time of the disinfection equipment match) and the operation records of the data collection personnel (such as the working hours and operation permissions of the data collection personnel). If the collection time exceeds the equipment operation time or the data collection personnel do not have operation permissions, the data is marked as suspicious. The second layer of verification is consistency verification. By comparing data from different sources under the same indicator, the consistency of the data is verified. For example, the data on "medical device disinfection and sterilization qualification rate" is compared with the qualification records collected by disinfection equipment and the qualification records of manual supervision. If the deviation between the two exceeds the preset threshold (set according to the type of indicator, the deviation threshold for disinfection qualification rate is ±5%, and the deviation threshold for infection incidence rate is ±3%), it is marked as suspicious data. All suspicious data is sent to the infection control management personnel's terminal for manual review. If the review is successful, the verification status is updated to "qualified" and the data is retained in the archive. If the review fails, the data is marked as "invalid data", deleted from the archive, and the reason for deletion is recorded. The data is also linked to a traceability tag to ensure that the deletion is traceable. Step B3, Redundant Data Identification: Establish rules for judging redundant data, identify redundant data based on key indicator dimensions, avoid the traditional "one-size-fits-all" redundancy judgment method, and ensure the accuracy of redundant data identification. Specifically, two types of redundant data are identified: Type 1: Completely duplicated data, which means that the core fields, indicator codes, and collection times of two data sets are completely identical. For example, two disinfection qualification records collected by the same disinfection equipment at the same time have completely identical core fields and are judged as completely duplicated data. Type 2: Invalid and redundant data, which means that the data content does not exceed the normal range of the corresponding indicator but has no actual reference value for prevention and control. For example, the disinfection qualification rate of a certain disinfection equipment is 100% for 7 consecutive days and there are no abnormal fluctuations. These repeated records have no additional reference value for hospital infection prevention and control decisions and are judged as invalid and redundant data. To accurately identify invalid and redundant data, a redundancy judgment formula is used. ,in This is a redundancy coefficient used to measure the degree of fluctuation in continuous data under the same indicator. The smaller the value, the smaller the data fluctuation and the higher the redundancy. For the first under the same indicator Each data point (core field value), for example, the disinfection compliance rate data for 7 consecutive days (all 100%, converted to a value of 1), then... ; The average data value under the same indicator, i.e. For example, the average disinfection pass rate over the above 7 consecutive days ; Set the number of consecutive data entries under the same indicator. (To avoid misjudging occasional fluctuations in a small amount of data as redundancy), when When the data is deemed invalid and redundant, it is considered to have minimal fluctuations and no practical reference value for prevention and control. Step B4, Redundant Data Cleaning and Update: Accurately clean up identified redundant data to avoid accidentally deleting valid data. Record all operations to ensure traceability. For completely duplicate data, the principle of "keeping the latest and deleting the rest" is adopted. The latest data is kept and the remaining duplicate data is deleted. The hash value associated with the record is deleted and the number of duplicate data records deleted, the deletion time, and the operator are clearly specified. For invalid and redundant data, the principle of "keeping the first and last records and deleting the middle records" is adopted. The first record (recording the initial state) and the last record (recording the latest state) are kept, and the duplicate and invalid data in the middle are deleted. For example, for disinfection qualification rate data for 7 consecutive days, the data of day 1 and day 7 are kept, and the data of day 2-6 are deleted. This reduces redundancy and preserves the time span information of the data. After redundant data cleanup is completed, the cleaned archived data is updated to the data storage unit, and the archived index is updated synchronously (indexes of redundant data are deleted, and index information of retained data is updated). This completes the final processing of data archiving, ensuring that the archived data in the data storage unit is concise, accurate, and traceable. The data storage unit stores archived data after it has been processed by the archived data traceability verification and redundancy cleanup module. Specifically, the data storage unit ensures the security, orderliness, and traceability of the archived data, while also adapting to subsequent data retrieval needs: a. Data Reception and Verification: The data storage unit receives cleaned archived data, archived indexes, and traceability tags from the archived data traceability and redundancy cleanup module in real time. Simultaneously, it receives preliminary archived data (used for temporary transitional storage before redundancy cleanup) from the multi-category key indicator dynamic matching archived module. After reception, data verification is performed first. Core verification content includes: data integrity verification, confirming that the received data contains the three core parts: the archived data body, the archived index, and the traceability tag, with no missing fields; data consistency verification, comparing the indicator codes, traceability hash values, and transmission records of the received data to ensure that the data has not been tampered with; and format compatibility verification, confirming that the data format conforms to the storage unit's preset standards (such as date format, numeric format, hash encoding format) to avoid storage failure due to format incompatibility. Data that fails verification is returned to the traceability cleanup module for reprocessing, while data that passes verification proceeds to the next storage step.

[0034] b. Initialization and Partition Configuration of Hierarchical Storage Structure: Following the pre-defined hierarchical storage structure, the storage partitions are initialized and configured, strictly corresponding to the hierarchical archiving logic of multiple key indicators, achieving hierarchical storage of "indicator category - subcategory - data": Six independent primary storage partitions are divided according to six major categories of key indicators (basic prevention and control indicators, infection occurrence indicators, etc.). Each primary partition corresponds to a unique indicator category code (e.g., the basic prevention and control indicator corresponds to code 01, and the primary partition is named "01-Basic Prevention and Control Indicator Storage Area"), ensuring independent storage of major category data and avoiding cross-contamination. Within each primary partition, secondary storage sub-partitions are divided according to subcategory codes. For example, within the "01-Basic Prevention and Control Indicator Storage Area," sub-partitions such as "01-01-Hand Hygiene Related" and "01-02-Disinfection and Sterilization Related" are divided, corresponding to different subcategories under the basic prevention and control indicators. Within each secondary sub-partition, a tertiary storage directory is divided according to the collection time (accurate to year, month, and day), enabling data to be archived in an orderly manner by time, facilitating subsequent retrieval by time dimension. Simultaneously, a mapping between partitions and indicator codes is established to ensure a one-to-one correspondence between data storage locations and indicator codes.

[0035] c. Data Encryption Storage and Privacy Protection: In accordance with the requirement of "supporting encrypted data storage to ensure data privacy and security" in the invention, the verified archived data is subjected to double encryption and then stored in the corresponding partitions according to a hierarchical structure: Data subject encryption: The AES encryption algorithm is used to encrypt the main body of the archived data (such as patient infection information, disinfection records, etc.). The encryption key is uniformly allocated by the system administrator and updated regularly (once a month). The key is stored in an independent key management module, which is physically isolated from the stored data to prevent key leakage from causing data cracking; Traceability marking and index encryption: The traceability hash value, archive index and other core traceability information are encrypted using symmetric encryption to ensure the security of the traceability chain and prevent the traceability information from being tampered with; Privacy data desensitization: The patient's personal privacy information (such as name, contact information, medical record number) contained in the archived data is desensitized (only the patient's unique identifier ID is retained, and other privacy information is hidden), which complies with the relevant regulations on hospital data privacy protection and takes into account both data traceability and privacy security.

[0036] d. Archived Index and Data Association Binding: The archived index is uniquely associated with the corresponding archived data, establishing an index database to achieve rapid mapping between "index retrieval and data location," adapting to the multi-dimensional retrieval needs of data retrieval units. The index database is updated synchronously with the hierarchical storage partitions. The index information for each archived data entry (including indicator code, main code + auxiliary code, data source, collection time, archiving time, and traceability hash value) is stored separately in the index database, while also being associated with the corresponding data's storage path (first-level partition - second-level sub-partition - third-level time directory). Multi-dimensional indexes are established, with indexes created according to indicator code, data source, collection time, and traceability hash value, ensuring that data retrieval units can quickly retrieve corresponding data through any dimension. For example, by using the indicator code "01-02-01," all relevant data within the sub-partition "Basic Prevention and Control - Disinfection and Sterilization - Medical Device Disinfection and Sterilization Qualification Rate" can be directly located.

[0037] e. Data Update and Maintenance: Establish a routine data maintenance mechanism to ensure the accuracy, integrity, and availability of stored data, aligning with the dynamic data update requirements outlined in the invention: Data Update: After the archived data traceability verification and redundancy cleanup module completes redundant data cleanup and correction, it synchronously receives the update instructions transmitted by the module, deletes redundant data, replaces the corrected data, and simultaneously updates the corresponding index information in the index database, recording data update records (including update time, update content, operator, and traceability hash value) to ensure data update traceability; Storage Status Monitoring: Monitor the storage space occupancy of each storage partition in real time. When the storage space occupancy rate of a partition reaches 80%, an early warning is automatically sent to the system administrator, reminding the administrator to expand capacity or clean up expired data; Data Integrity Maintenance: Perform integrity verification on stored data regularly (once a week), comparing archived data with index information. If data loss or index anomalies are found, the data recovery mechanism is automatically triggered to restore the corresponding content from backup data, preventing data loss.

[0038] f. Data Backup and Recovery: To further ensure data security and prevent data loss due to hardware failures or system anomalies, a dual backup mechanism is established, strictly adhering to the long-term storage needs of hospital infection control data: Local backup: Incremental backups are performed on all storage partitions every morning (avoiding peak business hours), and a full backup is performed every Sunday morning. Backup data is stored on a local independent backup server, using the same encryption method and hierarchical structure as the primary storage to ensure the security of backup data; Off-site backup: Full backup data is transferred to an off-site backup center every month to achieve off-site disaster recovery and prevent complete data loss due to local storage device failure; Data recovery: When primary storage data is lost or damaged, it can be quickly recovered from backup data. During the recovery process, data integrity and consistency are verified, and index information is updated after recovery to ensure that the recovered data can be retrieved and accessed normally.

[0039] g. Expired Data Management: In accordance with relevant standards for hospital infection control data storage, a data storage period is set (5 years for routine hospital infection data storage and 10 years for data storage related to key infection outbreaks). Expired data is cleaned up regularly (at the end of each year): Expired data is screened to distinguish between routine and key data. Key data can apply for an extension of its storage period as needed. Before cleaning up expired data, a final backup is performed, and the cleanup record is recorded (including the indicator type, quantity, cleanup time, and operator of the cleaned data), and traceability markers are associated to ensure the cleanup process is traceable. After cleanup, the storage partition space and index database are updated to release storage space and ensure the operating efficiency of the storage system. The data retrieval unit is used for users to retrieve archived data. The multi-category key indicator dynamic matching archiving module and the archived data traceability verification and redundancy purification module work together to solve the problems of disorganized multi-category data, disconnection from prevention and control indicators, difficulty in traceability, and high redundancy in existing hospital infection control data archiving. The data retrieval unit is used by users to retrieve archived data from the data storage unit; The specific implementation logic of the data retrieval unit is briefly explained here to aid understanding, as follows: I. Access Control and Login Verification: The data access unit adopts a hierarchical access control mechanism to adapt to the access needs of personnel in different positions within hospital infection control, ensuring data security and preventing unauthorized access. The first step involves users (infection control administrators, clinical infection control specialists, inspectors, etc.) logging into the system via a terminal, entering their account, password, and position access identifier. The system automatically verifies access information: Infection control administrators have the highest access privileges, able to access archived data, traceability records, and access logs for all six categories of indicators; clinical infection control specialists can only access indicator data and corresponding traceability records relevant to their own department and cannot access data across departments; inspectors can access indicator data and traceability records within a specified inspection scope, and can only export data for inspection work, unable to modify or delete any data. After successful access verification, the user enters the access operation interface; if verification fails, the system prompts insufficient permissions and records the login failure information, ensuring the compliance of data access.

[0040] II. Multi-dimensional Search Parameter Settings: The interface provides a clear multi-dimensional search entry point. All search dimensions correspond to the hierarchical structure, archive index, and indicator code library of the data storage unit mentioned above. Users can flexibly choose single-dimensional search or multi-dimensional combination search according to their actual needs. Specific search parameter settings are as follows: Indicator Dimension: Key indicator categories can be selected (six categories including basic prevention and control, infection occurrence, etc.), or specific sub-indicators can be further selected (such as "staff hand hygiene compliance rate" under the basic prevention and control category). After selection, the system automatically matches the corresponding indicator code and locates the corresponding storage partition; Time Dimension: Specific collection time range can be set (accurate to year, month, day, hour, and minute). Users can also select preset time intervals (such as the last 7 days, the last 30 days, the current month, and the current year) to suit the time requirements of data statistics; Data source dimensions: Users can select specific data sources (such as HIS systems, disinfection equipment terminals, hand hygiene monitoring equipment, etc.) or data source types (automatic collection / manual entry) to facilitate accurate location of data collected from specific terminals; Auxiliary retrieval: Supports fuzzy search, allowing users to input core data fields (such as "disinfection qualified" and "pneumonia infection"), archive index keywords, or traceability hash values ​​to quickly match relevant data. It also provides "data validity" filtering options (show only qualified data / show all data) to meet different search needs.

[0041] III. Retrieval Request Processing and Data Location: After the user completes the retrieval parameter settings and submits the retrieval request, the data retrieval unit connects to the index database and hierarchical storage partitions of the data storage unit to complete the rapid data location: The system first matches the corresponding indicator code according to the indicator dimension parameters to locate the corresponding primary storage partition and secondary sub-partition; then, combined with the time dimension and data source dimension parameters, it compares the collection time and data source information in the archived index to filter out archived data that meets the conditions; if the user sets auxiliary retrieval parameters, it further compares the core data fields and traceability hash values ​​to accurately filter the target data. The entire process is fast and efficient, ensuring the accuracy and timeliness of the retrieval results, and adapting to the needs of rapid data retrieval for hospital infection control.

[0042] IV. Search Results Display and Detailed Viewing: After the search is completed, the system displays the search results in a list format. The displayed content strictly corresponds to the archived data and traceability markers mentioned above. The core elements include: data subject (such as disinfection operation records, infection diagnosis information), archive index (indicator code, primary / secondary code, collection time, archive time), data source (collection device number, data source type), traceability markers (traceability hash value, collection personnel, transmission path), and data status (verification qualified / suspicious / invalid). Users can click on a single data entry to view detailed information: complete data content, including all core fields and supplementary fields; complete traceability chain, clicking on the traceability hash value allows viewing the entire process record of data collection, transmission, matching, archiving, and purification, including collection device operation logs, collection personnel operation records, and redundant purification records (if any); data correlation information, such as related data under the same indicator and other collected data from the same data source, facilitating multi-dimensional data analysis by users.

[0043] V. Data and Traceability Record Export: For the retrieved target data, users can choose to export the data body, traceability records, or both, depending on their work needs. Export formats support Excel and PDF, commonly used in hospital infection control work, adapting to needs such as data statistics and supervision report writing: Excel format: Used for data statistical analysis. The table is organized in the order of "indicator code - data body - collection time - data source - traceability hash value," retaining all core fields for easy editing and calculation. PDF format: Used for supervision archiving and emergency traceability. It completely retains detailed data information and the traceability chain, is non-editable, and ensures data authenticity. During the export process, the system automatically records export information (exporting user, export time, exported data range, export format), associates it with traceability tags to ensure traceability of the export behavior, and performs simple encryption on the exported data to protect data privacy and security.

[0044] VI. Retention of Call Records and Handling of Anomalies: The data retrieval unit automatically retains all call operation records, including the calling user, call time, search parameters, number of search results, export status (if any), and call duration. The retention period is consistent with the data storage period (5 years for routine data and 10 years for key data), facilitating infection control administrators to verify call behavior and investigate unauthorized calls. A simple anomaly handling mechanism is also established: if no search results are found, the system prompts "No data matching the criteria found" and provides optimization suggestions (such as adjusting the search time range or modifying indicator dimensions); if export fails, it prompts "Insufficient export permissions" or "Data is being updated," guiding the user to check permissions or try again later; if a system anomaly occurs during the search process, the anomaly information is automatically recorded and sent to the administrator terminal, ensuring the normal operation of the call function and not affecting daily infection control work.

[0045] VII. Multi-Scenario Adaptation and Optimization: The system optimizes and enhances usability by adapting to different hospital infection control scenarios: Supervision Scenario: Supports batch retrieval of data for multiple indicators, automatically generating simple statistical tables after export (e.g., a summary of hand hygiene compliance rates in various departments within a specific time period); Emergency Tracing Scenario: Supports quick retrieval of the entire traceability record of a single data point using traceability hash values, and one-click export of traceability reports to meet the traceability needs of sudden infection events; Data Statistics Scenario: Supports automatic data aggregation by indicator and time dimensions (e.g., monthly disinfection compliance rate, quarterly infection incidence rate), reducing manual statistical workload and aligning with the daily needs of hospital infection control.

[0046] This invention discloses a hospital infection control management data archiving system based on multiple categories of key indicators, belonging to the field of hospital infection control data management technology. It aims to solve the problems existing in hospital infection control data archiving, such as disorganized multi-category data, disconnect between archived data and key infection control indicators, difficulty in data traceability, high redundancy, and inability to adapt to dynamic adjustments in hospital infection control. The system includes a data access unit, a multi-category key indicator dynamic matching and archiving module, an archived data traceability verification and redundancy cleanup module, a data storage unit, and a data retrieval unit. The multi-category key indicator dynamic matching and archiving module and the archived data traceability verification and redundancy cleanup module work collaboratively. The multi-category key indicator dynamic matching and archiving module achieves accurate binding and hierarchical archiving of multi-dimensional hospital infection control data with key prevention and control indicators. The archived data traceability verification and redundancy cleanup module achieves authenticity verification of archived data, full-process traceability, and accurate cleanup of redundant data, solving the core pain points of poor usability, difficulty in traceability, and redundancy in existing technologies.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A hospital infection control management data archiving system based on multiple categories of key indicators, characterized in that, It includes a data access unit, a multi-category key indicator dynamic matching and archiving module, an archived data traceability verification and redundancy purification module, a data storage unit, and a data retrieval unit; the data access unit is used to collect multi-category raw data from the entire process of hospital infection control; The multi-category key indicator dynamic matching and archiving module is used to extract multi-category key indicators for hospital infection control from the raw data collected by the data access unit, establish dynamic mapping rules between indicators and data, and perform category identification and indicator matching on the raw data collected by the data access unit to achieve hierarchical archiving. The archived data traceability verification and redundancy cleanup module is used to verify the authenticity of the data after it is archived by the dynamic matching archiving module for multiple categories of key indicators, mark the traceability of the entire process, and identify and clean up redundant data to ensure the accuracy and simplicity of the archived data. The data storage unit is used to store archived data after it has been processed by the archived data traceability verification and redundancy cleanup module. The data retrieval unit is used by users to retrieve archived data from the data storage unit.

2. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 1, characterized in that: The data access unit is used to collect multiple types of raw data throughout the entire process of hospital infection control. It adopts a multi-interface adaptation design and connects to various data sources, including but not limited to hospital HIS system, LIS system, disinfection equipment terminal, and hand hygiene monitoring equipment. It supports both real-time and batch collection modes. The collected data includes, but is not limited to, patient basic information, infection diagnosis information, disinfection operation records, hand hygiene compliance data, surgical infection data, antimicrobial drug use data, and environmental microbial monitoring data.

3. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 1, characterized in that: The specific implementation steps of the multi-category key indicator dynamic matching and archiving module are as follows: Step A1, Indicator Extraction and Standardization: First, extract the six major categories of key indicators for hospital infection control and their corresponding sub-indicators, and clarify the definition, data format requirements, collection frequency and corresponding prevention and control scenarios for each sub-indicator; All indicators are standardized and coded to form an indicator coding library. The coding rule is "major category code - subcategory code - indicator serial number". Step A2, Establishing Mapping Rules: Combining hospital infection control guidelines and clinical practice experience, establish dynamic mapping rules between indicators and data, clarify the key indicators and matching thresholds corresponding to each type of raw data, and dynamically iterate the mapping rules according to indicator updates and adjustments to prevention and control needs. The rules are stored in the mapping rule library, including four core elements: data type, data field, matching indicator code, and matching conditions. Step A3, Raw Data Preprocessing: Receive the raw data collected by the data access unit, standardize the data format, remove data with incorrect format, supplement missing key fields, and form standardized raw data to ensure the accuracy of subsequent data matching; Step A4, Dynamic Matching and Hierarchical Archiving: Using an indicator code matching algorithm, the fields in the standardized raw data are compared with the data fields in the mapping rule base. Combined with the matching threshold, the key indicator code corresponding to each piece of raw data is determined. Then, according to the indicator category and subcategory, the data is archived to the corresponding storage partition, and an archive index is generated. The index contains four core pieces of information: indicator code, data source, collection time, and archiving time. The hierarchically archived data and index are synchronously transmitted to the data storage unit and the archived data traceability verification and redundancy purification module to complete the initial archiving.

4. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 3, characterized in that: The six major categories of indicators and core sub-indicators in step A1 are as follows: Basic prevention and control indicators include staff hand hygiene compliance rate, medical device disinfection and sterilization qualification rate, and medical waste classification and collection rate. Infection incidence indicators include the incidence rate of hospital-acquired infections per thousand days, the incidence rate of neonatal hospital-acquired infections per thousand days, and the incidence rate of specific multidrug-resistant bacterial infections per thousand days. High-risk device-related infection indicators include the incidence of catheter-associated urinary tract infections, ventilator-associated pneumonia, and vascular catheter-related bloodstream infections. Surgical-related infection indicators include: infection rate of Class I surgical sites and antibiotic prophylaxis rate for Class I surgical sites; Indicators for the rational use of antimicrobial drugs include the rate of etiological testing before hospitalized patients are treated with a combination of key antimicrobial drugs and the rate of rationality of the course of antimicrobial drug treatment. Infection control management and training indicators include the ratio of dedicated infection control personnel to beds, annual infection prevention and control training coverage rate, and awareness rate of emergency response plans for infection outbreaks.

5. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 3, characterized in that: The specific implementation steps for establishing the mapping rules in step A2 are as follows: Step A2.1: Preliminary matching of data fields and indicators: Extract all data fields from the standardized raw data, make preliminary associations with each sub-indicator in the indicator coding library, filter out candidate data fields related to the indicator, remove completely irrelevant fields, and form an "indicator-candidate field" correspondence table; Step A2.2, Quantitative Calculation of Association: The association degree between candidate data fields and corresponding sub-indicators is quantified using the association degree calculation formula, which is as follows: ,in : No. Sub-indicators and the first The correlation of each candidate data field, with a value range of [value range missing]. , The closer it is to 1, the higher the correlation between the data field and the sub-indicator, and the more suitable it is as a matching field for the indicator. : No. Sub-indicators and the first The semantic matching degree of each candidate data field is calculated by the system using a pre-set semantic dictionary based on hospital infection control guidelines, based on the similarity between the field's semantics and the indicator definition. The value range is [value range missing]. ; : No. Sub-indicators and the first The practical relevance of each candidate data field is determined based on its contribution to indicator statistics and prevention and control analysis in clinical practice, with a value range of [value range missing]. ; The semantic matching degree weight coefficient has a value of 0.6, which prioritizes ensuring the semantic consistency between the field and the indicator and avoids mismatches of irrelevant fields. The practical relevance weighting coefficient is set to 0.4 to balance clinical practice needs and ensure that the matched fields can support the practical application of the indicators and meet the requirements. ; Step A2.3, Determining the correlation threshold: Set the correlation threshold. ,when When, the candidate data field is determined to be the first... Matching fields for sub-indicators; when When this happens, the candidate field is removed to ensure the validity of the matching field; Step A2.4, Matching Threshold Setting: For each "indicator-matching field" pair, set a matching threshold in accordance with the hospital infection control guidelines. The matching threshold is the critical value that the field supports for indicator statistics and prevention and control analysis. Step A2.5, Rule Generation and Storage: Integrate the matching fields, correlation degree, and matching threshold corresponding to each sub-indicator to generate complete indicator-data mapping rules. Each rule contains four core elements: data type, data field, matching indicator code, and matching condition. All rules are stored in the mapping rule library, and an association index between rules and indicator codes is established to facilitate quick subsequent calls and updates. Step A2.6, Dynamic Iterative Optimization: Establish a rule iteration mechanism to recalculate the correlation degree when infection control indicators are updated or clinical practice needs change. Adjust matching fields, matching thresholds, and weighting coefficients. , This enables dynamic iteration of mapping rules, ensuring that the rules always adapt to the actual needs of hospital infection control.

6. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 5, characterized in that: In step A2.2 The specific content is calculated by the system using a pre-set semantic dictionary based on the hospital infection control guidelines, and by comparing the similarity between the semantics of the fields and the definitions of the indicators. S1. Construction of a pre-defined semantic dictionary for hospital infection control: First, based on the National Health Commission's "Medical Quality Control Indicators for Hospital Infection Management (2024 Edition)" and clinical hospital infection control practice specifications, a dedicated semantic dictionary for hospital infection control is constructed as the basis for semantic matching degree calculation; The semantic dictionary for hospital infection control includes three core modules: Indicator core terminology library: It includes the core terms, synonyms, and related terms of six major categories of key indicators and sub-indicators; Data field thesaurus: Includes core words and synonyms for all collected raw data fields related to hospital-acquired infections; Weighted thesaurus: Based on the importance of infection control in hospitals, semantic weights are assigned to each term in the dictionary. (range of values) The core terms are weighted at 0.8-1.0, and the auxiliary terms are weighted at 0.1-0.3 to ensure that the core semantics are matched first. S2, Semantic word segmentation and noise reduction: respectively for the first... The definition text of the sub-indicator, the first Semantic word segmentation is performed on the name / description text of each candidate data field to remove stop words with no actual semantic meaning and extract effective semantic words; S3. Preliminary Semantic Similarity Calculation: An improved cosine similarity algorithm is used to calculate the preliminary semantic similarity between the effective vocabulary set of the sub-indicators and the effective vocabulary set of the candidate data fields. The formula is as follows: The parameters are explained in conjunction with the content of this invention as follows: Preliminary semantic similarity between sub-indicators and candidate data fields, with a range of values. ; The number of valid words shared between the sub-indicator and the candidate data field. If there are no shared words, At this point, the initial similarity is 0; Sub-indicator The semantic weight of each effective word in the hospital infection semantic dictionary; Candidate data field number The semantic weight of each effective word in the hospital infection semantic dictionary; : Sub-indicators and candidate data fields The semantic relevance of shared words; if they are the same word, ; If they are synonyms If it is a related term, ; If it is an irrelevant word The correlation threshold is preset by the hospital infection semantic dictionary and can be dynamically updated; : Total number of effective words extracted from sub-indicators : Total number of valid words extracted from the candidate data field; S4. Infection Control Scenario Correction: Based on practical scenarios of infection control in hospitals, the initial semantic similarity is corrected to obtain the final semantic matching degree. The corrected formula is: ,in This is a scene correction factor, with a value range of... .

7. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 1, characterized in that: The specific implementation steps of the archived data traceability verification and redundancy cleanup module are as follows: Step B1, Traceability Information Tagging: Receive hierarchical archived data and archived indexes transmitted by the multi-category key indicator dynamic matching and archiving module. Add a traceability tag to each piece of archived data. The traceability tag uses a simplified blockchain hash encoding method, and the encoding formula is as follows: ,in The traceability hash value is a unique traceability identifier for each archived data entry. It is 18 characters long and consists of a combination of numbers and letters. The underlying hash value is used to represent the data source; each data source corresponds to a unique hash value. ; For the core field values ​​of archived data, convert the text information of the core field into the corresponding numerical value; The weighting coefficients for the corresponding core fields are set according to the importance of the fields in hospital infection control, with values ​​ranging from 0.1 to 0.

9. The weighting coefficients for core fields are set to 0.8 to 0.9, and the weighting coefficients for auxiliary fields are set to 0.1 to 0.2, such as the weight of disinfection compliance. Weight of disinfection time ; The number of core fields for each archived data entry is determined based on the corresponding indicator type. Step B2, Authenticity Verification: Based on the traceability chain, perform dual authenticity verification on the archived data to ensure the data is authentic and valid, and to prevent the archiving of false data. The first layer of verification is the legality verification. By comparing the operation logs of the data acquisition equipment and the operation records of the acquisition personnel, the legality of the data acquisition is verified. If the acquisition time exceeds the equipment operation time or the acquisition personnel do not have the operation authority, the data is marked as suspicious. The second layer of verification: consistency verification, which verifies the consistency of data by comparing data from different sources under the same indicator; All suspicious data is sent to the infection control management personnel's terminal for manual review. If the review is successful, the verification status is updated to "qualified" and the data is retained in the archive. If the review fails, the data is marked as "invalid data", deleted from the archive, and the reason for deletion is recorded. The data is also linked to a traceability tag to ensure that the deletion is traceable. Step B3, Redundant Data Identification: Establish rules for judging redundant data, identify redundant data based on key indicator dimensions, and specifically identify two types of redundant data: Type 1: Completely duplicated data, meaning that the core fields, indicator codes, and collection times of the two data sets are completely identical; Type 2: Invalid and redundant data, that is, the data content does not exceed the normal range of the corresponding indicator but has no actual reference value for prevention and control; To accurately identify invalid and redundant data, a redundancy judgment formula is used. ,in This is a redundancy coefficient used to measure the degree of fluctuation in continuous data under the same indicator. The smaller the value, the smaller the data fluctuation and the higher the redundancy. For the first under the same indicator Item data, then ; The average data value under the same indicator, i.e. ; Set the number of consecutive data entries under the same indicator. (To avoid misjudging occasional fluctuations in a small amount of data as redundancy), when When this happens, it is determined to be invalid redundant data; Step B4, Redundant Data Cleaning and Update: For completely duplicated data, retain the most recently collected data and delete the rest of the duplicate data; for invalid redundant data, retain the first and last data entries and delete the middle duplicate invalid data. At the same time, record the deletion record of the redundant data and associate it with the traceability mark to ensure that the deletion is traceable; update the cleaned archived data to the data storage unit and update the archived index synchronously to complete the final processing of data archiving.

8. The hospital infection control management data archiving system based on multiple categories of key indicators according to claim 1, characterized in that: The data storage unit adopts a hierarchical storage structure, corresponding to hierarchical archived data of multiple categories of key indicators. Each major category of indicators corresponds to an independent storage partition, and the data is stored in an orderly manner according to the collection time within the partition. It supports encrypted storage of data to ensure data privacy and security.

9. A hospital infection control management data archiving system based on multiple categories of key indicators according to claim 1, characterized in that: The data retrieval unit supports multi-dimensional retrieval, including but not limited to key indicator categories, sub-indicators, collection time, and data source, and can export archived data and traceability records.

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