Orthopedic anesthesia quality control data sharing and intelligent analysis system fused with cloud computing

Abstract

The application relates to the technical field of anesthesia quality control, in particular to an orthopedic anesthesia quality control data sharing and intelligent analysis system fusing cloud computing, which comprises a field fingerprint collection module, a structure difference identification module, a device information binding module, a field caliber correction module and a data output packaging module. In the application, the mapping relationship between the fields and standard fields is established by comprehensively analyzing the structural attributes of orthopedic anesthesia record fields, the structural consistency index is quantified, the standard matching accuracy is improved, the sampling frequency parameter participates in the field granularity conflict identification, the unit conversion and granularity comparison are performed in combination with the device model and protocol information, the dynamic correction and version record of the field attributes are completed, the field output link is checked and adjusted according to the correction record, the unit specification and time granularity are unified, the whole-process field standardization control is formed from structure identification to result output, and the quality consistency and adaptability of the shared data body are enhanced.

Description

Technical Field

[0001] This invention relates to the field of anesthesia quality control technology, and in particular to an orthopedic anesthesia quality control data sharing and intelligent analysis system that integrates cloud computing. Background Technology

[0002] The field of anesthesia quality control technology mainly involves the quality control and management of perioperative anesthesia-related data. Core aspects include the collection of anesthesia case information, recording of the anesthesia process, retention of vital signs and medication data, registration of adverse events, and statistical reporting of quality control indicators. This involves connecting the anesthesia information management system with the electronic medical record system, testing system, and monitoring equipment interface to store data in a structured manner according to unified field rules, and conducting summary review and traceability management based on the quality control indicator criteria. Among these, the traditional orthopedic anesthesia quality control data sharing and intelligent analysis system refers to a system designed for orthopedic surgical anesthesia scenarios, which aggregates and shares anesthesia quality control data within the hospital or across institutions and conducts indicator analysis. It addresses the consistency issues of data aggregation and sharing from multiple sources and indicator statistics. Traditionally, this method typically involves exporting case data from the anesthesia information management system, combining it with the results of the electronic medical record surgical anesthesia record sheet and complication registration form, classifying and statistically analyzing it according to the quality control indicator list, and then sharing it through an intranet database or uploading it to a cloud server for centralized storage before rule verification and trend statistics.

[0003] Current anesthesia quality control data processing primarily relies on information management systems for centralized export and static configuration management of multi-source data. Field structure rules are manually set or maintained using fixed templates. When faced with field differences arising from different systems or equipment, these systems lack dynamic identification and adaptation capabilities, easily leading to problems such as inconsistent coding, mixed units, and inconsistent recording granularity. During multi-source field aggregation, differences at the field attribute level are difficult to effectively identify, and unclear field mapping relationships can easily cause statistical caliber shifts, affecting the accuracy of quality control indicators. There is a lack of a linkage verification mechanism between the sampling frequency of monitoring equipment and the field recording granularity. Equipment parameters are not effectively involved in field consistency management, causing granularity conflicts to be ignored during the aggregation stage, resulting in potential data bias. Data verification before sharing mainly focuses on format integrity or missing data, lacking management methods for field structure consistency and configuration changes. Problems of inconsistent field units or time scales are amplified during cross-system sharing, reducing data comparability and analytical reliability. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide an orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing. The technical solution is as follows:

[0005] On the one hand, it provides a cloud-integrated orthopedic anesthesia quality control data sharing and intelligent analysis system, which includes: The field fingerprint acquisition module performs structured analysis on the coding type, data type, value unit, record granularity and enumeration value of the orthopedic anesthesia record field, constructs field fingerprint entries, and generates structural information and constructs field fingerprint upload unit by combining the monitoring device serial number and sampling frequency parameters. The structural difference identification module calls the structural information in the field fingerprint upload unit, compares the field code, type, unit, time granularity and enumeration set with the standard field dictionary, records the differences, establishes the mapping relationship, generates consistency index and constructs the field structure mapping map; The device information binding module identifies fields with different time granularities in the field structure mapping map, extracts the case sampling frequency parameters, converts and compares the field record granularity, identifies time granularity conflicting fields, and generates a device granularity conflict list by combining device model, interface protocol and driver service information. The field caliber correction module locates the field configuration table based on the equipment granularity conflict list, extracts the field standard type, unit and time granularity, compares it with the actual field attributes, and if there are differences, updates the field configuration content, records the parameters, operation time and configuration version, and generates a field configuration correction record. The data output encapsulation module uses the field configuration to correct records, verify field constraints, adjust timestamps of fields with mismatched time granularity, integrate compliant fields and equipment identifiers, and construct a standard quality control shared data body.

[0006] As a further embodiment of the present invention, the field fingerprint upload unit includes a field fingerprint entry set, a structural information encapsulation package, a case monitoring device serial number identifier, and a sampling frequency parameter set; the field structure mapping map includes a field attribute difference record set, a field mapping relationship table, a field structure consistency index set, and a field structure matching path set; the device granularity conflict list includes a time granularity difference field list, a unit-converted sampling frequency value set, a granularity conflict judgment result set, and a monitoring device model interface driver identifier set; the field configuration correction record includes configuration item update results, a field correction parameter set, a configuration change operation timestamp, and a field configuration version number; and the standard quality control shared data body includes a structurally compliant field dataset, a timestamp formatted field set, a field constraint verification pass identifier set, and a device identifier association information set.

[0007] As a further embodiment of the present invention, the standard field dictionary is a preset field set containing field definitions derived from medical information standards, including HL7, FHIR, ICD encoding set, LOINC, hospital-defined field specifications, or unified standards of the National Health Commission.

[0008] As a further aspect of the present invention, the structural difference identification includes the following comparison methods: fuzzy matching based on field name similarity, field encoding rule matching, field unit conversion rule comparison, data type compatibility verification, and enumeration set intersection analysis to construct field mapping relationships.

[0009] As a further aspect of the present invention, the field fingerprint acquisition module includes: The field attribute extraction submodule collects field attributes from the orthopedic anesthesia record, extracts field encoding type, data type, value unit, record granularity and enumeration value range, judges the enumeration value range and data type constraints, generates field attribute structure record, and obtains field attribute structure parameter set; The fingerprint entry construction submodule calls the field attribute structure parameter set, matches the encoding type and data type combination relationship according to the field structure composition rules, verifies the correspondence between the value unit and the record granularity, organizes the expression form of the enumerated value range, establishes the field fingerprint entry set, and generates the field fingerprint entry sequence. The protocol encapsulation generation submodule collects the serial number of the monitoring device corresponding to the case and the sampling frequency parameters according to the field fingerprint entry sequence, writes the structure header information and identifier bits according to the standard data packaging protocol, frames and encapsulates the entry sequence and device parameters, and generates the field fingerprint upload unit.

[0010] As a further aspect of the present invention, the structural difference identification module includes: The field structure difference comparison submodule, based on the structural information in the field fingerprint upload unit, calls the encoding, data type, value unit, time granularity and enumeration set of the corresponding field in the standard field dictionary, compares the uploaded field attributes with the standard fields item by item, records the attribute field differences, and generates a field attribute difference set. The structure mapping generation submodule calls the field attribute difference set, matches standard fields according to the difference rules of field encoding and time granularity, calculates the field structure correspondence value, marks the field mapping priority, establishes a field mapping set according to the field association mapping logic, and obtains the field structure mapping matrix; The consistency calculation output submodule calculates the structural matching degree value based on the structural correspondence of the fields in the field structure mapping matrix, allocates mapping weight values ​​according to the field coverage and matching accuracy, integrates the structural comparison coefficients of all fields, and generates a field structure mapping map.

[0011] As a further aspect of the present invention, the device information binding module includes: The time granularity identification submodule extracts fields with time granularity differences based on the field structure mapping map, collects the sampling frequency parameters bound to the corresponding cases, performs matching indexing in combination with the field record granularity, marks the field numbers with inconsistent granularity, and generates a granularity difference field index table. The unit conversion and comparison submodule calls the granularity difference field index table, converts the sampling frequency parameter into sampling interval time and unifies the unit to milliseconds, calculates the interval difference based on the conversion result and the granularity of the field record, determines whether the difference exceeds the set granularity difference threshold range, and obtains the granularity conflict judgment result set. The conflict information generation submodule extracts the monitoring device model, interface protocol and driver service name bound to the associated fields based on the granular conflict judgment result set, generates a binding relationship mapping table by combining the field number and device identifier, and compiles and generates a device granular conflict list.

[0012] As a further aspect of the present invention, the field caliber correction module includes: The configuration parameter extraction submodule locates the local field configuration table based on the field information in the device granularity conflict list, extracts the standard type, record unit and time granularity corresponding to the field to be corrected, establishes the mapping relationship between the field and the standard attribute, and generates a field standard parameter mapping table. The field attribute comparison submodule calls the field standard parameter mapping table to perform field-level comparison of the actual attribute value of each field with the standard type, unit and time granularity, determines the set of fields where the field configuration items are different, obtains the field difference identifier sequence, and obtains the field attribute difference identifier set. The configuration correction record submodule updates the standard type, unit and time granularity configuration items of the corresponding field according to the field attribute difference identifier set, records the field number, parameters before correction, parameters after correction, configuration version number and operation time, and generates a field configuration correction record.

[0013] As a further aspect of the present invention, the data output encapsulation module includes: The field constraint verification submodule performs a consistency verification operation on the current unit attribute of the field based on the correction result in the field configuration correction record, marks the inconsistent units of the fields according to the standard unit rules of the configured fields, constructs an available field index set, and obtains a field unit verification result set. The time granularity adjustment submodule calls the field unit verification result set, extracts the set of fields with inconsistent time granularity attributes, adjusts the timestamp format to the standard millisecond format, records the adjusted time field information, establishes a unified time field dataset, and obtains a standardized time field sequence. The structural data integration submodule integrates the attribute values ​​of the fields that meet the structural specifications and the corresponding equipment identification information according to the standardized time field sequence, reconstructs the data structure format according to the field order and equipment mapping relationship, assembles the field data block content, and generates a standard quality control shared data body.

[0014] As a further aspect of the present invention, the field configuration correction record includes a field identifier, original attribute value, corrected attribute value, correction basis, operation time, operation user identifier, and configuration version number, which is used to track the field configuration change process.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by comprehensively analyzing the structural attributes of orthopedic anesthesia record fields, a mapping relationship between the fields and standard fields is established, structural consistency indicators are quantified, and the accuracy of standard matching is improved. Sampling frequency parameters are used to identify field granularity conflicts. Combined with the conversion and granularity comparison of equipment model and protocol information, dynamic correction and version recording of field attributes are completed. The field output stage is verified and adjusted based on the correction records to ensure the uniformity of unit specifications and time granularity. From structural identification to result output, a full-process field standardization control is formed, enhancing the quality consistency and adaptability of the shared data body. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a system block diagram of the present invention; Figure 3 This is a flowchart of the field fingerprint acquisition module in this invention; Figure 4 This is a flowchart of the structural difference identification module in this invention; Figure 5 This is a flowchart of the device information binding module in this invention; Figure 6 This is a flowchart of the field caliber correction module in this invention; Figure 7 This is a flowchart of the data output encapsulation module in this invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] This invention provides an orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing, such as... Figure 1-2 The diagram shown illustrates a cloud-integrated orthopedic anesthesia quality control data sharing and intelligent analysis system. The system includes: The field fingerprint upload unit uploads data to the cloud server via the network; the structural difference identification module, field caliber correction module, and data output encapsulation module are deployed on cloud computing nodes and compare the data with the cloud-stored standard field dictionary; the standard quality control shared data body is encapsulated, written to cloud storage, and a shared access interface is provided. The field fingerprint acquisition module performs structured analysis on the field attributes in the orthopedic anesthesia record, including the field's encoding type, data type, value unit, record granularity, and enumeration value range. Based on the field structure composition rules, it constructs field fingerprint entries, combines the serial number of the monitoring equipment corresponding to the case with the sampling frequency parameters, encapsulates the structural information according to the standard data packaging protocol, and generates a field fingerprint upload unit. The structural difference identification module calls the structural information in the field fingerprint upload unit, compares the field's encoding, data type, value unit, time granularity and enumeration set based on the standard field dictionary, records the differences between the field attributes and the standard fields, establishes the field mapping relationship, and generates the field structure consistency index according to the degree of structural matching, and constructs the field structure mapping map. The device information binding module identifies fields with time granularity differences in the field structure mapping graph, extracts the sampling frequency parameters associated with the corresponding cases, performs unit conversion and compares them with the field record granularity to determine whether there are conflicts between granularities, and generates a device granularity conflict list by combining the model, interface protocol and driver service name of the monitoring device involved. The field caliber correction module locates the local field configuration table based on the field information in the equipment granularity conflict list, extracts the standard type, unit and time granularity of the field to be corrected, compares the difference with the actual attributes of the field, and if there is an inconsistency, updates the corresponding configuration item, records the field correction parameters, operation time and configuration version, and generates a field configuration correction record. The data output encapsulation module corrects the results in the record based on the field configuration, performs field constraint validation, marks fields with inconsistent units, adjusts the timestamp format of fields with mismatched time granularity, integrates fields that meet the structural specifications and their associated equipment identification information, and constructs a standard quality control shared data body.

[0021] The field fingerprint upload unit includes a field fingerprint entry set, a structural information encapsulation package, a case monitoring device serial number identifier, and a sampling frequency parameter set. The field structure mapping map includes a field attribute difference record set, a field mapping relationship table, a field structure consistency index set, and a field structure matching path set. The device granularity conflict list includes a time granularity difference field list, a unit-converted sampling frequency value set, a granularity conflict judgment result set, and a monitoring device model interface driver identifier set. The field configuration correction record includes configuration item update results, a field correction parameter set, a configuration change operation timestamp, and a field configuration version number. The standard quality control shared data body includes a structurally compliant field dataset, a timestamp formatted field set, a field constraint verification pass identifier set, and a device identifier association information set.

[0022] Specifically, such as Figure 2 , 3 As shown, the field fingerprint acquisition module includes: The field attribute extraction submodule collects field attributes from the orthopedic anesthesia record, extracts field encoding type, data type, value unit, record granularity and enumeration value range, judges the enumeration value range and data type constraints, generates field attribute structure record, and obtains field attribute structure parameter set; When collecting field attributes from orthopedic anesthesia records, the latest version of the anesthesia record for the target case is first retrieved from the hospital's medical record quality control shared database. The file is then subjected to structured parsing to extract a field list, which is sorted in ascending order by field number and processed line by line. For each field, the internal identifier and display name are read first, and the field encoding type is determined according to the pre-defined encoding rules in the field configuration table. The encoding type is limited to one of the following: numeric encoding, character encoding, date / time encoding, or enumeration encoding. The field number and encoding type are then written into the field's basic information record. Subsequently, the field storage column type and input control type are read and executed. For consistency checks, if the control type is numeric input but the storage column type is character, then the field is written to the data type conflict record and the next item is processed. During data type extraction, numeric fields are further read for allowed decimal places and maximum / minimum value constraints; character fields are read for maximum length constraints; date / time fields are read for timestamp format constraints; and enumeration fields are read for enumeration set configuration positions. During unit extraction, unit text is extracted from the unit column of the field configuration table and the unit annotation area on the record page. Space removal, full-width / half-width character unification, and synonym merging are performed on the unit text in both locations. If the merging result... In case of inconsistencies, the field configuration table takes precedence, and the page annotation is written into the unit difference flag. During the record granularity extraction process, it is checked whether the field carries a sampling time column, whether it is a continuously monitored waveform field, or whether it is a single manual entry field. The granularity flag is limited to one of millisecond granularity, second granularity, minute granularity, hourly granularity, or manual single entry granularity. During the enumeration value range extraction process, the enumeration set is read from the standard field dictionary cache for the enumeration encoded field. If it is not found, the enumeration list in the local configuration table is checked back. The enumeration display text and the stored value are checked one-to-one. If duplicate or empty stored values ​​are found, an enumeration exception flag is written. When judging the range of enumerated values ​​and data type constraints, the most recent 300 valid records are extracted from the case monitoring data entry records. Null values ​​and extreme values ​​exceeding 3 times the interquartile range are removed. For numerical fields, the number of records exceeding the maximum and minimum value constraints is checked and written into the out-of-bounds count. The statistical sample storage value set of enumerated fields is compared with the enumeration list item by item. If an unrecorded value is found, it is written into the enumeration out-of-bounds count. After completion, the field encoding type, data type, value unit, record granularity, enumerated value range summary, out-of-bounds count, and anomaly flag are written into the field attribute structure record to form the field attribute structure parameter set.

[0023] The fingerprint entry construction submodule calls the field attribute structure parameter set, matches the encoding type and data type combination relationship according to the field structure composition rules, verifies the correspondence between the value unit and the record granularity, organizes the expression form of the enumerated value range, establishes the field fingerprint entry set, and generates the field fingerprint entry sequence. After calling the field attribute structure parameter set, the field structure composition rule table is first loaded. The rule table records the combination relationship between encoding type and data type, the correspondence between value unit and record granularity, the expression form specification of enumerated value range, and the rules for writing exception flags. When performing combination matching for each field attribute structure record, the encoding type and data type are used as the joint key to search for matching items in the rule table. If a match is found, a combination identifier is written and the structure category of the field is recorded. If no match is found, a combination exception flag is written and the field is added to the list to be reviewed. When performing validation on the correspondence between value unit and record granularity, the record granularity is converted to a unified millisecond interval expression. The millisecond granularity is recorded as 1 millisecond, the second granularity as 1000 milliseconds, the minute granularity as 60000 milliseconds, the hour granularity as 3600000 milliseconds, and the manual single-time granularity as 0 milliseconds. The unit text is normalized. If the unit is a physiological quantity unit such as millimeter mercury, percentage, or times per minute, and the granularity is 1 millisecond and the field does not belong to the waveform type, a unit granularity conflict flag is written. The enumerated value range expression form is normalized. During processing, the enumeration set is sorted in ascending order by stored values. The displayed text is deduplicated and synonyms are merged. The mapping relationship before and after merging is written into the enumeration alias table, and an enumeration range summary string is generated. During entry generation, the field number, encoding type identifier, data type identifier, unit identifier, granularity identifier, enumeration range summary, out-of-bounds count, and exception flag set are written into the field fingerprint entry, forming a field fingerprint entry set in ascending order by field number. During sequence generation, the entry set is written into the entry sequence cache in a preset order, prioritizing core fields, followed by important fields, and then general fields. The core importance level is read from the anesthesia quality control indicator library and directly written into the entry sorting weight. The recording granularity of the heart rate field is 1000 milliseconds, with units of beats per minute. Combination rules match numerical encoding and integer types to generate entries. The recording granularity of the systolic blood pressure field is 1 millisecond, with units of millimeters of mercury. If the field type is identified as non-waveform, a unit granularity conflict flag is written. The entry is still written into the sequence and carries the conflict flag for subsequent encapsulation.

[0024] The protocol encapsulation generation submodule collects the serial number of the monitoring device corresponding to the case and the sampling frequency parameters according to the field fingerprint entry sequence, writes the structure header information and the identifier bit according to the standard data packaging protocol, frames and encapsulates the entry sequence and device parameters, and generates the field fingerprint upload unit. When encapsulating based on the field fingerprint entry sequence, the monitoring device serial number and device model identifier are first read from the case device binding table, and the sampling frequency parameters are statistically analyzed from the device communication log. The statistical window is limited to a continuous and stable connection interval from 5 minutes before the start of surgery to 5 minutes after the end of surgery. The median number of samples per second for each type of signal is taken as the sampling frequency and written into the device parameter set. Subsequently, the structure header information is written according to the standard data packaging protocol. The structure header information sequentially includes the case identifier, record version number, encapsulation timestamp, device serial number, device model identifier, number of sampling frequency parameters, and number of entries, and an identifier bit is written. The identifier bit is written as 0 or 1 depending on whether there is a combination anomaly, whether there is an enumeration out-of-bounds, whether there is a unit granularity conflict, and whether there is a data type conflict. During the frame encapsulation process, the structure header information segment, identifier bit segment, and entry segment are arranged according to the fixed... The data is sequentially concatenated and written to the output buffer. For each entry segment, the field number, type identifier, unit identifier, granularity identifier, enumeration digest length, and enumeration digest content are written. After writing, a byte-by-byte cumulative check is performed on the entire buffer, and the check result is written to the check segment. A field fingerprint upload unit is generated and written to the upload queue to await transmission. Sampling frequency statistics show that the sampling interval is 4 milliseconds for an ECG waveform of 250 Hz, 8 milliseconds for an arterial blood pressure waveform of 125 Hz, and 1000 milliseconds for a body temperature of 1 Hz. After writing these sampling frequency parameters into the structure header information, the number of entries is counted as 42, and the unit granularity conflict marker is 1. After writing, a byte-by-byte cumulative check is performed on the entire buffer, and the check result is written to the check segment. The length of the encapsulated field fingerprint upload unit is dynamically determined based on the number of entries and the enumeration digest length.

[0025] Specifically, such as Figure 2 , 4 As shown, the structural difference recognition module includes: The field structure difference comparison submodule, based on the structural information in the field fingerprint upload unit, calls the encoding, data type, value unit, time granularity and enumeration set of the corresponding field in the standard field dictionary, compares the uploaded field attributes with the standard fields item by item, records the attribute field differences, and generates a field attribute difference set. When performing comparison based on the structural information in the uploaded unit using field fingerprints, the structure header information and entry segment are parsed first, and a consistency check is performed on the verification segment. If the check is inconsistent, the uploaded unit is written to the invalid queue and the comparison is terminated. After the check passes, the entry segment is restored to the uploaded field attribute set. Each field in the set records the field number, encoding type, data type, value unit, time granularity, enumeration range summary, and anomaly flag. Then, the encoding, data type, value unit, time granularity, and enumeration set of the corresponding field are retrieved from the standard field dictionary. The index is retrieved first using the standard field number mapped by the field number. If no match is found, the field display name is used to perform a synonym search and return the candidate standard field set. During item-by-item comparison, a consistency check is first performed on the encoding type. If inconsistent, the encoding difference is recorded. Then, a consistency check is performed on the data type. If inconsistent, the difference is recorded. For type differences, the units of value are normalized and then compared. The normalization process performs synonym merging on unit text and splitting of composite units. If inconsistencies still exist, the unit difference is recorded. For time granularity comparison, the granularity is converted to millisecond intervals and numerical comparison is performed. If inconsistencies exist, the granularity difference is recorded. For enumeration set comparison, the uploaded enumeration range summary is expanded into an enumeration set and checked item by item against the standard enumeration set. Missing items are recorded as enumeration missing items, and new items are recorded as enumeration new items. All differences are summarized by field number to generate a field attribute difference set and written into the difference type tag sequence. If the uploaded field blood oxygen saturation unit is unitless while the standard unit is percentage, and inconsistencies still exist after normalization, a unit difference tag is written. If the uploaded field anesthesia mode enumeration set contains 0 general anesthesia and 1 spinal anesthesia, but the standard set additionally contains 2 combined anesthesia, then the enumeration missing item is recorded and written into the difference set.

[0026] The structure mapping generation submodule calls the field attribute difference set, matches standard fields according to the difference rules of field encoding and time granularity, calculates the field structure correspondence value, marks the field mapping priority, establishes a field mapping set according to the field association mapping logic, and obtains the field structure mapping matrix; After calling the field attribute difference set, standard fields are matched according to field coding and time granularity difference rules. First, a candidate standard field set is generated for each uploaded field. The candidate set is filtered by performing three types of retrieval actions: synonymous field name, identical coding prefix, and identical unit after normalization, and then merged and deduplicated. When the number of candidates exceeds 10, they are sorted according to the synonymous hit level and coding consistency priority, and the top 10 are retained. When calculating the field structure correspondence value, weight values ​​are assigned to coding matching, data type matching, unit matching, granularity matching, and enumeration matching. The weight values ​​are derived from the statistical results of manual mapping of 200 historical cases in the past 90 days: coding weight 0.30, data type weight 0.20, unit weight 0.20, granularity weight 0.20, and enumeration weight 0.10. The result of each matching item is converted to 0 or 1, multiplied by the corresponding weight, and summed to obtain the correspondence value. When marking field mapping priorities, fields with a corresponding degree value of 0.85 or higher are marked as high priority, those between 0.70 and 0.85 are marked as medium priority, and those below 0.70 are marked as low priority. When establishing a field mapping set based on the field association mapping logic, intra-group consistency constraints are applied to fields from the same device source. The constraint action limits the granularity level difference of fields within the group to no more than one level. If it exceeds this level, the low-priority field is written to the queue to be reviewed and does not enter the set. After completion, a field structure mapping matrix is ​​formed and the corresponding degree value and priority mark of each matrix element are written. If the uploaded field heart rate is consistent with the standard field heart rate encoding, type, unit, and granularity, the corresponding degree value is 1.00 and it is marked as high priority. If the uploaded field systolic blood pressure is consistent with the standard field systolic blood pressure encoding but the granularity is inconsistent, the corresponding degree value is 0.80 and it is marked as medium priority.

[0027] The consistency calculation output submodule calculates the structural matching degree value based on the structural correspondence of fields in the field structure mapping matrix, allocates mapping weight values ​​according to the field coverage and matching accuracy, integrates the structural comparison coefficients of all fields, and generates a field structure mapping map. When calculating the structure matching degree value based on the field structure mapping matrix, firstly, the standard field with the highest priority and largest corresponding degree value is selected as the temporary mapping result for each uploaded field, and the corresponding degree value is directly written into the structure corresponding degree of that field. When allocating mapping weight values ​​according to field coverage and matching accuracy, the field coverage is taken as the proportion of the number of temporarily mapped successful fields to the total number of uploaded fields, and the matching accuracy is taken as the average of all structure corresponding degrees. Both are weighted separately, with a weight of 0.50 for coverage and 0.50 for matching accuracy. The structure matching degree value is obtained by multiplying the coverage and matching accuracy by their respective weights and summing them. When integrating the structure comparison coefficients of all fields, the structure corresponding degree of each field is multiplied by the field's keyness level weight and summed. The keyness level weight is determined from the anesthesia... The quality control indicator library is read, with core fields having a weight of 1.00, important fields having a weight of 0.80, and general fields having a weight of 0.50. When establishing the structural relationship graph between fields, the field mapping relationship, the relationship with the same device, and the relationship with the same time granularity are written into the graph edge set, and the field number and standard field number are written into the node set with a difference type tag attached. The field structure mapping graph is generated and written into the output cache. A total of 42 fields are uploaded, and 39 are temporarily mapped successfully, resulting in a coverage range of 0.93. The average structural correspondence of the 39 fields is 0.88. Substituting the coverage range of 0.93 and the matching accuracy of 0.88 into the weighted logic, the structural matching degree value is 0.91. After comparing the structural matching degree value with the preset range of 0.90 to 1.00, it falls into the range that can enter the subsequent binding process.

[0028] Specifically, such as Figure 2 , 5 As shown, the device information binding module includes: The time granularity identification submodule extracts fields with time granularity differences based on the field structure mapping graph, collects the sampling frequency parameters bound to the corresponding cases, performs matching indexing in combination with the field record granularity, marks the field numbers with inconsistent granularity, and generates a granularity difference field index table. When extracting fields with temporal granularity differences based on the field structure mapping graph, the graph nodes are first filtered to identify the set of field nodes with granularity differences, and then grouped by device serial number. Fields within each group are then sorted in ascending order by field number. When collecting the sampling frequency parameters bound to the corresponding case, the device serial number is read from the structure header information, and the device communication log is checked back. The median number of samples per second for each type of signal is calculated for the stable connection interval within the surgical time window, forming a set of sampling frequency parameters. When performing matching indexing based on field record granularity, the field record granularity is converted into a millisecond interval value and compared with the sampling frequency. The converted sampling interval in milliseconds is written to the same index record. The index record contains the field number, device serial number, field record granularity interval in milliseconds, sampling interval in milliseconds, and granularity level difference flag. Field numbers with inconsistent granularity are marked and a granularity difference field index table is generated and written to the cache. For example, if the ECG waveform sampling frequency of 250 Hz is converted to a sampling interval of 4 milliseconds, and the field record granularity is 1000 milliseconds, then the granularity difference is marked as 1 and written to the index record. If the arterial blood pressure sampling frequency of 125 Hz is converted to a sampling interval of 8 milliseconds, and the field record granularity is 1 millisecond, then the granularity difference is marked as 1 and written to the index record.

[0029] The unit conversion and comparison submodule calls the granularity difference field index table, converts the sampling frequency parameter into sampling interval time and unifies the unit to milliseconds, calculates the interval difference based on the conversion result and the granularity of the field record, determines whether the difference exceeds the set granularity difference threshold range, and obtains the granularity conflict judgment result set. After calling the granularity difference field index table, the sampling frequency parameter is converted into a sampling interval time and the unit is unified to milliseconds. The conversion result is then reviewed, with the review rules limiting the sampling frequency to between 1 Hz and 1000 Hz. If the frequency exceeds this range, it is written to the frequency exception queue. When calculating the interval difference based on the conversion result and the field record granularity, the difference between the field record granularity interval in milliseconds and the sampling interval in milliseconds is calculated for each index record, and the absolute value is taken. The difference in milliseconds is then written to the index record. When determining whether the difference exceeds the set granularity difference threshold, the granularity difference threshold is set to 80 milliseconds. The data was based on a statistical experiment of the timestamp deviation of 30 monitoring devices entering the warehouse in 10 operating rooms for 3 consecutive days. The 99th percentile of the deviation was 80 milliseconds. The difference in milliseconds was compared with 80 milliseconds. A difference greater than 80 milliseconds was marked as a granularity conflict, and a difference less than or equal to 80 milliseconds was marked as alignable. A granularity conflict judgment result set was generated. The difference between the granularity of 1000 milliseconds and the sampling interval of 4 milliseconds was 996 milliseconds. If it was greater than 80 milliseconds, it was judged as a granularity conflict. The difference between the granularity of 1 milliseconds and the sampling interval of 8 milliseconds was 7 milliseconds. If it was less than or equal to 80 milliseconds, it was judged as alignable.

[0030] The conflict information generation submodule extracts the monitoring device model, interface protocol and driver service name bound to the associated fields based on the granular conflict judgment result set, and generates a binding relationship mapping table by combining the field number and device identifier, and compiles and generates a device granular conflict list. When extracting the monitoring device model, interface protocol, and driver service name bound to the associated fields based on the granularity conflict judgment result set, the device model and interface protocol are first retrieved from the device binding table for the field number determined to be in granularity conflict. Then, the driver service name is retrieved from the driver service registry using the device model and interface protocol and written into the field conflict record. When generating the binding relationship mapping table by combining the field number and device identifier, the field number, standard field number, device serial number, device model, interface protocol, driver service name, field record granularity interval in milliseconds, sampling interval in milliseconds, and difference in milliseconds are written into the mapping record and grouped by device serial number. When organizing the list of conflicting field device information, the number of conflicting fields for each device is counted and the conflicting items are sorted in descending order by difference in milliseconds, generating a device granularity conflict list and writing it into the output cache. Under device serial number 87340021, the field heart rate difference of 996 milliseconds and the field blood oxygen saturation difference of 992 milliseconds are marked as conflicting. The two conflicting records are summarized into the same device list and output after being sorted by the difference size.

[0031] Specifically, such as Figure 2 , 6 As shown, the field caliber correction module includes: The configuration parameter extraction submodule locates the local field configuration table based on the field information in the device granularity conflict list, extracts the standard type, record unit and time granularity corresponding to the field to be corrected, establishes the mapping relationship between the field and the standard attribute, and generates a field standard parameter mapping table. When locating the local field configuration table based on field information in the device granularity conflict list, the device model and interface protocol in the conflict list are read first. The configuration file path is then retrieved in the configuration index table using the device model and interface protocol as a composite key. If multiple paths are matched, the path with the highest version number is selected. When extracting the standard type, record unit, and time granularity corresponding to the field to be corrected, the standard field number of the field is read from the field structure mapping graph, and the standard type, standard unit, and standard time granularity are read from the standard field dictionary and written into the field standard parameter mapping record. When establishing the mapping relationship between fields and standard attributes, the field number, standard field number, standard type, standard unit, standard time granularity, and device serial number are written into the field standard parameter mapping table and cached. Field number 101 corresponds to standard field number 10101, with a standard unit of times per minute and a standard time granularity of milliseconds. This standard parameter is written into the mapping table and associated with device serial number 87340021.

[0032] The field attribute comparison submodule calls the field standard parameter mapping table to perform field-level comparison of the actual attribute value of each field with the standard type, unit and time granularity, determines the set of fields where there are differences in the field configuration items, obtains the field difference identifier sequence, and obtains the field attribute difference identifier set; After calling the field standard parameter mapping table, the actual type, actual unit, and actual time granularity of each field are read from the local field configuration table and compared with the standard type, standard unit, and standard time granularity in the mapping table at the field level. The comparison action performs a consistency check on the type marker; if inconsistent, a type difference identifier is written. The unit marker is normalized and then checked; if inconsistent, a unit difference identifier is written. The time granularity marker is compared at the granularity level; if inconsistent, a granularity difference identifier is written. The field numbers with any difference identifier are aggregated to form a difference field set, and the difference identifiers are written into the field difference identifier sequence in the order of the field numbers to generate a field attribute difference identifier set. For example, if field number 101 has an actual time granularity of seconds but a standard time granularity of milliseconds, a granularity difference identifier is written. If field number 115 has no unit in the actual unit but a standard unit of percentage, a unit difference identifier is written. Both fields are aggregated into the difference field set.

[0033] The configuration correction record submodule updates the standard type, unit and time granularity configuration items of the corresponding field according to the field attribute difference identifier set, records the field number, parameters before correction, parameters after correction, configuration version number and operation time, and generates a field configuration correction record. When updating the standard type, unit, and time granularity configuration items of the corresponding fields based on the field attribute difference identifier set, a writable lock is first performed on the configuration file and the lock timestamp is recorded. If the lock fails, the field number is written to the delayed correction queue and the writeback is skipped. After the lock succeeds, the standard type, standard unit, and standard time granularity are written back to the corresponding configuration items field by field. Before the writeback, a snapshot of the parameters before correction is read and written to the correction record cache. When recording the field number, parameters before correction, parameters after correction, configuration version number, and operation time, the configuration version number is taken from the version field in the configuration table header and incremented by 1 after the writeback is completed. The operation time is taken from the timestamp synchronized with the time server. When generating field configuration correction records, the field number, device serial number, parameters before correction, parameters after correction, configuration version number, and operation time are sorted by field number and written into the correction record table. An associated index is established between the correction record and the device granularity conflict list, with the index key being the device serial number plus the field number. Field number 101 was in seconds before correction and is now in milliseconds after correction. Field number 115 was in no unit before correction and is now a percentage after correction. The configuration version number is incremented from 17 to 18 and written into the correction record table.

[0034] Specifically, such as Figure 2 , 7 As shown, the data output encapsulation module includes: The field constraint verification submodule performs consistency verification on the current unit attribute of the field based on the correction results in the field configuration correction record, marks the fields with inconsistent units according to the standard unit rules of the configured field, constructs a set of available field indexes, and obtains a set of field unit verification results. When performing a consistency check on the current unit attribute of a field based on the correction results in the field configuration correction record, the set of correction field numbers in the correction record table is read first, and the current unit markers of these fields are read in the local field configuration table. When marking fields with inconsistent units according to the standard unit rules of the configured fields, the current unit markers are normalized and compared item by item with the standard unit markers in the correction record. If they are still inconsistent after normalization, the field number is written into the unit inconsistency list and removed from the available set. When constructing the available field index set, the field numbers that pass the unit consistency check are written into the index set in the field order of the standard field dictionary, and the field unit check result set is output and written into the cache. Field number 115 is retained in the available field index set if the unit after correction is a percentage and is consistent with the standard unit. If the unit of a field after correction is still millimeters of mercury but the standard unit is kilopascals, it is inconsistent after normalization and is removed.

[0035] The time granularity adjustment submodule calls the field unit verification result set, extracts the set of fields with inconsistent time granularity attributes, adjusts the timestamp format to the standard millisecond format, records the adjusted time field information, establishes a unified time field dataset, and obtains the standardized time field sequence. When extracting the set of fields with inconsistent time granularity attributes after calling the field unit validation result set, the set of field nodes marked with granularity differences is read from the field structure mapping graph, and the intersection with the set of available field indexes is taken to obtain the set of fields that need to be adjusted; when adjusting the timestamp format to the standard millisecond format, the original timestamp sequence in the monitoring data entry table is read for each field that needs to be adjusted. If the original timestamp is a second-granularity integer, it is multiplied by 1000 to convert it to a millisecond integer; if the original timestamp is a minute-granularity, it is first converted to seconds and then to milliseconds, and then written into a unified millisecond timestamp sequence; when recording the adjusted time field information, The field number, original time granularity, adjusted time granularity, number of adjusted entries, and first and last timestamps are written into the time adjustment record. When establishing a unified time field dataset, the millisecond timestamp sequences of all fields are merged into a unified time index, and null values ​​are written for non-existent time points to form a standardized time field sequence written into the cache. The original first timestamp of field number 101 is an integer with a second granularity of 1705560000, and the converted first millisecond timestamp is 1705560000000. This millisecond sequence is merged with the millisecond sequence of field number 112 to form a unified time index and record 920 adjusted entries.

[0036] The structural data integration submodule integrates attribute values ​​of fields that meet structural specifications and corresponding equipment identification information based on the standardized time field sequence, reconstructs the data structure format according to the field order and equipment mapping relationship, assembles the field data block content, and generates a standard quality control shared data body. When integrating attribute values ​​of fields that meet structural specifications with corresponding device identification information based on standardized time field sequences, the temporary mapping results in the field structure mapping graph are first read. Available fields are mapped one by one to unique standard field numbers, and the output field sequence is determined according to the dictionary order of standard fields. When reconstructing the data structure format according to the field order and device mapping relationship, the device serial number is written as a segment identifier into the structure header, and the field sequence is grouped by device and written into the field segment index. When assembling the field data block content, the values ​​of each field are aggregated for each millisecond point in the unified time index. During aggregation, conflict resolution is performed when multiple source fields appear for the same standard field, with resolution taking priority. Select the mapping entry with higher priority; if priorities are the same, select the most recent non-empty record. When generating the standard quality control shared data body, write the case identifier, equipment serial number set, time range start and end millisecond timestamp, number of fields, and number of records into the structure header, then write it into the data block segment and append a byte-by-byte cumulative verification segment to form the output encapsulation result. There are 39 available fields and 920 valid time points for the unified time index. Aggregate the values ​​of the 39 fields according to the 920 millisecond time points to generate a data block and output 920 records. The length of the output data body is dynamically determined according to the number of fields, the number of records, and the field value encoding method, and write the verification result for downstream receiving end verification.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A cloud-integrated orthopedic anesthesia quality control data sharing and intelligent analysis system, characterized in that: include: The field fingerprint upload unit uploads the fingerprint to the cloud server via the network; The structural difference identification module, field caliber correction module, and data output encapsulation module are deployed on cloud computing nodes, and compare the standard field dictionary in the cloud with the field configuration table stored in the cloud. The standard quality control shared data body is encapsulated, written to cloud storage, and a shared access interface is provided. The field fingerprint acquisition module analyzes the coding type, data type, value unit, record granularity and enumeration value of the orthopedic anesthesia record field, constructs field fingerprint entries, and generates structural information and constructs field fingerprint upload unit by combining the monitoring device serial number and sampling frequency parameters. The structural difference identification module calls the structural information in the field fingerprint upload unit, compares the field code, type, unit, time granularity and enumeration set with the standard field dictionary, records the differences, establishes the mapping relationship, generates consistency index and constructs the field structure mapping map; The device information binding module identifies fields with different time granularities in the field structure mapping map, extracts the case sampling frequency parameters, converts and compares the field record granularity, identifies time granularity conflicting fields, and generates a device granularity conflict list by combining device model, interface protocol and driver service information. The field caliber correction module, based on the device granularity conflict list, calls the field configuration table stored in the cloud, extracts the field standard type, unit and time granularity, compares it with the actual attributes of the field, and if there is a difference, updates the field configuration content, records the parameters, operation time and configuration version, and generates a field configuration correction record. The data output encapsulation module uses the field configuration to correct records, verify field constraints, adjust timestamps of fields with mismatched time granularity, integrate compliant fields and equipment identifiers, and construct a standard quality control shared data body.

2. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that: The field fingerprint upload unit includes a set of field fingerprint entries, a structural information encapsulation package, a case monitoring device serial number identifier, and a set of sampling frequency parameters. The field structure mapping map includes a set of field attribute difference records, a field mapping relationship table, a set of field structure consistency indicators, and a set of field structure matching paths. The device granularity conflict list includes a list of time granularity difference fields, a set of sampling frequency values ​​after unit conversion, a set of granularity conflict judgment results, and a set of monitoring device model interface driver identifiers. The field configuration correction record includes configuration item update results, a set of field correction parameters, a timestamp of configuration change operation, and a field configuration version number. The standard quality control shared data body includes a set of structurally compliant field datasets, a set of timestamp formatted field datasets, a set of field constraint verification pass identifiers, and a set of device identifier association information.

3. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that: The standard field dictionary is a preset set of fields, including field definitions of medical information standards, which include HL7, FHIR, ICD encoding set, and LOINC. The local field configuration table records the field standard type, unit, and time granularity configuration information. It originates from the database, is maintained by the hospital's information department, and is periodically synchronized to the cloud server via a network interface.

4. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that: The structural difference identification includes the following comparison methods: fuzzy matching based on field name similarity, field encoding rule matching, field unit conversion rule comparison, data type compatibility verification, and enumeration set intersection analysis to construct field mapping relationships.

5. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that, The field fingerprint collection module includes: The field attribute extraction submodule collects field attributes from the orthopedic anesthesia record, extracts field encoding type, data type, value unit, record granularity and enumeration value range, judges the enumeration value range and data type constraints, generates field attribute structure record, and obtains field attribute structure parameter set; The fingerprint entry construction submodule calls the field attribute structure parameter set, matches the encoding type and data type combination relationship according to the field structure composition rules, verifies the correspondence between the value unit and the record granularity, organizes the expression form of the enumerated value range, and generates a field fingerprint entry sequence. The protocol encapsulation generation submodule collects the serial number of the monitoring device corresponding to the case and the sampling frequency parameters according to the field fingerprint entry sequence, writes the structure header information and identifier bits according to the standard data packaging protocol, frames and encapsulates the entry sequence and device parameters, and generates the field fingerprint upload unit.

6. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that, The structural difference recognition module includes: The field structure difference comparison submodule, based on the structure information in the field fingerprint upload unit, calls the encoding, data type, value unit, time granularity and enumeration set of the corresponding field in the standard field dictionary, compares the uploaded field attributes with the standard fields item by item, records the attribute field differences, and generates a field attribute difference set. The structure mapping generation submodule calls the field attribute difference set, matches standard fields according to the difference rules of field encoding and time granularity, calculates the field structure correspondence value, marks the field mapping priority, establishes a field mapping set according to the field association mapping logic, and obtains the field structure mapping matrix; The consistency calculation output submodule calculates the structural matching degree value based on the structural correspondence of the fields in the field structure mapping matrix, allocates mapping weight values ​​according to the field coverage and matching accuracy, integrates the structural comparison coefficients of all fields, and generates a field structure mapping map.

7. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that, The device information binding module includes: The time granularity identification submodule extracts fields with time granularity differences based on the field structure mapping map, collects the sampling frequency parameters bound to the corresponding cases, performs matching indexing in combination with the field record granularity, marks the field numbers with inconsistent granularity, and generates a granularity difference field index table. The unit conversion and comparison submodule calls the granularity difference field index table, converts the sampling frequency parameter into sampling interval time and unifies the unit to milliseconds, calculates the interval difference based on the conversion result and the granularity of the field record, determines whether the difference exceeds the set granularity difference threshold range, and obtains the granularity conflict judgment result set. The conflict information generation submodule extracts the monitoring device model, interface protocol and driver service name bound to the associated fields based on the granular conflict judgment result set, generates a binding relationship mapping table by combining the field number and device identifier, and compiles and generates a device granular conflict list.

8. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that, The field caliber correction module includes: The configuration parameter extraction submodule locates the local field configuration table based on the field information in the device granularity conflict list, extracts the standard type, record unit and time granularity corresponding to the field to be corrected, establishes the mapping relationship between the field and the standard attribute, and generates a field standard parameter mapping table. The field attribute comparison submodule calls the field standard parameter mapping table to perform field-level comparison of the actual attribute value of each field with the standard type, unit and time granularity, determines the set of fields where the field configuration items are different, obtains the field difference identifier sequence, and obtains the field attribute difference identifier set. The configuration correction record submodule updates the standard type, unit and time granularity configuration items of the corresponding field according to the field attribute difference identifier set, records the field number, parameters before correction, parameters after correction, configuration version number and operation time, and generates a field configuration correction record.

9. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that, The data output encapsulation module includes: The field constraint verification submodule performs a consistency verification operation on the current unit attribute of the field based on the correction result in the field configuration correction record, marks the inconsistent units of the fields according to the standard unit rules of the configured fields, constructs an available field index set, and obtains a field unit verification result set. The time granularity adjustment submodule calls the field unit verification result set, extracts the set of fields with inconsistent time granularity attributes, adjusts the timestamp format to the standard millisecond format, records the adjusted time field information, establishes a unified time field dataset, and obtains a standardized time field sequence. The structural data integration submodule integrates the attribute values ​​of the fields that meet the structural specifications and the corresponding equipment identification information according to the standardized time field sequence, reconstructs the data structure format according to the field order and equipment mapping relationship, assembles the field data block content, and generates a standard quality control shared data body.

10. The orthopedic anesthesia quality control data sharing and intelligent analysis system integrating cloud computing as described in claim 1, characterized in that: The field configuration correction record includes the field identifier, original attribute value, corrected attribute value, correction basis, operation time, operation user identifier, and configuration version number, which is used to track the field configuration change process.

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