Pthoracic electrical impedance tomography data file storage method and system

Abstract

The invention relates to the field of medical data storage, and provides a chest electrical impedance tomography data file storage method, which comprises the following steps: acquiring monitoring data of a chest electrical impedance tomography, inquiring and optimizing a data storage format, extracting version identification information and authenticity verification parameters, integrating to obtain a verification data set, and storing the verification data set into a database; and analyzing a file reading protocol and a data frame format state, calculating a file analysis performance value, determining a storage optimization direction, analyzing data storage characteristics to calculate a storage capacity value, querying a data storage demand to construct a data storage instruction, and finally generating a file storage report. The storage efficiency of the medical data can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical data storage, and in particular to a method and system for storing data files of a chest electrical impedance tomography instrument. Background Art

[0002] In the field of medical data storage, medical imaging technology plays a vital role in the diagnosis and treatment of diseases. In addition, thoracic electrical impedance tomography (EIT), as a non-invasive technology that can reconstruct images of in vivo tissues based on the internal resistivity distribution of the human body, provides an important means for monitoring lung ventilation and blood perfusion.

[0003] At present, the existing data file storage methods mainly have the following problems: in terms of storage format, images or raw data lack file header information. This header-free storage method greatly limits the scalability of the data, making it difficult to effectively expand and optimize the existing data structure in the future. At the same time, the stored data is mostly image data. Compared with raw data, image data occupies a large amount of storage space and consumes a lot of memory resources during device operation. This not only puts higher requirements on the storage capacity of the device, but also affects the data processing efficiency of the device to a certain extent. Therefore, a new data file storage method is needed to standardize the storage structure of EIT data files and improve the storage efficiency of medical data. Summary of the invention

[0004] The present invention provides a method and system for storing data files of a chest electrical impedance tomography instrument, the main purpose of which is to improve the storage efficiency of medical data.

[0005] To achieve the above object, the present invention provides a method for storing data files of a chest electrical impedance tomography instrument, comprising:

[0006] Acquire a chest electrical impedance tomography instrument, collect monitoring data in the chest electrical impedance tomography instrument, query the data storage format in the monitoring data, optimize the data storage format, and obtain format-optimized data;

[0007] Extracting version identification information from the format-optimized data, collecting authenticity verification parameters from the version identification information, and performing data integration on the authenticity verification parameters to obtain a verification data set;

[0008] Based on the verification data set, analyzing the file reading protocol corresponding to the chest electrical impedance tomography instrument, determining the data frame format state corresponding to the file reading protocol, and calculating the file parsing performance value corresponding to the chest electrical impedance tomography instrument according to the data frame format state;

[0009] Based on the file parsing performance value, determining a storage optimization direction corresponding to the chest electrical impedance tomography instrument, analyzing data storage characteristics under the storage optimization direction, and calculating a storage capacity value corresponding to the chest electrical impedance tomography instrument based on the data storage characteristics;

[0010] The data storage requirement corresponding to the storage capacity value is queried, and based on the data storage requirement, a data storage instruction corresponding to the monitoring data is constructed; based on the data storage instruction, a file storage report corresponding to the chest electrical impedance tomography is generated.

[0011] Optionally, the step of optimizing the data storage format to obtain format-optimized data includes:

[0012] Identify existing structural features corresponding to the data storage format;

[0013] Based on the existing structural features, extracting key information identifiers in the data storage format;

[0014] Analyze the optimizable dimensions corresponding to the key information identifiers;

[0015] Based on the optimizable dimension, determining an optimizable direction corresponding to the data storage format;

[0016] Based on the optimizable direction, the data storage format is optimized to obtain format-optimized data.

[0017] Optionally, extracting the version identification information in the format-optimized data includes:

[0018] Dividing the format-optimized data into data blocks to obtain a set of data sub-blocks;

[0019] Scanning a header area of ​​each sub-block in the data sub-block set;

[0020] Identify a specific identification field in the header area;

[0021] Analyzing the encoding rules of the specific identification field;

[0022] Based on the encoding rule, version identification information in the format-optimized data is extracted.

[0023] Optionally, the data integration of the authenticity verification parameters to obtain a verification data set includes:

[0024] Analyze the parameter source and parameter type corresponding to the authenticity verification parameter;

[0025] Constructing an integration rule framework corresponding to the parameter source and the parameter type;

[0026] Based on the integration rule framework, query the repeated data part in the authenticity verification parameter;

[0027] Removing redundant information from the repeated data portion to obtain deduplicated data;

[0028] Associating logically related items in the deduplicated data;

[0029] Based on the logical correlation items, data integration is performed on the authenticity verification parameters to obtain a verification data set.

[0030] Optionally, analyzing a file reading protocol corresponding to the chest electrical impedance tomography device based on the verification data set includes:

[0031] Identifying key information identifiers in the verification data set;

[0032] Query the file reading element corresponding to the key information identifier;

[0033] Analyze the protocol parameter type corresponding to the file reading element;

[0034] Determine a protocol analysis dimension corresponding to the protocol parameter type;

[0035] Based on the protocol analysis dimension, the file reading protocol corresponding to the chest electrical impedance tomography is analyzed.

[0036] Optionally, calculating the file parsing performance value corresponding to the chest electrical impedance tomography device according to the data frame format state includes:

[0037] The file parsing performance value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula:

[0038]

[0039] Wherein, FX represents the file parsing performance value corresponding to the chest electrical impedance tomography, n represents the number of fields corresponding to the data frame in the data frame format state, i represents the field index corresponding to the data frame, l i represents the field length corresponding to the i-th field, m represents the number of types corresponding to the encoding type, j represents the type index corresponding to the encoding type, c j represents the number of times the jth encoding type appears in the data frame, p represents the total number of data frames, k represents the number index of data frames, and t k represents the frame processing time corresponding to the kth data frame, e k Indicates the number of parsing errors corresponding to the k-th data frame.

[0040] Optionally, determining the storage optimization direction corresponding to the chest electrical impedance tomography device based on the file parsing performance value includes:

[0041] Identify performance influencing factors corresponding to the file parsing performance value;

[0042] Classifying the performance influencing factors to obtain classified performance;

[0043] Analyzing the storage association degree corresponding to the classification performance;

[0044] Querying the storage priority corresponding to the storage association degree;

[0045] Based on the storage priority, a storage optimization direction corresponding to the chest electrical impedance tomography apparatus is determined.

[0046] Optionally, the calculating, based on the data storage characteristic, a storage capacity value corresponding to the chest electrical impedance tomography apparatus includes:

[0047] The storage capacity value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula:

[0048]

[0049] Wherein, Sr represents the storage capacity value corresponding to the chest electrical impedance tomography, K represents the data redundancy coefficient corresponding to the data storage characteristic, n represents the total number of different data types in the data storage characteristic, i′ represents the type index of the data type, V i′ represents the single data volume corresponding to the i′th data type, m′ represents the total number of data storage cycles in the data storage characteristic, j′ represents the number index corresponding to the storage cycle, and C i′j′ represents the data generation amount of the i′th data type in the j′th storage cycle, and R represents the data compression ratio corresponding to the data storage characteristic.

[0050] Optionally, querying the data storage demand corresponding to the storage capacity value includes:

[0051] Analyze the storage ratio characteristics of the storage capacity value under different data types;

[0052] Analyze the importance of the storage ratio corresponding to the storage ratio feature;

[0053] Based on the importance of the proportion, identifying the key data type and the secondary data type corresponding to the storage capacity value;

[0054] Constructing a storage allocation strategy corresponding to the key data type and the secondary data type;

[0055] Based on the storage allocation strategy, data storage requirements corresponding to the storage capacity value are analyzed.

[0056] Optionally, constructing a data storage instruction corresponding to the monitoring data based on the data storage requirement includes:

[0057] Analyze the core storage elements corresponding to the data storage requirements;

[0058] Determining storage impact parameters corresponding to the core storage elements;

[0059] Based on the storage influencing parameter, querying a standard storage template corresponding to the monitoring data;

[0060] Screening the adaptation data module in the standard storage template;

[0061] Extracting key storage parameters corresponding to the adaptation data module;

[0062] Based on the key storage parameters, data storage instructions corresponding to the monitoring data are constructed.

[0063] First, the present invention obtains a chest electrical impedance tomography instrument and collects monitoring data from the chest electrical impedance tomography instrument, which can obtain internal information of the human chest in real time and non-invasively, and can dynamically monitor the ventilation and blood perfusion conditions of the lungs, which helps to timely discover potential lung diseases or functional abnormalities, and provide key clues for early diagnosis of the disease, thereby greatly improving the cure rate of the disease and the prognosis of the patient. At the same time, the present invention can quickly locate the relevant version for investigation by extracting the version identification information in the format-optimized data, which helps to discover potential technical problems or sources of errors. Secondly, the version identification information can be used as an important basis for judging data compatibility, so as to take corresponding conversion or adaptation measures to ensure the smooth circulation and effective use of data in multiple devices and multiple application scenarios, and promote the integration and sharing of medical data resources. Based on the verification data set, the present invention analyzes the file reading protocol corresponding to the chest electrical impedance tomography instrument, which can provide rich and reliable information for accurate analysis of the file reading protocol. Reliable data foundation can clarify the potential differences and commonalities of file reading protocols of imagers under different versions and different production batches, which is helpful to develop more adaptable file reading software or tools, improve the success rate and accuracy of data reading, and reduce data reading errors or failures caused by protocol mismatch. The present invention determines the storage optimization direction corresponding to the chest electrical impedance tomography imager based on the file parsing performance value, which can clarify the bottleneck of the current imager in the data processing process, and can help remove unnecessary data storage, reasonably allocate storage space, and reduce the waste of storage resources, so as to store more valuable data under limited storage capacity. Further, the present invention can accurately understand how much storage space the chest electrical impedance tomography imager needs to store data during operation by querying the data storage demand corresponding to the storage capacity value, thereby avoiding the risk of data loss caused by insufficient storage space, or the waste of resources and cost increase caused by over-configuration of storage space. Therefore, a chest electrical impedance tomography data file storage method and system proposed by the present invention can improve the storage efficiency of medical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of a method for storing data files of a chest electrical impedance tomography device provided by an embodiment of the present invention;

[0065] Figure 2 A schematic diagram of modules for implementing a data file storage system for a chest electrical impedance tomography apparatus provided in one embodiment of the present invention.

[0066] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0068] The embodiment of the present application provides a method for storing data files of a chest electrical impedance tomography instrument. The execution subject of the method for storing data files of a chest electrical impedance tomography instrument includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for storing data files of a chest electrical impedance tomography instrument can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0069] Embodiment 1:

[0070] Reference Figure 1 FIG. 1 is a flow chart of a method for storing data files of a chest electrical impedance tomography apparatus provided by an embodiment of the present invention. In this embodiment, the method for storing data files of a chest electrical impedance tomography apparatus includes:

[0071] S1. Obtain a chest electrical impedance tomography device, collect monitoring data in the chest electrical impedance tomography device, query the data storage format in the monitoring data, optimize the data storage format, and obtain format-optimized data.

[0072] The present invention obtains a chest electrical impedance tomography imager and collects monitoring data from the chest electrical impedance tomography imager, which can obtain internal information of the human chest in real time and non-invasively, and can dynamically monitor the ventilation and blood perfusion conditions of the lungs, which helps to timely discover potential lung diseases or functional abnormalities, and provide key clues for the early diagnosis of the disease, thereby greatly improving the cure rate of the disease and the prognosis of the patient.

[0073] Among them, the chest electrical impedance tomography refers to a medical device that utilizes the impedance difference between different tissues in the human chest, applies safe tiny alternating current excitation to the human body, measures the surface voltage response, and then uses a specific algorithm to reconstruct the internal resistivity distribution image of the chest. It can non-invasively detect and image the structure and functional status of chest organs such as lungs, and provide important information support for the diagnosis of lung diseases, treatment effect evaluation, etc.; the monitoring data refers to various types of data related to the electrical impedance of chest tissue collected by the chest electrical impedance tomography during operation, including but not limited to voltage values ​​measured at multiple electrodes on the body surface under different excitation modes, time series information corresponding to these voltage data, data sets reflecting the resistivity distribution characteristics of chest tissue, and environmental parameters related to measurement recorded during the operation of the device, device working status parameters and other ancillary data. Optionally, the acquisition of monitoring data in the chest electrical impedance tomography can be achieved by a high-precision electrode array method, such as: using a high-precision electrode array method, attaching it to a specified position on the human chest, and using the good contact between the electrode and the skin to collect weak voltage signal changes, thereby obtaining original monitoring data.

[0074] Furthermore, by querying the data storage format in the monitoring data, the present invention can quickly locate the specific physiological or pathological information reflected by the data, such as clarifying whether it is monitoring data related to pulmonary ventilation function or a reflection of blood perfusion conditions. This helps to accurately judge the condition during disease diagnosis, avoid misdiagnosis and missed diagnosis, and provide an accurate basis for formulating personalized treatment plans.

[0075] Among them, the data storage format refers to the specific rules and structure used to store the monitoring data collected by the chest electrical impedance tomography, which includes: data organization method: for example, whether the data collected by each electrode is stored in sequence, or grouped and stored according to certain areas (such as the electrode areas corresponding to different lobes of the lungs), data type definition: clearly define the data type of the stored data, such as integer type, floating point type, etc., file header information format: the file header usually contains basic descriptive information about the data, such as the source of the data (what type of monitoring it is), the time of data collection, and the basic parameters of the instrument (such as the number of electrodes, excitation current parameters, etc.). Optionally, the query of the data storage format in the monitoring data can be achieved through format analysis methods, such as: byte-level analysis, file structure analysis and other methods, which can perform in-depth scanning and analysis on the files storing data, identify the structure and format of the files, and accurately obtain the data storage format information in the instrument parameters.

[0076] Furthermore, the present invention optimizes the data storage format to obtain format-optimized data, which can significantly improve data storage efficiency, reduce unnecessary data redundancy, and enable storage media of the same capacity to accommodate more monitoring data. This is extremely critical for medical monitoring scenarios where large amounts of data are accumulated over a long period of time, and reduces the capacity requirements for storage devices, thereby cutting storage costs.

[0077] Among them, the format-optimized data refers to a data set with a more efficient storage structure, more convenient data access characteristics, and higher compatibility obtained after the data storage format is optimized according to the determined optimizable direction. These data have better performance in storage, transmission, processing, etc. on the basis of maintaining the integrity of the original monitoring data information.

[0078] As an embodiment of the present invention, the format optimization of the data storage format to obtain format optimization data includes: identifying existing structural features corresponding to the data storage format; based on the existing structural features, extracting key information identifiers in the data storage format; analyzing optimizable dimensions corresponding to the key information identifiers; based on the optimizable dimensions, determining optimizable directions corresponding to the data storage format; based on the optimizable directions, format optimization of the data storage format to obtain format optimization data.

[0079] Among them, the existing structural features refer to the characteristics of the current organizational structure, data arrangement order, data block method, and the association form between the file header and the data body of the data storage format. For example, whether the data is arranged linearly according to the acquisition time sequence, or partitioned based on the electrode position; whether the file header information is complete and standardized, and whether it can clearly indicate the data source and related instrument parameters; the key information identifier refers to the mark or symbol that can play a key role in defining and explaining the nature, source, and purpose of the data in the data storage format, including the data type identifier (such as indicating that it is impedance numerical data, timestamp data, etc.), the data monitoring type identifier (distinguishing whether it is pulmonary ventilation monitoring data or blood perfusion monitoring data), instrument parameter association identifier (indicating the data part related to specific instrument setting parameters), etc.; the optimizable dimension refers to the various aspects of the data storage format that can be improved and perfected on the existing basis, such as data compression. Dimension reduction, consider whether a more efficient compression algorithm can be used to reduce the space occupied by data storage; data index dimension, that is, whether a more reasonable index mechanism can be built to quickly locate and retrieve data; data redundancy dimension, determine whether there is duplicate or unnecessary information in the data that can be deleted or streamlined; and data format compatibility dimension, think about how to adjust it to make it more compatible with general data processing software or other medical equipment data formats; the optimizable direction refers to the specific path for optimizing the data storage format determined by the optimizable dimension analysis. For example, if data compression is found to have great potential in the optimizable dimension, the optimizable direction may be to compress the data using a specific lossless or lossy compression algorithm; if there is a problem with data indexing, the optimizable direction is to establish a composite index system based on multiple dimensions such as monitoring type and time range; if the data redundancy is high, the optimizable direction is to remove redundant data fields or adopt a data merging strategy.

[0080] Furthermore, the identification of the existing structural features corresponding to the data storage format can be achieved through a binary data viewer, such as the HxD tool, by viewing the data storage file in binary form, the storage layout, byte order, data segment division and other structural features of the data can be intuitively understood; the extraction of key information identifiers in the data storage format can be achieved through a tag extraction tool, such as Data Tag Extractor and other tools, which can quickly locate and extract key information identifiers in the data storage format based on a preset identifier feature library; the analysis of the optimizable dimensions corresponding to the key information identifiers can be achieved through a data feature analysis platform, such as SPSS Modeler, etc., using the key information identifiers as input variables, and finding potential relationships and patterns related to the optimizable dimensions through data mining and analysis techniques; the determination of the optimizable direction corresponding to the data storage format can be achieved through a multi-factor analysis model, such as: data redundancy, storage efficiency, compatibility and other factors in the optimizable dimensions are input as variables into the model, and the weights and relationships of each factor are calculated through the model to determine the optimizable direction; the format optimization of the data storage format can be achieved through a storage optimization algorithm library, such as the LZMA algorithm, etc.

[0081] S2. Extract the version identification information in the format-optimized data, collect the authenticity verification parameters in the version identification information, and perform data integration on the authenticity verification parameters to obtain a verification data set.

[0082] By extracting the version identification information in the format-optimized data, the present invention can quickly locate the relevant version for troubleshooting, which is helpful to discover potential technical problems or sources of errors. Secondly, the version identification information can be used as an important basis for judging data compatibility, so as to take corresponding conversion or adaptation measures to ensure the smooth flow and effective use of data in multiple devices and multiple application scenarios, and promote the integration and sharing of medical data resources.

[0083] Among them, the version identification information refers to the information content that can clearly reflect the version of the relevant conditions under which the format-optimized data is generated. It can refer to the software version number, hardware version number of the chest electrical impedance tomography used when generating the data, or a specific version mark related to the data acquisition, processing, and storage process.

[0084] As an embodiment of the present invention, the extracting of version identification information from the format-optimized data includes: dividing the format-optimized data into data blocks to obtain a set of data sub-blocks; scanning the header area of ​​each sub-block in the set of data sub-blocks; identifying a specific identification field in the header area; analyzing an encoding rule of the specific identification field; and extracting the version identification information from the format-optimized data based on the encoding rule.

[0085] The data sub-block set refers to a plurality of relatively independent data subsets into which the entire format-optimized data is divided according to a certain division method (for example, according to factors such as the logical structure of the data, the storage order, and the functional module) when processing the format-optimized data; the header area refers to a specific part of the area at the beginning of each data sub-block. Usually, in the data storage structure, in order to facilitate the understanding, management, and subsequent processing of the data, an area is set at the starting position of each data sub-block to store some basic description information about the sub-block, such as the source, type, generation time, and related equipment parameters of the data. The specific identification field refers to the field content in the header area of ​​the data sub-block that is specially set to identify specific information. For example, there is a field specifically used to indicate which version of the chest electrical impedance tomography instrument is used to collect and generate the data, or a field used to identify the specific monitoring project or process to which the data belongs; the encoding rule refers to a set of specific encoding methods and specifications followed for the data in the specific identification field. For example, a specific digital encoding system may be adopted, in which each number represents a different meaning, such as using a certain digital range to represent different device version numbers; or a character encoding method is adopted to identify the relevant attributes of the data through a specific character combination.

[0086] Furthermore, the data block division of the format-optimized data can be implemented by a fixed-length-based division algorithm, such as: pre-setting a fixed byte length, and cutting the format-optimized data into data sub-blocks in sequence according to this length; the scanning of the header area of ​​each sub-block in the data sub-block set can be implemented by a sequential reading algorithm, such as: reading a certain length of data byte by byte in sequence from the starting position of each data sub-block as the header area; the identification of the specific identification field in the header area can be implemented by a field name matching algorithm, such as: it can be identified by searching for these names or keywords in the header area to obtain a specific identification field; the analysis of the encoding rules of the specific identification field can be implemented by a document parsing method, such as: the encoding method of the specific identification field can be understood by parsing the document; the extraction of version identification information in the format-optimized data can be implemented by an encoding rule application algorithm, such as: after determining the encoding rule of the specific identification field, an algorithm is written according to the rule to extract the version identification information.

[0087] By collecting the authenticity verification parameters in the version identification information, the present invention can effectively identify whether the imaging device used is a regular and qualified product, avoid serious deviations or errors in monitoring data caused by the use of counterfeit and shoddy equipment, greatly ensure the accuracy and reliability of medical monitoring data, and provide a solid foundation for clinical diagnosis and medical decision-making.

[0088] Among them, the authenticity verification parameters refer to a set of specific parameters used to verify whether the chest electrical impedance tomography is authentic and whether it meets the quality standards. These parameters can be related to the equipment hardware, such as the uniqueness and format standardization of the equipment serial number, the anti-counterfeiting mark on the hardware component (such as a specific chip serial number, a laser-etched anti-counterfeiting code, etc.), and whether the hardware performance indicators are within the specified standard range (such as the resistance value of the electrode, the accuracy of the excitation current, etc.); they can also be related to the software, such as whether the digital signature of the software is correct, whether the software version number is consistent with the official release, whether the encrypted authentication information inside the software is complete and valid, etc. Optionally, the authenticity verification parameters collected in the version identification information can be implemented through an anti-counterfeiting code recognition tool, such as: scanning and reading the anti-counterfeiting code (such as a QR code, a barcode or a specific encryption code) on the equipment hardware to obtain the authenticity verification parameters.

[0089] Furthermore, the present invention obtains a verification data set by performing data integration on the authenticity verification parameters, which helps to improve data management efficiency, and organizes the scattered authenticity verification parameters according to a unified standard and logical structure to form a verification data set, which facilitates data storage, retrieval and calling. Whether in daily equipment maintenance, quality inspection or troubleshooting, the required verification data can be quickly located to reduce time costs.

[0090] Among them, the verification data set refers to the final data set formed after the authenticity verification parameters are processed according to the above series of steps. This data set comprehensively and systematically integrates authenticity verification parameters from different sources and different types, and is presented in a form with clear structure, rigorous logic and accurate data by removing repeated redundant information and associating logically related items.

[0091] As an embodiment of the present invention, the data integration of the authenticity verification parameters to obtain a verification data set includes: analyzing the parameter source and parameter type corresponding to the authenticity verification parameters; constructing an integration rule framework corresponding to the parameter source and the parameter type; based on the integration rule framework, querying the repeated data part in the authenticity verification parameters; removing redundant information in the repeated data part to obtain deduplicated data; associating logically related items in the deduplicated data; based on the logically related items, performing data integration on the authenticity verification parameters to obtain a verification data set.

[0092] Among them, the parameter source refers to the origin or way of obtaining the authenticity verification parameters, which covers the identification information of the device itself, such as the serial number, model code, hardware version number, etc. engraved, printed or stored on the device housing, circuit board or chip; the data recorded by the device during the production and manufacturing process, such as the production batch number, production date, production process parameters, raw material supplier information, etc. of the device stored in the manufacturer's database; the parameter type refers to the different categories of attributes of the authenticity verification parameters, which can be divided into physical hardware parameters, including the physical characteristics of the device such as appearance size, weight, color, etc., and the specification parameters of hardware components, such as the material, size, quantity, resistance value range, circuit The chip model, performance indicators, etc.; software information parameters, such as the software name, version number, release date, copyright information, software hash value, digital signature algorithm and signature result, software function module list and corresponding version information, etc.; the integration rule framework refers to a set of systematic specifications and guidelines for guiding the data integration process based on the characteristics of parameter sources and parameter types, which includes data format unification rules, such as stipulating that all date formats are unified as "YYYY-MM-DD", hardware parameter units are unified in the international standard unit system, and software version numbers are unified in the format of major version number.minor version number.revision number, etc.; the repeated data part refers to the duplicate data in the authenticity verification parameter set. In general, due to repeated data collection during the data collection process, data synchronization errors, or partial overlapping information between different data sources, parameter data fragments that are completely identical or highly similar in key information may appear. For example, in the parameter data obtained simultaneously from the device's own storage system and the manufacturer's backup database, there may be multiple identical device serial number records, or in the software version information collected at different time points, there may be multiple duplicates of the same version number data. These identical or similar parts constitute the duplicate data part; the deduplicated data refers to the data set obtained after removing the redundant information in the duplicate data part according to the integration rule framework. The redundant information removal process Follow the data merging and deduplication rules. For example, for duplicate device serial numbers, only keep one copy and ensure that it is in a standardized format. The logically related items refer to those parameter items in the deduplication data that are logically internally connected, interrelated, and mutually influential based on the functional principles of the device, the production and manufacturing process, the collaborative working mechanism of software and hardware, or industry standards and specifications. For example, there is a logical relationship between the hardware production date of the device and the date when the software was first installed. If the order of the two is reversed, it may indicate that there is an abnormality in the device. There is a corresponding matching logical relationship between the hardware model of the device and the maximum software function version it can support. The specific hardware configuration determines the upper limit of the software function.

[0093] Furthermore, the analysis of the parameter sources and parameter types corresponding to the authenticity verification parameters can be achieved through data traceability analysis tools, such as: Data Provenance Analyzer and other tools, which can track the generation path and source information of the data; the construction of the integration rule framework corresponding to the parameter sources and the parameter types can be achieved through framework construction tools, such as: ArchiMate and other tools; the query of the duplicate data part in the authenticity verification parameters can be achieved through a hash algorithm, such as: calculating a hash value for each authenticity verification parameter, and parameters with the same hash value are duplicate data; the removal of redundant information in the duplicate data part can be achieved through a data cleaning tool, such as: OpenRefine and other tools; the association of logically related items in the deduplicated data can be achieved through a reasoning engine based on logic rules, such as: Drools, etc.; the data integration of the authenticity verification parameters can be achieved through a data fusion algorithm library, such as: Dedupe library, etc.

[0094] S3. Based on the verification data set, analyze the file reading protocol corresponding to the chest electrical impedance tomography instrument, determine the data frame format status corresponding to the file reading protocol, and calculate the file parsing performance value corresponding to the chest electrical impedance tomography instrument according to the data frame format status.

[0095] The present invention analyzes the file reading protocol corresponding to the chest electrical impedance tomography imager based on the verification data set, which can provide a rich and reliable data basis for the accurate analysis of the file reading protocol, and can clarify the potential differences and commonalities of the file reading protocol of the imager under different versions and different production batches, which is helpful to develop more adaptable file reading software or tools, improve the success rate and accuracy of data reading, and reduce data reading errors or failures caused by protocol mismatch.

[0096] Among them, the file reading protocol refers to the sum of a set of rules, processes and parameter settings followed by the chest electrical impedance tomography imager when reading relevant files, including how to identify the existence of files, how to open files, in what order to read data in files, how to handle errors and abnormal situations during the reading process, and how to close files and release related resources after reading is completed. A series of operating specifications are key criteria to ensure that the imager can correctly and efficiently read various files (such as monitoring data files, configuration files, image files, etc.).

[0097] As an embodiment of the present invention, the file reading protocol corresponding to the chest electrical impedance tomography instrument is analyzed based on the verification data set, including: identifying key information identifiers in the verification data set; querying file reading elements corresponding to the key information identifiers; analyzing protocol parameter types corresponding to the file reading elements; determining protocol analysis dimensions corresponding to the protocol parameter types; and analyzing the file reading protocol corresponding to the chest electrical impedance tomography instrument based on the protocol analysis dimensions.

[0098] Among them, the key information identifier refers to those specific marks, codes or data features in the verification data set that can directly or indirectly reflect the file reading protocol associated with the chest electrical impedance tomography imager. For example, it can be equipment model information, because different models of imagers may use different versions or types of file reading protocols; it may also be a specific software version identifier, and the update of the software version is often accompanied by the adjustment of the file reading protocol; the file reading element refers to the basic components and key links involved in the file reading process. The information content corresponding to the key links, including the storage path information of the file, determines where the imager looks for the file to be read; the file naming rules, different naming rules may imply the type, purpose and corresponding reading method of the file, for example, files named according to the date and monitoring project may require a specific time to decode. analysis and data classification reading process; the protocol parameter type refers to the classification form of various parameters involved in the file reading protocol, for example, there is a data transmission rate parameter type, which stipulates the speed limit and optimization strategy for data transmission from the storage medium to the imager memory or processing unit when reading the file. The high-speed parameter can be suitable for monitoring data reading with high real-time requirements, while the low-speed parameter may be used for stable transmission of large-capacity files; the protocol analysis dimension refers to analyzing the direction and scope of the file reading protocol from different angles and levels. From the time dimension, it includes the start time, duration and time interval requirements between different operation steps of the file reading. For example, some real-time monitoring data files need to start reading and complete preliminary analysis in a very short time, while the reading of some historical data files has relatively loose time requirements.

[0099] Furthermore, the identification of the key information identifier in the verification data set can be achieved through a rule-based matching engine, such as: using a pre-set rule set to identify the key information identifier through string matching, data format matching, etc.; the query of the file reading element corresponding to the key information identifier can be achieved through an index search algorithm, such as: constructing an index table of key information identifiers and file reading elements, and using algorithms such as hash indexes or B-tree indexes to quickly locate the corresponding file reading elements through the index after obtaining the key information identifier; the analysis of the protocol parameter type corresponding to the file reading element can be achieved through a protocol parsing model, such as: establishing a special file reading protocol parsing model, taking the file reading element as input, and outputting the corresponding protocol parameter type after model training and learning; the determination of the protocol analysis dimension corresponding to the protocol parameter type can be achieved by querying a dimension mapping table, such as: creating a mapping table of protocol parameter types and protocol analysis dimensions, and when the protocol parameter type is determined, directly querying the corresponding protocol analysis dimension in the mapping table; the analysis of the file reading protocol corresponding to the chest electrical impedance tomography can be achieved through a protocol analysis method, such as: syntax analysis, semantic analysis, and state machine analysis.

[0100] By determining the data frame format state corresponding to the file reading protocol, the present invention can ensure that the imager correctly identifies and extracts valid information when receiving data, avoids data loss or erroneous processing due to incorrect interpretation of the data frame format, and ensures the integrity and reliability of the monitoring data.

[0101] Among them, the data frame format status refers to a comprehensive description of various characteristic conditions of the data frame in the chest electrical impedance tomography imager, which includes the structural status of the data frame, that is, what parts the data frame is composed of, such as whether it contains a frame header, a frame footer, a data segment, a check segment, etc., and the arrangement order of each part. For example, the frame header part can contain information such as the source identification and timestamp of the data. Understanding these structures helps to accurately extract key information from the data frame. Optionally, the determination of the data frame format status corresponding to the file reading protocol can be achieved through a bytecode analysis method, such as: for data frames stored in the internal storage medium of the imager, the format status can be determined by bytecode analysis.

[0102] Furthermore, the present invention calculates the file parsing performance value corresponding to the chest electrical impedance tomography imager according to the data frame format status, which can accurately quantify key indicators such as resource consumption and processing speed of the imager when parsing files, and helps to timely discover potential performance bottlenecks and perform targeted optimization, thereby greatly improving the data processing efficiency of the imager and ensuring that reliable detection results can be provided quickly and accurately in clinical monitoring.

[0103] The file parsing performance value refers to a performance indicator used to measure the performance of the chest electrical impedance tomography device when processing files, which comprehensively considers multiple characteristics of the data frame, such as the number of fields, field length, encoding type, processing time, number of parsing errors and other factors.

[0104] As an embodiment of the present invention, the calculating, according to the data frame format state, the file parsing performance value corresponding to the chest electrical impedance tomography apparatus includes:

[0105] The file parsing performance value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula:

[0106]

[0107] Wherein, FX represents the file parsing performance value corresponding to the chest electrical impedance tomography, n represents the number of fields corresponding to the data frame in the data frame format state, i represents the field index corresponding to the data frame, l i represents the field length corresponding to the i-th field, m represents the number of types corresponding to the encoding type, j represents the type index corresponding to the encoding type, c j represents the number of times the jth encoding type appears in the data frame, p represents the total number of data frames, k represents the number index of data frames, and t k represents the frame processing time corresponding to the kth data frame, e k Indicates the number of parsing errors corresponding to the k-th data frame.

[0108] In detail, the field length refers to the length occupied by each field in the data frame, usually in bytes. For example, one field may be used to store image data and has a longer length; while another field may be used to store device status information and has a shorter length; the encoding type refers to the encoding method used for data in the data frame. Different encoding types include binary encoding, ASCII code, etc.; the frame processing time refers to the time taken by the imager to process each data frame, usually in seconds. For example, a shorter processing time means that the imager can process data frames faster, thereby improving overall work efficiency; while a longer processing time may cause delays in data processing; the number of parsing errors refers to the number of errors that occur during the imager's parsing of data frames. Parsing errors can cause data loss or inaccuracy, thereby affecting the working effect of the imager.

[0109] S4. Based on the file parsing performance value, determine the storage optimization direction corresponding to the chest electrical impedance tomography instrument, analyze the data storage characteristics under the storage optimization direction, and calculate the storage capacity value corresponding to the chest electrical impedance tomography instrument based on the data storage characteristics.

[0110] The present invention determines the storage optimization direction corresponding to the chest electrical impedance tomography imager based on the file parsing performance value, can clarify the bottleneck of the current imager in the data processing process, can help remove unnecessary data storage, reasonably allocate storage space, and reduce the waste of storage resources, so as to store more valuable data under limited storage capacity.

[0111] Among them, the storage optimization direction refers to the specific optimization direction and strategy determined based on storage priority to improve the storage system of the chest electrical impedance tomography instrument and thus improve the file parsing performance. For example, if the storage speed has the highest storage priority, then the storage optimization direction may be to replace it with a storage medium with a faster read and write speed (such as from a mechanical hard disk to a solid-state hard disk), or to optimize the cache mechanism of the existing storage medium.

[0112] As an embodiment of the present invention, determining the storage optimization direction corresponding to the chest electrical impedance tomography instrument based on the file parsing performance value includes: identifying the performance influencing factors corresponding to the file parsing performance value; performing performance classification on the performance influencing factors to obtain classification performance; analyzing the storage correlation degree corresponding to the classification performance; querying the storage priority corresponding to the storage correlation degree; and determining the storage optimization direction corresponding to the chest electrical impedance tomography instrument based on the storage priority.

[0113] Among them, the performance influencing factors refer to various conditions and elements that will affect the file parsing performance value of the chest electrical impedance tomography instrument. These factors include hardware factors, such as the read and write speed of the storage medium, the storage capacity, the interface type of the storage device (such as SATA, PCI-E, etc.), etc.; the classified performance refers to the performance category presented after the identified performance influencing factors are classified according to certain rules and standards. For example, in the hardware performance-related category, it is further subdivided into storage speed performance category, including factors such as the read and write speed of the storage medium; storage capacity performance category, mainly considering the capacity of the storage device; in the software performance-related category, there is a storage format performance category, involving data Whether the storage format is suitable for efficient parsing; the file system performance class is related to the efficiency of the file system in processing files; the storage correlation degree refers to the degree of close connection between each type of classification performance and the storage system of the chest electrical impedance tomography instrument. Taking the storage speed performance class in hardware performance as an example, its storage correlation degree is very high, because the storage speed directly determines the efficiency of data writing and reading, and has a vital impact on file parsing performance; the storage priority refers to the order of optimizing different storage-related factors determined according to the storage correlation degree. For example, if the storage speed performance class has the highest storage correlation degree, then when considering storage optimization, improving the storage speed will be given a higher priority.

[0114] Furthermore, the identification of the performance influencing factors corresponding to the file parsing performance value can be achieved through a performance monitoring tool, such as: Performance Monitor under the Windows system; the performance classification of the performance influencing factors can be achieved through a decision tree algorithm, such as: C4.5 and other algorithms; the analysis of the storage association degree corresponding to the classification performance can be achieved through a weight evaluation method, such as: using the analytic hierarchy process (AHP) to determine the association degree between different classification performance and storage; the query of the storage priority corresponding to the storage association degree can be achieved through a cost-benefit analysis method, such as: determining the storage priority by evaluating the benefits and costs brought by optimizing various performance factors; the determination of the storage optimization direction corresponding to the chest electrical impedance tomography can be achieved through a storage simulation tool, such as: DiskSim and other tools.

[0115] The present invention helps to accurately adapt storage resources by analyzing the data storage characteristics under the storage optimization direction. According to the strategies determined by the storage optimization direction, such as data layout and storage format adjustment, in-depth analysis of the data storage characteristics can clarify the actual space occupied by data in the storage medium, read and write modes, data update frequency and other details, so that the allocation of storage resources is more in line with the actual needs of the data, avoiding waste or shortage of resources and improving the overall utilization of the storage system.

[0116] Among them, the data storage characteristics refer to a series of characteristics exhibited by the data in the chest electrical impedance tomography device during the storage process, including the storage format characteristics of the data, that is, the specific format in which the data is stored in the storage medium (such as binary format, specific medical data format, etc.). Different storage formats will affect the storage efficiency, readability and operability of the data. For example, some efficient compression formats can save storage space, but may require more computing resources when reading and parsing. Optionally, the analysis of the data storage characteristics under the storage optimization direction can be achieved through data tracking methods, such as: analyzing the data storage characteristics by making detailed records during the data storage and reading process.

[0117] Furthermore, the present invention calculates the storage capacity value corresponding to the chest electrical impedance tomography device based on the data storage characteristics, so as to achieve accurate resource planning. By deeply analyzing the data storage characteristics, such as data format, data volume growth trend, data redundancy, etc., accurate calculation of the storage capacity value can help medical institutions estimate the required storage resource scale in advance, reasonably purchase or deploy storage devices, avoid the risk of data loss due to insufficient storage capacity or resource waste due to over-configuration, and effectively control costs.

[0118] The storage capacity value refers to the storage space required by the chest electrical impedance tomography device when storing data, which includes factors such as data redundancy, data type, data generation volume and data compression.

[0119] As an embodiment of the present invention, the calculating, based on the data storage characteristic, a storage capacity value corresponding to the chest electrical impedance tomography apparatus includes:

[0120] The storage capacity value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula:

[0121]

[0122] Wherein, Sr represents the storage capacity value corresponding to the chest electrical impedance tomography, K represents the data redundancy coefficient corresponding to the data storage characteristic, n represents the total number of different data types in the data storage characteristic, i′ represents the type index of the data type, V i′ represents the single data volume corresponding to the i′th data type, m′ represents the total number of data storage cycles in the data storage characteristic, j′ represents the number index corresponding to the storage cycle, and C i′j′ represents the data generation amount of the i′th data type in the j′th storage cycle, and R represents the data compression ratio corresponding to the data storage characteristic.

[0123] In detail, the data redundancy coefficient refers to the multiple of the increase in data volume due to data backup, duplicate data storage, etc. during the data storage process. For example, multiple backups are performed for data security, or there is some duplicate stored data. The multiple of these additional data volumes relative to the original data volume is the data redundancy coefficient; the single data volume refers to the size of the storage space occupied by a single data in each data type, usually in bytes, different types of data, such as imaging data, patient information data, etc.; the data storage cycle refers to the number of divisions of the time range in which data is stored in the storage medium. For example, the data storage cycle can be divided into time units such as days, weeks, and months; the data generation volume refers to the amount of data of a certain data type generated within a specific data storage cycle. For example, within a certain week, imaging data generates 1000 data units, and the 1000 here is the amount of data generated of the imaging data type within that week; the data compression ratio refers to the ratio of the compressed data size to the original data size after the data is compressed before storage. For example, if the original data size is 100MB and becomes 20MB after compression, then the data compression ratio is 100 / 20=5, which means that the original data size is 5 times the compressed data size.

[0124] S5. Query the data storage demand corresponding to the storage capacity value, construct a data storage instruction corresponding to the monitoring data based on the data storage demand, and generate a file storage report corresponding to the chest electrical impedance tomography instrument based on the data storage instruction.

[0125] By querying the data storage demand corresponding to the storage capacity value, the present invention can accurately understand how much storage space the chest electrical impedance tomography imager needs to store data during operation, thereby avoiding the risk of data loss due to insufficient storage space, or the waste of resources and increased costs due to excessive configuration of storage space.

[0126] Among them, the data storage demand refers to the comprehensive demand for storage resources and storage methods required by the chest electrical impedance tomography to meet its normal operation, clinical application and data management requirements. It is based on the analysis of the storage allocation strategy, including requirements for storage capacity, storage performance (such as read and write speed, reliability), data storage structure (such as storage layout of different data types), etc.

[0127] As an embodiment of the present invention, the query of the data storage demand corresponding to the storage capacity value includes: analyzing the storage proportion characteristics of the storage capacity value under different data types; analyzing the proportion importance corresponding to the storage proportion characteristics; based on the proportion importance, identifying the key data type and the secondary data type corresponding to the storage capacity value; constructing a storage allocation strategy corresponding to the key data type and the secondary data type; based on the storage allocation strategy, analyzing the data storage demand corresponding to the storage capacity value.

[0128] Among them, the storage proportion feature refers to the proportion of storage space occupied by different data types in the storage capacity of the chest electrical impedance tomography instrument and its distribution characteristics. For example, imaging data may occupy 60% of the total storage capacity, basic patient information data occupies 20%, equipment operation log data occupies 10%, and other auxiliary data occupies 10%; the proportion importance refers to the relative importance of different data types determined according to the storage proportion feature. For example, imaging data occupies a large storage proportion, so its importance in the data storage and processing process is higher because these data are directly related to clinical diagnosis results; the key data type refers to the data type that is identified as being of great significance to the storage system based on the proportion importance. For example, imaging data is a key data type because it not only accounts for a large proportion, but is also an important basis for doctors to make diagnoses, and its accuracy and completeness are very important. The secondary data type refers to a data type that is less important than the key data type. For example, equipment operation log data may belong to a secondary data type. Although it plays a certain role in equipment maintenance and troubleshooting, its direct impact on medical diagnosis is smaller than that of imaging data. The storage allocation strategy refers to a method for allocating and managing storage resources based on key data types and secondary data types. For key data types, the storage allocation strategy may include storing them in high-speed and reliable storage areas and using redundant storage mechanisms to ensure data security and accessibility, such as equipping imaging data with high-performance solid-state drives and performing multiple copy backups.

[0129] Furthermore, the analysis of the storage proportion characteristics of the storage capacity value under different data types can be achieved through disk management tools, such as DiskGenius and other tools; the analysis of the importance of the proportion corresponding to the storage proportion characteristics can be achieved through hierarchical analysis, such as by establishing a hierarchical model, constructing a judgment matrix, and calculating the weight of each data type, and the weight reflects the importance of the proportion; the identification of the key data type and the secondary data type corresponding to the storage capacity value can be achieved through data mining tools, such as IBM SPSS Modeler, etc.; the construction of the storage allocation strategy corresponding to the key data type and the secondary data type can be achieved through a storage simulator, such as DiskSim, etc.; the analysis of the data storage demand corresponding to the storage capacity value can be achieved through a demand modeling method, such as establishing a data storage demand model based on the determined key data types and secondary data types and their storage allocation strategies.

[0130] The present invention constructs data storage instructions corresponding to the monitoring data based on the data storage requirements, which can ensure the accuracy and efficiency of data storage. According to the storage capacity, storage structure and data type characteristics determined by the data storage requirements, the data storage instructions can enable the monitoring data to be stored in the most appropriate location, avoid confusion and errors in data storage, reduce time loss and resource waste in the data storage process, and improve the overall efficiency of data storage.

[0131] Among them, the data storage instructions refer to a series of clear operation commands or rules used to guide the storage process of monitoring data in the storage system, including where the data should be stored (specify the storage device and path), how to store it (such as whether to compress it, what encoding format to use), the frequency and timing of storage (such as real-time storage or scheduled storage), data backup strategy (backup time interval, number of backup copies and storage location) and data access permission settings (which users or systems can read and modify data), etc.

[0132] As an embodiment of the present invention, constructing the data storage instructions corresponding to the monitoring data based on the data storage requirements includes: analyzing the core storage elements corresponding to the data storage requirements; determining the storage impact parameters corresponding to the core storage elements; based on the storage impact parameters, querying the standard storage template corresponding to the monitoring data; screening the adaptation data module in the standard storage template; extracting the key storage parameters corresponding to the adaptation data module; and constructing the data storage instructions corresponding to the monitoring data based on the key storage parameters.

[0133] The core storage elements refer to the factors that play a key and decisive role in the data storage requirements. For example, for the monitoring data of the chest electrical impedance tomography, the core storage elements may include the type of data (such as imaging data, patient physiological parameter data, etc.), the importance level of the data (divided according to the criticality to diagnosis and treatment), the expected storage time of the data (short-term for immediate diagnosis or long-term for medical research, etc.) and the access frequency of the data (frequently viewed by doctors or rarely called), etc.; the storage influencing parameters refer to the specific parameters that are closely related to the core storage elements and can have a substantial impact on the data storage process, including the size range of different data types (such as imaging data may be large, while some identification data is small), data format (different formats such as binary and text have an impact on storage and reading efficiency) and data update rules (real-time update or regular update), etc.; the standard storage template refers to the pre-set parameters for different types of monitoring data. A standardized framework or pattern for storage. For example, there may be a special image data storage template for imaging data, which specifies the directory structure of data storage, file name naming rules, data compression method (if applicable), etc.; the adaptation data module refers to a specific part or module in the standard storage template that matches the core storage elements and storage influencing parameters of the current monitoring data. For example, if the monitoring data is imaging data of high importance and is updated frequently, then the imaging data storage module with high security level and efficient writing in the standard storage template is the adaptation data module; the key storage parameters refer to the parameter information that is further extracted from the adaptation data module that is most critical for constructing data storage instructions, such as the storage medium type (such as a solid-state hard drive or a specific network storage device), data block size (used to optimize storage and reading efficiency), storage path (specific location in the storage device), and data verification method (to ensure data accuracy) determined in the adaptation data module.

[0134] Furthermore, the analysis of the core storage elements corresponding to the data storage requirements can be achieved through a demand hierarchy analysis method, such as: decomposing the data storage requirements according to different levels, for example, analyzing from the aspects of the business purpose of the data (clinical diagnosis, scientific research analysis, etc.), data characteristics (type, format, etc.), and usage scenarios (real-time monitoring, historical review, etc.), to determine which core elements play a key role in storage; the determination of the storage influencing parameters corresponding to the core storage elements can be achieved through an association rule mining algorithm, such as: Apriori and other algorithms; the query of the standard storage template corresponding to the monitoring data can be achieved through a template indexing method, such as: establishing a standard storage template index library, and indexing the standard storage template according to key attributes such as data type, data importance level, and data application field. Classification index; the screening of the adaptation data module in the standard storage template can be implemented by programming languages ​​such as Python; the extraction of the key storage parameters corresponding to the adaptation data module can be implemented by parameter marking method, such as: in the text describing the imaging data storage module, the storage medium type is marked as "key parameter 1", the data block size is marked as "key parameter 2", etc., and when extracting, the key storage parameters are directly obtained according to these marks; the construction of the data storage instruction corresponding to the monitoring data can be implemented by the instruction template filling method, such as: pre-designing a data storage instruction template, the template contains placeholders for instruction fields such as storage location, storage method, backup strategy, etc., and according to the extracted key storage parameters, they are filled into the corresponding placeholders of the instruction template to form a complete data storage instruction.

[0135] The present invention is based on generating a file storage report corresponding to the chest electrical impedance tomography imager based on the data storage instruction, which helps to timely discover potential problems of the storage system. Through continuous monitoring of storage feedback parameters such as storage speed, storage success rate, data integrity verification results, etc., hidden dangers such as storage medium failure, storage link abnormality or software configuration error can be detected at an early stage, and measures can be taken in advance to repair or optimize, avoiding data loss or interruption of imager operation due to storage problems, thereby ensuring the security of medical data and the stability of equipment operation.

[0136] Among them, the file storage report refers to a comprehensive, systematic and structured document, which records in detail various types of information related to file storage during the operation of the chest electrical impedance tomography instrument. The report takes storage feedback parameters as the core content, covering the usage details of storage capacity, such as used space, remaining space and space usage trend; accurate data on storage speed, including the average speed, peak speed and speed fluctuation of data writing and reading; data integrity assessment results, such as the number, location and type of data verification errors; success rate statistics of storage operations, such as the number and proportion of success and failure of operations such as data writing, deletion, and modification; it also includes the response time distribution of the storage system, as well as the frequency, source and permission execution status of data access. Optionally, the generation of the file storage report corresponding to the chest electrical impedance tomography instrument can be achieved through a report generation tool, such as: JasperReports, FineReport and other tools.

[0137] First, the present invention obtains a chest electrical impedance tomography instrument and collects monitoring data from the chest electrical impedance tomography instrument, which can obtain internal information of the human chest in real time and non-invasively, and can dynamically monitor the ventilation and blood perfusion conditions of the lungs, which helps to timely discover potential lung diseases or functional abnormalities, and provide key clues for early diagnosis of the disease, thereby greatly improving the cure rate of the disease and the prognosis of the patient. At the same time, the present invention can quickly locate the relevant version for investigation by extracting the version identification information in the format-optimized data, which helps to discover potential technical problems or sources of errors. Secondly, the version identification information can be used as an important basis for judging data compatibility, so as to take corresponding conversion or adaptation measures to ensure the smooth circulation and effective use of data in multiple devices and multiple application scenarios, and promote the integration and sharing of medical data resources. Based on the verification data set, the present invention analyzes the file reading protocol corresponding to the chest electrical impedance tomography instrument, which can provide rich and reliable information for accurate analysis of the file reading protocol. Reliable data foundation can clarify the potential differences and commonalities of file reading protocols of imagers under different versions and different production batches, which is helpful to develop more adaptable file reading software or tools, improve the success rate and accuracy of data reading, and reduce data reading errors or failures caused by protocol mismatch. The present invention determines the storage optimization direction corresponding to the chest electrical impedance tomography imager based on the file parsing performance value, which can clarify the bottleneck of the current imager in the data processing process, and can help remove unnecessary data storage, reasonably allocate storage space, and reduce the waste of storage resources, so as to store more valuable data under limited storage capacity. Further, the present invention can accurately understand how much storage space the chest electrical impedance tomography imager needs to store data during operation by querying the data storage demand corresponding to the storage capacity value, thereby avoiding the risk of data loss caused by insufficient storage space, or the waste of resources and cost increase caused by over-configuration of storage space. Therefore, a chest electrical impedance tomography data file storage method and system proposed by the present invention can improve the storage efficiency of medical data.

[0138] Embodiment 2:

[0139] like Figure 2 FIG. 1 is a module schematic diagram of a chest electrical impedance tomography data file storage system provided by an embodiment of the present invention.

[0140] The chest electrical impedance tomography data file storage system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the chest electrical impedance tomography data file storage system 200 can include a format optimization module 201, a data integration module 202, a performance value calculation module 203, a capacity value calculation module 204 and a report generation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0141] In this embodiment, the functions of each module / unit are as follows:

[0142] The format optimization module 201 is used to obtain a chest electrical impedance tomography instrument, collect monitoring data in the chest electrical impedance tomography instrument, query the data storage format in the monitoring data, optimize the data storage format, and obtain format-optimized data;

[0143] The data integration module 202 is used to extract the version identification information in the format-optimized data, collect the authenticity verification parameters in the version identification information, and perform data integration on the authenticity verification parameters to obtain a verification data set;

[0144] The performance value calculation module 203 is used to analyze the file reading protocol corresponding to the chest electrical impedance tomography instrument based on the verification data set, determine the data frame format state corresponding to the file reading protocol, and calculate the file parsing performance value corresponding to the chest electrical impedance tomography instrument according to the data frame format state;

[0145] A capacity value calculation module 204 is used to determine a storage optimization direction corresponding to the chest electrical impedance tomography instrument based on the file parsing performance value, analyze the data storage characteristics under the storage optimization direction, and calculate the storage capacity value corresponding to the chest electrical impedance tomography instrument based on the data storage characteristics;

[0146] The report generation module 205 is used to query the data storage requirements corresponding to the storage capacity value, construct data storage instructions corresponding to the monitoring data based on the data storage requirements, and generate a file storage report corresponding to the chest electrical impedance tomography instrument based on the data storage instructions.

[0147] In detail, the modules described in the chest electrical impedance tomography data file storage system 200 described in the embodiment of the present invention adopt the same technical means as the chest electrical impedance tomography data file storage method described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0148] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for storing data files of a chest electrical impedance tomography instrument, characterized in that: The method comprises: Acquire a chest electrical impedance tomography instrument, collect monitoring data in the chest electrical impedance tomography instrument, query the data storage format in the monitoring data, optimize the data storage format, and obtain format-optimized data; Extracting version identification information from the format-optimized data, collecting authenticity verification parameters from the version identification information, and performing data integration on the authenticity verification parameters to obtain a verification data set; Based on the verification data set, analyzing the file reading protocol corresponding to the chest electrical impedance tomography instrument, determining the data frame format state corresponding to the file reading protocol, and calculating the file parsing performance value corresponding to the chest electrical impedance tomography instrument according to the data frame format state; Based on the file parsing performance value, determining a storage optimization direction corresponding to the chest electrical impedance tomography instrument, analyzing data storage characteristics under the storage optimization direction, and calculating a storage capacity value corresponding to the chest electrical impedance tomography instrument based on the data storage characteristics; The data storage requirement corresponding to the storage capacity value is queried, and based on the data storage requirement, a data storage instruction corresponding to the monitoring data is constructed; based on the data storage instruction, a file storage report corresponding to the chest electrical impedance tomography is generated.

2. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: Optimizing the data storage format to obtain format-optimized data includes: Identify existing structural features corresponding to the data storage format; Based on the existing structural features, extracting key information identifiers in the data storage format; Analyze the optimizable dimensions corresponding to the key information identifiers; Based on the optimizable dimension, determining an optimizable direction corresponding to the data storage format; Based on the optimizable direction, the data storage format is optimized to obtain format-optimized data.

3. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The extracting the version identification information in the format-optimized data includes: Dividing the format-optimized data into data blocks to obtain a set of data sub-blocks; Scanning a header area of ​​each sub-block in the data sub-block set; Identify a specific identification field in the header area; Analyzing the encoding rules of the specific identification field; Based on the encoding rule, version identification information in the format-optimized data is extracted.

4. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The data integration of the authenticity verification parameters to obtain a verification data set includes: Analyze the parameter source and parameter type corresponding to the authenticity verification parameter; Constructing an integration rule framework corresponding to the parameter source and the parameter type; Based on the integration rule framework, query the repeated data part in the authenticity verification parameter; Removing redundant information from the repeated data portion to obtain deduplicated data; Associating logically related items in the deduplicated data; Based on the logical correlation items, data integration is performed on the authenticity verification parameters to obtain a verification data set.

5. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The analyzing, based on the verification data set, a file reading protocol corresponding to the chest electrical impedance tomography instrument comprises: Identifying key information identifiers in the verification data set; Query the file reading element corresponding to the key information identifier; Analyze the protocol parameter type corresponding to the file reading element; Determine a protocol analysis dimension corresponding to the protocol parameter type; Based on the protocol analysis dimension, the file reading protocol corresponding to the chest electrical impedance tomography is analyzed.

6. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The calculating, according to the data frame format state, a file parsing performance value corresponding to the chest electrical impedance tomography apparatus comprises: The file parsing performance value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula: Wherein, FX represents the file parsing performance value corresponding to the chest electrical impedance tomography, n represents the number of fields corresponding to the data frame in the data frame format state, i represents the field index corresponding to the data frame, l i represents the field length corresponding to the i-th field, m represents the number of types corresponding to the encoding type, j represents the type index corresponding to the encoding type, c j represents the number of times the jth encoding type appears in the data frame, p represents the total number of data frames, k represents the number index of data frames, and t k represents the frame processing time corresponding to the kth data frame, e k Indicates the number of parsing errors corresponding to the k-th data frame.

7. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The step of determining the storage optimization direction corresponding to the chest electrical impedance tomography device based on the file parsing performance value includes: Identify performance influencing factors corresponding to the file parsing performance value; Classifying the performance influencing factors to obtain classified performance; Analyzing the storage association degree corresponding to the classification performance; Querying the storage priority corresponding to the storage association degree; Based on the storage priority, a storage optimization direction corresponding to the chest electrical impedance tomography apparatus is determined.

8. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The calculating, based on the data storage characteristic, a storage capacity value corresponding to the chest electrical impedance tomography apparatus comprises: The storage capacity value corresponding to the chest electrical impedance tomography instrument is calculated using the following formula: Wherein, Sr represents the storage capacity value corresponding to the chest electrical impedance tomography, K represents the data redundancy coefficient corresponding to the data storage characteristic, n represents the total number of different data types in the data storage characteristic, i′ represents the type index of the data type, V i′ represents the single data volume corresponding to the i′th data type, m′ represents the total number of data storage cycles in the data storage characteristic, j′ represents the number index corresponding to the storage cycle, and C i′j′ represents the data generation amount of the i′th data type in the j′th storage cycle, and R represents the data compression ratio corresponding to the data storage characteristic.

9. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The querying of the data storage demand corresponding to the storage capacity value includes: Analyze the storage ratio characteristics of the storage capacity value under different data types; Analyze the importance of the storage ratio corresponding to the storage ratio feature; Based on the importance of the proportion, identifying the key data type and the secondary data type corresponding to the storage capacity value; Constructing a storage allocation strategy corresponding to the key data type and the secondary data type; Based on the storage allocation strategy, data storage requirements corresponding to the storage capacity value are analyzed.

10. A method for storing data files of a chest electrical impedance tomography apparatus as claimed in claim 1, characterized in that: The constructing the data storage instruction corresponding to the monitoring data based on the data storage requirement includes: Analyze the core storage elements corresponding to the data storage requirements; Determining storage impact parameters corresponding to the core storage elements; Based on the storage influencing parameter, querying a standard storage template corresponding to the monitoring data; Screening the adaptation data module in the standard storage template; Extracting key storage parameters corresponding to the adaptation data module; Based on the key storage parameters, data storage instructions corresponding to the monitoring data are constructed.

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