Archives searching system based on internet of things technology

Abstract

The application discloses an archive searching system based on Internet of Things technology and relates to the technical field of archive management.The archive searching system solves the problem of slow archive data indexing progress.The archive searching system is characterized in that, in the aspect of archive positioning and coding, the system finely confirms the storage position of clinical trial archives through an archive position confirmation terminal, and generates unique position coding according to the hierarchy and sorting features of the storage path.The coding mode directly reflects the storage path of the archives, realizes accurate identification of the storage position of massive archives, and makes the sorting coding of each hierarchy one-to-one correspond to the actual sorting position of the node, so that the position coding has clear logic and traceability, an operator can quickly locate the physical storage path of the archives through the coding, time waste caused by position ambiguity in traditional archive searching is greatly reduced, and the efficiency of archive positioning is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of archives management technology, specifically an archives search system based on Internet of Things (IoT) technology. Background Technology

[0002] In the field of clinical trial research, the increasing number of research projects and the growing complexity of the research process have generated a massive amount of clinical trial archive data. This archive data contains key information such as trial protocols, subject information, test reports, and data analysis results. It serves as an important carrier of clinical trial research findings and has irreplaceable value for subsequent research references, results traceability, and compliance reviews.

[0003] Traditional clinical trial record management relies heavily on manual recording and retrieval. Record data is typically stored on fixed physical media or local storage systems, with different records scattered across various locations. During the storage phase, operators must manually record the storage path and location information for each record, which is not only time-consuming and labor-intensive but also prone to errors due to human negligence. During the retrieval phase, the lack of a unified and efficient indexing mechanism forces operators to rely on memory or tedious manual searching to find target records. When the number of records is large, this retrieval method becomes extremely inefficient, often resulting in excessively long search times or even failure to accurately locate the target record.

[0004] Furthermore, as clinical trials progress, archival data may be added or its storage location may change. Under traditional management models, changes in archival storage locations are difficult to synchronize with the retrieval system in real time, easily leading to a mismatch between indexed information and actual storage locations, further increasing the difficulty of archival searches. Simultaneously, the lack of targeted parameter optimization during the indexing process for different archival data formats results in unstable indexing speeds, failing to meet the demands of rapid retrieval of massive amounts of archival data. These problems severely restrict the efficiency of clinical trial archival management, affecting the progress of clinical trial research and the translation of results. Therefore, there is an urgent need for an archival management system that can achieve accurate archival location, efficient indexing, and good adaptability to address the many pain points of traditional management methods. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an archive search system based on Internet of Things (IoT) technology, which solves the problem of slow progress in archive data indexing.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an archive search system based on Internet of Things (IoT) technology, comprising:

[0007] The archive database contains a large amount of clinical trial archive data, with different clinical trial archive data stored in different storage locations.

[0008] The archive location confirmation end identifies the storage locations associated with different clinical trial archive data and assigns different location codes based on the number of storage locations associated with different levels. Specifically:

[0009] Confirm the storage location of the corresponding clinical trial archive data and generate the storage path associated with the corresponding storage location. Then, confirm the level of the path node from the storage path, confirm the sorting position of the corresponding path node from the sorting features of the corresponding level, and adaptively generate a sorting code based on the sorting position. The sorting code is consistent with the node sorting position parameter.

[0010] Similarly, following the order of corresponding levels from front to back, the associated sorting codes are sorted to generate the location codes belonging to the corresponding clinical trial archive data, and the different location codes associated with different clinical trial archive data are confirmed in turn.

[0011] At the index feature recording end, based on the total number of different paths associated with different levels, corresponding feature polygons are constructed. Then, based on the location code associated with the corresponding clinical trial archive data, the feature vector associated with the clinical trial archive data is confirmed and recorded. The specific method is as follows:

[0012] The total number of different paths associated with different levels is denoted as Z. i , where i represents different levels, the outermost level is identified from the storage features, and the levels are sorted sequentially starting from the outermost level to confirm the level column;

[0013] Identify the Z associated with the first group of levels from the hierarchy column. i And based on Z i Generate a set of corresponding polygons, the total number of sides of which is related to Z. i Consistent, and then based on the Z associated with the corresponding level. i The associated polygons are generated sequentially, with each polygon in a subsequent level containing polygons in the previous level. The inner diameter of the polygons in subsequent levels gradually increases. The proposed sorting position of the corresponding level in the hierarchy column is P. k Where k represents different levels, and the inner diameter of the polygon associated with the corresponding level is (P k ×C1), where C1 is a preset unit length, and the center points of several polygons associated with the hierarchical column are all at the same point. Construct a set of perpendicular lines to the center points, and control a certain corner point of the corresponding polygon to coincide with this perpendicular line. Starting from the confirmed perpendicular line, sort the corner points associated with the corresponding polygons in a clockwise direction. The total number of these sorted numbers is equal to the Z associated with the corresponding level. iConsistent;

[0014] Based on the location codes associated with the corresponding clinical trial archive data and the constructed multi-level polygons, the code numbers associated with the corresponding path nodes are identified from the location codes, and the corner points associated with the corresponding level polygons are locked. Starting from the innermost level polygon, the identification is carried out step by step outward, and the associated corner points are connected in sequence to generate the feature vector associated with the corresponding clinical trial archive data and record it.

[0015] The index center identifies the clinical trial archive data to be indexed based on the input feature text, then directly identifies the feature vector associated with the clinical trial archive data, and directly identifies the path node where the corresponding clinical trial archive data is located based on the constructed hierarchical polygon, and directly outputs the index.

[0016] Preferably, after the construction of the layered polygon is completed, the sorting position is matched one-to-one with the corner point of the corresponding number sorting according to the sorting position associated with the corresponding path node of the corresponding layer, and the path node associated with the corresponding corner point is confirmed.

[0017] Preferred options also include:

[0018] The historical data processing center identifies the index characteristics of data in different formats during historical processing. From the identified large number of index characteristics, it then identifies and records the operational characteristics of the corresponding data formats. Specifically, the process is as follows:

[0019] The different indexing processes associated with different data formats are identified, the index bandwidth and associated index rate are identified from the indexing processes, and the average rate of several sets of index rates associated with the same index bandwidth is calculated to identify the average rate, and the identified average rate is used as the characteristic rate of the corresponding index bandwidth.

[0020] The index bandwidths are sorted in ascending order of numerical value, and rate change curves associated with the corresponding format data are generated based on the different characteristic rates associated with different index bandwidths.

[0021] Line segments are selected from the generated rate change curve. The selected line segments must be at least five index bandwidth points in length. The selected line segments are then feature-confirmed: the average of several characteristic rates associated with the corresponding line segment is taken as the first characteristic of the corresponding line segment. The maximum and minimum values ​​are then identified from the several characteristic rates associated with the corresponding line segment, and the difference between the maximum and minimum values ​​is locked as the second characteristic of the corresponding line segment. Based on the confirmed first and second characteristics, the calibration characteristics associated with the corresponding line segment are identified. The calibration characteristic is calculated as (first characteristic × A1) ÷ (second characteristic × A2), where A1 and A2 are preset fixed coefficient factors.

[0022] The different calibration features associated with the selected line segments are confirmed in sequence, and the maximum value is selected from the confirmed calibration features. The corresponding line segment associated with the maximum value is taken as the determined line segment, and the index bandwidth associated with the determined line segment is recorded as the running feature of the corresponding format data.

[0023] Preferably, the index center selects the median value of the operating characteristics associated with different data formats as the operating bandwidth, and uses the operating bandwidth for index output during the indexing process of the corresponding data formats.

[0024] This invention provides an archive search system based on Internet of Things (IoT) technology. Compared with existing technologies, it has the following advantages:

[0025] This invention addresses the issue of archive location and coding. The system uses an archive location confirmation terminal to precisely identify the storage location of clinical trial archives and generates unique location codes based on the hierarchy and sorting characteristics of the storage path. This coding method intuitively reflects the archive storage path, achieving accurate identification of the storage location of massive amounts of archives. Each level of sorting code corresponds one-to-one with the actual sorting position of the node, giving the location codes clear logic and traceability. Operators can quickly locate the physical storage path of the archives through the codes, significantly reducing the time wasted due to ambiguous locations in traditional archive searches and significantly improving the efficiency of archive location.

[0026] For the construction and updating of index features, the index feature recorder constructs feature polygons based on the total number of paths at different levels and generates feature vectors by combining them with location encoding, transforming the location information of the archives into visual feature graphics. This graphical index feature facilitates rapid identification and comparison by the system, laying a precise foundation for subsequent indexing work. Simultaneously, the system possesses good adaptability; when a new archive storage location is added, an update command can trigger automatic updates to the index features, ensuring that the feature graphics remain consistent with the actual storage location. This effectively avoids indexing errors caused by changes in storage location, guaranteeing the accuracy and reliability of the indexing results.

[0027] In terms of indexing efficiency optimization, the historical data processing center analyzes historical indexed data to extract operational characteristics of different data formats and determine the optimal indexing bandwidth. Based on these operational characteristics, the indexing center selects an appropriate bandwidth for index output, adaptively adjusting indexing parameters according to differences in data formats to ensure the indexing process remains highly efficient and stable. This historical data-optimized indexing strategy reduces invalid indexing operations and improves indexing speed, especially when processing large amounts of clinical trial archive data, significantly shortening indexing time and improving the overall efficiency of archive searching. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] First Embodiment

[0031] Please see Figure 1 This application provides an archive search system based on Internet of Things technology, including an archive location confirmation terminal, an index feature recording terminal, an archive database, an index center, and a historical data processing center. The archive database is electrically linked to the archive location confirmation terminal and the input node of the index center, respectively. The archive location confirmation terminal, the index feature recording terminal, and the index center are electrically linked from the output node to the input node in sequence, and the historical data processing center is electrically linked to the input node of the index center.

[0032] Among them, the archive database contains a large amount of clinical trial archive data. Different clinical trial archive data are stored in different storage locations and are all stored in advance by the operators.

[0033] The archive location confirmation end confirms the storage location associated with different clinical trial archive data, assigns different location codes based on the number of storage locations associated with different levels, and transmits the location codes associated with different clinical trial archive data to the index feature record end. Specifically, each different clinical trial archive data has a preset storage location, and each different storage folder is pre-determined and generally will not be added or changed. If there is a new addition, an update instruction needs to be generated and transmitted to the index feature record end. The index feature record end then needs to update the associated index features to ensure the accuracy of the corresponding graph and avoid related errors between the updated graph and the original graph.

[0034] The specific method for assigning codes at different positions is as follows:

[0035] The system identifies the storage location of the corresponding clinical trial archive data and generates the storage path associated with that location. It then identifies the level of the path nodes within the storage path, determines the sorting position of the corresponding path nodes based on the sorting characteristics of that level, and adaptively generates a sorting code based on the sorting position. The sorting code is consistent with the node sorting position parameter. For example, if the corresponding storage node is the fifth one from top to bottom, then the sorting code associated with the corresponding storage node is 5. Specifically, drives C, D, E, and F belong to the first level.

[0036] Similarly, following the order of corresponding levels from front to back, the associated sorting codes are sorted to generate the location codes belonging to the corresponding clinical trial archive data, and the different location codes associated with different clinical trial archive data are confirmed in turn.

[0037] Specifically, in the corresponding hierarchical sorting process, each level has different path nodes. Based on the sorting method of the path nodes, the sorting code associated with the corresponding node can be generated. Then, the determined sorting codes can be sorted from front to back to obtain the position code associated with the corresponding sorting code.

[0038] Specifically, the index feature recording end constructs corresponding feature polygons based on the total number of different paths associated with different levels. Then, based on the location code associated with the corresponding clinical trial archive data, it confirms and records the feature vectors associated with the clinical trial archive data. The specific method for confirming the feature vectors is as follows:

[0039] The total number of different paths associated with different levels is denoted as Z. i , where i represents different levels, the outermost level is identified from the storage features, and the levels are sorted sequentially starting from the outermost level to confirm the level column;

[0040] Identify the Z associated with the first group of levels from the hierarchy column. i And based on Z i Generate a set of corresponding polygons, the total number of sides of which is related to Z. i Consistent, and then based on the Z associated with the corresponding level. i The associated polygons are generated sequentially, with each polygon in a subsequent level containing polygons in the previous level. The inner diameter of the polygons in subsequent levels gradually increases. The proposed sorting position of the corresponding level in the hierarchy column is P. k Where k represents different levels, and the inner diameter of the polygon associated with the corresponding level is (P k×C1), where C1 is a preset unit length, determined in advance by the operator based on experience, and the center points of several polygons associated with the hierarchical column are all at the same point. A set of perpendicular lines are constructed to the center point, and a corner point of the corresponding polygon is controlled to coincide with this perpendicular line. Starting from the confirmed perpendicular line, the corner points associated with the corresponding polygon are numerically sorted in a clockwise direction. The total number of these numerical sorts is equal to the Z associated with the corresponding level. i In a consistent manner, after constructing the hierarchical polygons, the sorting positions of the path nodes associated with each level are matched one-to-one with the corresponding numerically sorted corner points. This confirms the path nodes associated with each corner point. For example, if there are five levels, each level is associated with a corresponding total number of paths, and a polygon with the corresponding number of sides is generated, then five polygons are associated with each level. The center point of each polygon is the same. Following the confirmed perpendicular line, the polygons are rotated around their center points so that the corresponding corner points coincide with the perpendicular line. Then, the associated corner points are sorted clockwise, with different corner points associated with different numbers. Subsequently, if new storage nodes are added within a corresponding level (generally not, as corresponding storage ranges are pre-defined during storage and may be updated monthly), the same processing method is used to update and adjust the constructed hierarchical polygons to achieve better adjustment results.

[0041] Based on the location codes associated with the corresponding clinical trial archive data and the constructed multi-level polygons, the code numbers associated with the corresponding path nodes are identified from the location codes, and the corner points associated with the corresponding level polygons are locked. Then, starting from the innermost level polygon, the identification is carried out step by step outwards. The associated corner points are connected in sequence to generate the feature vector associated with the corresponding clinical trial archive data and record it. This facilitates subsequent comparison and verification, quick search, and quick indexing of the storage location of the corresponding data.

[0042] The index center identifies the clinical trial archive data to be indexed based on the input feature text, then directly identifies the feature vector associated with the clinical trial archive data, and directly identifies the path node where the corresponding clinical trial archive data is located based on the constructed hierarchical polygon, and directly outputs the index.

[0043] Second Embodiment

[0044] In this embodiment, compared to the above embodiments, this embodiment mainly focuses on the indexing characteristics of the indexing process, modifies the indexing parameters, and ensures the rapid output of the corresponding process data of the corresponding indexing process.

[0045] The historical data processing center identifies the index characteristics of data in different formats during the historical processing process. From the identified large number of index characteristics, it then identifies and records the operational characteristics of the corresponding data formats. The specific method for identifying these operational characteristics is as follows:

[0046] The different indexing processes associated with different data formats are identified, the index bandwidth and associated index rate are identified from the indexing processes, and the average rate of several sets of index rates associated with the same index bandwidth is calculated to identify the average rate, and the identified average rate is used as the characteristic rate of the corresponding index bandwidth.

[0047] The index bandwidths are sorted in ascending order of numerical value, and rate change curves associated with the corresponding format data are generated based on the different characteristic rates associated with different index bandwidths.

[0048] Line segments are selected from the generated rate change curve. The selected line segments must be at least five index bandwidth points in length (that is, the corresponding line segment must have at least five sets of points). The selected line segments are then feature-confirmed: the average of several characteristic rates associated with the corresponding line segment is taken as the first characteristic of the corresponding line segment. The maximum and minimum values ​​are then determined from the several characteristic rates associated with the corresponding line segment, and the difference between the maximum and minimum values ​​is locked as the second characteristic of the corresponding line segment. Based on the confirmed first and second characteristics, the calibration characteristics associated with the corresponding line segment are confirmed. The calibration characteristic is calculated as (first characteristic × A1) ÷ (second characteristic × A2), where A1 and A2 are preset fixed coefficient factors, and their specific values ​​are determined by the operator based on experience.

[0049] The different calibration features associated with the selected line segments are confirmed in sequence, and the maximum value is selected from the confirmed calibration features. The corresponding line segment associated with the maximum value is taken as the determined line segment, and the index bandwidth associated with the determined line segment is recorded as the running feature of the corresponding format data.

[0050] Specifically, in the corresponding curve, line segments can be selected sequentially from front to back. The length of the selected line segments can be varied, and different lengths of line segments can be selected during the actual process. The calibration features associated with line segments of different lengths are different. In the actual feature verification selection process, the line segment with the most obvious calibration feature can be identified and used as the determined line segment. Then, the running features can be quickly confirmed, which facilitates subsequent feature processing and verification.

[0051] The indexing center selects the median value of the operating characteristics associated with different data formats as the operating bandwidth, and uses the operating bandwidth to output the index during the indexing process of the corresponding data formats, thus completing the indexing process of the corresponding clinical trial archive data.

[0052] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0053] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An archive search system based on Internet of Things (IoT) technology, characterized in that: include: The archive database contains a large amount of clinical trial archive data, with different clinical trial archive data stored in different storage locations. The archive location confirmation end confirms the storage location associated with different clinical trial archive data and assigns different location codes based on the number of storage locations associated with different levels; The index feature recording end constructs corresponding feature polygons based on the total number of different paths associated with different levels, and then confirms and records the feature vectors associated with the clinical trial archive data based on the location code associated with the corresponding clinical trial archive data. The index center identifies the clinical trial archive data to be indexed based on the input feature text, then directly identifies the feature vector associated with the clinical trial archive data, and directly identifies the path node where the corresponding clinical trial archive data is located based on the constructed hierarchical polygon, and directly outputs the index. The historical data processing center confirms the index characteristics of data in different formats during the historical processing process, and identifies and records the operational characteristics of the corresponding data formats from the large number of confirmed index characteristics. The historical data processing center confirms the specific methods for determining the operational characteristics of data in the corresponding format as follows: The different indexing processes associated with different data formats are identified, the index bandwidth and associated index rate are identified from the indexing processes, and the average rate of several sets of index rates associated with the same index bandwidth is calculated to identify the average rate, and the identified average rate is used as the characteristic rate of the corresponding index bandwidth. The index bandwidths are sorted in ascending order of numerical value, and rate change curves associated with the corresponding format data are generated based on the different characteristic rates associated with different index bandwidths. Line segments are selected from the generated rate change curve. The selected line segments must be at least five index bandwidth points in length. The selected line segments are then feature-confirmed: the average of several characteristic rates associated with the corresponding line segment is taken as the first characteristic of the corresponding line segment. The maximum and minimum values ​​are then identified from the several characteristic rates associated with the corresponding line segment, and the difference between the maximum and minimum values ​​is locked as the second characteristic of the corresponding line segment. Based on the confirmed first and second characteristics, the calibration characteristics associated with the corresponding line segment are identified. The calibration characteristic = (first characteristic × A1) ÷ (second characteristic × A2), where A1 and A2 are preset fixed coefficient factors. The different calibration features associated with the selected line segments are confirmed in sequence, and the maximum value is selected from the confirmed calibration features. The corresponding line segment associated with the maximum value is taken as the determined line segment, and the index bandwidth associated with the determined line segment is recorded as the running feature of the corresponding format data.

2. The archive search system based on Internet of Things technology according to claim 1, characterized in that, The specific method by which the archive location confirmation terminal assigns different location codes is as follows: Confirm the storage location of the corresponding clinical trial archive data and generate the storage path associated with the corresponding storage location. Then, confirm the level of the path node from the storage path, confirm the sorting position of the corresponding path node from the sorting features of the corresponding level, and adaptively generate a sorting code based on the sorting position. The sorting code is consistent with the node sorting position parameter. Similarly, following the order of corresponding levels from front to back, the associated sorting codes are sorted to generate the location codes belonging to the corresponding clinical trial archive data, and the different location codes associated with different clinical trial archive data are confirmed in turn.

3. The archive search system based on Internet of Things technology according to claim 2, characterized in that, The specific method for confirming the feature vector at the index feature recording terminal is as follows: The total number of different paths associated with different levels is denoted as Z. i , where i represents different levels, the outermost level is identified from the storage features, and the levels are sorted sequentially starting from the outermost level to confirm the level column; Identify the Z associated with the first group of levels from the hierarchy column. i And based on Z i Generate a set of corresponding polygons, the total number of sides of which is related to Z. i Consistent, and then based on the Z associated with the corresponding level. i The associated polygons are generated sequentially, with each polygon in a subsequent level containing polygons in the previous level. The inner diameter of the polygons in subsequent levels gradually increases. The proposed sorting position of the corresponding level in the hierarchy column is P. k Where k represents different levels, and the inner diameter of the polygon associated with the corresponding level is (P k ×C1), where C1 is a preset unit length, and the center points of several polygons associated with the hierarchy column are all at the same point. Construct a set of perpendicular lines to the center points, and control a corner point of the corresponding polygon to coincide with this perpendicular line. Starting from the confirmed perpendicular line, sort the corner points associated with the corresponding polygons in a clockwise direction. The total number of these sorted numbers is equal to the Z-axis of the corresponding hierarchy. i Consistent; Based on the location codes associated with the corresponding clinical trial archive data and the constructed multi-level polygons, the code numbers associated with the corresponding path nodes are identified from the location codes, and the corner points associated with the corresponding level polygons are locked. Starting from the innermost level polygon, the identification is carried out step by step outward, and the associated corner points are connected in sequence to generate the feature vector associated with the corresponding clinical trial archive data and record it.

4. The archive search system based on Internet of Things technology according to claim 3, characterized in that, After the construction of the layered polygons is completed, the sorting positions are matched one-to-one with the corner points of the corresponding numerical sorting according to the sorting positions associated with the corresponding path nodes of the corresponding layers, and the path nodes associated with the corresponding corner points are confirmed.

5. The archive search system based on Internet of Things technology according to claim 1, characterized in that, The index center selects the median value of the operating characteristics associated with different data formats as the operating bandwidth, and uses the operating bandwidth for index output during the indexing process of the corresponding data formats.

Images