Archive searching system based on Internet of Things technology

Abstract

The invention discloses an archive searching system based on the Internet of Things technology, relates to the technical field of archive management, solves the problem that the archive data indexing progress is slow, and fines and confirms the storage position of a clinical test archive through an archive position confirmation end in the aspects of archive positioning and encoding. Generating a unique position code according to the hierarchy and sorting characteristics of the storage path; the coding mode visually reflects the storage path of the archives, and accurate identification of the storage positions of the massive archives is realized; the sorting codes of each level are in one-to-one correspondence with the actual sorting positions of the nodes, so that the position codes have clear logicality and traceability, an operator can quickly position a physical storage path of the archive through the codes, time waste caused by fuzzy positions in traditional archive searching is greatly reduced, and archive positioning efficiency is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of archive management, in particular to an archive searching system based on Internet of Things technology. BACKGROUND

[0002] In the field of clinical trial research, with the increasing number of research projects and the increasing complexity of research process, a large amount of clinical trial archive data is generated. These archive data contain key contents such as test plan, subject information, test report, data analysis result, etc., and are important carriers of clinical trial research results, which have irreplaceable value for subsequent research reference, achievement tracing and compliance review, etc.

[0003] The traditional clinical trial archive management mainly relies on manual recording and retrieval, and the archive data is usually stored in fixed physical media or local storage system. Different archive data is scattered in different storage locations. During the archive storage stage, the operator needs to manually record the storage path and location information of each archive, which not only consumes a lot of manpower and time, but also is prone to location recording errors due to human negligence. During the archive retrieval stage, due to the lack of unified and efficient indexing mechanism, the operator often needs to rely on memory or one-by-one checking method to find the target archive. When the number of archives is large, this retrieval method is extremely inefficient, and often takes too long to find the target archive, or even cannot accurately find the target archive.

[0004] In addition, with the advancement of clinical trials, archive data may be added or the storage location may be adjusted. Under the traditional management mode, the change of archive storage location is difficult to be synchronized to the retrieval system in real time, which easily causes the mismatch between index information and actual storage location, further increasing the difficulty of archive searching. At the same time, different formats of archive data lack targeted parameter optimization in the indexing process, resulting in unstable indexing rate, which cannot adapt to the rapid retrieval demand of massive archive data. These problems seriously restrict the efficiency of clinical trial archive management, and affect the progress and achievement transformation of clinical trial research. Therefore, an archive management system capable of accurate positioning, efficient indexing and good adaptability is urgently needed to solve the many pain points existing in the traditional management mode. SUMMARY

[0005] In view of the deficiencies in the prior art, the present application provides an archive searching system based on Internet of Things technology, which solves the problem of slow archive data indexing progress.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: an archive searching system based on Internet of Things technology, comprising:

[0007] An archive database internally stores a large amount of clinical trial archive data, and different clinical trial archive data is stored at different storage locations.

[0008] The archive position confirmation end confirms the storage location associated with different clinical trial archive data, and gives different position encodings according to the number of storage locations associated with different levels. The specific method is:

[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 to which the path node belongs from the storage path. From the ordering feature of the corresponding level, confirm the ordering position of the corresponding path node, and adaptively generate the ordering encoding according to the ordering position. The ordering encoding is consistent with the node ordering position parameter;

[0010] In this way, the associated several ordering encodings are sorted in the order of the corresponding levels from front to back, and the position encoding belonging to the corresponding clinical trial archive data is generated, and different position encodings associated with different clinical trial archive data are confirmed in turn;

[0011] The index feature recording end constructs the corresponding feature polygon according to the different path total number associated with different levels, and then confirms and records the feature vector associated with the clinical trial archive data according to the position encoding associated with the corresponding clinical trial archive data. The specific method is:

[0012] The different path total number associated with different levels is denoted as Z i , where i represents different levels. The outermost level is identified from the storage feature, and the levels are sorted in turn from the outermost level to confirm the level column.

[0013] The Z i associated with the first group of levels is identified from the level column, and a corresponding polygon is generated according to Z i , the total number of edges of the polygon is consistent with Z i , and Z i associated with the corresponding level is generated in turn for the subsequent polygons associated with the subsequent levels. The polygon associated with the subsequent level includes the polygon associated with the previous level inside, and the inner diameter of the polygon associated with the subsequent level gradually increases. The ordering position of the corresponding level in the level column is P k , where k represents different levels. The inner diameter of the corresponding polygon associated with the corresponding level is (P k ×C1), where C1 is a preset unit length, and the center points of the polygons associated with the level column are the same point. A set of perpendicular lines perpendicular to the center point are constructed, and a certain edge corner point of the corresponding polygon is controlled to coincide with the vertical line. From the confirmed vertical line, the edge corner points associated with the corresponding polygon are digitally sorted in the clockwise direction. The total number of digital sorting is consistent with Z iConsistent;

[0014] According to the position code associated with the corresponding clinical trial file data and the constructed multiple sets of hierarchical polygons, the code number associated with the corresponding path node is confirmed within the position code, and the edge corner point associated with the corresponding hierarchical polygon is locked, and in turn, from the innermost hierarchical polygon, the associated edge corner points are connected in turn, the feature vector associated with the corresponding clinical trial file data is generated and recorded;

[0015] The index center confirms the clinical trial file data to be indexed according to the input feature text, directly confirms the feature vector associated with the clinical trial file data, directly confirms the path node where the corresponding clinical trial file data is located according to the constructed hierarchical polygon, and directly indexes and outputs.

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

[0017] Preferably, it also includes:

[0018] The historical data processing center confirms the index features of different format data in the historical processing process, and from the confirmed large number of index features, the running features of the corresponding format data are confirmed and recorded, and the specific method is:

[0019] The different index processes associated with different format data are confirmed, the index bandwidth and the associated index rate are confirmed from the index process, and the average value of the several groups of index rates associated with the index bandwidth is processed, the average rate is confirmed, and the confirmed average rate is taken as the characteristic rate of the corresponding index bandwidth;

[0020] According to the sorting method from small to large, the several index bandwidths are sorted, and according to the different characteristic rates associated with the different index bandwidths, the rate change curve associated with the corresponding format data is generated;

[0021] Line segments are selected from the generated rate change curve, the selected line segment cannot be lower than the range length of five index bandwidth points, and the selected line segment is characterized: the average value of the several characteristic rates associated with the corresponding line segment is processed as the first characteristic of the corresponding line segment, and the maximum value and the minimum value are confirmed from the several characteristic rates associated with the corresponding line segment, and the difference between the maximum value and the minimum value is locked as the second characteristic of the corresponding line segment. According to the first characteristic and the second characteristic confirmed, the calibration characteristic associated with the corresponding line segment is confirmed, which is (first characteristic × A1) ÷ (second characteristic × A2), wherein A1 and A2 are both preset fixed coefficient factors.

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

[0023] Preferably, the index center selects the middle value of the running feature as the running bandwidth according to the different running features associated with different format data, and adopts the running bandwidth for index output in the index process of the corresponding format data.

[0024] The present application provides an archive searching system based on Internet of Things technology.

[0025] The present application realizes accurate identification of the storage position of a large number of archives by intuitively reflecting the storage path of the archives and achieving accurate identification of the storage position of a large number of archives. The sorting code of each level corresponds to the actual sorting position of the node, so that the position code has clear logic and traceability, and the operator can quickly locate the physical storage path of the archives, greatly reducing the time waste caused by ambiguous position in traditional archive searching, and significantly improving the efficiency of archive positioning.

[0026] For the construction and update of the index feature, the index feature recording end constructs a feature polygon according to the total number of paths of different levels, and generates a feature vector in combination with the position code, thereby converting the position information of the archives into a visual feature graph. This graphical index feature facilitates quick identification and comparison by the system, laying a precise foundation for subsequent indexing work. At the same time, the system has good adaptability, and when new archive storage positions appear, the system can trigger automatic update of the index feature through an update instruction, ensuring that the feature graph always matches the actual storage position, effectively avoiding index errors caused by changes in storage position, and ensuring the accuracy and reliability of the index results.

[0027] In terms of index efficiency optimization, the historical data processing center extracts the running features of different format data by analyzing historical index data, and determines the optimal index bandwidth. The index center selects the appropriate running bandwidth for index output according to the running features, which can adaptively adjust the index parameters according to the differences in data formats, so that the index process is always in an efficient and stable state. This index strategy based on historical data optimization reduces invalid index operations and improves index speed. Especially when dealing with a large amount of clinical trial archive data, it can significantly shorten the index time and improve the overall efficiency of archive searching. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The schematic diagram of the principle framework of the present application. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0030] First embodiment

[0031] Please refer to Figure 1 The present application provides an archive searching system based on Internet of Things technology, which comprises an archive location confirmation end, an index feature recording end, an archive database, an index center and a historical data processing center. The archive database is electrically connected to the archive location confirmation end and the input node of the index center, and the archive location confirmation end, the index feature recording end and the index center are electrically connected in sequence from the output node to the input node. The historical data processing center is electrically connected to the input node of the index center.

[0032] The archive database internally stores a large amount of clinical trial archive data. Different clinical trial archive data are stored in different storage locations and are stored in advance by an operator.

[0033] The archive location confirmation end confirms the storage locations associated with different clinical trial archive data, and gives different location codes according to 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 recording end. Specifically, each different clinical trial archive data exists in a preset storage location, and each different storage folder is prepared in advance and generally will not be added or changed. If there is an addition, an update instruction needs to be generated and transmitted to the index feature recording end. The index feature recording end needs to update the associated index features to ensure the accuracy of the corresponding graphics and avoid errors between the graphics and the original graphics after the update.

[0034] The specific way of giving different location codes is as follows:

[0035] The storage location corresponding to the clinical trial file data is confirmed, and a storage path associated with the corresponding storage location is generated. The hierarchy of the path node is then confirmed from the storage path, and the sorting position of the corresponding path node is confirmed from the sorting feature of the corresponding hierarchy. The sorting code is adaptively generated according to the sorting position, and the sorting code is consistent with the node sorting position parameter. If the corresponding storage node is ranked fifth from top to bottom, the sorting code associated with the corresponding storage node is 5. Specifically, for example, the C disk, the D disk, the E disk, and the F disk belong to the first hierarchy.

[0036] By analogy, the associated sorting codes are sorted in the order of the corresponding hierarchy from front to back, and the position code corresponding to the clinical trial file data is generated. The different position codes associated with different clinical trial file data are sequentially confirmed.

[0037] Specifically, in the corresponding hierarchy sorting process, different path nodes exist in each hierarchy, and the sorting code associated with the corresponding node can be generated according to the sorting method of the path node. Then the determined sorting code can be effectively sorted from front to back to obtain the position code associated with the corresponding sorting code.

[0038] Among them, the index feature records end, according to the different path total number associated with different hierarchies, constructs the corresponding feature polygon, and according to the position code associated with the corresponding clinical trial file data, confirms and records the feature vector associated with the clinical trial file data. The specific way of confirming the feature vector is:

[0039] The different path total number associated with different hierarchies is marked as Z i , where i represents different hierarchies. The outermost hierarchy is identified from the storage feature, and the hierarchies are sequentially sorted from the outermost hierarchy to identify the hierarchy column.

[0040] Z i associated with the first group of hierarchies is identified from the hierarchy column. i A group of corresponding polygons is generated according to Z i , the total number of edges of the polygon is consistent with Z i , and the subsequent polygons associated with the corresponding hierarchy are sequentially generated. The polygon associated with the subsequent hierarchy includes the polygon associated with the previous hierarchy, and the inner diameter of the polygon associated with the subsequent hierarchy gradually increases. The sorting position of the corresponding hierarchy in the hierarchy column is P k , where k represents different hierarchies. The inner diameter of the polygon associated with the corresponding hierarchy is (P kX C1), wherein C1 is a preset unit length, which is determined in advance by an operator according to experience, and the center points of the several polygons associated with the level column are the same point position, a set of perpendicular lines perpendicular to the center point is constructed, and a certain corner point of the corresponding polygon is controlled to coincide with the perpendicular line, starting from the confirmed perpendicular line, the corner points associated with the corresponding polygon are sequentially numbered in a clockwise direction, and the total number of the digital sequence is Z i Consistency, after the construction of the level polygon is completed, the corresponding edge corner point is one-to-one corresponding to the sequence position and the corresponding digital sequence according to the sequence position associated with the corresponding path node of the corresponding level, the path node associated with the corresponding edge corner point is confirmed, for example: five levels are determined to exist, each level is associated with a corresponding path total number, and a polygon with a corresponding edge number is generated, and the five levels are associated with five polygons, the center points associated with the five polygons are the same point position, and the polygons are rotated according to the confirmed perpendicular line, so that the corresponding edge corner point coincides with the perpendicular line, and then the edge corner points associated with the corresponding level are sequentially numbered in a clockwise sequence, and different edge corner points are associated with different numbers. Subsequently, if there is a newly added storage node in the corresponding level (generally not, the corresponding storage interval is determined in advance when storing, and it may be updated once a month), the same processing method is used to update and adjust the several level polygons constructed to achieve better adjustment and processing effect.

[0041] According to the position code associated with the corresponding clinical trial file data and the constructed multiple sets of level polygons, the code number associated with the corresponding path node is confirmed from the position code, and the edge corner point associated with the corresponding level polygon is locked, and then the several edge corner points associated with the corresponding level polygon are sequentially connected from the innermost level polygon to the outermost level polygon, and the feature vector associated with the corresponding clinical trial file data is generated and recorded, so that subsequent comparison and verification, fast search and fast index of the storage position of the corresponding data are facilitated.

[0042] Among them, the index center, according to the input feature text, confirms the clinical trial file data to be indexed, and directly confirms the feature vector associated with the clinical trial file data, directly confirms the path node where the corresponding clinical trial file data is located according to the constructed level polygon, and directly indexes and outputs.

[0043] Second embodiment

[0044] In the specific implementation process of the present embodiment, compared with the above-mentioned embodiments, the present embodiment mainly aims at the index feature of the index process, modifies the index parameter, and guarantees the fast output of the process data of the corresponding index process.

[0045] The historical data processing center confirms the index features of different format data in the historical processing process, and from the confirmed large number of index features, the running features of the corresponding format data are confirmed and recorded, and the specific way of confirming the running features is:

[0046] The different index processes associated with different format data are confirmed, the index bandwidth and the associated index rate are confirmed from the index process, and several groups of index rates associated with the index bandwidth are processed by mean value, the mean rate is confirmed, and the confirmed mean rate is taken as the characteristic rate of the corresponding index bandwidth;

[0047] According to the ordering from small to large, the several index bandwidths are sorted, and according to the different characteristic rates associated with different index bandwidths, the rate change curve associated with the corresponding format data is generated;

[0048] Line segments are selected from the generated rate change curve, the selected line segment cannot be lower than the range length of five index bandwidth points (that is, the minimum number of points inside the corresponding line segment is five groups), and the selected line segment is confirmed: the several characteristic rates associated with the corresponding line segment are processed by mean value as the first characteristic of the corresponding line segment, and the maximum value and the minimum value are confirmed from the several characteristic rates associated with the corresponding line segment, and the difference between the maximum value and the minimum value is locked as the second characteristic of the corresponding line segment. According to the confirmed first characteristic and second characteristic, the calibration characteristic associated with the corresponding line segment is confirmed, which is (first characteristic x A1) ÷ (second characteristic x A2), wherein A1 and A2 are both preset fixed coefficient factors, and their specific values are determined by the operator according to experience;

[0049] The different calibration characteristics associated with the selected different line segments are confirmed in turn, and the maximum value is selected from the confirmed several calibration characteristics, and 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 characteristic of the corresponding format data and recorded;

[0050] Specifically, in the corresponding curve, the selection of line segments can be performed in sequence from front to back, and the length of the selected line segment can be changed, and in the actual change process, line segments of different lengths can be selected. The calibration characteristics associated with line segments of different length types are different, so in the actual feature checking and selecting process, the line segment with the most obvious calibration characteristic can be confirmed and taken as the determined line segment, and then the running characteristic can be quickly confirmed, which facilitates subsequent feature processing and checking.

[0051] In the formula, the index center is selected according to different running characteristics associated with different format data, and the intermediate value of the running characteristics is selected as the running bandwidth, and the running bandwidth is used for index output in the index process of the corresponding format data, so as to complete the index processing procedure of the corresponding clinical trial file data.

[0052] Some data in the above formula are dimensionless for numerical calculation, and the contents not described in detail in the specification all belong to the prior art known by those skilled in the art.

[0053] The above examples are only used to illustrate the technical method of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

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.

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 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. 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, Also includes: The historical data processing center confirms the index characteristics of data in different formats during the historical processing process, and from the large number of confirmed index characteristics, identifies and records the operational characteristics of the corresponding data formats.

6. The archive search system based on Internet of Things technology according to claim 5, characterized in that, 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 is calculated as (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.

7. The archive search system based on Internet of Things technology according to claim 6, 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.

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