Data intelligent management system and method for whole cycle of hair sample detection

Abstract

The application provides a data intelligent management system and method for a whole cycle of hair sample detection, relates to the technical field of data processing, and comprises the following: a sample acquisition module is used for acquiring a hair sample; a grid division module is used for multi-granularity grid division to determine a plurality of division grids; a sampling determination module is used for determining a plurality of first management coefficients; a scheme determination module is used for acquiring a detection result and determining an associated detection scheme; a data volume determination module is used for determining a detection data volume; a mapping matching module is used for mapping matching to determine a plurality of second management coefficients; and a resource configuration module is used for resource configuration of a plurality of data management units. The application can solve the technical problem of unreasonable data resource allocation due to the lack of an effective matching mechanism for detection data volume and data management resource configuration in the prior art, and improves the data management quality and efficiency by multi-granularity grid division, in combination with a sampling position and an associated detection scheme, to determine a detection data volume.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a data intelligent management system and method for a whole cycle of hair sample detection. BACKGROUND

[0002] With the development of biological detection technology, hair sample detection is increasingly widely applied in the fields of forensic science, environmental monitoring and medical diagnosis. As a kind of biological material, hair sample can reflect the physiological state and environmental exposure of an individual, so it is of great significance to analyze it. However, the existing hair sample detection technology has some deficiencies in data management. In the existing technology, a unified detection scheme is generally used to process hair samples, ignoring the possible differences between different sampling locations. Since hair samples at different locations may be affected by different environments or contain different material components, the unified detection scheme cannot fully consider these differences, resulting in inaccurate detection results. Hair samples at different locations have different detection needs, and the existing unified detection scheme cannot meet these individualized needs, affecting the accuracy of the detection results and leading to unreasonable allocation and waste of resources.

[0003] In summary, in the existing technology, there is a technical problem that due to the lack of effective matching mechanism between the detection data volume and the data management resource configuration, the detection data volume of certain sampling locations is large, the data management resources are insufficient, while the resources of other locations are over-configured, leading to unreasonable allocation of data resources. SUMMARY

[0004] The purpose of the present application is to provide a data intelligent management system and method for a whole cycle of hair sample detection, to solve the technical problem in the existing technology that due to the lack of effective matching mechanism between the detection data volume and the data management resource configuration, the detection data volume of certain sampling locations is large, the data management resources are insufficient, while the resources of other locations are over-configured, leading to unreasonable allocation of data resources.

[0005] In view of the above problems, the present application provides a data intelligent management system and method for a whole cycle of hair sample detection.

[0006] In a first aspect, the present application provides a data intelligent management system for a whole cycle of hair sample detection, wherein the data intelligent management system for the whole cycle of hair sample detection comprises: a sample acquisition module, configured to acquire K hair samples of a target area, wherein the K hair sample sampling points comprise K sampling positions; a grid division module, configured to perform multi-granularity grid division on the target area based on the K sampling positions, to determine a plurality of division grids, wherein the plurality of division grids comprise a plurality of sampling position sets; a sampling determination module, configured to determine a plurality of first management coefficients according to the number of sampling positions in the plurality of sampling position sets in the plurality of division grids; a scheme determination module, configured to acquire K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results; a data amount determination module, configured to determine K detection data amounts in combination with the K sampling positions and the K associated detection schemes; a mapping matching module, configured to perform mapping matching based on the K sampling positions corresponding to the K detection data amounts, in combination with the plurality of sampling position sets of the plurality of division grids, to determine a plurality of second management coefficients; and a resource configuration module, configured to perform resource configuration on a plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients, to determine a data management scheme.

[0007] In a second aspect, the present application further provides a data intelligent management method for a whole cycle of hair sample detection, wherein the data intelligent management method for the whole cycle of hair sample detection comprises: acquiring K hair samples of a target area, wherein the K hair sample sampling points comprise K sampling positions; performing multi-granularity grid division on the target area based on the K sampling positions, to determine a plurality of division grids, wherein the plurality of division grids comprise a plurality of sampling position sets; determining a plurality of first management coefficients according to the number of sampling positions in the plurality of sampling position sets in the plurality of division grids; acquiring K detection results of the K hair samples, and determining K associated detection schemes according to the K detection results; determining K detection data amounts in combination with the K sampling positions and the K associated detection schemes; performing mapping matching based on the K sampling positions corresponding to the K detection data amounts, in combination with the plurality of sampling position sets of the plurality of division grids, to determine a plurality of second management coefficients; and performing resource configuration on a plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients, to determine a data management scheme.

[0008] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The method comprises the following steps: a sample acquisition module for acquiring K hair samples from a target area, wherein the K hair sample sampling points include K sampling positions; a grid division module for performing multi-granularity grid division on the target area based on the K sampling positions, and determining a plurality of divided grids, wherein the plurality of divided grids include a plurality of sampling position sets; a sampling determination module for determining a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets within the plurality of divided grids; and a scheme determination module for acquiring K detection points of the K hair samples. The invention also provides a method for determining the K detection results and determining K associated detection schemes based on the K detection results; a data volume determination module, which is used to determine the K detection data volumes in combination with the K sampling positions and the K associated detection schemes; a mapping and matching module, which is used to perform mapping and matching based on the K sampling positions corresponding to the K detection data volumes and in combination with the multiple sampling position sets of the multiple divided grids, and determine multiple second management coefficients; a resource allocation module, which is used to perform resource allocation for multiple data management units based on the multiple first management coefficients and the multiple second management coefficients, and determine the data management scheme. In other words, through multi-granularity grid division, the target area is accurately selected for sampling positions, and the associated detection scheme is determined in combination with the sampling positions and detection results, so that samples at different positions can adopt the most suitable detection scheme, calculate the first and second management coefficients, realize dynamic allocation of data management resources, avoid resource waste or shortage, and improve data management quality and efficiency.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0012] Figure 1 This is a schematic diagram of the structure of the intelligent data management system for the entire cycle of hair sample testing in this application;

[0013] Figure 2 This is a flow chart of the intelligent data management method for the entire cycle of hair sample testing in this application.

[0014] Explanation of reference numerals: sample acquisition module 11 , grid division module 12 , sampling determination module 13 , plan determination module 14 , data volume determination module 15 , mapping and matching module 16 , resource configuration module 17 . DETAILED DESCRIPTION

[0015] This application provides an intelligent data management system and method for the entire hair sample testing cycle, resolving the existing technical issues of irrational data resource allocation, which arise from the lack of an effective matching mechanism between test data volume and data management resource allocation. This leads to high test data volumes and insufficient data management resources at certain sampling locations, while resources are over-allocated at other locations, resulting in irrational data resource allocation. By using multi-granularity gridding, precise sampling locations are selected for the target area. By combining sampling locations with test results, a correlation test scheme is determined, enabling the most appropriate test scheme to be used for samples at different locations. The first and second management coefficients are then calculated, enabling dynamic allocation of data management resources, avoiding resource waste or insufficiency, and improving data management quality and efficiency.

[0016] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0017] For example, see the attached Figure 1 The present application provides an intelligent data management system for the entire hair sample detection cycle, wherein the intelligent data management system for the entire hair sample detection cycle is used to implement the steps of the intelligent data management method for the entire hair sample detection cycle, and the intelligent data management system for the entire hair sample detection cycle includes:

[0018] A sample acquisition module 11, the sample acquisition module 11 is used to acquire K hair samples in a target area, wherein the K hair sample sampling points include K sampling positions;

[0019] Specifically, a target area for hair sample collection is identified, including the head or other body parts, a specific geographic area, or a specific sampling environment. Within the target area, K specific sampling locations are marked based on research needs or standard operating procedures. These locations can be several specific points distributed throughout the target area to ensure representative hair samples collected from different locations. Random, systematic, or stratified sampling methods are used to select the K sampling locations, and hair samples are obtained from each sampling location, resulting in K hair samples. Each hair sample has its own specific sampling location. By selecting multiple sampling locations within the target area, sample diversity and representativeness are ensured, thereby improving the accuracy and reliability of the test results.

[0020] A grid division module 12 is configured to perform multi-granularity grid division on the target area based on the K sampling positions, and determine a plurality of division grids, wherein the plurality of division grids include a plurality of sampling position sets;

[0021] Specifically, multiple sampling locations are randomly selected from K sampling locations as multiple first partition centers, and multiple sampling locations are again selected as multiple second partition centers. Distance verification is performed on the multiple first and second partition centers. If verification passes, the partition centers with higher dispersion are selected as the multiple target partition centers, and a diffusion partitioning instruction is determined. Based on the diffusion partitioning instruction, multi-granularity meshing is performed within the target area using the multiple target partition centers as starting points, using a preset partitioning step size. Multi-granularity meshing refers to meshing the target area at different precisions or scales, gradually refining the mesh from coarse to fine, so that the meshing scale can adapt to the distribution of different sampling locations. In the initial stage, the target area is coarsely divided to generate relatively large grids, resulting in the first layer of partitioned grids. Each grid may cover multiple sampling locations. After the first layer of coarse-grained partitioning is completed, the edges of the initial partitioned grid are further partitioned using the preset partitioning step size to obtain the second layer of partitioned grids.

[0022] Based on the K sampling positions, determine the density of the first-layer division grid and the second-layer division grid. Determine whether the density of the second division is greater than the density of the first division. If so, continue to divide according to the preset division step size to further divide the grid into smaller sub-grids. If not, stop the diffusion division and record the current multiple diffusion division grids and undivided areas. Use the undivided area as a division grid and combine multiple diffusion division grids to generate multiple division grids. In the multi-granularity grid division process, dynamically adjust the size and level of the grid, and optimize the grid according to the distribution of sampling points. For areas with dense sampling points, the grid will be refined and divided into more and smaller sub-grids. For areas with sparse sampling points, the grid division may be maintained at a coarser granularity to avoid over-division.

[0023] After completing the multi-granularity grid division, determine the set of sampling locations within each grid, that is, the sampling points in each grid. Each divided grid will contain several sampling locations, called a sampling location set, which reflects the distribution of sampling points within the grid. The sampling location set within each grid may contain different numbers of sampling points. By counting the number of sampling locations in each grid, the sampling density of different areas can be analyzed to assist in further resource allocation or data analysis. By multi-granularity grid division based on K sampling locations, the target area is divided into multiple grids, ensuring that each grid contains a sampling location set, effectively improving the rationality of the division and processing efficiency.

[0024] a sampling determination module 13 configured to determine a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets within the plurality of divided grids;

[0025] Specifically, the set of sampling locations within each grid reflects the number of sampling points contained in that grid. This number directly affects the grid's first management coefficient: the greater the number of sampling locations in a grid, the greater its management coefficient, indicating a higher relative importance within the system. The number of sampling locations within multiple grids is determined by the set of sampling locations within each grid. The sum of the number of sampling locations across all grids is calculated. The first management coefficient for each grid is the ratio of the number of sampling locations within that grid to the total number of sampling locations, reflecting the relative importance of each grid relative to the entire area. The first management coefficient reflects the relative weight of each grid within the overall system. A grid with more sampling locations has a larger management coefficient, meaning that it will receive higher priority than other grids in subsequent resource allocation and data processing. The calculation of the first management coefficient identifies the density of sampling locations across different grids and allocates resources accordingly. Processing resource allocation is automatically adjusted based on the density of sampling locations, eliminating manual intervention and dynamically adjusting allocation strategies for more flexible management.

[0026] a scheme determination module 14, configured to obtain K test results of the K hair samples and determine K associated detection schemes according to the K test results;

[0027] Specifically, K hair samples are tested, generating test results for each sample, including information such as the type and concentration of the pollutant. Based on the monitoring results, a corresponding correlation testing plan is developed for each sample. A correlation testing plan identifies additional locations or environmental samples requiring further testing based on the pollutants or abnormal components found in the hair samples. This is done to further confirm the source or transmission path of the pollutants in the hair samples. Certain pollutants or abnormal components detected in hair samples may be linked to other environmental sources, including pipes, water pipes, sewers, and sewage treatment systems. Each test result corresponds to one or more areas requiring further testing (such as pipes and sewers), which then generates a specific testing plan. For example, if a hair sample detects heavy metals, it is necessary to test nearby household water pipes and water supply pipes to identify the source of the heavy metals in the water. Based on the different hair sample test results, multiple different testing locations are identified, ensuring that the correlation between the test results and potential pollution sources is clearly identified. By combining hair testing with environmental testing, pollution sources or potential environmental issues can be accurately located, implementing a multi-dimensional correlation testing plan. Correlating detection solutions helps to accurately identify areas where contamination or other problems may exist, allowing for more effective action or in-depth analysis.

[0028] a data volume determination module 15, configured to determine K detection data volumes by combining the K sampling positions and the K associated detection schemes;

[0029] Specifically, the environment of each sampling location is recorded and collected in detail, including but not limited to ambient temperature, humidity, pollution level, soil type, water quality, and surrounding facility types (such as industrial, residential, and commercial areas). The sampling environment information can be multi-dimensional, depending on the different detection scenarios. According to the associated detection scheme, the detection type and detection parameters required for each scheme are analyzed to determine the amount of detection data required for each sampling location. For example, areas with high pollution levels usually require higher-precision detection, so the amount of data is larger. By combining the environmental information of the sampling location with the associated detection scheme, the amount of detection data for each location is dynamically determined, avoiding data processing overload or insufficient resources.

[0030] a mapping and matching module 16 configured to perform mapping and matching based on the K sampling positions corresponding to the K detection data amounts and in combination with the plurality of sampling position sets of the plurality of divided grids to determine a plurality of second management coefficients;

[0031] Specifically, the K detection data volumes refer to the detection data volumes at each sampling location, which determine the detection complexity and resource requirements at that location. A multi-granularity gridding process divides the target area into multiple grids, with each grid containing a set of sampling locations. Through mapping and matching, the detection data volumes at each sampling location are associated with the set of sampling locations in the grid within which it resides, ensuring that resources are allocated appropriately to the detection needs within each grid. The second management coefficient for each grid is determined based on the detection data volumes at the sampling locations contained in each grid. Based on the spatial coordinates of each sampling location, the detection data volumes corresponding to its corresponding grid are mapped to the grid within which it resides. For each set of sampling locations in each grid, the detection data volumes of all sampling locations are aggregated to determine the total detection demand for that grid. The total detection data volume for each grid is then compared with the total detection data volume for all grids to calculate the detection data ratio for that grid. For example, if a grid requires more data collection, the second management coefficient for that grid unit may be higher. The second management coefficient reflects the proportion of each grid unit in the detection resource demand relative to all grids, dynamically reflecting the resource needs and priorities of different grids. By reasonably determining the second management coefficient, we can ensure that grids with high inspection demands obtain more resources and optimize the efficiency of the overall inspection process.

[0032] The resource configuration module 17 is configured to perform resource configuration on the plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients, and determine a data management solution.

[0033] Specifically, the first management coefficient reflects the proportion of sampling locations in each grid relative to the total number of sampling locations, determining the relative importance of each grid. The more sampling locations a grid has, the higher the first management coefficient and the greater the weight of that grid in resource allocation. The second management coefficient reflects the proportion of detection data volume in each grid relative to the detection needs of all grids, reflecting the detection resources required for each grid and used to assess the detection complexity and data processing requirements of each grid. The greater the detection data volume, the higher the second management coefficient, indicating the grid's need for more resources. By combining these two management coefficients, each grid receives appropriate resource allocation, including computing power, data storage, and detection equipment. Resources are allocated based on each grid's first and second management coefficients, ensuring that key grids receive priority. The data management unit is responsible for processing detection data, analyzing results, and managing resources for each grid. Based on the management coefficient, resources are allocated to each data management unit to ensure it can meet data collection, processing, and storage requirements. By combining these two management coefficients, the resource needs of each grid are accurately assessed, ensuring rational and efficient resource allocation.

[0034] Furthermore, the grid division module 12 in the intelligent data management system for the entire hair sample detection cycle is further used to:

[0035] randomly select a plurality of positions from the K sampling positions as first partition centers, and randomly select a plurality of positions from the K sampling positions as second partition centers; perform distance authentication on the plurality of first partition centers and the plurality of second partition centers, and if the authentication is passed, obtain a plurality of target partition centers and a diffusion partition instruction; and based on the diffusion partition instruction, perform diffusion iteration in the target region with the plurality of target partition centers as starting points and according to a preset partition step length, to obtain the plurality of partition grids.

[0036] Specifically, a plurality of positions are randomly selected from K sampling positions as first partition centers, and through random selection, the randomness of the distribution of the sampling positions is ensured, human bias is avoided, and the representativeness and diversity of the samples are increased. After the selection of the first partition centers, a plurality of positions are again randomly selected from the K sampling positions as second partition centers. Through two random selections, the diversity of the partition centers is ensured, the coverage of the partition region is more extensive, and the local concentration phenomenon that may be caused by single random selection is avoided. The first and second partition centers are subjected to distance authentication to ensure that the two partition centers have sufficient distance and avoid overlapping or excessively dense grid partitioning. The standard for passing the authentication may be based on a minimum distance threshold or other statistical standards of spatial distribution, such as being less than or equal to a preset distance threshold. Distance authentication can avoid excessively dense or excessively dispersed partition center distribution.

[0037] After the distance authentication is passed, a plurality of target partition centers are determined, and a diffusion partition instruction is generated for controlling the subsequent partition diffusion process and specifying how each center performs regional partitioning. According to the diffusion partition instruction, diffusion iteration is performed in the target region with the plurality of target partition centers as starting points and according to a preset partition step length as a radius, to obtain a plurality of preliminary partition grids. Each preliminary partition grid is a region constructed with a target partition center as a starting point and a preset partition step length as a radius. The edges of the preliminary partition grid are again diffused according to the preset partition step length to form a stage partition grid. According to the K sampling positions, a preliminary grid density and a stage grid density of the preliminary partition grid are determined. If the stage grid density is greater than or equal to the preliminary grid density, the diffusion of the stage partition grid is continued according to the preset partition step length to form a diffusion partition grid. If the stage grid density is less than the preliminary grid density, the diffusion partition is stopped, and the current diffusion partition grid and the unpartitioned region are retained. The partition region after stopping the diffusion is taken as a partition grid, and the plurality of diffusion partition grids are combined to generate a plurality of final partition grids.

[0038] Through twice random extraction and distance authentication, the division center is distributed uniformly and reasonably, and the division grid formed by diffusion iteration can cover different parts of the target area, thereby ensuring the comprehensiveness and representativeness of sample collection. The introduction of the diffusion division instruction makes each target division center more orderly and accurate when performing regional division, avoids the blindness and overlapping phenomenon existing in the traditional grid division, and improves the accuracy of the entire regional division.

[0039] Further, the grid division module 12 in the data intelligent management system of the whole cycle of the hair sample detection is further used for:

[0040] The plurality of first division centers and the plurality of second division centers are enumerated respectively, and a plurality of first division center enumeration combinations and a plurality of second division center enumeration combinations are obtained. A plurality of first combination sampling distances and a plurality of second combination sampling distances of the plurality of first division center enumeration combinations and the plurality of second division center enumeration combinations are calculated based on the K sampling positions. It is judged whether there is a combination sampling distance less than or equal to a preset distance threshold in the plurality of first combination sampling distances and / or the plurality of second combination sampling distances. If yes, the authentication fails, and the division center selection is performed again.

[0041] Further, the grid division module 12 in the data intelligent management system of the whole cycle of the hair sample detection is further used for:

[0042] If no, the authentication passes, and a first dispersion degree of the plurality of first combination sampling distances and a second dispersion degree of the plurality of second combination sampling distances are compared. When the first dispersion degree is greater than the second dispersion degree, the plurality of first division centers are taken as the plurality of target division centers.

[0043] Specifically, the first division centers and the second division centers are enumerated respectively, each position of the first division center is paired with all other positions, and a first division center enumeration combination is formed. Similarly, the same operation is performed on the second division center, and a second division center enumeration combination is formed. For each enumeration combination, based on its corresponding sampling position, the first combination sampling distance and the second combination sampling distance are calculated through the Euclidean distance, the Manhattan distance or other suitable spatial distance. Through the distance calculation of each enumeration combination, a plurality of first combination sampling distances and a plurality of second combination sampling distances are obtained. The plurality of first combination sampling distances refers to the distance set of all first division center combinations, and the plurality of second combination sampling distances refers to the distance set of all second division center combinations, reflecting the distribution of the division centers. The greater the distance, the more dispersed the division centers; the smaller the distance, the more concentrated the division centers. A preset distance threshold is set in advance, which is a minimum allowed distance, used to ensure the reasonable distribution between the division centers, prevent the division centers from being too close to each other, and avoid grid division overlapping or unevenness.

[0044] All calculated sampling distances for the first and second combinations are evaluated to see if any distance is less than or equal to a preset distance threshold. If all combined distances are greater than the preset distance threshold, authentication passes, indicating that the distribution of partition centers is reasonable, and the next step can be performed. If any combined distance is less than or equal to the preset distance threshold, authentication fails, indicating that the distribution of partition centers is too concentrated. Reselecting partition centers requires random sampling of multiple sampling locations from the K sampling locations. For example, assume that three first partition centers and three second partition centers are selected. Pairwise enumeration of the first partition centers yields three combinations: (A1, A2), (A1, A3), and (A2, A3). Similarly, pairwise enumeration of the second partition centers yields three combinations: (B1, B2), (B1, B3), and (B2, B3). Calculating the distances between these combinations reveals that the sampling distances for the first combination are d(A1, A2) = 5 mm, d(A1, A3) = 8 mm, and d(A2, A3) = 6 mm. The second combined sampling distance is d(B1, B2) = 7mm, d(B1, B3) = 9mm, and d(B2, B3) = 10mm. If the preset distance threshold is 8mm, the distribution of the division centers is too concentrated, the authentication fails, and re-division is required.

[0045] The dispersion of the first and second combined sampling distances (i.e., the first and second dispersions) is calculated to indicate the degree of dispersion of the data distribution between the partition centers. Dispersion measures the uniformity of the distribution of sampling locations and reflects the overall difference in distances between different partition centers. Statistical methods such as variance or standard deviation are used to indicate the fluctuation of distances. High dispersion indicates a wider distribution of data, while low dispersion indicates a more concentrated distribution. Comparing the first and second dispersions, if the first dispersion is greater than the second dispersion, the distribution of the first partition center is more dispersed and uniform; if the second dispersion is greater than the first dispersion, the distribution of the second partition center is more dispersed.

[0046] Based on the results of the dispersion comparison, the group of partition centers with the most even distribution is selected as the target partition centers. When the first dispersion is greater than the second dispersion, multiple first partition centers are selected as target partition centers. When the second dispersion is greater than the first dispersion, the second partition center is selected. By selecting partition centers with greater dispersion, the distribution of partition centers within the target area is more balanced, avoiding an uneven grid caused by excessive concentration of partition centers. For example, suppose the calculated sampling distances for the first combination are 3mm, 7mm, and 11mm, and the sampling distances for the second combination are 4mm, 8mm, and 6mm. If the first dispersion (e.g., standard deviation) is 3.3mm and the second dispersion is 1.6mm, the first partition center is selected as the target partition center. This two-step selection of partition centers and their distance calculation ensures a reasonable spatial distribution between the partition centers. By comparing the dispersions of the two groups of partition centers, the system selects the group with the more even distribution as the final target partition center, further ensuring the uniformity and rationality of the grid division.

[0047] Furthermore, the grid division module 12 in the intelligent data management system for the entire hair sample detection cycle is further used to:

[0048] Based on the diffusion partitioning instruction, with the multiple target partitioning centers as the starting point, diffusion iterations are performed in the target area according to a preset partitioning step size to obtain multiple preliminary partitioning grids; the edges of the multiple preliminary partitioning grids are diffused again according to the preset partitioning step size to obtain multiple stage partitioning grids; based on the K sampling positions, multiple preliminary grid densities and multiple stage grid densities of the multiple preliminary partitioning grids are determined; whether the multiple stage grid densities are greater than or equal to the multiple preliminary grid densities is judged respectively; if so, the multiple stage partitioning grids are continued to be diffused according to the preset partitioning step size to obtain multiple diffusion partitioning grids; if not, the diffusion partitioning is stopped to obtain multiple diffusion partitioning grids and undivided areas; the undivided area is taken as a partitioning grid, and the multiple partitioning grids are generated in combination with the multiple diffusion partitioning grids.

[0049] Specifically, according to the diffusion partitioning instruction, it is instructed how to start from the target partitioning center and perform grid division in the target area according to the preset step size. The target partitioning center determined after the distance certification is passed is used as the starting point, and the pre-set partitioning step size is used to determine the size of each grid unit. Starting from the target partitioning center, diffusion iteration is performed in the target area according to the preset partitioning step size to generate multiple preliminary partitioning grids. Each preliminary grid is a circular or other geometric area with the target partitioning center as the center point and the preset partitioning step size as the radius. On the basis of the preliminary partitioning grid, the edge of each preliminary partitioning grid is diffused again according to the preset partitioning step size to generate a new stage partitioning grid. An expansion operation is performed on the edge of each preliminary grid to form the next layer of diffusion area, gradually expanding the influence range of the target partitioning center, and further refining the grid through staged division.

[0050] Based on the K sampling locations, the preliminary grid density and the stage grid density are determined. This means the number of sampling locations within each preliminary grid cell and each stage grid cell is calculated. Preliminary grid density refers to the number of sampling points or sample distribution within each preliminary grid cell; stage grid density refers to the number of sampling points within each stage grid cell. Density can be calculated by dividing the number of sampling points within a grid by the grid area, or based on the spatial distribution of sampling points and the number of samples. By calculating the density of the preliminary and stage grids, it is possible to assess whether a reasonable grid distribution has been generated during the diffusion process, avoiding uneven or overly concentrated distributions.

[0051] Compare the stage mesh density to the preliminary mesh density to determine if a higher density distribution exists. If the stage mesh density is greater than or equal to the preliminary mesh density, this means that diffusion has produced denser areas, indicating that further diffusion can refine the mesh. The stage meshes are then further divided at the preset division step size until the mesh density stops increasing, resulting in multiple diffusion meshes.

[0052] If the stage grid density is less than the preliminary grid density, it indicates that the diffusion region may have become too sparse or difficult to further refine, and continuing the diffusion partitioning will not bring more information gain. At this time, stop the diffusion and record the current multiple diffusion partition grids and unpartitioned regions. The purpose of stopping the diffusion partitioning is to prevent over-refining the grid, thereby saving computing resources and avoiding unnecessary complexity. At the same time, the existence of unpartitioned regions can help identify blind spots in data collection or areas that require additional attention. The unpartitioned region after stopping the diffusion is treated as a partition grid, combined with the multiple diffusion partition grids, to form multiple partition grids, ensuring that the entire target region is effectively covered, and each grid cell has a clear identification and attributes, including those regions that are not further subdivided in the diffusion partitioning process. The final partition grid is a refined partition of the entire target region, with each grid representing a specific sub-region in the target region. By including unpartitioned regions in the partition grid, it ensures that all regions within the target region are considered, reducing blind spots in data collection and analysis. Through multiple diffusion steps and density comparisons, the generated grid can finely cover the target region, and through the automatic diffusion and stopping mechanism, the partitioning range is dynamically adjusted according to the density changes, improving the partitioning accuracy and efficiency.

[0053] Further, the sampling determination module 13 in the data intelligent management system of the entire hair sample detection cycle is further used for:

[0054] The number of sampling locations in the set of sampling locations in the multiple partition grids is counted to obtain a plurality of grid sampling location quantities. The ratio of each grid sampling location quantity to the sum of all grid sampling location quantities is calculated to obtain a plurality of first management coefficients.

[0055] Specifically, the number of sampling locations in the set of sampling locations in each partition grid is counted, the number of sampling locations in each grid is calculated, and a plurality of grid sampling location quantities are obtained. For each grid sampling location quantity, the ratio of the sum of all grid sampling location quantities is calculated to obtain a plurality of first management coefficients, which reflect the sampling location distribution of each grid relative to the entire target region. For example, assuming there are 4 grids, grid 1 includes 3 sampling locations, grid 2 includes 1 sampling location, grid 3 includes 2 sampling locations, and grid 4 includes 4 sampling locations, the sum of the plurality of grid sampling location quantities is 10, and the ratio of each grid sampling location quantity and the sum is calculated to obtain a plurality of first management coefficients: grid 1 is 0.3, grid 2 is 0.1, grid 3 is 0.2, and grid 4 is 0.4. Through the management coefficient, appropriate computing resources are allocated during data collection or processing to ensure that the processing of each grid is optimized according to the number of sampling locations, improving the accuracy of the overall analysis.

[0056] Further, the data amount determination module 15 in the data intelligent management system of the whole cycle of the hair sample detection is further used for:

[0057] K sampling environmental information of the K sampling positions is acquired; and the K detection data amounts are determined according to the K correlation detection schemes and the K sampling environmental information.

[0058] Specifically, the environmental information of each sampling position is acquired, including surrounding physical conditions, environmental pollution level, water quality, temperature and humidity, etc. The sampling environmental information can be multidimensional, and is specifically determined according to different detection scenes. The detection type and detection parameter required by each correlation detection scheme are analyzed according to each sampling position, and the detection data amount required by each position is determined in combination with the correlation detection scheme and the sampling environmental information. For example, if the environmental information of a position shows that the pollution level is high, more data needs to be collected at the position to ensure the accuracy of the result. By combining the sampling environmental information and the correlation detection scheme, the detection data amount required by each position is determined in a targeted manner, avoiding the problems of excessive data collection or insufficient data.

[0059] In summary, the data intelligent management system of the whole cycle of the hair sample detection provided in the present application has the following technical effects:

[0060] The sample acquisition module is used to acquire K hair samples of a target area, wherein the K hair sample sampling points include K sampling positions; the grid division module is used to perform multi-granularity grid division on the target area based on the K sampling positions to determine a plurality of division grids, wherein the plurality of division grids include a plurality of sampling position sets; the sampling determination module is used to determine a plurality of first management coefficients according to the number of sampling positions in the plurality of sampling position sets in the plurality of division grids; the scheme determination module is used to acquire K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results; the data amount determination module is used to determine K detection data amounts in combination with the K sampling positions and the K associated detection schemes; the mapping matching module is used to perform mapping matching based on the K sampling positions corresponding to the K detection data amounts, in combination with the plurality of sampling position sets of the plurality of division grids, to determine a plurality of second management coefficients; and the resource configuration module is used to perform resource configuration on a plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients to determine a data management scheme. That is, through multi-granularity grid division, accurate sampling position selection is performed on the target area, the associated detection scheme is determined in combination with the sampling positions and the detection results, so that samples at different positions can adopt the most suitable detection scheme, the first and second management coefficients are calculated, dynamic allocation of data management resources is realized, resource waste or deficiency is avoided, and the data management quality and efficiency are improved.

[0061] In the second embodiment, based on the same inventive concept as the data intelligent management system for the whole cycle of hair sample detection in the foregoing first embodiment, the present application also provides a data intelligent management method for the whole cycle of hair sample detection. Please refer to the accompanying drawings Figure 2 , the data intelligent management method for the whole cycle of hair sample detection includes:

[0062] K hair samples of a target area are obtained, wherein the K hair sample sampling points include K sampling positions; the target area is divided into a plurality of division grids based on the K sampling positions, and a plurality of division grids are determined, wherein the plurality of division grids include a plurality of sampling position sets; a plurality of first management coefficients are determined according to the number of sampling positions in the plurality of sampling position sets in the plurality of division grids; K detection results of the K hair samples are obtained, and K associated detection schemes are determined according to the K detection results; K detection data amounts are determined in combination of the K sampling positions and the K associated detection schemes; the K sampling positions corresponding to the K detection data amounts are mapped and matched in combination of the plurality of sampling position sets of the plurality of division grids, and a plurality of second management coefficients are determined; a plurality of data management units are resource configured according to the plurality of first management coefficients and the plurality of second management coefficients, and a data management scheme is determined.

[0063] Further, the data intelligent management method of the whole cycle of hair sample detection further includes:

[0064] A plurality of sampling positions are randomly extracted from the K sampling positions as a plurality of first division centers; a plurality of sampling positions are randomly extracted from the K sampling positions again as a plurality of second division centers; the plurality of first division centers and the plurality of second division centers are distance authenticated, and if the authentication is passed, a plurality of target division centers and a diffusion division instruction are obtained; based on the diffusion division instruction, the plurality of target division centers are taken as starting points, and diffusion iteration is performed in the target area according to a preset division step length, and the plurality of division grids are obtained.

[0065] Further, the data intelligent management method of the whole cycle of hair sample detection further includes:

[0066] The plurality of first division centers and the plurality of second division centers are enumerated two by two respectively, and a plurality of first division center enumeration combinations and a plurality of second division center enumeration combinations are obtained; a plurality of first combination sampling distances and a plurality of second combination sampling distances of the plurality of first division center enumeration combinations and the plurality of second division center enumeration combinations are calculated based on the K sampling positions; it is judged whether there is a combination sampling distance less than or equal to a preset distance threshold in the plurality of first combination sampling distances and / or the plurality of second combination sampling distances, and if so, the authentication is not passed, and the division center selection is performed again.

[0067] Further, the data intelligent management method of the whole cycle of hair sample detection further includes:

[0068] If no, the authentication is passed, and a first dispersion degree of the plurality of first combined sampling distances and a second dispersion degree of a plurality of second combined sampling distances are compared; when the first dispersion degree is greater than the second dispersion degree, the plurality of first division centers are taken as a plurality of target division centers.

[0069] Further, the data intelligent management method of the whole cycle of the hair sample detection further comprises:

[0070] Based on the diffusion division instruction, diffusion iteration is performed in the target region from the plurality of target division centers as the starting point according to a preset division step, a plurality of preliminary division grids are obtained; the edges of the plurality of preliminary division grids are diffused again according to the preset division step, a plurality of stage division grids are obtained; based on the K sampling positions, a plurality of preliminary grid densities and a plurality of stage grid densities of the plurality of preliminary division grids are determined; it is respectively judged whether the plurality of stage grid densities are greater than or equal to the plurality of preliminary grid densities, if yes, the diffusion of the plurality of stage division grids is continued according to the preset division step, a plurality of diffusion division grids are obtained; if no, the diffusion division is stopped, a plurality of diffusion division grids and an un-divided region are obtained; the un-divided region is taken as a division grid, and the plurality of diffusion division grids are combined to generate the plurality of division grids.

[0071] Further, the data intelligent management method of the whole cycle of the hair sample detection further comprises:

[0072] The number of sampling positions in the plurality of sampling position sets in the plurality of division grids is counted, a plurality of grid sampling position quantities are obtained; the ratio of the plurality of grid sampling position quantities to the sum of the plurality of grid sampling position quantities is calculated respectively, and a plurality of first management coefficients are obtained.

[0073] Further, the data intelligent management method of the whole cycle of the hair sample detection further comprises:

[0074] K sampling environment information of the K sampling positions is obtained; the K detection data quantities are determined according to the K correlation detection schemes and the K sampling environment information.

[0075] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. Figure 1The intelligent data management system for the entire hair sample testing cycle and the specific examples described in Example 1 are also applicable to the intelligent data management method for the entire hair sample testing cycle of this embodiment. The aforementioned detailed description of the intelligent data management system for the entire hair sample testing cycle will clearly indicate the intelligent data management method for the entire hair sample testing cycle of this embodiment. For the sake of brevity, a detailed description will not be given here. Since the method disclosed in this embodiment corresponds to the system disclosed in this embodiment, its description is relatively brief. For relevant details, please refer to the description of the system.

[0076] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one 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 application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent data management system for the entire hair sample testing cycle, characterized by: include: A sample acquisition module, the sample acquisition module is used to acquire K hair samples from a target area, wherein the K hair sample sampling points include K sampling positions; A grid division module, the grid division module is used to perform multi-granularity grid division on the target area based on the K sampling positions, and determine a plurality of division grids, wherein the plurality of division grids include a plurality of sampling position sets; a sampling determination module, the sampling determination module being configured to determine a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets within the plurality of divided grids; a scheme determination module, configured to obtain K test results of the K hair samples and determine K associated detection schemes according to the K test results; a data volume determination module, configured to determine K detection data volumes in combination with the K sampling positions and the K associated detection schemes; a mapping and matching module, the mapping and matching module being configured to map the amount of detection data corresponding to each sampling position to the divided grid in which it is located based on the spatial coordinates thereof, and for each set of sampling positions in each grid, summarizing the amount of detection data of all sampling positions, and determining a second management coefficient for the grid based on the amount of detection data of the sampling positions contained in the grid; a resource configuration module, configured to perform resource configuration on a plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients, wherein the resource configuration includes computing power, data storage, and detection equipment, and determine a data management plan; The grid division module is also used to: Randomly selecting multiple sampling positions from the K sampling positions as multiple first division centers; Randomly selecting a plurality of sampling positions from the K sampling positions again as a plurality of second division centers; Performing distance authentication on the plurality of first division centers and the plurality of second division centers, and if the authentication passes, obtaining a plurality of target division centers and a diffusion division instruction; Based on the diffusion partitioning instruction, taking the multiple target partitioning centers as starting points, performing diffusion iterations in the target area according to a preset partitioning step size to obtain the multiple partitioning grids; The grid division module is also used to: Based on the diffusion partitioning instruction, taking the multiple target partitioning centers as starting points, diffusion iteration is performed in the target area according to a preset partitioning step size to obtain multiple preliminary partitioning grids; Diffusion is again performed on the edges of the plurality of preliminary divided grids according to the preset division step size to obtain a plurality of stage divided grids; Determining a plurality of preliminary grid densities and a plurality of stage grid densities of the plurality of preliminary divided grids based on the K sampling positions; respectively determining whether the plurality of stage grid densities are greater than or equal to the plurality of preliminary grid densities, and if so, continuing to diffuse the plurality of stage divided grids according to the preset division step to obtain a plurality of diffused divided grids; If not, the diffusion partitioning is stopped, and multiple diffusion partitioning grids and unpartitioned areas are obtained; The undivided area is used as a divided grid, and the multiple diffusion divided grids are combined to generate the multiple divided grids.

2. The intelligent data management system for the entire hair sample detection cycle according to claim 1, characterized in that: The grid division module is also used to: Enumerating the plurality of first division centers and the plurality of second division centers in pairs respectively to obtain a plurality of first division center enumeration combinations and a plurality of second division center enumeration combinations; Calculating a plurality of first combination sampling distances and a plurality of second combination sampling distances of the plurality of first partition center enumeration combinations and the plurality of second partition center enumeration combinations based on the K sampling positions; It is determined whether there is a combined sampling distance less than or equal to a preset distance threshold among the plurality of first combined sampling distances and / or the plurality of second combined sampling distances. If so, the authentication fails and the division center is reselected.

3. The intelligent data management system for the entire hair sample detection cycle according to claim 2, characterized in that: The grid division module is also used to: If not, the authentication is passed, and the first dispersion of the plurality of first combined sampling distances is compared with the second dispersion of the plurality of second combined sampling distances; When the first dispersion is greater than the second dispersion, the plurality of first division centers are used as a plurality of target division centers.

4. The intelligent data management system for the entire hair sample detection cycle according to claim 1, characterized in that: The sampling determination module is further configured to: Counting the number of sampling positions in a plurality of sampling position sets within the plurality of divided grids to obtain a plurality of grid sampling position numbers; The ratios of the number of the plurality of grid sampling positions to the total number of the plurality of grid sampling positions are respectively calculated to obtain the plurality of first management coefficients.

5. The intelligent data management system for the entire hair sample detection cycle according to claim 1, characterized in that: The data amount determination module is further configured to: Acquiring K sampling environment information of the K sampling locations; The K detection data amounts are determined according to the K associated detection schemes and the K sampling environment information.

6. A method for intelligent data management during the entire hair sample testing cycle, characterized in that: The method is performed by the intelligent data management system for the entire hair sample detection cycle according to any one of claims 1 to 5, wherein the intelligent data management method for the entire hair sample detection cycle comprises: Acquire K hair samples from a target area, wherein the K hair sample sampling points include K sampling positions; Performing multi-granularity grid division on the target area based on the K sampling positions to determine a plurality of division grids, wherein the plurality of division grids include a plurality of sampling position sets; determining a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets within the plurality of divided grids; Obtaining K test results of the K hair samples, and determining K associated detection schemes according to the K test results; Determining K detection data amounts in combination with the K sampling positions and the K associated detection schemes; Based on the K sampling positions corresponding to the K detection data amounts, mapping and matching are performed in combination with the multiple sampling position sets of the multiple divided grids to determine a plurality of second management coefficients; Resources are configured for multiple data management units according to the multiple first management coefficients and the multiple second management coefficients to determine a data management solution.

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