Intelligent data management system and method for full cycle of hair sample detection

Abstract

The invention provides an intelligent data management system and method for a complete cycle of hair sample detection, and relates to the technical field of data processing, and the system comprises a sample obtaining module which is used for obtaining a hair sample; the grid division module is used for performing multi-granularity grid division and determining a plurality of division grids; the sampling determination module is used for determining a plurality of first management coefficients; the scheme determination module is used for acquiring a detection result and determining an associated detection scheme; the data volume determination module is used for determining a detection data volume; the mapping matching module is used for mapping, matching and determining a plurality of second management coefficients; and the resource configuration module is used for performing resource configuration on the plurality of data management units. According to the data management method and device, the technical problem that in the prior art, due to the fact that configuration of the detection data volume and the data management resources lacks an effective matching mechanism, data resource allocation is unreasonable can be solved, the detection data volume is determined through multi-granularity grid division in combination with the sampling position and the association detection scheme, and the data management quality and efficiency are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an intelligent data management system and method for the entire cycle of hair sample detection. Background Art

[0002] With the development of biological detection technology, hair sample detection is increasingly widely used in forensic medicine, environmental monitoring, medical diagnosis and other fields. As a biological material, hair samples can reflect the physiological state and environmental exposure of individuals, so it is of great significance to analyze them. However, the existing hair sample detection technology has some shortcomings in data management. In the prior art, 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, a unified detection scheme cannot fully consider these differences, resulting in inaccurate detection results. Hair samples at different locations have different detection requirements, and the existing unified detection scheme cannot meet these personalized needs, which affects the accuracy of the detection results and leads to unreasonable allocation and waste of resources.

[0003] To sum up, the existing technology has a technical problem of unreasonable data resource allocation due to the lack of an effective matching mechanism between the amount of detection data and the configuration of data management resources, resulting in a large amount of detection data and insufficient data management resources at some sampling locations, while other locations have an excess of resources. Summary of the invention

[0004] The purpose of this application is to provide an intelligent data management system and method for the entire cycle of hair sample detection, so as to solve the technical problem in the prior art that due to the lack of an effective matching mechanism between the amount of detection data and the configuration of data management resources, the amount of detection data at some sampling locations is large and the data management resources are insufficient, while other locations have an excess of resources, resulting in unreasonable allocation of data resources.

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

[0006] In a first aspect, the present application provides an intelligent data management system for a full cycle of hair sample detection, wherein the intelligent data management system for a full cycle of hair sample detection includes: a sample acquisition module, the sample acquisition module is used to acquire K hair samples in 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 is used to determine a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets in the plurality of division grids; a scheme determination module a determination module, the scheme determination module is used to obtain K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results; a data volume determination module, the data volume determination module is used 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 is used to perform mapping and matching based on the K sampling positions corresponding to the K detection data volumes and in combination with a plurality of sampling position sets of the plurality of divided grids, to determine a plurality of second management coefficients; a resource configuration module, 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.

[0007] In a second aspect, the present application further provides a method for intelligent data management during the entire detection cycle of hair samples, wherein the method for intelligent data management during the entire detection cycle of hair samples comprises: obtaining K hair samples of 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, and determining a plurality of divided grids, wherein the plurality of divided 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 detection results of the K hair samples, and determining K associated detection schemes according to the K detection results; determining K detection data volumes in combination with the K sampling positions and the K associated detection schemes; performing mapping and matching based on the K sampling positions corresponding to the K detection data volumes and in combination with a plurality of sampling position sets of the plurality of divided grids, and determining a plurality of second management coefficients; and performing resource allocation for a plurality of data management units according to the plurality of first management coefficients and the plurality of second management coefficients, and determining a data management scheme.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

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

[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, it can be implemented according to the contents of the specification, and in order 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 cited 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 the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any 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 , scheme determination module 14 , data volume determination module 15 , mapping and matching module 16 , resource configuration module 17 . DETAILED DESCRIPTION

[0015] The present application solves the technical problem in the prior art that the amount of detection data and the configuration of data management resources lack an effective matching mechanism, the amount of detection data at certain sampling locations is large and the data management resources are insufficient, while other locations have excessive resource configuration, resulting in unreasonable data resource allocation, by providing an intelligent data management system and method for the entire cycle of hair sample detection. Through multi-granularity grid division, accurate sampling location selection is performed for the target area, and the associated detection scheme is determined in combination with the sampling location and the detection result, so that the most suitable detection scheme can be adopted for samples at different locations, the first and second management coefficients are calculated, and the dynamic allocation of data management resources is realized, resource waste or insufficiency is avoided, and the quality and efficiency of data management are improved.

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

[0017] For example, please refer to the attached Figure 1 The present application provides a data intelligent management system for the entire cycle of hair sample detection, wherein the data intelligent management system for the entire cycle of hair sample detection is used to implement the steps of the data intelligent management method for the entire cycle of hair sample detection, and the data intelligent management system for the entire cycle of hair sample detection 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, determine the target area for hair sample collection, including the human head or other parts of the body, a specific geographical area, or a sample environment. In the target area, K specific sampling locations are marked according to research needs or standard operating procedures. These can be several specific points distributed in the target area to ensure that the hair samples collected from different locations are representative. Use random sampling, systematic sampling, or stratified sampling to select K sampling locations, obtain hair samples from each sampling location, and obtain K hair samples. Each hair sample sampling point has its specific sampling location. By selecting multiple sampling locations in the target area, the diversity and representativeness of the samples are ensured, thereby improving the accuracy and reliability of the test results.

[0020] A grid division module 12, the grid division module 12 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;

[0021] Specifically, multiple sampling positions are randomly selected from K sampling positions as multiple first division centers, and multiple sampling positions are again selected as multiple second division centers. Distance authentication is performed on multiple first division centers and multiple second division centers. If the authentication is passed, the division centers with higher dispersion are used as multiple target division centers, and the diffusion division instruction is determined. According to the diffusion division instruction, multiple target division centers are used as starting points, and multi-granularity grid division is performed in the target area according to the preset division step. Multi-granularity grid division refers to gridding the target area at different precisions or scales, and gradually refining the grid from coarse to fine in a step-by-step manner, so that the scale of grid division can adapt to the distribution of different sampling positions. In the initial stage, the target area is coarse-grained to generate a relatively large grid to obtain the first layer of division grids, each of which may cover multiple sampling positions. After the first layer of coarse-grained division is completed, the edge of the preliminary division grid is further divided according to the preset division step to obtain the second layer of division grid.

[0022] According to the K sampling positions, determine the density of the first layer of division grids and the second layer of division grids. 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, and 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. Take 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 positions in each grid, that is, the sampling points in each grid. Each divided grid will contain several sampling positions, called the sampling position set, which reflects the distribution of sampling points in the grid. The sampling position set in each grid may contain different numbers of sampling points. By counting the number of sampling positions in each grid and analyzing the sampling density in different areas, further resource allocation or data analysis can be helped. Through multi-granularity grid division based on K sampling positions, the target area is divided into multiple grids to ensure that each grid contains a sampling position set, effectively improving the rationality of the division and processing efficiency.

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

[0025] Specifically, the set of sampling positions in each divided grid reflects the number of sampling points contained in the grid. The number will directly affect the first management coefficient of the grid, that is, the grid with more sampling positions has a larger management coefficient, indicating that the grid has a higher relative importance in the system. The number of sampling positions in the grid is determined by the sets of sampling positions in multiple divided grids. The sum of the number of sampling positions in all grids is calculated. The first management coefficient of each grid is the ratio of the number of sampling positions in each grid to the total number of sampling positions, reflecting the relative importance of each grid relative to the entire area. The first management coefficient reflects the relative weight of each grid in the overall system. The grid with more sampling positions has a larger management coefficient, which means that in the subsequent resource allocation and data processing process, the priority of the grid will be higher than that of other grids. Through the calculation of the first management coefficient, the density of sampling positions of different grids is identified, and the corresponding resources are allocated accordingly. The processing resource allocation is automatically adjusted according to the density of the sampling positions to avoid manual intervention, dynamically adjust the allocation strategy, and achieve more flexible management.

[0026] A scheme determination module 14, the scheme determination module 14 is used to obtain K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results;

[0027] Specifically, K hair samples are tested to generate the test results of each sample, including information such as the type and concentration of pollutants. According to the monitoring results, a corresponding correlation detection plan is formulated for each sample. The correlation detection plan refers to determining other locations or environmental samples that need to be further tested based on the pollutants or abnormal components in the hair samples, in order to further confirm the source or propagation path of the pollutants in the hair samples. Some pollutants or abnormal components detected in the hair samples may be related to other sources in the environment, including pipes, water pipes, sewers, sewage treatment systems, etc. Each test result corresponds to one or more areas that need further testing (such as pipes, sewers, etc.), so as to generate a specific test plan. For example, if heavy metals are detected in the hair sample, it is necessary to test the household water pipes and water supply pipes in the nearby area to check the source of heavy metals in the water quality. According to different hair sample test results, multiple different test locations are pointed to ensure that the association between the test results and potential pollution sources can be clearly identified. Through the combination of hair testing and environmental testing, the pollution source or potential environmental problems can be accurately located, and a multi-dimensional correlation detection plan can be realized. 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, the data volume determination module 15 is used to determine K detection data volumes in combination with 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 generally 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, the mapping and matching module 16 is used to perform mapping and matching based on the K sampling positions corresponding to the K detection data amounts and in combination with the multiple sampling position sets of the multiple divided grids, to determine a plurality of second management coefficients;

[0031] Specifically, the K detection data volumes refer to the detection data volumes of each sampling position, which determines the detection complexity and required resources of the position. The target area is divided into multiple grids through a multi-granularity grid partitioning process, and each grid contains a set of several sampling positions. Through mapping and matching, the detection data volume of each sampling position is associated with the grid sampling position set where it is located, ensuring that the detection demand in each grid can reasonably allocate resources. According to the detection data volume of the sampling position contained in each grid, the second management coefficient of the grid is determined. According to the spatial coordinates of each sampling position, the corresponding detection data volume is mapped to the partitioned grid where it is located. For the sampling position set in each grid, the detection data volume of all sampling positions is summarized to obtain the total detection demand of the grid, and the total detection data volume of each grid is compared with the total detection data volume of all grids to calculate the detection data ratio of the grid. For example, if the grid needs to collect more data, the second management coefficient of this grid unit may be higher. The second management coefficient reflects the proportion of each grid in the detection resource demand relative to all grids, and dynamically reflects the resource demand and priority of different grids. By reasonably determining the second management coefficient, we can ensure that grids with high detection demands obtain more resources and optimize the efficiency of the overall detection process.

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

[0033] Specifically, the first management coefficient reflects the proportion of the number of sampling positions in each grid to the total sampling positions, and determines the relative importance of each grid. The more sampling positions there are in the grid, the higher the first management coefficient, and the greater the weight of the grid in resource allocation. The second management coefficient reflects the proportion of the amount of detection data in each grid in the detection requirements of all grids, reflects the detection resources required for each grid, and is used to evaluate the detection complexity and data processing requirements of each grid. The larger the amount of detection data, the higher the second management coefficient, and the more resources the grid requires. By combining the two management coefficients, it is ensured that each grid obtains appropriate resource allocation, including computing power, data storage, detection equipment, etc., and resources are allocated according to the first management coefficient and the second management coefficient of each grid to ensure that key grids are given priority. The data management unit is responsible for processing the detection data, analysis results and resource management of each grid. According to the management coefficient, resources are configured for each data management unit to ensure that it can meet the needs of data collection, processing and storage. By combining the two management coefficients, the resource requirements of each grid are accurately evaluated to ensure reasonable and efficient resource allocation.

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

[0035] A plurality of sampling positions are randomly selected from the K sampling positions as a plurality of first division centers; a plurality of sampling positions are randomly selected again from the K sampling positions as a plurality of second division centers; distance authentication is performed on the plurality of first division centers and the plurality of second division centers, and if the authentication is passed, a plurality of target division centers and diffusion division instructions are obtained; based on the diffusion division instructions, diffusion iterations are performed in the target area with the plurality of target division centers as starting points according to a preset division step size to obtain the plurality of division grids.

[0036] Specifically, multiple locations are randomly selected from K sampling locations as the first division center. Through random sampling, the randomness of the distribution of the sampling locations is ensured, human bias is avoided, and the representativeness and diversity of the samples are increased. After the first division center is selected, the second division center is randomly sampled again from the K sampling locations. Through two random samplings, the diversity of the division centers is ensured, so that the coverage of the division area is wider, and the local concentration phenomenon that may be caused by a single random sampling is avoided. The distance authentication is performed on the first and second division centers extracted to ensure that there is enough distance between the two division centers to avoid overlapping or overly dense grid divisions. The authentication pass standard may be based on a minimum distance threshold or other statistical standards of spatial distribution, such as less than or equal to a preset distance threshold. Distance authentication can avoid overly dense or overly dispersed distribution of division centers.

[0037] After the distance authentication is passed, the final multiple target division centers are determined, and a diffusion division instruction is generated to control the subsequent division and diffusion process and specify how each center is divided into regions. According to the diffusion division instruction, with multiple target division centers as the starting point and the preset division step length as the radius, diffusion iteration is performed in the target area to obtain multiple preliminary division grids. Each preliminary division grid is an area constructed with the target division center as the starting point and the preset division step length as the radius. The edge of the preliminary division grid is diffused again according to the preset division step length to form a stage division grid. According to K sampling positions, the preliminary grid density and stage grid density of the preliminary division grid are determined. If the stage grid density is greater than or equal to the preliminary grid density, the stage division grid is continued to be diffused according to the preset division step length to form a diffusion division grid. If the stage grid density is less than the preliminary grid density, the diffusion division is stopped, and the current diffusion division grid and the undivided area are retained. The division area after the diffusion is stopped is used as a division grid, and multiple diffusion division grids are combined to generate the final multiple division grids.

[0038] Through two random extractions and distance authentication, the distribution of the division centers is uniform and reasonable, 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, avoiding the blindness and overlap in traditional grid division, and improving the accuracy of the entire regional division.

[0039] Furthermore, the grid division module 12 in the data intelligent management system for the entire hair sample detection cycle is also used for:

[0040] The multiple first division centers and the multiple second division centers are enumerated in pairs respectively to obtain multiple first division center enumeration combinations and multiple second division center enumeration combinations; multiple first combination sampling distances and multiple second combination sampling distances of the multiple first division center enumeration combinations and the multiple second division center enumeration combinations are calculated based on the K sampling positions; it is determined whether there is a combination sampling distance less than or equal to a preset distance threshold among the multiple first combination sampling distances and / or the multiple second combination sampling distances. If so, the authentication fails and the division center selection is performed again.

[0041] Furthermore, the grid division module 12 in the data intelligent management system for the entire hair sample detection cycle is also used for:

[0042] If not, the authentication is passed, and the first dispersion of the plurality of first combined sampling distances and the second dispersion of the plurality of second combined sampling distances are compared; 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.

[0043] Specifically, the first division center and the second division center are enumerated in pairs, and each position of the first division center is paired with all other positions to form an enumeration combination of the first division center. Similarly, the same operation is performed on the second division center to form an enumeration combination of the second division center. For each enumeration combination, based on its corresponding sampling position, the first combination sampling distance and the second combination sampling distance are calculated by Euclidean distance, Manhattan distance or other suitable spatial distance. By calculating the distance of each enumeration combination, a plurality of first combination sampling distances and 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, which reflects the distribution of the division centers. The larger the distance, the more dispersed the division centers are; the smaller the distance, the more concentrated the division centers are. A preset distance threshold is pre-set, which is a minimum allowable distance, used to ensure the reasonable distribution between the division centers, prevent the division centers from being too close, and avoid overlapping or uneven grid division.

[0044] All calculated sampling distances of the first and second combinations are judged to check whether any distance is less than or equal to the preset distance threshold. If all combination distances are greater than the preset distance threshold, the authentication is passed, indicating that the distribution of the division center is reasonable, and the next step can be performed. If there is a combination distance less than or equal to the preset distance threshold, it means that the distribution of the division center is too concentrated, the authentication fails, and the division center needs to be reselected, that is, multiple sampling positions are randomly selected from the K sampling positions. For example, it is assumed that 3 first division centers and 3 second division centers are selected. The first division centers are enumerated in pairs to obtain 3 combinations: (A1, A2), (A1, A3), (A2, A3). Similarly, the second division centers are enumerated in pairs to obtain 3 combinations: (B1, B2), (B1, B3), (B2, B3). The distances between these combinations are calculated, and the sampling distances of the first combination are d(A1, A2) = 5mm, d(A1, A3) = 8mm, and d(A2, A3) = 6mm. The second combined sampling distance is d(B1, B2) = 7mm, d(B1, B3) = 9mm, d(B2, B3) = 10mm. If the preset distance threshold is 8mm, the distribution of the division center is too concentrated, the authentication fails, and re-division is required.

[0045] Calculate the dispersion of the first combination sampling distance and the second combination sampling distance, that is, the first dispersion and the second dispersion, which indicate the degree of dispersion of the data distribution between the partition centers. Dispersion measures the uniformity of the distribution of sampling positions and reflects the overall difference in distances between different partition centers. Calculated by statistical methods, such as variance or standard deviation, it indicates the fluctuation of the distance. A high dispersion means that the data is more widely distributed, and a low dispersion means that the data is more concentrated. Comparing the first dispersion and the second dispersion, if the first dispersion is greater than the second dispersion, it means that the distribution of the first partition center is more dispersed and more uniform; if the second dispersion is greater than the first dispersion, it means that the distribution of the second partition center is more dispersed.

[0046] According to the results of the dispersion comparison, a group of division centers with more uniform distribution is selected as the target division centers. When the first dispersion is greater than the second dispersion, multiple first division centers are used as multiple target division centers; when the second dispersion is greater than the first dispersion, the second division center is selected. By selecting the division center with greater dispersion, it is ensured that the distribution of the division center in the target area is more reasonable, and the situation where the division center is too concentrated and causes uneven grid is avoided. For example, assuming that the calculated sampling distance of the first combination is 3mm, 7mm, 11mm, and the sampling distance of the second combination is 4mm, 8mm, 6mm. The first dispersion (such as standard deviation) is 3.3mm, and the second dispersion is 1.6mm, then the first division center is used as multiple target division centers. Through the two division center selections and their distance calculations, it is ensured that there is a reasonable spatial distribution between the division centers. By comparing the dispersion of the two groups of division centers, the system selects the group with more uniform distribution as the final target division center, which further ensures the uniformity and rationality of the grid division.

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

[0048] Based on the diffusion partitioning instruction, taking the multiple target partitioning centers as the starting point, diffusion iterations are performed in the target area according to the preset partitioning step to obtain multiple preliminary partitioning grids; the edges of the multiple preliminary partitioning grids are diffused again according to the preset partitioning step 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 determined respectively; if so, the multiple stage partitioning grids are continued to be diffused according to the preset partitioning step 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 division step size is preset 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 division 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 division 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 division step size to generate a new stage partitioning grid. The 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 K sampling positions, the preliminary grid density and stage grid density are determined, that is, the number of sampling positions in each preliminary grid unit and stage grid unit is calculated. The preliminary grid density refers to the number of sampling points or sample distribution in each preliminary divided grid; the stage grid density refers to the number of sampling points in the stage divided grid. The density can be calculated by the number of sampling points / grid area in the grid, or it can be determined based on the spatial distribution of the sampling points and the number of samples. By calculating the density of the preliminary grid and the stage grid, it can be evaluated whether a reasonable grid distribution is generated during the diffusion process to avoid unevenness or excessive concentration.

[0051] Compare the stage mesh density with the preliminary mesh density to determine whether there is a higher density distribution. If the stage mesh density is greater than or equal to the preliminary mesh density, it means that diffusion has produced denser areas, indicating that continued diffusion can refine the mesh, and then continue to further divide the multiple stage partition meshes according to the preset division step size until the mesh density no longer increases, and multiple diffusion partition meshes are obtained.

[0052] If the stage grid density is less than the initial grid density, it means that the diffusion area may be too sparse or difficult to further refine. Continuing the diffusion division will not bring more information gain. At this time, the diffusion is stopped and the current multiple diffusion division grids and undivided areas are recorded. The purpose of stopping the diffusion division is to prevent over-refining of the grid, thereby saving computing resources and avoiding unnecessary complexity. At the same time, the existence of undivided areas can help identify blind spots in data collection or areas that need extra attention. The undivided area after stopping the diffusion is used as a division grid, combined with multiple diffusion division grids to form multiple division grids, ensuring that the entire target area is effectively covered and each grid cell has clear identification and attributes, including those areas that are not further subdivided during the diffusion division process. The final division grid is a refined division of the entire target area, and each grid represents a specific sub-area in the target area. By incorporating the undivided area into the division grid, it is ensured that all areas in the target area are taken into account, reducing the blind spots of data collection and analysis. Through multiple diffusion steps and density comparison, the generated grid can finely cover the target area. Through the automated diffusion and stop mechanism, the division range is dynamically adjusted according to the density change, which improves the division accuracy and efficiency.

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

[0054] The number of sampling positions in the plurality of sampling position sets within the plurality of divided grids is counted to obtain the number of multiple grid sampling positions; and the ratio of the number of the multiple grid sampling positions to the sum of the number of the multiple grid sampling positions is respectively calculated to obtain the plurality of first management coefficients.

[0055] Specifically, the number of sampling positions in the sampling position set within each divided grid is counted, and the number of sampling positions in each grid is calculated to obtain multiple grid sampling position numbers. For each grid sampling position number, the ratio of the number of sampling positions to the sum of the number of sampling positions of all grids is calculated to obtain multiple first management coefficients, which reflect the distribution of sampling positions of each grid relative to the entire target area. For example, assuming that there are 4 grids, grid 1 includes 3 sampling positions, grid 2 includes 1 sampling position, grid 3 includes 2 sampling positions, and grid 4 includes 4 sampling positions. The sum of the number of sampling positions of multiple grids is 10. The ratio of the number of sampling positions of each grid to the sum is calculated to obtain multiple first management coefficients: 0.3 for grid 1, 0.1 for grid 2, 0.2 for grid 3, and 0.4 for grid 4. Through the management coefficient, appropriate computing resources are allocated during data acquisition or processing to ensure that the processing of each grid is optimized according to the number of its sampling positions, thereby improving the accuracy of the overall analysis.

[0056] Furthermore, the data volume determination module 15 in the intelligent data management system for the entire hair sample detection cycle is also used for:

[0057] Acquire K sampling environment information of the K sampling positions; and determine the K detection data amounts according to the K associated detection schemes and the K sampling environment information.

[0058] Specifically, the environmental information of each sampling location is obtained, including the surrounding physical conditions, degree of environmental pollution, water quality, temperature and humidity, etc. The sampling environmental information can be multi-dimensional, depending on different detection scenarios. According to the associated detection scheme of each sampling location, the detection type and detection parameters required for each scheme are analyzed, and the amount of detection data required for each location is determined in combination with the associated detection scheme and the sampling environmental information. For example, if the environmental information of a location shows that the pollution level is high, more data needs to be collected at that location to ensure the accuracy of the results. By combining the sampling environmental information and the associated detection scheme, the amount of detection data required for each location is determined in a targeted manner, avoiding the problem of excessive or insufficient data collection.

[0059] In summary, the intelligent data management system for the entire hair sample detection cycle provided by this application has the following technical effects:

[0060] The method comprises the following steps: a sample acquisition module, which is used to acquire K hair samples of a target area, wherein the K hair sample sampling points include K sampling positions; a grid division module, which is used to perform multi-granularity grid division on the target area based on the K sampling positions, and determine a plurality of divided grids, wherein the plurality of divided grids include a plurality of sampling position sets; a sampling determination module, which is used to determine a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets in the plurality of divided grids; and a scheme determination module, which is used to acquire K detection points of the K hair samples. The invention provides a method for determining the K detection results and determining K associated detection schemes according to 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 determine multiple second management coefficients 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 a resource configuration module, which is used to configure resources for multiple data management units according to 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 sampling position of the target area is accurately selected, and the associated detection scheme is determined in combination with the sampling position and the detection results, so that the samples at different positions can adopt the most suitable detection scheme, calculate the first and second management coefficients, realize the dynamic allocation of data management resources, avoid resource waste or shortage, and improve the quality and efficiency of data management.

[0061] Embodiment 2, based on the same inventive concept as the data intelligent management system for the entire cycle of hair sample detection in the aforementioned embodiment 1, the present application also provides a data intelligent management method for the entire cycle of hair sample detection, please refer to the attached Figure 2 The method for intelligent management of data during the entire hair sample detection cycle includes:

[0062] Acquire K hair samples of a target area, wherein the K hair sample sampling points include K sampling positions; divide the target area into multi-granularity grids based on the K sampling positions to determine a plurality of divided grids, wherein the plurality of divided grids include a plurality of sampling position sets; 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; acquire K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results; determine K detection data volumes in combination with the K sampling positions and the K associated detection schemes; perform mapping and matching based on the K sampling positions corresponding to the K detection data volumes in combination with a plurality of sampling position sets of the plurality of divided grids to determine a plurality of second management coefficients; perform resource allocation for 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.

[0063] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0064] A plurality of sampling positions are randomly selected from the K sampling positions as a plurality of first division centers; a plurality of sampling positions are randomly selected again from the K sampling positions as a plurality of second division centers; distance authentication is performed on the plurality of first division centers and the plurality of second division centers, and if the authentication is passed, a plurality of target division centers and diffusion division instructions are obtained; based on the diffusion division instructions, diffusion iterations are performed in the target area with the plurality of target division centers as starting points according to a preset division step size to obtain the plurality of division grids.

[0065] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0066] The multiple first division centers and the multiple second division centers are enumerated in pairs respectively to obtain multiple first division center enumeration combinations and multiple second division center enumeration combinations; multiple first combination sampling distances and multiple second combination sampling distances of the multiple first division center enumeration combinations and the multiple second division center enumeration combinations are calculated based on the K sampling positions; it is determined whether there is a combination sampling distance less than or equal to a preset distance threshold among the multiple first combination sampling distances and / or the multiple second combination sampling distances. If so, the authentication fails and the division center selection is performed again.

[0067] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0068] If not, the authentication is passed, and the first dispersion of the plurality of first combined sampling distances and the second dispersion of the plurality of second combined sampling distances are compared; 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.

[0069] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0070] Based on the diffusion partitioning instruction, taking the multiple target partitioning centers as the starting point, diffusion iterations are performed in the target area according to the preset partitioning step to obtain multiple preliminary partitioning grids; the edges of the multiple preliminary partitioning grids are diffused again according to the preset partitioning step 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 determined respectively; if so, the multiple stage partitioning grids are continued to be diffused according to the preset partitioning step 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.

[0071] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0072] The number of sampling positions in the plurality of sampling position sets within the plurality of divided grids is counted to obtain the number of multiple grid sampling positions; and the ratio of the number of the multiple grid sampling positions to the sum of the number of the multiple grid sampling positions is respectively calculated to obtain the plurality of first management coefficients.

[0073] Furthermore, the method for intelligent management of data during the entire hair sample detection cycle also includes:

[0074] Acquire K sampling environment information of the K sampling positions; and determine the K detection data amounts according to the K associated detection schemes and the K sampling environment information.

[0075] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The data intelligent management system for the entire detection period of hair samples and the specific examples in the first embodiment are also applicable to the data intelligent management method for the entire detection period of hair samples in the present embodiment. Through the above detailed description of the data intelligent management system for the entire detection period of hair samples, those skilled in the art can clearly understand the data intelligent management method for the entire detection period of hair samples in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. As for the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the system part.

[0076] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can 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 belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent data management system for the entire hair sample testing cycle, characterized in that: include: A sample acquisition module, the sample acquisition module is used to acquire K hair samples in the 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 is used to determine a plurality of first management coefficients according to the number of sampling positions in a plurality of sampling position sets in the plurality of divided grids; A scheme determination module, the scheme determination module is used to obtain K detection results of the K hair samples, and determine K associated detection schemes according to the K detection results; A data volume determination module, the data volume determination module is used to determine K detection data volumes in combination with the K sampling positions and the K associated detection schemes; A mapping matching module, 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 multiple sampling position sets of the multiple divided grids to determine a plurality of second management coefficients; A resource configuration module is used to perform resource configuration on multiple data management units according to the multiple first management coefficients and the multiple second management coefficients to determine a data management plan.

2. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 1, characterized in that: The grid partitioning module is also used for: Randomly select multiple sampling positions from the K sampling positions as multiple first division centers; Again randomly selecting a plurality of sampling positions from the K sampling positions as a plurality of second partition centers; Performing distance authentication on the plurality of first division centers and the plurality of second division centers, and if the authentication is passed, obtaining a plurality of target division centers and diffusion division instructions; Based on the diffusion partitioning instruction, taking the multiple target partitioning centers as starting points, diffusion iterations are performed in the target area according to a preset partitioning step length to obtain the multiple partitioning grids.

3. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 2, characterized in that: The grid partitioning module is also used for: Enumerate 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; Calculate 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 selected again.

4. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 3, characterized in that: The grid partitioning module is also used for: 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.

5. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 2, characterized in that: The grid partitioning module is also used for: 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 length to obtain multiple preliminary partitioning grids; Diffusion is performed again on the edges of the plurality of preliminary divided grids according to the preset division step length to obtain a plurality of stage divided grids; Based on the K sampling positions, determining a plurality of preliminary grid densities and a plurality of stage grid densities of the plurality of preliminary divided grids; 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 division grids according to the preset division step to obtain a plurality of diffused division grids; If not, the diffusion partitioning is stopped, and a plurality of diffusion partitioning grids and unpartitioned regions are obtained; The undivided area is taken as a divided grid, and the multiple divided grids are generated by combining the multiple diffusion divided grids.

6. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 1, characterized in that: The sampling determination module is also used for: Counting the number of sampling positions in a plurality of sampling position sets in the plurality of divided grids to obtain the number of sampling positions in a plurality of grids; The ratios of the number of the plurality of grid sampling positions to the sum of the number of the plurality of grid sampling positions are respectively calculated to obtain the plurality of first management coefficients.

7. The intelligent data management system for the entire hair sample detection cycle as claimed in claim 1, characterized in that: The data volume determination module is also used for: Acquire 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.

8. A method for intelligent management of data during the entire hair sample detection 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 7, wherein the intelligent data management method for the entire hair sample detection cycle comprises: Acquire K hair samples from the 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 in the plurality of divided grids; Obtaining K test results of the K hair samples, and determining K associated test schemes according to the K test results; Determine 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 multiple 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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