Drowning point speculation system and method based on diatom type distribution

Abstract

The invention discloses a drowning point speculation system and method based on diatom type distribution, and the system comprises a data collection module which is used for automatically collecting water sample information at a sampling point through intelligent water sample collection equipment according to the preset coordinates of the sampling point, monitoring the environmental parameters of the water sample in real time, and marking and recording the environmental parameters and the water sample together; the database construction module is used for constructing a database comprising a water sample information table, a diatom classification table and a sampling point information table, and storing and querying data according to an association relationship among different data tables; and the data analysis and application module is used for carrying out similarity comparison on the diatom data extracted from the body of the victim and the diatom data in the database and speculating a drowning place. According to the invention, automation and intellectualization of data acquisition, storage, analysis and application are realized, the working efficiency is greatly improved, and errors possibly caused by manual operation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of forensic medicine, and particularly to a drowning point speculation system and method based on the distribution of diatom species. Background Art

[0002] In the field of forensic medicine, determining the drowning location in a drowning death case is of great significance for solving the case. Traditional methods for judging the drowning location mainly rely on on-site investigation, autopsy features, etc., but these methods often have certain limitations. On-site investigation may be affected by environmental factors, and autopsy features may also be inaccurate due to individual differences. In addition, traditional methods often require a large amount of experience and subjective judgment when determining the drowning location, lacking objective scientific basis.

[0003] As an aquatic microalgae organism, the species and distribution of diatoms are closely related to the water environment, and there are differences in the species and quantities of diatoms in different waters. Therefore, by analyzing the species and distribution of diatoms in the victim's body, important clues can be provided for determining the drowning location. However, current diatom analysis methods often require a large amount of laboratory work, including microscopic observation and image analysis, which is not only time-consuming and laborious, but also requires professional technical personnel to operate.

[0004] With the emergence of intelligent water sample collection devices, the field of water quality monitoring has undergone a major revolution. These devices can realize the whole process automation of water sample collection, pretreatment, analysis and data processing, reduce human error, and improve the efficiency and accuracy of data collection. Intelligent water sample collection devices are usually equipped with high-precision sensors and intelligent control systems, supporting various sampling modes, such as timed, quantitative, proportional mixing or flow-triggered sampling schemes. In addition, these devices also have a portable design and environmental adaptability, can adapt to complex environments such as rugged terrain in the wild, and realize remote control and data management through the Internet of Things module.

[0005] Although intelligent water sample collection devices have made remarkable progress in the field of water quality monitoring, their application in the field of forensic medicine is still in its infancy. At present, there is no systematic drowning point speculation system based on the distribution of diatom species that can efficiently collect, store and analyze diatom data and accurately speculate the drowning location. Therefore, developing a drowning point speculation system and method based on the distribution of diatom species is of great significance for improving the detection efficiency and accuracy of drowning cases in the field of forensic medicine. Summary of the Invention

[0006] To solve the above technical problems existing in the prior art, the present invention proposes a drowning point speculation system and method based on the distribution of diatom species, which realizes the efficient collection, storage, analysis of diatom data and its convenient application in the investigation of drowning cases, and improves the accuracy of drowning location inference and the detection efficiency of cases.

[0007] On the one hand, to achieve the above object, the present invention provides a drowning point speculation system based on diatom species distribution, including:

[0008] Data acquisition module: Through intelligent water sample collection equipment, according to the preset sampling point coordinates, automatically collect water sample information at the sampling points, real-time monitor the environmental parameters of the water samples, and mark and record the environmental parameters together with the water samples;

[0009] Database construction module: Used to construct a database including a water sample information table, a diatom classification table, and a sampling point information table, and store and query data through the association relationships between different data tables;

[0010] Data analysis and application module: Used to compare the diatom data extracted from the victim's body with the diatom data in the database to speculate the drowning location.

[0011] Preferably, the data acquisition module includes:

[0012] Water sample collection unit: Used to automatically locate and collect water samples through the preset sampling point coordinates by intelligent water sample collection equipment, and use high-precision sensors to real-time monitor environmental parameters such as temperature, acidity and alkalinity, and dissolved oxygen to obtain preliminary environmental parameter data.

[0013] Preferably, the data acquisition module further includes a diatom classification unit, which is used to bind environmental parameters with water sample data based on the preliminary environmental parameter data by using marking and recording technology to generate a water sample data set with environmental parameter marks; use a microscope imaging device to image the diatoms in the water sample data set to obtain morphological characteristic image data of the diatoms, and qualitatively analyze the diatoms in the water sample data set through a molecular biology detection tool to judge the types of diatoms and obtain the diatom species classification result.

[0014] Preferably, the diatom classification unit scans the diatoms in the water sample data set through a microscope imaging device to obtain initial morphological characteristic image data, and processes the initial morphological characteristic image data through an image segmentation algorithm to obtain the segmented diatom morphological image;

[0015] Extract the gene sequence of the diatom sample in the water sample data set through a molecular biology detection tool to obtain gene sequence data. If the gene sequence data matches the preset species database, determine the diatom species classification result.

[0016] Preferably, the database construction module includes:

[0017] Structured data storage unit: It is used to parse the water sample information and sampling point information, obtain the classification identifier and sampling location, generate an initial data record, and combine the sampling location and recording time in the sampling point information to generate a structured database record. Through inter-table association, the generated database record is stored in the corresponding table of the database to determine the completion of storage.

[0018] Preferably, the structured data storage unit includes a data parsing subunit, which is used to parse the water sample information and sampling point information, obtain the classification identifier and sampling location therefrom, generate an initial data record, and if the classification identifier exists, perform matching according to the pre-established diatom classification table to obtain the corresponding classification details.

[0019] Preferably, the data analysis and application module includes:

[0020] Data analysis unit: It is used to obtain diatom data from the victim's body through the sample analysis method, store it as the first data set, use the comparison algorithm to perform similarity comparison between the first data set and the database content, and generate the first similarity matrix; if there is at least one element in the first similarity matrix that exceeds the preset threshold, extract the location information corresponding to the corresponding element according to the environmental characteristics to obtain the first candidate location set;

[0021] Drowning point speculation unit: It is used to perform secondary verification on the environmental characteristics in the first candidate location set and the first data set through data matching to generate the second similarity matrix. If the second candidate location set contains a single location, it is determined as the drowning point; if it contains multiple locations, the environmental characteristics are sorted according to the location inference algorithm to obtain the final drowning point.

[0022] Preferably, the drowning point speculation unit includes an environmental characteristic sorting subunit, which is used to sort the environmental characteristics of multiple locations in the second candidate location set through the location inference algorithm to obtain the sorting result, use the sorting result to determine the final drowning point, and obtain the diatom data corresponding to the final drowning point from the database;

[0023] Perform data matching on the diatom data and the first data set to generate a matching result. If the matching result shows that at least one data point is consistent, determine the verification data through consistency verification, use the feature extraction method according to the verification data to obtain the distribution pattern of the environmental characteristics, and compare the distribution pattern with the environmental characteristics of the second candidate location set to obtain the confirmation result; if the confirmation result points to a single location, perform secondary verification on the diatom data corresponding to the corresponding location through the location inference algorithm to generate the final confirmation data.

[0024] Preferably, the data analysis and application module can also collect data at different sampling points to obtain corresponding diatom data, classify the diatom data according to seasonal changes to obtain a time series data set, and analyze the time series data set using statistical methods to generate distribution law information.

[0025] On the other hand, to achieve the above object, the present invention also provides a method for inferring drowning points based on diatom species distribution, including:

[0026] Using an intelligent water sample collection device, according to the preset sampling point coordinates, automatically collect water sample information at the sampling point, real-time monitor the environmental parameters of the water sample, and mark and record the environmental parameters together with the water sample;

[0027] Construct a database containing a water sample information table, a diatom classification table, and a sampling point information table based on the recorded data, and perform data storage and query through the association relationships between different data tables;

[0028] Compare the diatom data extracted from the victim's body with the diatom data in the database to infer the drowning location.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] The diatom database constructed by the present invention integrates comprehensive diatom data, provides a rich reference basis for the investigation of drowning cases, and improves the accuracy of inferring drowning locations; the integrated system design realizes the automation and intelligence of data collection, storage, analysis, and application, greatly improves work efficiency, reduces errors that may be brought by manual operations, and the functions of the data analysis and application module contribute to the in-depth study of diatom distribution laws, which can not only be applied to the detection of current cases, but also provide support for subsequent forensic research and water ecological protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0032] Figure 1 is a schematic structural diagram of a system for inferring drowning points based on diatom species distribution according to an embodiment of the present invention;

[0033] Figure 2 is a flowchart of a method for inferring drowning points based on diatom species distribution according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The following will describe the present application in detail with reference to the drawings and in combination with the embodiments.

[0035] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0036] The present invention provides a drowning point speculation system based on the diatom species distribution, as Figure 1 , including:

[0037] Data acquisition module: Through an intelligent water sample collection device, according to the preset sampling point coordinates, automatically collect water sample information at the sampling points, real-time monitor the environmental parameters of the water samples, and mark and record the environmental parameters together with the water samples;

[0038] Database construction module: Used to construct a database containing a water sample information table, a diatom classification table, and a sampling point information table, and store and query data through the association relationships between different data tables;

[0039] Data analysis and application module: Used to compare the diatom data extracted from the victim's body with the diatom data in the database, calculate the similarity index, and speculate the drowning location.

[0040] Furthermore, the data acquisition module includes:

[0041] Water sample collection unit: Used to automatically locate and collect water samples through the preset sampling point coordinates by an intelligent water sample collection device, and use high-precision sensors to real-time monitor environmental parameters such as temperature, pH value, and dissolved oxygen to obtain preliminary environmental parameter data.

[0042] Specifically, in the water quality monitoring scenario, the intelligent device can be an unmanned boat equipped with a GPS module, and the preset coordinates are multiple key points in the lake, such as the water inlet, the central area, etc. The unmanned boat accurately locates to the coordinate points through GPS, such as longitude 114.35° and latitude 30.52°, with the error controlled within 1 meter. This method ensures the consistency of the sampling points, avoids the randomness of manual operation, and improves the reliability of the data. Control the collection device according to the positioning data to collect the corresponding water samples and obtain water sample specimens. In a possible implementation manner, after receiving the positioning data, the unmanned boat automatically lowers the sampling tube to a specified depth, such as 2 meters, and extracts 500 milliliters of water samples. The sampling tube can be equipped with a flow meter to ensure the stability of the water sample volume. Compared with manual sampling, this automatic control reduces human interference and ensures the representativeness of the samples.

[0043] The environmental parameters are monitored in real time for a water sample using sensors to obtain parameter data. Specifically, the sensors include a pH sensor, a turbidity sensor, and a dissolved oxygen sensor. For example, the measured pH value is 6.8, the turbidity is 20 NTU, and the dissolved oxygen is 5 mg / L. These parameters reflect the water quality status in real time. The high-frequency monitoring ability of the sensors makes the data more timely, providing a basis for subsequent analysis.

[0044] Furthermore, the data acquisition module also includes a diatom classification unit, which is used to bind the environmental parameters with the water sample data based on the preliminary environmental parameter data by using the marker recording technique to generate a water sample data set with environmental parameter markers; a microscope imaging device is used to image the diatoms in the water sample data set to obtain the morphological feature image data of the diatoms. The diatoms in the water sample data set are qualitatively analyzed by a molecular biology detection tool to determine the types of diatoms and obtain the diatom type classification result.

[0045] Specifically, the diatoms in the water sample data set are scanned by a microscope imaging device to obtain the initial morphological feature image data. For example, in the water sample at sampling point A, the microscope device scans the diatoms at a magnification of 1000 times to obtain a high-resolution image, clearly showing the cell outline and internal structure of the diatoms. This high-magnification imaging can capture the minute features of the diatoms, such as the cell wall texture, laying a foundation for subsequent analysis.

[0046] An image segmentation algorithm is used to process the initial morphological feature image data to obtain the segmented diatom morphological image. In this embodiment, the threshold segmentation method is used to separate the diatom cells in the image from the background. Suppose the gray value range of an image is 0 - 255, and the threshold is set to 150. The pixels below this value are classified as the background, and those above this value are classified as the diatom part, finally obtaining a clear single-cell image. The gene sequence of the diatom sample in the water sample data set is extracted by a molecular biology detection tool to obtain the gene sequence data. In this embodiment, the PCR technology is used to amplify the 18S rRNA gene fragment of the diatoms, and then the sequence is read by a sequencer. For example, a 300-base pair sequence fragment is measured for the water sample at sampling point A, and the data is stored in the FASTA format. If the gene sequence data matches the preset species database, the diatom type classification result is determined. The database contains known diatom gene libraries, such as the sequence entries in NCBI. This matching process is fast and reliable, providing a basis for subsequent classification.

[0047] Furthermore, the database construction module includes:

[0048] Structured Data Storage Unit: It is used to parse water sample information and sampling point information, obtain classification identifiers and sampling locations, generate initial data records. If the classification identifier exists, it will be matched according to the diatom species classification result to obtain the corresponding classification details, and combined with the sampling location and recording time in the sampling point information, generate a structured database record. Through inter-table association, the generated database record will be stored in the corresponding table of the database to determine the completion of storage.

[0049] Specifically, when generating standardized data records by parsing the input water sample information, diatom species classification results, and sampling point information, it can be understood as a process of integrating scattered data into a unified format. For example, the water sample information may include parameters such as temperature and turbidity during sampling, diatom classification includes species and quantity, and the sampling point information has longitude and latitude coordinates and timestamps. First, these data are sorted according to the preset field standards, such as setting the temperature as a floating-point number and the timestamp as a date format to ensure consistency in subsequent processing.

[0050] If the water sample information contains a classification identifier, it will be matched with a pre-established diatom classification table to obtain the diatom classification details. Specifically, assuming the input classification identifier is "D-001", the system will query the classification table, match the corresponding diatom species such as "Navicula diatom", and attach its ecological habit description. This matching process depends on the completeness of the classification table. Exemplarily, if the classification table records 100 diatom species and their characteristics, it can quickly locate the specific species and reduce the time for manual verification.

[0051] When generating a structured database record by combining the obtained diatom classification details with the sampling point information, in this embodiment, the longitude and latitude of the sampling location such as "31.2 degrees north latitude, 121.5 degrees east longitude" and the time "April 7, 2025, 10:00" can be bound to the diatom species to form a complete record.

[0052] In one embodiment, this record may be expressed as "Navicula diatom, quantity 500 per milliliter, sampled at 31.2 degrees north latitude, 121.5 degrees east longitude, time April 7, 2025, 10:00".

[0053] Such a structured design facilitates subsequent analysis of the water sample distribution pattern. When storing data in the database through inter-table association, the primary key and foreign key mechanisms of the relational database can be utilized. For example, the water sample information is stored in the "Water Sample Table", the diatom classification is stored in the "Diatom Table", and the sampling point information is stored in the "Sampling Point Table". The three are associated through a unique identifier. This method can ensure data integrity and can quickly locate relevant records during querying, which is suitable for large-scale data management.

[0054] When obtaining the query conditions input by the user and retrieving data, the user may input "The sampling time is April 2025, and the sampling point is near 31 degrees north latitude". Filter the data that meets the conditions from the database, such as returning 10 records. This flexible query can help users quickly focus on the diatom distribution in the target water area.

[0055] When generating an ordered data set by sorting the query results according to the sampling location or time, if sorted by time, the results may be displayed in the order of "April 1st, April 3rd, April 7th". Such sorting helps to observe the changing trend of water sample parameters over time and facilitates the analysis of environmental evolution. The output format can also support export to a table file, further enhancing its practicality.

[0056] Furthermore, the structured data storage unit includes a data parsing subunit, which is used to obtain the classification identifier and sampling location by parsing the water sample information and sampling point information, generate an initial data record, and if the classification identifier exists, perform matching according to the pre-established diatom classification table to obtain the corresponding classification details.

[0057] Specifically, when obtaining the classification identifier and sampling location by parsing the water sample information and sampling point information and generating an initial data record, it can be understood as a process of extracting key elements from the original input.

[0058] For example, if the water sample information contains the classification identifier "D-002" and turbidity data, and the sampling point information provides the location "30.5 degrees north latitude, 120.8 degrees east longitude", the initial record may be a simple field combination, such as "D-002, 30.5 degrees north latitude, 120.8 degrees east longitude". This method facilitates the rapid collation of scattered data.

[0059] If the classification identifier exists, when performing matching according to the pre-established diatom classification table to obtain the corresponding classification details, it should be noted that the preset content of the classification table is crucial. Assuming that "D-002" corresponds to "Needle-shaped diatom", and the table also records its characteristics such as "Prefers cold water environment", these details can be obtained after matching.

[0060] This matching process depends on the accuracy of the classification table. For example, if the table covers 200 species of diatoms and their ecological habits, the species represented by "D-002" can be quickly located.

[0061] Furthermore, the data analysis and application module includes:

[0062] Data analysis unit: It is used to obtain diatom data from the victim's body through the sample analysis method, store it as the first data set, and use the comparison algorithm to perform similarity comparison between the first data set and the content of the database to generate the first similarity matrix; if there is at least one element in the first similarity matrix that exceeds the preset threshold, then extract the location information corresponding to the corresponding element according to the environmental characteristics to obtain the first candidate location set;

[0063] Drowning point speculation unit: It is used to perform secondary verification on the environmental characteristics in the first candidate location set and the first data set through data matching to generate the second similarity matrix. If the second candidate location set contains a single location, it is determined as the drowning location; if it contains multiple locations, then sort the environmental characteristics according to the location inference algorithm to obtain the final drowning location.

[0064] Specifically, diatoms are isolated from the victim's lung samples, their types and densities are recorded, and a data set containing 500 diatoms / ml of "round diatoms" and 150 diatoms / ml of "star diatoms" is compiled. This extraction relies on microscopic observation and sample pretreatment techniques to ensure data integrity.

[0065] When using the comparison algorithm to perform similarity comparison between the first data set and the pre-built database to generate the first similarity matrix, the diatom distribution in the known waters in the database is used as a reference. Specifically, the database records that a certain Lake X contains 480 diatoms / ml of "round diatoms" and 160 diatoms / ml of "star diatoms". After comparison with the first data set, the similarity is 0.92, while the similarity of another River Y is 0.4. The matrix elements reflect the matching degree between the sample and the water area, which is convenient for preliminary screening. If at least one element in the first similarity matrix exceeds the preset threshold such as 0.9, then extract the environmental characteristics of the corresponding location to form the first candidate location set.

[0066] In one embodiment, Lake X is included in the candidate set because its similarity of 0.92 exceeds the threshold, and at the same time, its characteristics such as a water depth of 5 meters and a low flow rate are recorded. If the similarity of Water Area Z is 0.91, it is also included, generating a candidate set containing Lake X and Water Area Z. When using the clustering algorithm to group the environmental characteristics in the location subset to obtain the location classification set, preferably, classification is based on characteristics such as water depth and vegetation coverage rate. Lake X and the similar Lake W are grouped into the same group because of their similar water depths, while other locations form another group. This grouping is convenient for focusing on similar environments. For the environmental characteristics of each group in the location classification set, obtain the matching degree with the first data set. When determining the location group with the highest priority, comprehensively compare the diatom types and water quality parameters.

[0067] When determining the final drowning location by comparing the diatom data of the location group with the highest priority with the first data set through consistency verification, it can be understood as the final verification. For example, the density of "circular diatoms" and "star diatoms" in Lake X has an error of less than 8% from the first data set, and the results are consistent, so it is determined as the drowning location. This verification ensures the reliability of the inference.

[0068] Furthermore, the drowning point speculation unit includes an environmental feature sorting subunit, which is used to sort the environmental features of multiple locations in the second candidate location set through a location inference algorithm to obtain a sorting result, and use the sorting result to determine the final drowning location, and obtain the corresponding diatom data of the final drowning location from the database;

[0069] Perform data matching on the diatom data and the first data set to generate a matching result. If the matching result shows that at least one data point is consistent, determine the verification data through consistency verification. According to the verification data, use a feature extraction method to obtain the distribution pattern of environmental features, and compare the distribution pattern with the environmental features of the second candidate location set to obtain a confirmation result; if the confirmation result points to a single location, perform secondary verification on the corresponding diatom data of the location through a location inference algorithm to generate final confirmation data.

[0070] Furthermore, the data analysis and application module can also collect data at different sampling points to obtain corresponding diatom data, classify the diatom data according to seasonal changes to obtain a time series data set, and use statistical methods to analyze the time series data set to generate distribution law information.

[0071] Specifically, collect data at different sampling points to obtain corresponding diatom data. Classify the diatom data according to seasonal changes to obtain a time series data set. Use statistical methods to analyze the time series data set to generate distribution law information.

[0072] If at least one feature in the distribution law information exceeds the preset threshold, adjust the sampling points through environmental factors to determine the optimized point set. For the optimized point set, obtain the updated diatom data to generate new distribution law data. Group the new distribution law data through a clustering algorithm to obtain a classification law set. Generate corresponding chart data according to the classification law set to judge the final distribution law trend.

[0073] This embodiment also provides a method for speculating drowning points based on the distribution of diatom species, such as Figure 2 , including:

[0074] Through an intelligent water sample collection device, according to the preset sampling point coordinates, automatically collect water sample information at the sampling point, real-time monitor the environmental parameters of the water sample, and mark and record the environmental parameters together with the water sample;

[0075] Construct a database including a water sample information table, a diatom classification table, and a sampling point information table based on the recorded data, and perform data storage and query through the association relationships between different data tables;

[0076] Compare the diatom data extracted from the victim's body with the diatom data in the database to infer the drowning location.

[0077] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A drowning point speculation system based on the diatom species distribution, characterized in that, Including: Data acquisition module: Through intelligent water sample collection equipment, according to the preset sampling point coordinates, automatically collect water sample information at the sampling points, real-time monitor the environmental parameters of the water samples, and mark and record the environmental parameters together with the water samples; Database construction module: Used to construct a database containing a water sample information table, a diatom classification table, and a sampling point information table, and store and query data through the association relationships between different data tables; Data analysis and application module: Used to perform similarity comparison between the diatom data extracted from the victim's body and the diatom data in the database, and infer the drowning location.

2. The drowning point speculation system based on the diatom species distribution according to claim 1, wherein The data acquisition module includes: Water sample collection unit: Used to automatically locate and collect water samples through the preset sampling point coordinates by intelligent water sample collection equipment, and use high-precision sensors to real-time monitor environmental parameters such as temperature, pH value, and dissolved oxygen to obtain preliminary environmental parameter data.

3. The drowning point speculation system based on diatom species distribution according to claim 2, characterized in that, The data acquisition module further includes a diatom classification unit. The diatom classification unit is used to bind environmental parameters with water sample data based on the preliminary environmental parameter data by using marking and recording technology to generate a water sample data set with environmental parameter marks; use a microscope imaging device to perform imaging processing on the diatoms in the water sample data set to obtain morphological characteristic image data of the diatoms, and perform qualitative analysis on the diatoms in the water sample data set through a molecular biology detection tool to determine the types of diatoms and obtain the diatom type classification result.

4. The drowning point speculation system based on diatom species distribution according to claim 3, wherein The diatom classification unit scans the diatoms in the water sample data set through a microscope imaging device to obtain initial morphological characteristic image data, and processes the initial morphological characteristic image data through an image segmentation algorithm to obtain the segmented diatom morphological image; Extract the gene sequence data of the diatom samples in the water sample data set through a molecular biology detection tool. If the gene sequence data matches the preset species database, determine the diatom type classification result.

5. The drowning point speculation system based on diatom species distribution according to claim 1, wherein The database construction module includes: Structured data storage unit: Used to analyze the water sample information and sampling point information, obtain classification identifiers and sampling locations, generate initial data records, and combine the sampling locations and recording times in the sampling point information to generate structured database records. Through inter-table association, store the generated database records into the corresponding tables of the database and determine that the storage is completed.

6. The drowning point speculation system based on diatom species distribution according to claim 5, characterized in that, The structured data storage unit includes a data analysis subunit. The data analysis subunit is used to analyze the water sample information and sampling point information to obtain classification identifiers and sampling locations therefrom, generate initial data records, and if the classification identifier exists, perform matching according to the pre-established diatom classification table to obtain the corresponding classification details.

7. The drowning point prediction system based on the diatom species distribution according to claim 1, wherein The data analysis and application module includes: Data analysis unit: Used to obtain diatom data from the victim's body through a sample analysis method, store it as a first data set, perform similarity comparison between the first data set and the database content using a comparison algorithm to generate a first similarity matrix; if there is at least one element in the first similarity matrix that exceeds the preset threshold, extract the location information corresponding to the corresponding element according to the environmental characteristics to obtain a first candidate location set; Drowning location speculation unit: used to perform secondary verification on the environmental characteristics in the first candidate location set and the first data set through data matching to generate a second similarity matrix. If the second candidate location set contains a single location, it is determined as the drowning location; if it contains multiple locations, the environmental characteristics are sorted according to the location inference algorithm to obtain the final drowning location.

8. The drowning point speculation system based on diatom species distribution according to claim 7, characterized in that, The drowning location speculation unit includes an environmental characteristic sorting subunit, which is used to sort the environmental characteristics of multiple locations in the second candidate location set through the location inference algorithm to obtain a sorting result, and use the sorting result to determine the final drowning location, and obtain the diatom data corresponding to the final drowning location from the database; Perform data matching on the diatom data and the first data set to generate a matching result. If the matching result shows that at least one data point is consistent, determine the verification data through consistency verification, use the feature extraction method according to the verification data to obtain the distribution pattern of the environmental characteristics, and compare the distribution pattern with the environmental characteristics of the second candidate location set to obtain a confirmation result; if the confirmation result points to a single location, perform secondary verification on the diatom data corresponding to the corresponding location through the location inference algorithm to generate the final confirmation data.

9. The drowning point speculation system based on diatom species distribution according to claim 1, characterized in that The data analysis and application module can also collect data at different sampling points to obtain the corresponding diatom data, classify the diatom data according to seasonal changes to obtain a time series data set, and use statistical methods to analyze the time series data set to generate distribution law information.

10. A method for inferring drowning points based on the species distribution of diatoms, characterized in that, Including: Through an intelligent water sample collection device, automatically collect water sample information at the sampling point according to the preset sampling point coordinates, real-time monitor the environmental parameters of the water sample, and mark and record the environmental parameters together with the water sample; Build a database containing a water sample information table, a diatom classification table, and a sampling point information table based on the recorded data, and perform data storage and query through the association relationship between different data tables; Compare the diatom data extracted from the victim's body with the diatom data in the database to speculate the drowning location.

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