Intelligent sample storage and management system based on laser positioning and RFID

Abstract

The invention discloses an intelligent sample storage and management system based on laser positioning and RFID, relates to the technical field of intelligent sample storage, and aims to solve the problems of unclear sample position and poor sample information integrity during sample storage. According to the method, the moving path on the two-dimensional map is generated through the moving track of the three-dimensional model, tracking and management of the samples in the two-dimensional space are facilitated, the number and the state of the samples can be displayed in real time through two-dimensional map and chart visualization, timely monitoring information is provided for managers, the sample IDs are automatically written into the RFID tags through the RFID reader-writer, and the management efficiency is improved. Compared with the prior art, manual operation is reduced, meanwhile, errors caused by improper manual operation are reduced, after the RFID tags are distributed to the samples and data are written in, the tag state is immediately updated to be unavailable in the RFID tag database, repeated use of the tags is avoided, and the one-to-one correspondence relation between the tags and the samples is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sample storage, and specifically to an intelligent sample storage and management system based on laser positioning and RFID. Background Art

[0002] Intelligent sample storage refers to the use of modern information technology, Internet of Things technology, automation technology and other means to efficiently, accurately and safely manage and store biological samples, chemical samples, physical samples, etc.

[0003] The patent application with the publication number CN115215023B discloses a folder access path intelligent security analysis system and method based on big data. It mainly stores files through pick-up QR codes, is easy to operate, requires no consumables, no special person on duty, saves daily use and management costs, is safe and fast at the same time, has low equipment investment cost, improves the efficiency of enterprise operations, predicts the failure rate of the transmission mechanism through the processing of test data, and takes remedial measures in advance to reduce the risk of jamming and crashing during its operation. Although the above patent solves the problem of intelligent storage, there are still the following problems in actual operation: 1. There is no more effective electronic tag allocation for sample data, resulting in a reduction in the security and manageability of sample data.

[0004] 2. There is no more accurate laser positioning for samples, and there is no further data fusion between the laser positioning information of samples and the electronic tags of samples, resulting in a reduction in data flexibility.

[0005] 3. There is no more comprehensive data visualization conversion and data analysis for the obtained sample information, resulting in the inability to obtain abnormal data in a timely manner and process abnormal data. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent sample storage and management system based on laser positioning and RFID. By generating a moving path on a two-dimensional map through the moving trajectory of a three-dimensional model, it helps to realize the tracking and management of samples in a two-dimensional space. The two-dimensional map and chart visualization can display the sample quantity and status in real time, providing timely monitoring information for managers. Using an RFID reader / writer to automatically write the sample ID into the RFID tag reduces manual operations and also reduces errors caused by improper human operations. After the RFID tag is assigned to the sample and written with data, the tag status is immediately updated to unavailable in the RFID tag database, avoiding the reuse of tags and ensuring a one-to-one correspondence between the tag and the sample, which can solve the problems in the prior art.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: Intelligent sample storage and management system based on laser positioning and RFID, including: Sample information acquisition unit, used for: Read the basic information of each sample from the database, uniquely encode and label the basic information of each sample, and label the sample with the completed unique encoding label as the target sample information data; RFID tag confirmation unit, used for: Allocate RFID electronic tags to the target sample information data, and establish the correspondence between the sample ID and the RFID electronic tag ID. After the RFID electronic tag allocation of the target sample information data is completed, the target sample electronic tag data is obtained; Laser positioning unit, used for: Use laser positioning technology to locate the target sample information data, and construct a three-dimensional model of the located sample. After the three-dimensional model construction is completed, the target sample positioning data is obtained; Positioning information fusion unit, used for: Preprocess the target sample positioning data and the target sample electronic tag data, perform data fusion after the preprocessing is completed, and obtain the sample storage information data after the data fusion; Sample storage simulation analysis unit, used for: Convert the sample storage information data into visualization data, perform monitoring simulation on the sample storage information data after the visualization data conversion, and obtain the storage data to be analyzed after the monitoring simulation; Analysis data evaluation unit, used for: Perform location analysis on the storage data to be analyzed, evaluate the sample storage quality according to the location analysis results, and transmit the sample storage quality evaluation results to the display terminal for display.

[0008] Preferably, the sample storage simulation analysis unit is also used for: Convert the sample storage information data into visualization data; The visualization data includes three-dimensional space visualization data, two-dimensional map visualization data, and chart visualization data; Perform monitoring simulation on the three-dimensional space visualization data, two-dimensional map visualization data, and chart visualization data; During the monitoring simulation of the two-dimensional map visualization data, real-time monitor the change of the sample quantity and status in each area, and dynamically adjust the refresh frequency of the sample quantity and status and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model according to the change, including: Extract the sample quantity change frequency and status change frequency corresponding to each unit time in each area in the historical record; wherein, the unit time is 12h - 36h; Obtain the data change degree coefficient corresponding to each area according to the change frequency of the number of samples and the change frequency of the state corresponding to each unit time of each area; Among them, the data change degree coefficient is obtained through the following formula: Among them, R represents the data change degree coefficient; n represents the number of unit times experienced by the two-dimensional map display operation corresponding to each area; w f represents the weight value corresponding to the change frequency of the number of samples; w t represents the weight value corresponding to the change frequency of the state; f yi and f ti respectively represent the change frequency of the number of samples and the change frequency of the state corresponding to the i-th unit time; f ymax represents the maximum value of the change frequency of the number of samples corresponding to n unit times; f yt represents the change frequency of the state corresponding to the unit time where the maximum value of the change frequency of the number of samples is located; f tmax represents the maximum value of the change frequency of the state corresponding to n unit times; f ty represents the change frequency of the number of samples corresponding to the unit time where the maximum value of the change frequency of the state is located; f yp and f tp respectively represent the average value of the change frequency of the number of samples and the average value of the change frequency of the state corresponding to n unit times; f yb and f tb respectively represent the standard deviation of the change frequency of the number of samples and the standard deviation of the change frequency of the state corresponding to n unit times; Compare the data change degree coefficient with a preset change degree coefficient threshold; Mark the areas where the data change degree coefficient exceeds the preset change degree coefficient threshold to obtain the target areas; Compare the number of the target areas with a preset number threshold; When the number of the target areas exceeds the preset number threshold, dynamically adjust the number of samples and the state refresh frequency. At the same time, adjust the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model.

[0009] Preferably, the sample information acquisition unit is further configured to: Read the basic information of each sample from the database; The basic information of the sample includes sample ID, sample type, sample attribute, sample source, collection time, storage location, and sample name; Perform a unique coding label on the samples whose basic information has been read; Define the coding rules before assigning unique coding labels. The coding rules include the format, structure, combination of random numbers or letters, and specific information of the coding. Generate unique codes for the basic information of each sample according to the defined coding rules. After generating the unique codes, obtain the target sample information data.

[0010] Preferably, the RFID tag confirmation unit is further configured to: Before allocating RFID electronic tags to the target sample information data, query the status of each RFID tag from the RFID tag database. The status of the RFID tag is divided into available status and unavailable status, and then obtain the tag ID of the RFID tag in the available status. Read the ID of each sample in the target sample information data. Associate the tag ID of the RFID tag in the available status with the ID of each sample. After the association is completed, use the RFID reader / writer to write the ID of each sample into the RFID tag. After the writing is completed, update the status of the corresponding RFID tag in the RFID tag database to unavailable status. After the status update is completed, the target sample information data completes the allocation of RFID electronic tags, and the allocated data is marked as target sample electronic tag data.

[0011] Preferably, the laser positioning unit is further configured to: The positioning device corresponding to the laser positioning technology includes a laser emitter, a laser receiver, a positioning sensor, and a controller. Retrieve the three-dimensional model of the storage area from the model database, where the reference points of each sample position are marked in the three-dimensional model. Use the laser emitter to emit lasers for the positions of each sample in the target sample information data. When the laser beam hits the sample surface and reflects back, the laser receiver receives the reflected signal. According to the measured time difference between the laser emission and reception, obtain the distance between the sample and the laser emitter, and convert the distance between the sample and the laser emitter into specific coordinates. Associate the converted specific coordinates with the sample ID in the target sample information data. After the association is completed, obtain the sample positioning data. Construct a three-dimensional model of the sample based on the sample size, shape, and position data obtained by laser in the sample positioning data. After the three-dimensional model is constructed, obtain the target sample positioning data.

[0012] Preferably, the positioning information fusion unit is further configured to: Perform data cleaning, data standardization, data screening, and data dimensionality reduction on the target sample positioning data and the target sample electronic tag data in sequence; After data cleaning, data standardization, data screening, and data dimensionality reduction, obtain the target sample positioning data and the target sample electronic tag data with data preprocessing completed; Associate the sample information in the target sample positioning data and the target sample electronic tag data according to the sample ID; Merge the associated target sample positioning data and the target sample electronic tag data into a unified data set, where the data set includes the ID, location coordinates, RFID tag ID, and sample attributes of the samples; Then extract the feature data in the data set, where the feature data includes the storage location, storage environment parameters, and sample status; Label the merged data set and the feature data in the data set as sample storage information data.

[0013] Preferably, the sample storage simulation analysis unit is further configured to: Convert the three-dimensional space visualization data to construct a three-dimensional model of the sample storage information data using 3D modeling software. At the same time, use different colors, shapes, or sizes in the constructed three-dimensional model to represent different sample types, states, or attributes. Among them, the location coordinates of the sample correspond to the location in the three-dimensional model, and the RFID tag information is presented by displaying the tag ID on the sample model or through a floating tooltip; Convert the two-dimensional map visualization data to construct a two-dimensional map of the sample storage information data using a map library. Among them, the location coordinates of the sample are mapped to specific locations on the two-dimensional map, and different markers are used to represent different samples; Convert the chart visualization data to display the quantity distribution of different sample types using a bar chart or a pie chart, display the change trend of the storage environment parameters over time using a line chart, display the relationship between sample attributes using a scatter plot, and display key indicators using a dashboard.

[0014] Preferably, the sample storage simulation analysis unit is further configured to: Monitor and simulate the three-dimensional space visualization data by adding an animation effect to the constructed three-dimensional model, generating a movement trajectory for the movement process of the three-dimensional model. At the same time, the user views the sample distribution by rotating, zooming in, and zooming out the detailed information of the samples in the three-dimensional model on the interaction terminal; Monitor and simulate the two-dimensional map visualization data by dividing the generated two-dimensional map into different regions, and real-time display the quantity and status of samples in each region. At the same time, generate a two-dimensional movement path according to the movement trajectory of the three-dimensional model; The monitoring simulation of the visualized data in the chart classifies each data in the chart according to the attributes of the data, and after the classification is completed, the parameter values of the data of each attribute are obtained; The monitoring simulation data of the three-dimensional spatial visualized data, the two-dimensional map visualized data, and the chart visualized data are uniformly labeled as the data to be analyzed and stored.

[0015] Preferably, when the number of the target areas exceeds a preset number threshold, the sample number and the status refresh frequency are dynamically adjusted. At the same time, the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model is adjusted, including: When the number of the target areas exceeds a preset number threshold, the current sample number and the status refresh frequency are retrieved; The current sample number and the status refresh frequency are adjusted by using the data change degree coefficient to obtain the adjusted sample number and the status refresh frequency; Among them, the adjusted sample number and the status refresh frequency are obtained through the following formula: Among them, f y01 represents the adjusted sample number and the status refresh frequency; f y represents the sample number and the status refresh frequency before adjustment; m represents the number of the target areas; R b represents the standard deviation of the data change degree coefficients of the m target areas; R i represents the data change degree coefficient of the i-th target area; R y represents the preset change degree coefficient threshold; Retrieve the frequency of the two-dimensional movement path generated by the two-dimensional map of each area according to the movement track of the three-dimensional model; The display refresh frequency of the two-dimensional movement path is adjusted by using the frequency of the two-dimensional movement path generated by the two-dimensional map of each area according to the movement track of the three-dimensional model in combination with the data change degree coefficient to obtain the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model after adjustment; Among them, the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model after adjustment is obtained through the following formula: Among them, f e01 represents the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model after adjustment; f e represents the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model before adjustment; m represents the number of the target areas; k represents the number of non-target areas; R iIt represents the data change degree coefficient of the i-th target area; R j It represents the data change degree coefficient of the j-th non-target area; f ei It represents the frequency of generating a two-dimensional movement path for the two-dimensional map of the i-th target area according to the movement trajectory of the three-dimensional model; f ej It represents the frequency of generating a two-dimensional movement path for the two-dimensional map of the j-th non-target area according to the movement trajectory of the three-dimensional model.

[0016] Preferably, the analysis data evaluation unit is further configured to: The location analysis of the data to be analyzed and stored includes sample partition analysis, spatial density analysis, sample distance analysis, and dynamic tracking analysis; The sample partition analysis is to partition the samples into different regions according to the position coordinates of the samples in the data to be analyzed and stored, count the sample data in each region, and at the same time use a clustering algorithm to identify the distribution of similar samples; The spatial density analysis is to visualize the distribution density of the samples in the storage area through the spatial density analysis method and distinguish the aggregated or sparse regions; The sample distance analysis is to use the Euclidean distance algorithm to calculate and analyze the distance between samples and the positional relationship between samples and storage devices and environmental sensors; The dynamic tracking analysis is to analyze the movement trajectory of the samples in the storage area, and the movement trajectory includes the frequency and direction of the samples being moved; Compare the analysis results of the location analysis with the preset threshold determination rule; When the threshold of the analysis result exceeds the preset threshold determination rule, the exceeded threshold is determined as abnormal data; Associate the abnormal data with the corresponding samples abnormally, and after the abnormal association, give a warning display on the display terminal.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The intelligent sample storage and management system based on laser positioning and RFID provided by the present invention uses an RFID reader-writer to automatically write the sample ID into the RFID tag, reducing manual operations, improving work efficiency, and at the same time reducing errors caused by improper human operations. After the RFID tag is assigned to the sample and written with data, the tag status is immediately updated to unavailable in the RFID tag database, avoiding the reuse of tags and ensuring the one-to-one correspondence between tags and samples.

[0018] 2. The sample intelligent storage and management system based on laser positioning and RFID provided by the present invention. The laser positioning technology can obtain the position information of the samples in real time and is applicable to the dynamically changing sample storage environment. Using the sample size, shape, and position data obtained by the laser, a three-dimensional model of the sample can be constructed. The three-dimensional model provides intuitive and visual support for the storage, management, and retrieval of samples.

[0019] 3. The sample intelligent storage and management system based on laser positioning and RFID provided by the present invention generates a movement path on the two-dimensional map according to the movement trajectory of the three-dimensional model, which helps to realize the tracking and management of samples in the two-dimensional space. The two-dimensional map and chart visualization can display the sample quantity and status in real time, as well as the change trend of the storage environment parameters, providing timely monitoring information for the manager. The sample partition analysis can quickly allocate a large number of samples to different regions and count the sample data in each region, thus simplifying the sample management and retrieval process. An early warning display is carried out on the display terminal to timely notify the management personnel for handling, thereby avoiding potential risks and losses. By combining laser positioning and RFID technologies, the intelligent storage and management of samples are realized, and the automation and intelligence level of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the unit for sample intelligent storage and management of the present invention; Figure 2 It is a schematic diagram of the process for sample intelligent storage and management of the present invention; Figure 3 It is a schematic diagram of the body structure of the sample intelligent storage cabinet of the present invention; In the figure: 1. Body of the sample intelligent storage cabinet; 2. Operation display; 3. Specimen model scanner; 4. Specimen classification cabinet. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] In order to solve the problem in the prior art that after the sample data is collected, the sample data is not more effectively allocated with electronic tags, resulting in a reduction in the security and manageability of the sample data, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment: The sample intelligent storage and management system based on laser positioning and RFID includes: A sample information acquisition unit, configured to: Read the basic information of each sample from a database, uniquely encode and label the basic information of each sample, and label the sample with the completed unique encoding label as target sample information data; An RFID tag confirmation unit, configured to: Allocate RFID electronic tags to the target sample information data, and establish a correspondence between the sample ID and the RFID electronic tag ID. After the allocation of the RFID electronic tags for the target sample information data is completed, target sample electronic tag data is obtained; A laser positioning unit, configured to: Use laser positioning technology to locate the target sample information data, and construct a three-dimensional model of the located sample. After the three-dimensional model construction is completed, target sample positioning data is obtained; A positioning information fusion unit, configured to: Perform data preprocessing on the target sample positioning data and the target sample electronic tag data. After the data preprocessing is completed, data fusion is performed. After the data fusion, sample storage information data is obtained; A sample storage simulation analysis unit, configured to: Convert the sample storage information data into visual data, and perform monitoring simulation on the sample storage information data after the visual data conversion. After the monitoring simulation, data to be analyzed for storage is obtained; An analysis data evaluation unit, configured to: Perform location analysis on the data to be analyzed for storage, and evaluate the sample storage quality according to the location analysis result. Transmit the sample storage quality evaluation result to a display terminal for display.

[0023] Specifically, each sample is identified by a unique code through the sample information acquisition unit, enabling the system to easily trace historical information such as the source, collection time, and storage location of the sample. The RFID tag confirmation unit reuses the RFID tags, thereby reducing the sample management cost and improving the resource utilization efficiency. The laser positioning unit integrates the sample positioning data and the three-dimensional model data into a unified database to achieve data sharing and unified management. The feature data extracted by the positioning information fusion unit can be flexibly adjusted and extended according to actual needs to meet different application scenarios and requirements. The sample storage simulation analysis unit adopts a variety of visualization means, which can be flexibly selected and combined according to actual needs to adapt to the sample storage and management needs of different scales and types. The analysis data evaluation unit can perform early warning display on the display terminal, timely notify the management personnel for processing, thereby avoiding potential risks and losses; Among them, such as Figure 3As shown in the figure, the intelligent sample storage and management system based on laser positioning and RFID further includes the main body 1 of the intelligent sample storage cabinet. An operation display 2 is provided on the main body 1 of the intelligent sample storage cabinet. The basic information of the sample and the status of the RFID tag can be viewed on the operation display 2. At the same time, a number of specimen classification cabinets 4 are provided on the main body 1 of the intelligent sample storage cabinet. Samples are stored in the corresponding specimen classification cabinets 4 according to different specimen types. And the initial three-dimensional model of the sample is scanned by the specimen model scanner 3. After the initial three-dimensional model scanning is completed, it is stored in the model database, and the three-dimensional model of the storage area is retrieved from the model database.

[0024] The sample information acquisition unit is further used for: Read the basic information of each sample from the database; The basic information of the sample includes sample ID, sample type, sample attribute, sample source, collection time, storage location and sample name; Perform unique coding and labeling on the samples after reading the basic information; Define the coding rule before performing unique coding and labeling. The coding rule is the format, structure, random number or letter combination, and specific information of the coding; Generate a unique code for the basic information of each sample according to the defined coding rule; After the unique code is generated, the target sample information data is obtained.

[0025] Specifically, the RFID technology realizes real-time data transmission through radio waves, can quickly read the basic information of the sample, including key data such as sample ID and sample type, and significantly improves the recognition and reading speed of the sample. The laser positioning technology can accurately locate the storage location of the sample, reducing the time for manual search of the sample. Each sample is identified by a unique code, enabling the system to easily trace historical information such as the source, collection time, and storage location of the sample, providing strong support for scientific research or medical diagnosis, etc.

[0026] The RFID tag confirmation unit is further used for: Before allocating RFID electronic tags to the target sample information data, first query the status of each RFID tag from the RFID tag database. The status of the RFID tag is divided into available status and unavailable status, and then obtain the tag ID of the RFID tag in the available status; Read the ID of each sample in the target sample information data; Associate the tag ID of the RFID tag in the available status with the ID of each sample; After the association is completed, write the ID of each sample into the RFID tag by using the RFID reader and writer; After the writing is completed, update the status of the corresponding RFID tag in the RFID tag database to the unavailable status; After the status update is completed, the target sample information data completes the allocation of RFID electronic tags, and marks the allocated data as target sample electronic tag data.

[0027] Specifically, by first querying the tag status (available or unavailable) from the RFID tag database to ensure that only available tags are allocated to samples, thus avoiding resource waste and tag conflicts, reading the ID of each sample in the target sample information data, and making a one-to-one association with the ID of the available RFID tags. This precise matching reduces human errors and improves data accuracy. Using an RFID reader / writer to automatically write the sample ID into the RFID tag reduces manual operations, improves work efficiency, and also reduces errors caused by improper human operations. After the RFID tag is allocated to the sample and the data is written, immediately update the tag status in the RFID tag database to unavailable to avoid the reuse of tags and ensure a one-to-one correspondence between tags and samples. Since each sample is allocated a unique RFID tag and the corresponding tag ID is recorded, this greatly enhances the traceability of data, facilitates subsequent management and querying, allows for dynamic management of RFID tags. When a certain tag is no longer needed, its status can be updated to available, and then reallocated to other samples, improving the flexibility and scalability of the system.

[0028] To solve the problems in the prior art that there is no more accurate laser positioning for samples and no further data fusion between the laser positioning information of samples and the electronic tags of samples, resulting in reduced data flexibility, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The laser positioning unit is further used for: The positioning device corresponding to the laser positioning technology includes a laser emitter, a laser receiver, a positioning sensor, and a controller; Retrieve the three-dimensional model of the storage area from the model database, where the benchmark points of each sample position are marked in the three-dimensional model; Use the laser emitter to emit lasers at the positions of each sample in the target sample information data; When the laser beam is reflected back after irradiating the sample surface, the laser receiver receives the reflected signal, obtains the distance between the sample and the laser emitter according to the measured time difference between laser emission and reception, and converts the distance between the sample and the laser emitter into specific coordinates; Associate the converted specific coordinates with the sample ID in the target sample information data, and after the association is completed, obtain the sample positioning data; Construct a three-dimensional model of the sample using the sample size, shape, and position data obtained by laser in the sample positioning data; After the three-dimensional model is constructed, the target sample positioning data is obtained.

[0029] Specifically, through the measurement of the time difference between laser emission and reception, the distance between the sample and the laser emitter can be accurately calculated, and then converted into high-precision specific coordinates. Laser positioning technology usually has a positioning accuracy of millimeters and is suitable for scenarios with extremely high requirements for the sample position. The laser positioning technology uses a non-contact measurement method, avoiding possible damage or contamination to the sample caused by physical contact. This is particularly important for scenarios that require maintaining the integrity and cleanliness of the sample. The laser positioning technology can obtain the position information of the sample in real time and is suitable for a dynamically changing sample storage environment. The system can update the sample position in real time to ensure the accuracy and timeliness of the information. Using the sample size, shape, and position data obtained by laser, a three-dimensional model of the sample can be constructed. The three-dimensional model provides intuitive and visual support for the storage, management, and retrieval of the sample. The sample positioning data and the three-dimensional model data can be integrated into a unified database to achieve data sharing and unified management. This helps to improve the efficiency and accuracy of sample management and supports data sharing and collaboration across departments and institutions.

[0030] The positioning information fusion unit is also used for: Perform data cleaning, data standardization, data screening, and data dimensionality reduction processing on the target sample positioning data and the target sample electronic tag data in sequence; After data cleaning, data standardization, data screening, and data dimensionality reduction processing, the target sample positioning data and the target sample electronic tag data with completed data preprocessing are obtained; Associate the sample information in the target sample positioning data and the target sample electronic tag data according to the sample ID; Merge the associated target sample positioning data and the target sample electronic tag data into a unified data set, and the data set includes the sample ID, position coordinates, RFID tag ID, and sample attributes; Then extract the feature data in the data set, and the feature data includes the storage location, storage environment parameters, and sample status; Label the merged data set and the feature data in the data set as sample storage information data.

[0031] Specifically, the positioning data and electronic tag data are associated through the sample ID to achieve data integration and unified management, facilitating subsequent data analysis and mining. The associated dataset contains complete information of the samples, such as location coordinates, RFID tag IDs, and sample attributes, improving the integrity and relevance of the data. Key feature data, such as storage location, storage environment parameters, and sample status, are extracted from the integrated dataset to provide a strong basis for subsequent decision-making support. The feature data is labeled to form sample storage information data, facilitating data management and query. The preprocessed data has higher quality, helping to improve the efficiency and accuracy of the system in processing data. The integrated dataset and feature data provide a basis for the intelligent management of the system, such as realizing automatic tracking, positioning, and management of samples. The complete dataset and feature data provide comprehensive information support for decision-makers, helping to formulate and optimize sample storage management strategies. By analyzing the feature data, problems in the storage environment, such as abnormal temperature and humidity, can be detected in a timely manner, and corresponding measures can be taken for improvement. The preprocessing process and data integration method have certain generality and scalability and can adapt to the storage requirements of different types and scales of sample data. The extracted feature data can be flexibly adjusted and expanded according to actual needs to meet different application scenarios and requirements.

[0032] To solve the problem in the prior art that the obtained sample information is not subjected to more comprehensive data visualization conversion and data analysis, resulting in the inability to obtain abnormal data in a timely manner and process the abnormal data, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The sample storage simulation analysis unit is further configured to: Convert the sample storage information data into visual data; The visual data includes three-dimensional space visual data, two-dimensional map visual data, and chart visual data; The conversion of the three-dimensional space visual data is to use 3D modeling software to construct a three-dimensional model of the sample storage information data. At the same time, different colors, shapes, or sizes are used in the constructed three-dimensional model to represent different sample types, states, or attributes. Among them, the location coordinates of the sample correspond to the position in the three-dimensional model, and the RFID tag information is presented by displaying the tag ID on the sample model or through a floating prompt; The conversion of the two-dimensional map visual data is to use a map library to construct a two-dimensional map of the sample storage information data. Among them, the location coordinates of the sample are mapped to specific positions on the two-dimensional map, and different markers are used to represent different samples; The conversion of chart visual data is to use bar charts or pie charts to display the quantity distribution of different sample types, line charts to show the change trend of storage environment parameters over time, scatter plots to display the relationships between sample attributes, and dashboards to show key indicators.

[0033] Monitor and simulate the three-dimensional space visualization data, two-dimensional map visualization data, and chart visualization data; The monitoring and simulation of three-dimensional space visualization data is to add animation effects to the constructed three-dimensional model, generate the movement trajectory of the three-dimensional model during the movement process. At the same time, the user can view the detailed information of the samples in the three-dimensional model on the interactive terminal through operations such as rotation, scaling, and zooming to view the sample distribution; The monitoring and simulation of two-dimensional map visualization data is to divide the generated two-dimensional map into different regions, and display the quantity and status of samples in each region in real time. At the same time, generate a two-dimensional movement path according to the movement trajectory of the three-dimensional model; The monitoring and simulation of chart visualization data is to classify each data in the chart according to the attributes of the data, and obtain the parameter values of the data for each attribute after classification; Unify the monitoring and simulation data of the three-dimensional space visualization data, two-dimensional map visualization data, and chart visualization data and label them as data to be analyzed and stored.

[0034] Specifically, the three-dimensional model constructed by 3D modeling software can intuitively display the spatial layout of sample storage, enabling managers to quickly understand the distribution and status of samples. The two-dimensional map generated using the map library maps the sample locations to specific places, facilitating managers to directly view sample information on the map, enhancing the intuitiveness and readability of the information. Through chart forms such as bar charts, pie charts, line charts, scatter plots, and dashboards, information such as sample type distribution, storage environment parameter change trends, sample attribute relationships, and key indicators can be clearly displayed. Displaying RFID tag information on the three-dimensional model and two-dimensional map makes the tracking and management of samples more efficient. Through floating tooltips or label ID displays, detailed sample information can be quickly obtained. The data classification and parameter value extraction in chart visualization help managers quickly understand the data distribution of different attributes, providing support for decision-making. Adding animation effects to the three-dimensional model can simulate the movement process of samples and generate movement trajectories, assisting managers in viewing and managing sample distribution on the interactive terminal. Generating a movement path on the two-dimensional map based on the movement trajectory of the three-dimensional model helps achieve the tracking and management of samples in the two-dimensional space. The two-dimensional map and chart visualization can display the sample quantity and status in real time, as well as the change trends of storage environment parameters, providing timely monitoring information for managers. Unifying the three-dimensional space visualization, two-dimensional map visualization, and chart visualization data as data to be analyzed and stored helps managers conduct comprehensive analysis and formulate more scientific storage and management strategies. Multiple visualization means are adopted and can be flexibly selected and combined according to actual needs to adapt to the sample storage and management requirements of different scales and types.

[0035] Specifically, during the monitoring simulation of two-dimensional map visualization data, the change situations of the sample quantity and status in each area are monitored in real time, and the refresh frequencies of the sample quantity and status and the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model are dynamically adjusted according to the change situations, including: Extracting the change frequencies of the sample quantity and status corresponding to each unit time in each area in the historical record; wherein, the unit time is 12h - 36h; Obtaining the data change degree coefficient corresponding to each area according to the change frequencies of the sample quantity and status corresponding to each unit time in each area; Among them, the data change degree coefficient is obtained through the following formula: Among them, R represents the data change degree coefficient; n represents the number of unit times experienced by the two-dimensional map display operation corresponding to each area; w f represents the weight value corresponding to the change frequency of the sample quantity; w tThe weight value corresponding to the state change frequency; f yi and f ti respectively represent the sample quantity change frequency and the state change frequency corresponding to the i-th unit time; f ymax represents the maximum value of the sample quantity change frequency corresponding to n unit times; f yt represents the state change frequency corresponding to the unit time where the maximum value of the sample quantity change frequency is located; f tmax represents the maximum value of the state change frequency corresponding to n unit times; f ty represents the sample quantity change frequency corresponding to the unit time where the maximum value of the state change frequency is located; f yp and f tp respectively represent the average value of the sample quantity change frequency and the average value of the state change frequency corresponding to n unit times; f yb and f tb respectively represent the standard deviation of the sample quantity change frequency and the standard deviation of the state change frequency corresponding to n unit times; Compare the data change degree coefficient with a preset change degree coefficient threshold; Mark the area where the data change degree coefficient exceeds the preset change degree coefficient threshold to obtain the target area; Compare the quantity of the target area with a preset quantity threshold; When the quantity of the target area exceeds the preset quantity threshold, dynamically adjust the sample quantity and the state refresh frequency. At the same time, adjust the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model.

[0036] The technical effects of the above technical solution are as follows: By monitoring the changes in the number of samples and status in each area in real time, the system can quickly respond to the dynamic changes in the area, thereby improving the real-time performance and accuracy of monitoring. Dynamically adjusting the refresh frequency of the number of samples and status enables the system resources to be more effectively allocated to the areas with more frequent changes, improving the overall monitoring efficiency. Calculating the data change degree coefficient for each area based on the change frequency of the number of samples and the change frequency of the status in the historical record, this step helps the system identify which areas are the key monitoring objects. By comparing the data change degree coefficient with the preset threshold, the system can automatically mark the target areas, that is, those areas that need to be monitored more frequently, thus achieving reasonable allocation of resources. Dynamically adjusting the display refresh frequency of the two-dimensional movement path generated according to the movement trajectory of the three-dimensional model on the two-dimensional map enables the movement path on the map to more smoothly and accurately reflect the actual movement of the object. This dynamic adjustment not only improves the real-time performance of the map but also enhances the user's visual experience, enabling the user to more intuitively understand the dynamic changes in the monitored area. The technical solution enables the system to be flexibly adjusted according to the actual situation by introducing parameters such as the data change degree coefficient and the preset threshold. Whether it is the refresh frequency of the number of samples and status or the display refresh frequency of the movement path on the two-dimensional map, it can be dynamically adjusted according to the actual needs, thereby enhancing the flexibility and adaptability of the system. The technical solution realizes the effective monitoring and management of the visualized data of the two-dimensional map through automated and intelligent means. Through steps such as real-time monitoring and dynamic adjustment, the system can automatically adapt to the changes in the monitored area, providing strong support for intelligent management.

[0037] In summary, the technical solution improves the monitoring efficiency, enhances the user experience, improves the system flexibility, and promotes intelligent management through means such as real-time monitoring, dynamic adjustment, and optimized resource allocation. These technical effects make the technical solution have significant advantages and application value in the monitoring simulation process of the visualized data of the two-dimensional map.

[0038] Specifically, when the number of the target areas exceeds the preset number threshold, the refresh frequency of the number of samples and status is dynamically adjusted. At the same time, the display refresh frequency of the two-dimensional movement path generated according to the movement trajectory of the three-dimensional model on the two-dimensional map is adjusted, including: When the number of the target areas exceeds the preset number threshold, retrieve the current refresh frequency of the number of samples and status; Adjust the current refresh frequency of the number of samples and status using the data change degree coefficient to obtain the adjusted refresh frequency of the number of samples and status; Among them, the adjusted refresh frequency of the number of samples and status is obtained through the following formula: Among them, f y01 represents the adjusted sample quantity and status refresh frequency; f y represents the sample quantity and status refresh frequency before adjustment; m represents the number of target regions; R b represents the standard deviation of the data change degree coefficient of the m target regions; R i represents the data change degree coefficient of the i-th target region; R y represents the preset threshold of the change degree coefficient; The frequency of retrieving the two-dimensional map of each region to generate a two-dimensional movement path according to the movement trajectory of the three-dimensional model; Adjust the display refresh frequency of the two-dimensional movement path by combining the frequency of generating the two-dimensional movement path according to the movement trajectory of the three-dimensional model of the two-dimensional map of each region with the data change degree coefficient, and obtain the display refresh frequency of the two-dimensional movement path generated by the adjusted two-dimensional map according to the movement trajectory of the three-dimensional model; Among them, the display refresh frequency of the two-dimensional movement path generated by the adjusted two-dimensional map according to the movement trajectory of the three-dimensional model is obtained through the following formula: Among them, f e01 represents the display refresh frequency of the two-dimensional movement path generated by the adjusted two-dimensional map according to the movement trajectory of the three-dimensional model; f e represents the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model before adjustment; m represents the number of target regions; k represents the number of non-target regions; R i represents the data change degree coefficient of the i-th target region; R j represents the data change degree coefficient of the j-th non-target region; f ei represents the frequency of generating a two-dimensional movement path by the two-dimensional map of the i-th target region according to the movement trajectory of the three-dimensional model; f ej represents the frequency of generating a two-dimensional movement path by the two-dimensional map of the j-th non-target region according to the movement trajectory of the three-dimensional model.

[0039] The technical effects of the above technical solution are as follows: When the number of target areas exceeds the preset threshold, the system automatically adjusts the sample quantity, the refresh frequency of the status, and the display refresh frequency of the moving path of the two-dimensional map. This dynamic adjustment mechanism enables the system to quickly respond to changes in the monitored area, improving the adaptability and flexibility of the system. By using the data change degree coefficient to adjust the refresh frequency, the system can allocate resources more reasonably. Areas with frequent changes will obtain a higher refresh frequency, thus ensuring the accuracy and real-time nature of the data. This resource optimization strategy helps to improve the overall monitoring efficiency of the system. The adjusted refresh frequency can more accurately reflect the actual status of the monitored area, providing more accurate data support for decision-makers. This helps decision-makers make more informed decisions and improve the effectiveness of monitoring and management. This technical solution not only considers the changes in the target area but also combines the data change degree coefficient of the non-target area. This balance strategy helps to avoid overloading the system while meeting the monitoring requirements. By reasonably adjusting the refresh frequency, the system can maintain a stable operating state while ensuring the monitoring effect. The display refresh frequency of the moving path of the two-dimensional map after dynamic adjustment can more smoothly display the moving trajectory of the object, enhancing the user's visual experience. This real-time and accurate display method helps to improve the user's trust and satisfaction with the monitoring system. This technical solution realizes the intelligent and automated management of the monitoring system by introducing the data change degree coefficient and the dynamic adjustment mechanism. This management method not only improves the monitoring efficiency but also reduces the cost and risk of manual intervention.

[0040] In summary, the above technical solution improves the adaptability and flexibility of the monitoring system, enhances the user experience, and promotes intelligent and automated management through strategies such as dynamically adjusting the refresh frequency and optimizing resource allocation. These technical effects give this technical solution significant advantages and application value in the monitoring simulation process of two-dimensional map visualization data.

[0041] The data analysis and evaluation unit is also used for: The location analysis of the data to be analyzed and stored includes sample partition analysis, spatial density analysis, sample distance analysis, and dynamic tracking analysis; Sample partition analysis is to partition samples into different regions according to the position coordinates of the samples in the data to be analyzed and stored, count the sample data in each region, and at the same time use the clustering algorithm to identify the distribution of similar samples; Spatial density analysis is to visualize the distribution density of samples in the storage area through the spatial density analysis method and distinguish the aggregated or sparse regions; Sample distance analysis is to calculate and analyze the distance between samples and the positional relationships between samples, storage devices, and environmental sensors using the Euclidean distance algorithm; Dynamic tracking analysis is to analyze the movement trajectory of samples in the storage area, and the movement trajectory includes the frequency and direction of sample movement; Compare the analysis result of location analysis with the preset threshold determination rule; When the threshold of the analysis result exceeds the preset threshold determination rule, the exceeded threshold is determined as abnormal data; Associate the abnormal data with the corresponding sample, and after the abnormal association, give an early warning display on the display terminal.

[0042] Specifically, sample partition analysis can quickly allocate a large number of samples to different regions and count the sample data in each region, thus simplifying the sample management and retrieval process. The application of clustering algorithms can further identify the distribution of similar samples, which helps in sample classification and quick positioning. Spatial density analysis can clearly show which regions are clustered and which are sparse by visualizing the distribution density of samples in the storage area, thus helping managers optimize the use of storage space and avoid waste. Sample distance analysis uses the Euclidean distance algorithm to calculate the distance between samples and their positional relationships with storage devices and environmental sensors, providing accurate sample position information. Dynamic tracking analysis can track the movement trajectory of samples in the storage area in real time, including movement frequency and direction, which helps ensure the safety and accuracy of samples. By comparing the analysis result of location analysis with the preset threshold determination rule, the system can automatically detect abnormal data, such as incorrect sample positions and abnormal movement frequencies. After abnormal association, the system can give an early warning display on the display terminal and notify the manager in time for processing, thus avoiding potential risks and losses. Combining laser positioning and RFID technology, it realizes the intelligent storage and management of samples and improves the automation and intelligence level of the system. Through data analysis and early warning functions, the system can self-learn and optimize, continuously improving management efficiency and accuracy.

[0043] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0044] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. An intelligent sample storage and management system based on laser positioning and RFID, characterized in that, Including: A sample information acquisition unit, configured to: Read the basic information of each sample from a database, uniquely encode and label the basic information of each sample, and label the sample with the completed unique encoding label as target sample information data; An RFID tag confirmation unit, configured to: Allocate RFID electronic tags to the target sample information data, and establish a correspondence between the sample ID and the RFID electronic tag ID. After the RFID electronic tag allocation of the target sample information data is completed, target sample electronic tag data is obtained; A laser positioning unit, configured to: Use laser positioning technology to locate the target sample information data, and construct a three-dimensional model of the located sample. After the three-dimensional model construction is completed, target sample positioning data is obtained; A positioning information fusion unit, configured to: Perform data preprocessing on the target sample positioning data and the target sample electronic tag data, perform data fusion after the data preprocessing is completed, and obtain sample storage information data after the data fusion; A sample storage simulation analysis unit, configured to: Convert the sample storage information data into visual data, perform monitoring simulation on the sample storage information data after the visual data conversion, and obtain data to be analyzed for storage after the monitoring simulation; An analysis data evaluation unit, configured to: Perform location analysis on the data to be analyzed for storage, evaluate the sample storage quality according to the location analysis result, and transmit the sample storage quality evaluation result to a display terminal for display.

2. The intelligent sample storage and management system based on laser positioning and RFID according to claim 1, wherein, The sample storage simulation analysis unit is further configured to: Convert the sample storage information data into visual data; The visual data includes three-dimensional space visual data, two-dimensional map visual data, and chart visual data; Perform monitoring simulation on the three-dimensional space visual data, two-dimensional map visual data, and chart visual data; During the monitoring simulation of the two-dimensional map visual data, real-time monitor the change of the sample quantity and status in each area, and dynamically adjust the sample quantity and status refresh frequency and the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model, including: Extract the sample quantity change frequency and status change frequency corresponding to each unit time in each area from the historical record; wherein, the unit time is 12h - 36h; Obtain the data change degree coefficient corresponding to each area according to the sample quantity change frequency and status change frequency corresponding to each unit time in each area; Wherein, the data change degree coefficient is obtained through the following formula: Among them, R represents the data change degree coefficient; n represents the number of unit times experienced by the two-dimensional map display operation corresponding to each region; w f represents the weight value corresponding to the sample quantity change frequency; w t represents the weight value corresponding to the state change frequency; f yi and f ti respectively represent the sample quantity change frequency and the state change frequency corresponding to the i-th unit time; f ymax represents the maximum value of the sample quantity change frequency corresponding to n unit times; f yt represents the state change frequency corresponding to the unit time where the maximum value of the sample quantity change frequency is located; f tmax represents the maximum value of the state change frequency corresponding to n unit times; f ty represents the sample quantity change frequency corresponding to the unit time where the maximum value of the state change frequency is located; f yp and f tp respectively represent the average value of the sample quantity change frequency and the average value of the state change frequency corresponding to n unit times; f yb and f tb respectively represent the standard deviation of the sample quantity change frequency and the standard deviation of the state change frequency corresponding to n unit times; Compare the data change degree coefficient with a preset change degree coefficient threshold; Mark the areas where the data change degree coefficient exceeds the preset change degree coefficient threshold to obtain target areas; Compare the quantity of the target areas with a preset quantity threshold; When the quantity of the target areas exceeds the preset quantity threshold, dynamically adjust the sample quantity and status refresh frequency, and at the same time, adjust the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement track of the three-dimensional model.

3. The intelligent sample storage and management system based on laser positioning and RFID according to claim 2, wherein, The sample information acquisition unit is further configured to: Read the basic information of each sample from a database; The basic information of the samples includes sample ID, sample type, sample attributes, sample source, collection time, storage location, and sample name; The samples for which the basic information has been read are assigned unique encoding labels; Before assigning unique encoding labels, first define the encoding rules, which include the encoding format, structure, random combination of numbers or letters, and specific information; Generate unique encodings for the basic information of each sample according to the defined encoding rules; After the unique encodings are generated, the target sample information data is obtained.

4. The intelligent sample storage and management system based on laser positioning and RFID according to claim 3, wherein The RFID tag confirmation unit is also used for: Before allocating RFID electronic tags to the target sample information data, first query the status of each RFID tag from the RFID tag database. The status of the RFID tag is divided into available status and unavailable status, and then obtain the tag ID of the RFID tags in the available status; Read the ID of each sample in the target sample information data; Associate the tag ID of the RFID tags in the available status with the ID of each sample; After the association is completed, use the RFID reader to write the ID of each sample into the RFID tag; After the writing is completed, update the status of the corresponding RFID tag in the RFID tag database to unavailable status; After the status update is completed, the target sample information data completes the allocation of RFID electronic tags, and the allocated data is marked as target sample electronic tag data.

5. The intelligent sample storage and management system based on laser positioning and RFID according to claim 4, characterized in that The laser positioning unit is also used for: The positioning equipment corresponding to the laser positioning technology includes a laser emitter, a laser receiver, a positioning sensor, and a controller; Retrieve the three-dimensional model of the storage area from the model database, where the benchmark points of each sample position are marked in the three-dimensional model; Use the laser emitter to emit lasers at the positions of each sample in the target sample information data; When the laser beam is reflected back after hitting the sample surface, the laser receiver receives the reflected signal. According to the measured time difference between the laser emission and reception, obtain the distance between the sample and the laser emitter, and convert the distance between the sample and the laser emitter into specific coordinates; Associate the converted specific coordinates with the sample ID in the target sample information data. After the association is completed, obtain the sample positioning data; Construct a three-dimensional model of the sample based on the sample size, shape, and position data obtained by the laser in the sample positioning data; After the three-dimensional model is constructed, the target sample positioning data is obtained.

6. The intelligent sample storage and management system based on laser positioning and RFID according to claim 5, characterized in that The positioning information fusion unit is also used for: Perform data cleaning, data standardization, data screening, and data dimensionality reduction processing on the target sample positioning data and the target sample electronic tag data in sequence; After data cleaning, data standardization, data screening, and data dimensionality reduction processing, obtain the target sample positioning data and the target sample electronic tag data for which the data preprocessing is completed; Associate the sample information in the target sample positioning data and the target sample electronic tag data according to the sample ID; Merge the associated target sample positioning data and target sample electronic tag data into a unified data set, and the data set includes the sample ID, position coordinates, RFID tag ID, and sample attributes; Next, extract the feature data from the dataset. The feature data includes storage location, storage environment parameters, and sample status; Label the merged dataset and the feature data in the dataset as sample storage information data.

7. The intelligent sample storage and management system based on laser positioning and RFID according to claim 6, wherein The sample storage simulation analysis unit is further configured to: Convert the three-dimensional space visualization data into a three-dimensional model construction of the sample storage information data using 3D modeling software. At the same time, different colors, shapes, or sizes are used in the constructed three-dimensional model to represent different sample types, statuses, or attributes. Among them, the position coordinates of the sample correspond to the position in the three-dimensional model, and the RFID tag information is presented by displaying the tag ID on the sample model or through a floating prompt; Convert the two-dimensional map visualization data into a two-dimensional map construction of the sample storage information data using a map library. Among them, the position coordinates of the sample are mapped to a specific position on the two-dimensional map, and different markers are used to represent different samples; Convert the chart visualization data into a bar chart or pie chart to display the quantity distribution of different sample types, a line chart to display the change trend of storage environment parameters over time, a scatter plot to display the relationship between sample attributes, and a dashboard to display key indicators.

8. The intelligent sample storage and management system based on laser positioning and RFID according to claim 7, characterized in that The sample storage simulation analysis unit is further configured to: Monitor and simulate the three-dimensional space visualization data by adding animation effects to the constructed three-dimensional model and generating a movement trajectory for the movement process of the three-dimensional model. At the same time, the user views the detailed information of the samples in the three-dimensional model on the interactive terminal through operations such as rotation, scaling, and zooming; Monitor and simulate the two-dimensional map visualization data by dividing the generated two-dimensional map into different regions and real-time displaying the number and status of samples in each region. At the same time, generate a two-dimensional movement path according to the movement trajectory of the three-dimensional model; Monitor and simulate the chart visualization data by classifying each data in the chart according to the attributes of the data, and obtaining the parameter values of the data of each attribute after classification; Unify the monitored and simulated data of the three-dimensional space visualization data, two-dimensional map visualization data, and chart visualization data and label them as data to be analyzed for storage.

9. The intelligent sample storage and management system based on laser positioning and RFID according to claim 8, wherein When the number of the target regions exceeds the preset number threshold, dynamically adjust the refresh frequency of the sample quantity and status, and at the same time, adjust the display refresh frequency of the two-dimensional movement path generated by the two-dimensional map according to the movement trajectory of the three-dimensional model, including: When the number of the target regions exceeds the preset number threshold, retrieve the current refresh frequency of the sample quantity and status; Adjust the current refresh frequency of the sample quantity and status using the data change degree coefficient to obtain the adjusted refresh frequency of the sample quantity and status; Among them, the adjusted refresh frequency of the sample quantity and status is obtained through the following formula: Among them, f y01 represents the adjusted sample quantity and status refresh frequency; f y represents the sample quantity and status refresh frequency before adjustment; m represents the number of target regions; R b represents the standard deviation of the data change degree coefficients of the m target regions; R i represents the data change degree coefficient of the i-th target region; R y represents a preset threshold of the change degree coefficient; Retrieve the frequency of generating the two-dimensional movement path by the two-dimensional map of each region according to the movement trajectory of the three-dimensional model; Adjust the display refresh frequency of the two-dimensional movement path generated according to the movement trajectory of the three-dimensional model by using the frequency combination data change degree coefficient of the two-dimensional movement path generated from the two-dimensional map of each region, and obtain the display refresh frequency of the two-dimensional movement path generated from the adjusted two-dimensional map according to the movement trajectory of the three-dimensional model; Among them, the display refresh frequency of the two-dimensional movement path generated from the adjusted two-dimensional map according to the movement trajectory of the three-dimensional model is obtained through the following formula: Among them, f e01 represents the display refresh frequency of the two-dimensional movement path generated according to the movement trajectory of the three-dimensional model by the adjusted two-dimensional map; f e represents the display refresh frequency of the two-dimensional movement path generated according to the movement trajectory of the three-dimensional model by the two-dimensional map before adjustment; m represents the number of target areas; k represents the number of non-target areas; R i represents the data change degree coefficient of the i-th target area; R j represents the data change degree coefficient of the j-th non-target area; f ei represents the frequency of generating a two-dimensional movement path by the two-dimensional map of the i-th target area according to the movement trajectory of the three-dimensional model; f ej represents the frequency of generating a two-dimensional movement path by the two-dimensional map of the j-th non-target area according to the movement trajectory of the three-dimensional model.

10. The intelligent sample storage and management system based on laser positioning and RFID according to claim 9, characterized in that The analysis data evaluation unit is also used for: The position analysis of the data to be analyzed and stored includes sample partition analysis, spatial density analysis, sample distance analysis, and dynamic tracking analysis; The sample partition analysis is to partition the samples into different regions according to the position coordinates of the samples in the data to be analyzed and stored, count the sample data in each region, and at the same time use the clustering algorithm to identify the distribution of similar samples; The spatial density analysis is to visualize the distribution density of the samples in the storage area through the spatial density analysis method and distinguish the aggregated or sparse regions; The sample distance analysis is to use the Euclidean distance algorithm to calculate and analyze the distance between samples and the positional relationship between samples and storage devices and environmental sensors; The dynamic tracking analysis is to analyze the movement trajectory of the samples in the storage area, and the movement trajectory includes the frequency and direction of the samples being moved; Compare the threshold of the analysis result of the position analysis with the preset threshold determination rule; When the threshold of the analysis result exceeds the preset threshold determination rule, it is determined that the exceeded threshold is abnormal data; Associate the abnormal data with the corresponding samples abnormally, and after the abnormal association, give a warning display on the display terminal.

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