Sample intelligent storage and management system based on laser positioning and RFID

Abstract

The application discloses a sample intelligent storage and management system based on laser positioning and RFID, relates to the technical field of sample intelligent storage, and aims at solving the problems of unclear sample position and poor sample information integrity during sample storage. The application generates a moving path on a two-dimensional map through the moving track of a three-dimensional model, helps realize the tracking and management of samples in a two-dimensional space, and can display the sample quantity and state in real time through two-dimensional map and chart visualization, so as to provide timely monitoring information for managers. The sample ID is automatically written into the RFID tag by using an RFID reader and writer, manual operation is reduced, and errors caused by improper human operation are reduced. After the RFID tag is assigned to the sample and data is written, the tag state is updated to unavailable in the RFID tag database, the repeated use of the tag is avoided, and the one-to-one correspondence between the tag and the sample is ensured.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of sample intelligent storage, in particular to a sample intelligent storage and management system based on laser positioning and RFID. BACKGROUND

[0002] Sample intelligent storage refers to efficient, accurate and safe management and storage of biological samples, chemical samples, physical samples and the like by means of modern information technology, Internet of Things technology and automation technology.

[0003] Patent application with publication number CN115215023B discloses a folder access path intelligent safety analysis system and method based on big data, which mainly stores files by taking a two-dimensional code, is easy to operate, does not need consumables and does not need special personnel on duty, saves daily use and management cost, is safe and fast, has low equipment investment cost, improves enterprise work efficiency, predicts the failure rate of a transmission mechanism through processing of test data, makes early remediation, and reduces the risk of jam and downtime in the running process. The above patent solves the problem of intelligent storage, but there are still the following problems in actual operation:

[0004] 1. The sample data is not effectively assigned with electronic tags, so that the safety and manageability of the sample data are reduced.

[0005] 2. The sample is not accurately positioned by laser, and the laser positioning information of the sample and the electronic tag of the sample are not further fused, so that the flexibility of the data is reduced.

[0006] 3. The obtained sample information is not comprehensively converted and analyzed, so that abnormal data cannot be obtained in time and the abnormal data cannot be processed. SUMMARY

[0007] The application aims to provide a sample intelligent storage and management system based on laser positioning and RFID, which generates a moving path on a two-dimensional map through the moving track of a three-dimensional model, helps to realize tracking and management of the sample in a two-dimensional space, and realizes real-time display of the sample quantity and state through two-dimensional map and chart visualization, so as to provide timely monitoring information for managers. The RFID reader is used to automatically write the sample ID into the RFID tag, reduces manual operation, and reduces errors caused by improper human operation. After the RFID tag is assigned to the sample and data is written, the tag state is immediately updated to unavailable in the RFID tag database, so that the reuse of the tag is avoided, and the one-to-one correspondence between the tag and the sample is ensured, so that the problems in the prior art can be solved.

[0008] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0009] The sample intelligent storage and management system based on laser positioning and RFID comprises:

[0010] A sample information acquisition unit is configured to:

[0011] read basic information of each sample from a database, encode the basic information of each sample with a unique code, and label the sample with the unique code as target sample information data;

[0012] An RFID tag confirmation unit is configured to:

[0013] allocate an RFID electronic tag to the target sample information data, establish a corresponding relationship between the sample ID and the RFID electronic tag ID, and obtain target sample electronic tag data after the RFID electronic tag allocation of the target sample information data is completed;

[0014] A laser positioning unit is configured to:

[0015] position the target sample information data by using laser positioning technology, construct a three-dimensional model of the positioned sample, and obtain target sample positioning data after the three-dimensional model construction is completed;

[0016] A positioning information fusion unit is configured to:

[0017] 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;

[0018] A sample storage simulation analysis unit is configured to:

[0019] convert the sample storage information data into visual data, monitor and simulate the sample storage information data after the visual data conversion, and obtain to-be-analyzed storage data after the monitoring and simulation;

[0020] An analysis data evaluation unit is configured to:

[0021] perform position analysis on the to-be-analyzed storage data, perform sample storage quality evaluation according to the position analysis result, and transmit the sample storage quality evaluation result to a display terminal for display.

[0022] Preferably, the sample storage simulation analysis unit is further configured to:

[0023] convert the sample storage information data into visual data;

[0024] The visual data comprises three-dimensional space visual data, two-dimensional map visual data, and chart visual data;

[0025] The three-dimensional space visualized data, the two-dimensional map visualized data and the chart visualized data are monitored and simulated;

[0026] During the monitoring and simulation of the two-dimensional map visualized data, the sample quantity and the state change in each region are monitored in real time, and the sample quantity and the state refresh frequency and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track of the three-dimensional model are dynamically adjusted according to the change, including:

[0027] The sample quantity change frequency and the state change frequency corresponding to each unit time of each region in the historical record are extracted; wherein the unit time is 12h-36h;

[0028] The data change degree coefficient corresponding to each region is obtained according to the sample quantity change frequency and the state change frequency corresponding to each unit time of each region;

[0029] The data change degree coefficient is obtained by the following formula:

[0030]

[0031] Wherein, R represents the data change degree coefficient; n represents the number of unit times experienced by the two-dimensional map display running 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;

[0032] The data change degree coefficient is compared with the preset change degree coefficient threshold value;

[0033] The region whose data change degree coefficient exceeds the preset change degree coefficient threshold value is marked to obtain a target region;

[0034] comparing the number of the target areas with a preset number threshold;

[0035] when the number of the target areas exceeds the preset number threshold, dynamically adjusting the sample number and the state refresh frequency, and adjusting the display refresh frequency of the two-dimensional moving path of the two-dimensional map generated according to the moving track of the three-dimensional model.

[0036] Preferably, the sample information acquisition unit is further configured to:

[0037] read the basic information of each sample from a database;

[0038] The basic information of the sample includes a sample ID, a sample type, a sample attribute, a sample source, a collection time, a storage location, and a sample name.

[0039] uniquely encode the sample whose basic information reading is completed;

[0040] Before the unique coding is performed, a coding rule is defined, and the coding rule is a coding format, a structure, a random number or letter combination, and specific information.

[0041] According to the defined coding rule, the basic information of each sample is uniquely coded.

[0042] After the unique coding is completed, target sample information data is obtained.

[0043] Preferably, the RFID tag confirmation unit is further configured to:

[0044] Before the target sample information data is allocated with an RFID electronic tag, the state of each RFID tag is queried from an RFID tag database, the state of the RFID tag is divided into an available state and an unavailable state, and the tag ID of the RFID tag in the available state is obtained.

[0045] The ID of each sample in the target sample information data is read;

[0046] The tag ID of the RFID tag in the available state is associated with the ID of each sample;

[0047] After the association is completed, the ID of each sample is written into the RFID tag by using an RFID reader-writer;

[0048] After the writing is completed, the state of the corresponding RFID tag in the RFID tag database is updated to the unavailable state;

[0049] After the state updating is completed, the target sample information data is allocated with the RFID electronic tag, and the data after the allocation is completed is marked as target sample electronic tag data.

[0050] Preferably, the laser positioning unit is further configured to:

[0051] The positioning device corresponding to the laser positioning technology comprises a laser emitter, a laser receiver, a positioning sensor, and a controller.

[0052] A three-dimensional model of the storage area is called from the model database, wherein the three-dimensional model marks the reference points of each sample position;

[0053] The laser emitter is used to emit laser light at the position of each sample in the target sample information data;

[0054] When the laser beam is reflected back after irradiating the sample surface, the laser receiver receives the reflected signal, and according to the time difference between the emission and reception of the laser light, the distance between the sample and the laser emitter is obtained, and the distance between the sample and the laser emitter is converted into specific coordinates;

[0055] The converted specific coordinates are associated with the sample ID in the target sample information data, and after the association is completed, sample positioning data is obtained;

[0056] The sample size, shape, and position data obtained by the laser in the sample positioning data are used to construct a three-dimensional model of the sample;

[0057] After the three-dimensional model is constructed, target sample positioning data is obtained.

[0058] Preferably, the positioning information fusion unit is further configured to:

[0059] The target sample positioning data and the target sample electronic tag data are sequentially subjected to data cleaning, data standardization, data screening, and data dimensionality reduction processing;

[0060] After the data cleaning, data standardization, data screening, and data dimensionality reduction processing, the target sample positioning data and the target sample electronic tag data are obtained after the data preprocessing is completed;

[0061] According to the sample ID, the sample information in the target sample positioning data and the target sample electronic tag data is associated;

[0062] The associated target sample positioning data and target sample electronic tag data are merged into a unified data set, and the data set includes the ID, position coordinates, RFID tag ID, and sample attribute of the sample;

[0063] The feature data in the data set is extracted, and the feature data includes the storage location, storage environment parameters, and sample state;

[0064] The merged data set and the feature data in the data set are labeled as sample storage information data.

[0065] Preferably, the sample storage simulation analysis unit is further configured to:

[0066] The conversion of the three-dimensional space visualization data is to construct a three-dimensional model using 3D modeling software based on the sample storage information data, and different colors, shapes, or sizes are used in the constructed three-dimensional model to represent different sample types, states, or attributes, wherein the position coordinates of the samples correspond to the positions in the three-dimensional model, and the RFID tag information is presented by displaying the tag ID on the sample model or by a floating prompt;

[0067] The conversion of the two-dimensional map visualization data is to construct a two-dimensional map using a map library based on the sample storage information data, wherein the position coordinates of the samples are mapped to specific positions on the two-dimensional map, and different markers are used to represent different samples;

[0068] The conversion of the chart visualization data is to use a column chart or a pie chart to display the number distribution of different sample types, use a line chart to display the trend of the storage environment parameters over time, use a scatter plot to display the relationship between sample attributes, and use a dashboard to display key indicators.

[0069] Preferably, the sample storage simulation analysis unit is further configured to:

[0070] The monitoring simulation of the three-dimensional space visualization data is to add animation effects to the constructed three-dimensional model and generate a moving trajectory of the three-dimensional model, and the user can view the sample distribution by rotating, zooming, and enlarging the detailed information of the samples in the three-dimensional model on the interactive terminal;

[0071] The monitoring simulation of the two-dimensional map visualization data is to divide the generated two-dimensional map into different regions and display the number and state of the samples in each region in real time, and generate a two-dimensional moving path based on the moving trajectory of the three-dimensional model;

[0072] The monitoring simulation of the 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 of each attribute after classification;

[0073] The monitoring simulation data of the three-dimensional space visualization data, the two-dimensional map visualization data, and the chart visualization data are uniformly labeled as the storage data to be analyzed.

[0074] Preferably, when the number of target regions exceeds a preset number threshold, the sample number and state refresh frequency are dynamically adjusted, and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map based on the moving trajectory of the three-dimensional model is also adjusted, including:

[0075] When the number of the target regions exceeds a preset number threshold, the current sample number and state refresh frequency are called;

[0076] The current sample number and state refresh frequency are adjusted by using the data change degree coefficient, to obtain an adjusted sample number and state refresh frequency;

[0077] The adjusted sample number and state refresh frequency are obtained by the following formula:

[0078]

[0079] Wherein, f y01 represents the adjusted sample number and state refresh frequency; f y represents the sample number and state 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 a preset change degree coefficient threshold;

[0080] The two-dimensional map of each region is called, and the frequency of generating a two-dimensional moving path according to the moving track of the three-dimensional model is called;

[0081] The display refresh frequency of the two-dimensional moving path is adjusted by using the frequency of generating a two-dimensional moving path according to the moving track of the three-dimensional model of each region combined with the data change degree coefficient, to obtain the display refresh frequency of the two-dimensional moving path generated by the adjusted two-dimensional map according to the moving track of the three-dimensional model;

[0082] The display refresh frequency of the two-dimensional moving path generated by the adjusted two-dimensional map according to the moving track of the three-dimensional model is obtained by the following formula:

[0083]

[0084] Wherein, f e01 represents the display refresh frequency of the two-dimensional moving path generated by the adjusted two-dimensional map according to the moving track of the three-dimensional model; f e represents the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track 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 moving path according to the moving track of the three-dimensional model of the i-th target region; f ejThe two-dimensional map representing the jth non-target area generates a two-dimensional movement path according to the frequency of the movement trajectory of the three-dimensional model.

[0085] Preferably, the analysis data evaluation unit is further configured to:

[0086] The location analysis of the storage data to be analyzed includes sample partition analysis, spatial density analysis, sample distance analysis and dynamic tracking analysis.

[0087] The sample partition analysis is to partition the samples into different areas according to the location coordinates of the samples in the storage data to be analyzed, and to count the sample data in each area, while using a clustering algorithm to identify the distribution of similar samples.

[0088] The spatial density analysis is to visualize the distribution density of the samples in the storage area by a spatial density analysis method, and to distinguish the areas of aggregation or sparseness.

[0089] The sample distance analysis is to calculate and analyze the distance between the samples and the position relationship between the samples and the storage device and the environmental sensor by using the Euclidean distance algorithm.

[0090] The dynamic tracking analysis is to analyze the movement trajectory of the samples in the storage area, including the frequency and direction of the movement of the samples.

[0091] The analysis results of the location analysis are compared with the preset threshold determination rule.

[0092] When the threshold of the analysis results exceeds the preset threshold determination rule, the exceeded threshold is determined as abnormal data.

[0093] The abnormal data is associated with the corresponding sample, and the abnormal association is displayed on the display terminal.

[0094] Compared with the prior art, the present application has the following advantages:

[0095] 1. The sample intelligent storage and management system based on laser positioning and RFID provided by the present application uses an RFID reader to automatically write the sample ID into the RFID tag, reducing manual operation, improving work efficiency, and reducing errors caused by improper human operation. After the RFID tag is assigned to the sample and the data is written, the tag state is immediately updated to unavailable in the RFID tag database, avoiding repeated use of the tag and ensuring one-to-one correspondence between the tag and the sample.

[0096] 2.The sample intelligent storage and management system based on laser positioning and RFID provided by the application, laser positioning technology can obtain real-time sample position information, is suitable for dynamic sample storage environment, and a three-dimensional model of the sample can be constructed by using sample size, shape and position data obtained by laser.

[0097] 3.The sample intelligent storage and management system based on laser positioning and RFID provided by the application, a moving path on a two-dimensional map is generated according to the moving track of the three-dimensional model, which helps to realize tracking and management of the sample in the two-dimensional space, the two-dimensional map and chart visualization can display the sample quantity and state in real time and the change trend of the storage environment parameters, and provide timely monitoring information for the manager, sample partition analysis can quickly distribute a large number of samples to different areas and count sample data in each area, thereby simplifying the sample management and retrieval process, and pre-warning display is performed on the display terminal to timely inform the manager to process, thereby avoiding potential risks and losses, and the laser positioning and RFID technology are combined to realize intelligent storage and management of the sample and improve the automation and intelligent level of the system. BRIEF DESCRIPTION OF DRAWINGS

[0098] Figure 1 A unit schematic diagram of the sample intelligent storage and management of the application;

[0099] Figure 2 A flowchart of the sample intelligent storage and management of the application;

[0100] Figure 3 A sample intelligent storage cabinet body structure schematic diagram of the application;

[0101] In the figure: 1, sample intelligent storage cabinet body; 2, operation display; 3, specimen model scanner; 4, specimen classification cabinet. DETAILED DESCRIPTION

[0102] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0103] In order to solve the problem that sample data is not effectively allocated to an electronic tag after sample data collection in the prior art, thereby reducing the safety and manageability of sample data, please refer to Figure 1 and Figure 2 The technical solutions are provided in the embodiments as follows:

[0104] The sample intelligent storage and management system based on laser positioning and RFID includes:

[0105] The sample information acquisition unit is used for:

[0106] Reading the basic information of each sample from the database, and uniquely coding the basic information of each sample, and labeling the sample with a unique coding label as target sample information data;

[0107] The RFID tag confirmation unit is used for:

[0108] Allocating RFID electronic tags to the target sample information data, and establishing a corresponding relationship between the sample ID and the RFID electronic tag ID, and obtaining target sample electronic tag data after the RFID electronic tag allocation of the target sample information data is completed;

[0109] The laser positioning unit is used for:

[0110] Positioning the target sample information data using laser positioning technology, and constructing a three-dimensional model of the positioned sample, and obtaining target sample positioning data after the three-dimensional model construction is completed;

[0111] The positioning information fusion unit is used for:

[0112] Data preprocessing is performed on the target sample positioning data and the target sample electronic tag data, data fusion is performed after the data preprocessing is completed, and sample storage information data is obtained after the data fusion;

[0113] The sample storage simulation analysis unit is used for:

[0114] Visual data conversion is performed on the sample storage information data, and monitoring simulation is performed on the sample storage information data after the visual data conversion, and the analyzed storage data is obtained after the monitoring simulation;

[0115] The analysis data evaluation unit is used for:

[0116] The analyzed storage data is subjected to position analysis, and sample storage quality evaluation is performed according to the position analysis result, and the sample storage quality evaluation result is transmitted to the display terminal for display.

[0117] Specifically, each sample is identified by a unique code through the sample information acquisition unit, allowing the system to easily trace the sample's origin, collection time, storage location, and other historical information. The RFID tag is reused through the RFID tag confirmation unit, thereby reducing sample management costs and improving resource utilization efficiency. The sample positioning data and three-dimensional model data are integrated into a unified database through the laser positioning unit, enabling data sharing and unified management. The feature data extracted by the positioning information fusion unit can be flexibly adjusted and expanded according to actual needs to meet different application scenarios and requirements. The sample storage simulation analysis unit uses various visualization methods that can be flexibly selected and combined according to actual needs to adapt to different sizes and types of sample storage and management requirements. The analysis data evaluation unit can display warnings on the display terminal and notify management personnel for processing in a timely manner, thereby avoiding potential risks and losses.

[0118] As shown in Figure 3 The sample intelligent storage and management system based on laser positioning and RFID also includes a sample intelligent storage cabinet body 1. An operation display 2 is provided on the sample intelligent storage cabinet body 1. The basic information of the sample and the state of the RFID tag can be viewed on the operation display 2. At the same time, the sample intelligent storage cabinet body 1 is provided with a plurality of specimen classification cabinets 4. Different types of specimens are stored in the corresponding specimen classification cabinets 4. A specimen model scanner 3 is used to scan the initial three-dimensional model of the sample. After the initial three-dimensional model scanning is completed, the three-dimensional model is stored in the model database. The three-dimensional model of the storage area is retrieved from the model database.

[0119] The sample information acquisition unit is also used for:

[0120] Reading the basic information of each sample from the database;

[0121] The basic information of the sample includes sample ID, sample type, sample attribute, sample source, collection time, storage location, and sample name.

[0122] The sample whose basic information has been read is given a unique code number;

[0123] The coding rule is defined before the unique code number is given. The coding rule includes the format, structure, random number or letter combination, and specific information of the code.

[0124] Each sample's basic information is generated into a unique code according to the defined coding rule;

[0125] The unique code generates target sample information data.

[0126] Specifically, the RFID technology realizes real-time data transmission through radio waves, can quickly read the basic information of the sample, including the sample ID, sample type and other key data, and significantly improves the identification and reading speed of the sample. The laser positioning technology can accurately position the storage position of the sample, reduce the time of manually searching for the sample, and each sample is identified by a unique code, so that the system can easily trace the source, collection time and storage position of the sample. Historical information provides strong support for scientific research or medical diagnosis.

[0127] The RFID tag confirmation unit is further configured to:

[0128] Before the RFID electronic tag allocation of the target sample information data, the state of each RFID tag is queried from the RFID tag database, and the state of the RFID tag is divided into an available state and an unavailable state. Then, the tag ID of the RFID tag in the available state is obtained.

[0129] The ID of each sample in the target sample information data is read.

[0130] The tag ID of the RFID tag in the available state is associated with the ID of each sample.

[0131] After the association is completed, the ID of each sample is written into the RFID tag by using an RFID reader-writer.

[0132] After the writing is completed, the state of the corresponding RFID tag in the RFID tag database is updated to the unavailable state.

[0133] After the state is updated, the target sample information data completes the RFID electronic tag allocation, and the data after the allocation is completed is marked as target sample electronic tag data.

[0134] Specifically, by first querying the tag state (available or unavailable) from the RFID tag database, it is ensured that only available tags are assigned to samples, thereby avoiding resource waste and tag conflicts, the ID of each sample in the target sample information data is read and one-to-one associated with the ID of the available RFID tag, this precise matching reduces human error and improves data accuracy, the sample ID is automatically written into the RFID tag using the RFID reader, reducing manual operations and improving work efficiency, while also reducing errors caused by improper human operation, after the RFID tag is assigned to the sample and the data is written, the tag state is immediately updated to unavailable in the RFID tag database, avoiding repeated use of the tag and ensuring a one-to-one correspondence between the tag and the sample, since each sample is assigned a unique RFID tag and the corresponding tag ID is recorded, this greatly enhances the traceability of the data, facilitating subsequent management and query, allowing dynamic management of RFID tags, when a certain tag is no longer needed, its state can be updated to available, thereby being reassigned to other samples, improving the flexibility and scalability of the system.

[0135] To solve the problem in the prior art that the sample is not positioned more accurately by laser, and the laser positioning information of the sample and the electronic tag of the sample are not further fused, thereby reducing the flexibility of the data, please refer to Figure 1 and Figure 2 The embodiment provides the following technical solutions:

[0136] The laser positioning unit is also used to:

[0137] The positioning device corresponding to the laser positioning technology comprises a laser emitter, a laser receiver, a positioning sensor and a controller;

[0138] The three-dimensional model of the storage area is called from the model database, wherein the three-dimensional model marks the reference points of the positions of each sample;

[0139] The position of each sample in the target sample information data is laser emitted by the laser emitter;

[0140] When the laser beam is reflected back after irradiating the surface of the sample, the laser receiver receives the reflected signal, the distance between the sample and the laser emitter is obtained according to the time difference between the laser emission and the reception, and the distance between the sample and the laser emitter is converted into specific coordinates;

[0141] The converted specific coordinates are associated with the sample ID in the target sample information data, and the sample positioning data is obtained after the association is completed;

[0142] The sample size, shape and position data obtained by the laser in the sample positioning data are used to construct a three-dimensional model of the sample;

[0143] After the three-dimensional model is constructed, the target sample positioning data is obtained.

[0144] Specifically, by measuring 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 generally has a positioning accuracy of millimeters, which is suitable for scenarios with extremely high requirements for sample position. Laser positioning technology uses a non-contact measurement method, which avoids damage or contamination to the sample caused by physical contact. This is particularly important for scenarios that require sample integrity and cleanliness. Laser positioning technology can obtain real-time sample position information, which is suitable for dynamic sample storage environments. 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 sample storage, management, and retrieval. The sample positioning data and 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, while supporting cross-department and cross-institution data sharing and collaboration.

[0145] The positioning information fusion unit is also configured to:

[0146] sequentially perform data cleaning, data standardization, data filtering, and data dimensionality reduction processing on the target sample positioning data and the target sample electronic tag data;

[0147] After data cleaning, data standardization, data filtering, and data dimensionality reduction processing, the target sample positioning data and the target sample electronic tag data are obtained.

[0148] According to the sample ID, the sample information in the target sample positioning data and the target sample electronic tag data is associated.

[0149] The associated target sample positioning data and target sample electronic tag data are merged into a unified data set, which includes the sample's ID, position coordinates, RFID tag ID, and sample attributes.

[0150] The feature data in the data set is extracted, including storage location, storage environment parameters, and sample state.

[0151] The merged data set and the feature data in the data set are labeled as sample storage information data.

[0152] Specifically, the positioning data and the electronic tag data are associated by the sample ID to realize integration and unified management of the data, facilitating subsequent data analysis and mining. The associated data set contains complete information of the sample, such as the position coordinates, the RFID tag ID and the sample attributes, improving the completeness and relevance of the data. Key feature data such as the storage location, the storage environment parameters and the sample state are extracted from the integrated data set, providing a strong basis for subsequent decision support. The feature data is labeled to form sample storage information data, facilitating data management and query, and the preprocessed data has higher quality, which helps to improve the efficiency and accuracy of the system in processing data. The integrated data set and the feature data provide a basis for intelligent management of the system, such as realizing automatic tracking, positioning and management of the sample, and the complete data set and the feature data provide comprehensive information support for decision makers, which helps to develop and optimize sample storage management strategies. By analyzing the feature data, problems in the storage environment such as temperature and humidity abnormalities can be found in a timely manner, so that corresponding measures can be taken for improvement. The preprocessing process and the data integration method have certain universality and scalability, and can adapt to different types and scales of sample data storage requirements. The extracted feature data can be flexibly adjusted and expanded according to actual needs to meet different application scenarios and requirements.

[0153] In order to solve the problem that in the prior art, the obtained sample information is not subjected to more comprehensive data visualization conversion and data analysis, so that abnormal data cannot be obtained in time and processed, please refer to Figure 1 and Figure 2 The embodiment provides the following technical scheme:

[0154] The sample storage simulation analysis unit is also used for:

[0155] visualizing data conversion of the sample storage information data;

[0156] The visualized data includes three-dimensional space visualized data, two-dimensional map visualized data and chart visualized data;

[0157] The three-dimensional space visualized data is converted by using a 3D modeling software to construct a three-dimensional model of the sample storage information data, and different colors, shapes or sizes are used in the constructed three-dimensional model to represent different sample types, states or attributes, wherein 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 by a floating prompt;

[0158] The two-dimensional map visualized data is converted by using a map library to construct a two-dimensional map of the sample storage information data, wherein the position coordinates of the sample are mapped to a specific position on the two-dimensional map, and different marks are used to represent different samples;

[0159] The chart visualization data is converted to display the number distribution of different sample types by using a column chart or a pie chart, to display the change trend of the storage environment parameters over time by using a line chart, to display the relationship between sample attributes by using a scatter plot, and to display key indicators by using a dashboard.

[0160] The three-dimensional space visualization data, the two-dimensional map visualization data, and the chart visualization data are monitored and simulated.

[0161] The monitoring and simulation of the three-dimensional space visualization data includes adding animation effects to the constructed three-dimensional model, generating a moving track of the three-dimensional model, and allowing a user to view the sample distribution by rotating, zooming in, and zooming out the sample details in the three-dimensional model on an interactive terminal.

[0162] The monitoring and simulation of the two-dimensional map visualization data includes dividing the generated two-dimensional map into different regions, displaying the number and state of samples in each region in real time, and generating a two-dimensional moving path according to the moving track of the three-dimensional model.

[0163] The monitoring and simulation of the chart visualization data includes classifying each data in the chart according to the attribute of the data, and obtaining the parameter value of the data of each attribute after the classification.

[0164] The monitoring and simulation data of the three-dimensional space visualization data, the two-dimensional map visualization data, and the chart visualization data are uniformly labeled as to-be-analyzed storage data.

[0165] Specifically, the three-dimensional model constructed by the 3D modeling software can intuitively display the spatial layout of sample storage, so that the manager can quickly understand the distribution and state of the samples. The two-dimensional map generated by the map library can map the sample location to a specific location, which is convenient for the manager to directly view the sample information on the map, and improves the intuitiveness and readability of the information. The bar chart, pie chart, line chart, scatter chart and dashboard can clearly display the sample type distribution, storage environment parameter change trend, sample attribute relationship and key indicators, and display the RFID tag information on the three-dimensional model and two-dimensional map, so that the tracking and management of the samples are more efficient. Through the floating prompt or label ID display, the detailed information of the samples can be quickly obtained. The data classification and parameter value extraction in the chart visualization help the manager quickly understand the data distribution of different attributes and provide support for decision-making. Adding animation effects to the three-dimensional model can simulate the movement process of the samples and generate a moving track, which helps the manager to view and manage the sample distribution on the interactive terminal. According to the moving track of the three-dimensional model, a moving path on the two-dimensional map is generated, which helps to realize the tracking and management of the samples in the two-dimensional space. The two-dimensional map and chart visualization can display the sample quantity and state in real time, as well as the change trend of the storage environment parameters, so as to provide timely monitoring information for the manager. The three-dimensional space visualization, two-dimensional map visualization and chart visualization data are uniformly marked as to-be-analyzed storage data, which helps the manager to conduct comprehensive analysis and develop more scientific storage and management strategies. A variety of visualization methods are adopted, which can be flexibly selected and combined according to actual needs to adapt to different scales and types of sample storage and management requirements.

[0166] Specifically, in the monitoring simulation process of the two-dimensional map visualization data, the change of the sample quantity and state in each region is monitored in real time, and the sample quantity and state refresh frequency and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track of the three-dimensional model are dynamically adjusted according to the change, including:

[0167] extracting the sample quantity change frequency and state change frequency corresponding to each unit time of each region in the historical record; wherein the unit time is 12h-36h;

[0168] obtaining the data change degree coefficient corresponding to each region according to the sample quantity change frequency and state change frequency corresponding to each unit time of each region;

[0169] wherein the data change degree coefficient is obtained by the following formula:

[0170]

[0171] wherein, R represents a data variation degree coefficient; n represents the number of unit time experienced by each region corresponding to the two-dimensional map display operation; w f represents a weight value corresponding to the sample quantity variation frequency; w t represents a weight value corresponding to the state variation frequency; f yi and f ti respectively represent the sample quantity variation frequency and the state variation frequency corresponding to the i th unit time; f ymax represents the maximum value of the sample quantity variation frequency corresponding to n unit time; f yt represents the state variation frequency corresponding to the unit time where the maximum value of the sample quantity variation frequency is located; f tmax represents the maximum value of the state variation frequency corresponding to n unit time; f ty represents the sample quantity variation frequency corresponding to the unit time where the maximum value of the state variation frequency is located; f yp and f tp respectively represent the average value of the sample quantity variation frequency and the average value of the state variation frequency corresponding to n unit time; f yb and f tb respectively represent the standard deviation of the sample quantity variation frequency and the standard deviation of the state variation frequency corresponding to n unit time;

[0172] comparing the data variation degree coefficient with a preset variation degree coefficient threshold value;

[0173] marking the region where the data variation degree coefficient exceeds the preset variation degree coefficient threshold value to obtain a target region;

[0174] comparing the number of target regions with a preset number threshold value;

[0175] when the number of target regions exceeds the preset number threshold value, dynamically adjusting the sample quantity and the state refresh frequency, and simultaneously adjusting the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track of the three-dimensional model.

[0176] The technical effects of the above technical solution are: by monitoring the sample quantity and state change in each region in real time, the system can quickly respond to the dynamic changes in the region, thereby improving the real-time and accuracy of monitoring. Dynamically adjusting the refresh frequency of the sample quantity and state makes the system resources more effectively allocated to the regions with more frequent changes, improving the overall monitoring efficiency. According to the sample quantity change frequency and state change frequency in the historical record, the data change degree coefficient of each region is calculated, which helps the system identify which regions are the focus of monitoring. By comparing the data change degree coefficient with the preset threshold, the system can automatically mark the target region, i.e. the region that needs more frequent monitoring, thereby achieving rational allocation of resources. Dynamically adjusting the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track of the three-dimensional model makes the moving path on the map more smooth and accurate to reflect the actual movement of the object. This dynamic adjustment not only improves the real-time of the map, but also enhances the user's visual experience, making the user more intuitive to understand the dynamic changes in the monitoring region. The technical solution introduces parameters such as data change degree coefficient and preset threshold, so that the system can be flexibly adjusted according to the actual situation. Whether it is the refresh frequency of the sample quantity and state, or the display refresh frequency of the moving path on the two-dimensional map, it can be dynamically adjusted according to the actual demand, thereby improving the flexibility and adaptability of the system. The technical solution realizes effective monitoring and management of two-dimensional map visualization data through automation and intelligent means. Through real-time monitoring, dynamic adjustment and other steps, the system can automatically adapt to changes in the monitoring region, providing strong support for intelligent management.

[0177] In summary, the technical solution improves monitoring efficiency, enhances user experience, improves system flexibility and promotes intelligent management through real-time monitoring, dynamic adjustment and optimization of resource allocation. These technical effects make the technical solution have significant advantages and application value in the monitoring simulation process of two-dimensional map visualization data.

[0178] Specifically, when the number of target regions exceeds the preset number threshold, the sample quantity and state refresh frequency is dynamically adjusted, and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving track of the three-dimensional model is adjusted, including:

[0179] When the number of target regions exceeds the preset number threshold, the current sample quantity and state refresh frequency is retrieved;

[0180] The current sample quantity and state refresh frequency is adjusted using the data change degree coefficient to obtain the adjusted sample quantity and state refresh frequency;

[0181] Wherein, the adjusted sample quantity and state refresh frequency is obtained by the following formula:

[0182]

[0183] wherein f y01 represents the adjusted sample quantity and state refresh frequency; f y represents the unadjusted sample quantity and state refresh frequency; m represents the number of target regions; R b represents the standard deviation of the data variation degree coefficient of the m target regions; R i represents the data variation degree coefficient of the i-th target region; R y represents a preset variation degree coefficient threshold value;

[0184] the frequency of generating a two-dimensional movement path according to the movement trajectory of the three-dimensional model using the two-dimensional map of each region;

[0185] adjusting the display refresh frequency of the two-dimensional movement path in combination with the data variation degree coefficient, obtaining 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;

[0186] wherein 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 by the following formula:

[0187]

[0188] wherein 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 unadjusted two-dimensional map according to the movement trajectory of the three-dimensional model; m represents the number of target regions; k represents the number of non-target regions; R i represents the data variation degree coefficient of the i-th target region; R j represents the data variation degree coefficient of the j-th non-target region; f ei represents the frequency of generating a two-dimensional movement path according to the movement trajectory of the three-dimensional model using the two-dimensional map of the i-th target region; f ej represents the frequency of generating a two-dimensional movement path according to the movement trajectory of the three-dimensional model using the two-dimensional map of the j-th non-target region.

[0189] The technical effects of the above technical solution are: when the number of target areas exceeds the preset threshold, the system automatically adjusts the sample quantity and the refresh frequency of the state, as well as the display refresh frequency of the two-dimensional map moving path. This dynamic adjustment mechanism enables the system to quickly respond to changes in the monitoring area, improving the adaptability and flexibility of the system. By using the data change degree coefficient to adjust the refresh frequency, the system can more reasonably allocate resources. Areas with frequent changes will have a higher refresh frequency, ensuring the accuracy and real-time nature of the data. This resource optimization strategy helps improve the overall monitoring efficiency of the system. The adjusted refresh frequency can more accurately reflect the actual state of the monitoring area, providing more accurate data support for decision-makers. This helps decision-makers make more informed decisions and improve monitoring and management effectiveness. The technical solution not only considers the changes in target areas, but also combines the data change degree coefficient of non-target areas. This balanced strategy helps meet monitoring needs while avoiding excessive system load. By reasonably adjusting the refresh frequency, the system can maintain stable operation while ensuring monitoring effectiveness. The dynamically adjusted two-dimensional map moving path display refresh frequency can more smoothly display the moving trajectory of the object, enhancing the user's visual experience. This real-time and accurate display method helps improve user trust and satisfaction with the monitoring system. The technical solution realizes intelligent and automated management of the monitoring system by introducing the data change degree coefficient and dynamic adjustment mechanism. This management method not only improves monitoring efficiency, but also reduces the cost and risk of human intervention.

[0190] In summary, the above technical solution improves the adaptability and flexibility of the monitoring system, enhances user experience, and promotes intelligent and automated management through dynamic adjustment of refresh frequency and optimization of resource allocation. These technical effects make the technical solution have significant advantages and application value in the monitoring simulation process of two-dimensional map visualization data.

[0191] The analysis data evaluation unit is also used for:

[0192] The location analysis of the storage data to be analyzed includes sample partition analysis, spatial density analysis, sample distance analysis, and dynamic tracking analysis;

[0193] The sample partition analysis is to partition samples into different areas according to the location coordinates of the samples in the storage data to be analyzed, and to count the sample data in each area, while using a clustering algorithm to identify the distribution of similar samples;

[0194] The spatial density analysis is to visualize the distribution density of samples in the storage area through a spatial density analysis method, and to distinguish between areas with high or low density;

[0195] The sample distance analysis is to calculate and analyze the distance between samples and the positional relationship between the samples and the storage device and the environmental sensor by using the Euclidean distance algorithm.

[0196] The dynamic tracking analysis is to analyze the moving track of the sample in the storage area, and the moving track includes the frequency and direction of the sample movement.

[0197] The analysis result of the position analysis is compared with the preset threshold determination rule.

[0198] When the threshold of the analysis result exceeds the preset threshold determination rule, the exceeded threshold is determined as abnormal data.

[0199] The abnormal data is associated with the corresponding sample, and the abnormal association is displayed on the display terminal for early warning.

[0200] Specifically, the sample partition analysis can quickly distribute a large number of samples to different areas and count the sample data in each area, thereby simplifying the management and retrieval process of the samples. The application of the clustering algorithm can further identify the distribution of similar samples, which helps the classification and rapid positioning of the samples. The spatial density analysis can clearly show which areas are concentrated and which areas are sparse by visualizing the distribution density of the samples in the storage area, thereby helping the management personnel to optimize the utilization of the storage space and avoid waste. The sample distance analysis uses the Euclidean distance algorithm to calculate the distance between the samples and the positional relationship between the samples and the storage device and the environmental sensor, thereby providing accurate sample position information. The dynamic tracking analysis can track the moving track of the sample in the storage area in real time, including the moving frequency and direction, which helps to ensure the safety and accuracy of the sample. By comparing the analysis result of the position analysis with the preset threshold determination rule, the system can automatically detect abnormal data such as sample position error and abnormal moving frequency. After the abnormal association, the system can display early warning on the display terminal to timely notify the management personnel to handle, thereby avoiding potential risks and losses. Combined with the laser positioning and RFID technology, the intelligent storage and management of the samples are realized, and the automation and intelligence level of the system is improved. Through the data analysis and early warning function, the system can learn and optimize itself, thereby continuously improving the management efficiency and accuracy.

[0201] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0202] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A sample intelligent storage and management system based on laser positioning and RFID, characterized in that, include: The sample information acquisition unit is used for: Read the basic information of each sample from the database, and assign a unique code to the basic information of each sample. Mark the sample with the unique code as the target sample information data. RFID tag verification unit, used for: The target sample information data is assigned RFID electronic tags, and the correspondence between sample ID and RFID electronic tag ID is established. After the RFID electronic tag assignment of the target sample information data is completed, the target sample electronic tag data is obtained. Laser positioning unit, used for: Laser positioning technology is used to locate the target sample information data, and a three-dimensional model is constructed from the located sample. After the three-dimensional model is constructed, the target sample positioning data is obtained. The positioning information fusion unit is used for: The target sample location data and target sample electronic tag data are preprocessed, and then fused to obtain the sample storage information data. The sample storage simulation analysis unit is used for: The sample storage information data is visualized and transformed, and the visualized and transformed sample storage information data is monitored and simulated. The storage data to be analyzed is obtained after the monitoring and simulation. The data analysis and evaluation unit is used for: The data to be analyzed is subjected to location analysis, and the sample storage quality is evaluated based on the location analysis results. The sample storage quality evaluation results are then transmitted to the display terminal for display. The sample storage simulation analysis unit is also used for: Visualize and transform sample storage information data; Visualized data includes 3D spatial visualization data, 2D map visualization data, and chart visualization data; Monitoring and simulation of 3D spatial visualization data, 2D map visualization data, and chart visualization data; During the monitoring and simulation of 2D map visualization data, the changes in the number and status of samples within each area are monitored in real time. Based on these changes, the refresh frequency of the sample number and status, as well as the display refresh frequency of the 2D movement path generated by the 2D map according to the movement trajectory of the 3D model, are dynamically adjusted. This includes: Extract the frequency of sample quantity change and the frequency of state change for each region in the historical record for each unit of time; wherein the unit of time is 12h-36h; The data change coefficient for each region is obtained based on the frequency of change in the number of samples and the frequency of change in the state for each unit of time in each region. The coefficient of data change is obtained by the following formula: wherein, R represents a data variation degree coefficient; n represents the number of unit time experienced by each region corresponding to the two-dimensional map display operation; w f represents a weight value corresponding to the sample quantity variation frequency; w t represents a weight value corresponding to the state variation frequency; f yi and f ti respectively represent the sample quantity variation frequency and the state variation frequency corresponding to the i th unit time; f ymax represents the maximum value of the sample quantity variation frequency corresponding to n unit time; f yt represents the state variation frequency corresponding to the unit time where the maximum value of the sample quantity variation frequency is located; f tmax represents the maximum value of the state variation frequency corresponding to n unit time; f ty represents the sample quantity variation frequency corresponding to the unit time where the maximum value of the state variation frequency is located; f yp and f tp respectively represent the average value of the sample quantity variation frequency and the average value of the state variation frequency corresponding to n unit time; f yb and f tb respectively represent the standard deviation of the sample quantity variation frequency and the standard deviation of the state variation frequency corresponding to n unit time; The data change coefficient is compared with a preset change coefficient threshold. Mark areas where the data change coefficient exceeds a preset change coefficient threshold to obtain the target area; Compare the number of the target areas with a preset quantity threshold; When the number of target areas exceeds a preset threshold, the number of samples 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 based on the movement trajectory of the three-dimensional model is adjusted. Real-time monitoring of the sample quantity and state change in each area, dynamically adjusting the refresh frequency of the sample quantity and state, calculating the data change degree coefficient of each area according to the sample quantity change frequency and state change frequency in the historical record, identifying the key monitoring object, automatically marking the target area by comparing the data change degree coefficient with the preset threshold, and realizing the reasonable allocation of resources.

2. The laser positioning and RFID based smart sample storage and management system as claimed in claim 1, wherein, 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; The sample with completed basic information reading is uniquely coded; Define the coding rules before coding, including the format, structure, random number or letter combination, and specific information of the code; Generate a unique code for each sample based on the defined coding rules; After the unique code is generated, the target sample information data is obtained.

3. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 2, wherein, The RFID tag confirmation unit is further configured to: Before assigning the RFID electronic tag to the target sample information data, query the state of each RFID tag from the RFID tag database, and the state of the RFID tag is divided into available state and unavailable state, and then obtain the tag ID of the RFID tag in the available state; Read the ID of each sample in the target sample information data; Associate the tag ID of the RFID tag in the available state with the ID of each sample; After the association is completed, write the ID of each sample into the RFID tag using the RFID reader and writer; After writing is completed, update the state of the corresponding RFID tag in the RFID tag database to the unavailable state; After the state is updated, the target sample information data completes the assignment of the RFID electronic tag, and the data after the assignment is completed is marked as target sample electronic tag data.

4. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 3, wherein, The laser positioning unit is further configured to: The positioning device corresponding to the laser positioning technology includes a laser transmitter, a laser receiver, a positioning sensor, and a controller; Retrieve the three-dimensional model of the storage area from the model database, wherein the three-dimensional model marks the reference point of the position of each sample; Use the laser transmitter to emit the position 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, calculates the distance between the sample and the laser transmitter according to the time difference between the emission and reception of the laser, and converts the distance between the sample and the laser transmitter into specific coordinates; Associate the converted specific coordinates with the sample ID in the target sample information data, and obtain the sample positioning data after the association is completed; Construct a three-dimensional model of the sample by using 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.

5. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 4, wherein, The positioning information fusion unit is further configured to: sequentially perform data cleaning, data standardization, data filtering, and data dimensionality reduction processing on the target sample positioning data and the target sample electronic tag data; After data cleaning, data standardization, data screening and data dimensionality reduction processing, target sample positioning data and target sample electronic tag data after data preprocessing are obtained; According to the sample ID, the sample information in the target sample positioning data and the target sample electronic tag data is associated; The associated target sample positioning data and target sample electronic tag data are merged into a unified data set, and the data set includes the ID, position coordinates, RFID tag ID and sample attribute of the sample; Then, the feature data in the data set is extracted, including the storage location, storage environment parameters and sample state; The merged data set and the feature data in the data set are labeled as sample storage information data.

6. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 5, wherein, The sample storage simulation analysis unit is also used for: The conversion of three-dimensional space visualization data is to use 3D modeling software to construct a three-dimensional model of the sample storage information data, and different colors, shapes or sizes are used to represent different sample types, states or attributes in the constructed three-dimensional model, wherein 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 by hovering. The conversion of two-dimensional map visualization data is to use a map library to construct a two-dimensional map of the sample storage information data, wherein 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. The conversion of chart visualization data is to use a column chart or a pie chart to display the number distribution of different sample types, a line chart to display the trend of storage environment parameters over time, a scatter plot to display the relationship between sample attributes, and an instrument panel to display key indicators.

7. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 6, wherein, The sample storage simulation analysis unit is also used for: The monitoring simulation of three-dimensional space visualization data is to add animation effects to the constructed three-dimensional model, and generate a moving trajectory of the three-dimensional model, and the user can view the sample distribution in the three-dimensional model by rotating, zooming and enlarging the detailed information of the sample in the three-dimensional model. The monitoring simulation of two-dimensional map visualization data is to divide the generated two-dimensional map into different regions and display the number and state of samples in each region in real time, and generate a two-dimensional moving path according to the moving trajectory of the three-dimensional model. The monitoring simulation of chart visualization data is to classify each data in the chart according to the attribute of the data, and obtain the parameter value of each attribute data after classification. The monitoring simulation data of three-dimensional space visualization data, two-dimensional map visualization data and chart visualization data are uniformly labeled as to-be-analyzed storage data.

8. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 7, wherein, When the number of target areas exceeds the preset number threshold, the sample number and state refresh frequency are dynamically adjusted, and the display refresh frequency of the two-dimensional moving path generated by the two-dimensional map according to the moving trajectory of the three-dimensional model is adjusted, including: When the number of target areas exceeds the preset number threshold, the current sample number and state refresh frequency are retrieved; The current sample number and state refresh frequency are adjusted using a data change degree coefficient to obtain an adjusted sample number and state refresh frequency. Wherein, the adjusted sample quantity and state refresh frequency are obtained by the following formula: Wherein, f y01 represents the adjusted sample quantity and state refresh frequency; f y represents the sample quantity and state refresh frequency before adjustment; m represents the number of target regions; R b represents the standard deviation of the data variation degree coefficient of the m target regions; R i represents the data variation degree coefficient of the i-th target region; R y represents a preset variation degree coefficient threshold value; The frequency of generating the two-dimensional moving path according to the moving track of the three-dimensional model by using the two-dimensional map of each region; The display refresh frequency of the two-dimensional moving path is adjusted by combining the data change degree coefficient with the frequency of generating the two-dimensional moving path according to the moving track of the three-dimensional model by using the two-dimensional map of each region, to obtain the display refresh frequency of the two-dimensional moving path generated by the adjusted two-dimensional map according to the moving track of the three-dimensional model; Wherein, the display refresh frequency of the two-dimensional moving path generated by the adjusted two-dimensional map according to the moving track of the three-dimensional model is obtained by the following formula: wherein 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 unadjusted two-dimensional map according to the movement trajectory of the three-dimensional model; 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 the two-dimensional movement path generated 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 the two-dimensional movement path generated by the two-dimensional map of the j-th non-target region according to the movement trajectory of the three-dimensional model.

9. The laser positioning and RFID based intelligent sample storage and management system as claimed in claim 8, wherein, The analysis data evaluation unit is further configured to: The position analysis of the to-be-analyzed storage data 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 to-be-analyzed storage data, and to count the sample data in each region, while using 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 region by using a spatial density analysis method, and to distinguish the regions with high or low density; The sample distance analysis is to calculate and analyze the distance between the samples, and the position relationship between the samples and the storage device and the environmental sensor by using the Euclidean distance algorithm; The dynamic tracking analysis is to analyze the moving track of the samples in the storage region, including the frequency and direction of the movement of the samples; The analysis result of the position analysis is compared with a 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; The abnormal data is associated with the corresponding sample, and the abnormal association is displayed on a display terminal for early warning.

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