An intelligent sample collection management method, device, equipment and medium

Abstract

The present application relates to an intelligent sample collection management method, device, equipment and medium. An intelligent sample collection management method includes obtaining a transfer request of a sample, determining in real time the location data corresponding to the basic data, and generating a corresponding sample transfer log based on the basic data and the location data; analyzing the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result; if the recognition result is an abnormal result, performing a corresponding abnormal handling operation and / or an alarm operation according to the abnormal result; if the recognition result is a normal result, synchronizing the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules. It improves the efficiency of sample transfer, ensures the real-time and accuracy of data, and fundamentally solves the problems of lagging information update and poor system interaction under the traditional management mode.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sample collection management, and particularly to an intelligent sample collection management method, device, equipment and medium. Background Art

[0002] In the traditional sample transfer management process, the collection, storage and transfer of samples mainly rely on manual operations and single-point information entry, resulting in lagging data updates and prone to information inconsistency problems. Taking the Warehouse Management System (WMS) and Laboratory Information Management System (LIMS) as examples, the entry and exit of samples usually require manual scanning or manual entry, which not only increases the possibility of human errors, but also causes delays in information synchronization. Due to the lack of an efficient data interaction mechanism between different systems, during the sample transfer process, data missing, duplicate records or status errors will occur in multiple links. For example, a batch of samples has been sent to the laboratory for testing, but due to the failure of the information system to update in time, the WMS still shows that it is in the warehouse state, which will lead to confusion for subsequent management personnel in inventory checking and experiment arrangement. Especially when involving collaborative management of multiple terminals (such as Web terminals, PDA terminals, APP terminals), the untimely information update will directly affect the traceability and transfer efficiency of samples. Summary of the Invention

[0003] In order to solve the problem that when involving collaborative management of multiple terminals (such as Web terminals, PDA terminals, APP terminals), the untimely information update will directly affect the traceability and transfer efficiency of samples, the present application provides an intelligent sample collection management method, device, equipment and medium.

[0004] An intelligent sample collection management method, the intelligent sample collection management method includes:

[0005] Obtain a transfer request of a sample, and synchronize the basic data of the sample between information systems based on the transfer request, where the information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device;

[0006] Through multi-threaded positioning technology, determine the location data corresponding to the basic data in real time, and generate a corresponding sample transfer log based on the basic data and the location data;

[0007] Analyze the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result;

[0008] If the recognition result is an abnormal result, perform corresponding abnormal handling operations and / or alarm operations according to the abnormal result;

[0009] If the recognition result is a normal result, according to the pre-determined synchronization rules, synchronize the corresponding log segments in the sample transfer log between each of the information systems.

[0010] By adopting the above technical solution, a sample transfer request is obtained and the basic sample data is synchronized among a warehouse management system (WMS), a laboratory information management system (LIMS), and a mobile terminal device, enabling real-time updates of information such as sample receipt, delivery, and transfer among systems, and avoiding information inconsistency problems caused by manual entry or system delays. Secondly, the method uses multi-threaded positioning technology to determine the location information of the sample in real time, and generates a sample transfer log in combination with the basic data, thereby ensuring the traceability of the sample transfer between the warehouse and the laboratory, no longer relying on manual records or single-point scanning, and reducing management chaos caused by the inability of traditional technologies to track the sample location in real time. Further, based on the established transfer anomaly recognition model, the method can automatically analyze the transfer log, identify abnormal transfer situations, and timely detect and warn of problems such as missing data, duplicate records, or incorrect statuses, ensuring the integrity and accuracy of the data. When an anomaly is recognized, the system can automatically trigger an anomaly handling or alarm operation, enabling managers to intervene in a timely manner to prevent inventory confusion or experimental arrangement errors caused by untimely information updates. In addition, for normal transfer data, the method uses preset synchronization rules to ensure that each log segment of the sample transfer log is consistent among the WMS, LIMS, and mobile terminals, avoiding information errors caused by out-of-sync data updates in different systems in traditional technologies. This intelligent sample receiving management method not only improves the efficiency of sample transfer, but also ensures the real-time nature and accuracy of the data, fundamentally solving the problems of lagging information updates and poor system interaction in the traditional management mode.

[0011] In a preferred example of the present application, it can be further configured that: in the step of synchronizing the basic sample data between information systems based on the transfer request, it includes:

[0012] Extract the key fields in the transfer request;

[0013] Match the corresponding sample codes according to the key fields;

[0014] Based on the sample codes, match the corresponding shelving codes in each information system, determine whether each of the shelving codes is consistent, and if not, generate corresponding anomaly identification data;

[0015] If they are consistent, add a transferred identifier to the information system where the shelving code is matched, and add an untransferred identifier to the information system where the shelving code is not matched;

[0016] Integrate the transferred identifier, the shelving code, and / or the exception identifier data to generate corresponding basic data.

[0017] By adopting the above technical solution, it is possible to automatically parse the keyword fields in the transfer request and verify whether the shelving codes of the samples are consistent based on the matching rules, enabling accurate matching and verification of sample data between different systems. When inconsistencies are detected, the system automatically generates exception identifier data, allowing managers to promptly discover and handle potential transfer errors. At the same time, for samples with normal transfers, the system can assign corresponding transfer status identifiers to achieve automatic identification and management of transfer data between systems. This not only reduces the manual verification workload but also improves the reliability of data verification and reduces the risk of data omission or duplicate recording during the sample transfer process.

[0018] In a preferred example of the present application, it can be further configured as follows: in the step of determining the position data corresponding to the basic data in real time through the multi-threaded positioning technology, it includes:

[0019] Match the corresponding multi-thread management mechanism according to the shelving code in the basic data;

[0020] Based on the multi-thread management mechanism, configure an independent thread and acquisition adjustment parameters for the corresponding mobile terminal device;

[0021] Based on the mobile terminal device configured with an independent thread and acquisition adjustment parameters, obtain the corresponding single-thread data, store the single-thread data in a temporary buffer, and record the corresponding data attributes in the temporary buffer. The data attributes include timestamp and signal strength;

[0022] Substitute each single-thread data into the determined fusion algorithm to generate corresponding preliminary data;

[0023] Call historical transfer data, and determine whether to determine the preliminary data as position data according to the comparison result between the historical transfer data and the preliminary data.

[0024] By adopting the above technical solution, it is possible to achieve parallel processing of sample positioning data based on the multi-thread management mechanism, enabling the mobile terminal device to simultaneously collect data in different threads and improving the positioning efficiency. At the same time, the data collected by each thread will be stored in a temporary buffer and additional attribute information such as timestamp and signal strength will be attached for accurate calculation during subsequent data fusion. By setting a fusion algorithm to synthesize and calculate the data of multiple threads and combining historical transfer data for verification, the finally generated sample position data is more accurate, avoiding positioning deviations caused by large errors in single positioning source data, thereby improving the accuracy and real-time performance of sample tracking.

[0025] In a preferred example, the present application can be further configured as follows: in the step of generating a corresponding sample transfer log based on the basic data and the location data, it includes:

[0026] Determine the classification rules for the sample transfer log, where the classification rules include field standardization processing rules and field arrangement rules. Among them, the classification rules are determined according to the category of the information system;

[0027] Based on the field standardization processing rules, integrate and standardize the basic data and the location data to generate each classification field, as well as the associated fields and / or exception identification fields associated with each classification field. The exception identification fields are generated based on the exception identification data;

[0028] Based on the field arrangement rules, rearrange the classification fields, the associated fields and / or the exception identification fields to generate a plurality of transfer entries arranged according to the classification fields. Among them, the classification fields are arranged at the head of the transfer entries;

[0029] Determine the transfer entries containing the exception identification fields as exception entries, and perform a highlighting prompt operation and a statistical positioning operation;

[0030] Integrate a plurality of transfer entries arranged according to the classification fields, and / or the exception entries after performing the highlighting prompt operation and the statistical positioning operation, to generate a corresponding sample transfer log.

[0031] By adopting the above technical solution, it is possible to standardize the fields of the transfer log, classify and arrange the fields according to the category of the information system, so that the log data can be stored and managed in a structured manner. During the log generation process, the system can organize the transfer entries according to the order of the classification fields based on the field arrangement rules, and automatically detect the exception identification fields therein, so as to perform a highlighting prompt and statistical positioning when abnormal data appears. This can not only ensure the uniform format of the transfer log, improve the readability of the log data, but also quickly identify and locate abnormal samples when sample transfer anomalies occur, improve the efficiency of anomaly handling, and enhance the reliability of sample transfer management.

[0032] In a preferred example, the present application can be further configured as follows: in the step of analyzing the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result, the recognition result includes an abnormal result and a normal result, and the abnormal result includes a first abnormal recognition result and a second abnormal recognition result. The step further includes:

[0033] Retrieve whether there are abnormal entries in the sample transfer log. If so, generate a corresponding first abnormal recognition result according to the abnormal entries;

[0034] If not, based on the established transfer anomaly recognition model, select the target fields required by the established transfer anomaly recognition model from the classification fields and the associated fields in each of the transfer entries;

[0035] If the target fields are timestamp fields and location coordinate fields, perform logical matching on the target fields according to the predefined anomaly condition rule library in the established transfer anomaly recognition model, and output the corresponding anomaly marks;

[0036] If the target field is an operation type field, call the historical abnormal operation data set, compare features with the operation type field, and output the corresponding anomaly probability value;

[0037] If it is detected that the anomaly mark appears and / or the anomaly probability value exceeds the preset probability threshold, generate the corresponding second anomaly recognition result.

[0038] By adopting the above technical solutions, it is possible to automatically retrieve the abnormal entries based on the data in the sample transfer log and generate the first anomaly recognition result to ensure that known abnormal data can be quickly recognized. At the same time, the system can also deeply analyze the target fields in the log based on the established transfer anomaly recognition model, and output anomaly marks or anomaly probability values through the logical matching of timestamp fields and location coordinate fields, or the feature comparison of operation type fields. When it is detected that the anomaly mark appears or the anomaly probability value exceeds the preset threshold, the system can generate the second anomaly recognition result, thereby realizing the intelligent prediction of potential anomalies, improving the comprehensiveness and accuracy of sample anomaly detection, enabling managers to take corresponding measures in a timely manner, and reducing the management risks brought by abnormal transfers.

[0039] In a preferred example of the present application, it can be further configured that the generation steps of the predefined anomaly condition rule library in the established transfer anomaly recognition model include:

[0040] Obtain the timestamp conflict events and location offset events in the historical abnormal transfer log, and extract the conflict time interval and the offset distance threshold;

[0041] Generate a timestamp conflict rule according to the conflict time interval;

[0042] Generate a location offset rule according to the offset distance threshold, specifically: if the Euclidean distance between the location coordinate field in the current transfer entry and the standard coordinate of the preset transfer path exceeds the offset distance threshold, trigger a location offset mark;

[0043] Store the timestamp conflict rule and the location offset rule in the anomaly condition rule library;

[0044] In the step of performing logical matching on the target field according to the predefined exception condition rule library in the established transfer exception recognition model and outputting corresponding exception marks, the exception marks include a timestamp conflict mark and a position offset mark, and the step further includes:

[0045] If the target field is a timestamp field, based on the timestamp conflict rule, determine whether the difference between the timestamp field and the timestamp field in the adjacent transfer entry is less than a preset time threshold. If it is less, trigger the timestamp conflict mark;

[0046] If the target field is a position coordinate field, based on the position offset rule, determine whether the Euclidean distance between the position coordinate field and the standard coordinate of the preset transfer path exceeds the offset distance threshold. If it exceeds, trigger the position offset mark.

[0047] By adopting the above technical solution, it is possible to construct an exception condition rule library based on historical exception transfer logs, and generate corresponding timestamp conflict rules and position offset rules by extracting the key features of timestamp conflict events and position offset events. The system can use these rules to perform logical matching on newly collected transfer data, and determine whether the transfer data conforms to the standard operation specification based on a preset threshold. When the system detects that the deviation of the timestamp field or the position coordinate field exceeds the set threshold, it automatically triggers an exception mark to ensure that any time conflict or position anomaly can be discovered and processed in a timely manner. This can not only reduce the omission of exceptions caused by misjudgment or information lag, but also improve the safety and controllability during the sample transfer process, and optimize the overall exception management mechanism.

[0048] The second invention object of the present application is achieved by the following technical solutions:

[0049] An intelligent sample receiving management device, the intelligent sample receiving management device includes:

[0050] A first acquisition module, configured to acquire a transfer request of a sample, and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device;

[0051] A determination module, configured to determine the position data corresponding to the basic data in real time through multi-threaded positioning technology, and generate a corresponding sample transfer log based on the basic data and the position data;

[0052] An analysis module, configured to analyze the sample transfer log based on the established transfer exception recognition model to generate a corresponding recognition result;

[0053] An execution module, configured to, if the recognition result is an abnormal result, perform corresponding exception handling operations and / or alarm operations according to the abnormal result;

[0054] A synchronization module, configured to, if the recognition result is a normal result, synchronize corresponding log segments in the sample transfer logs between each of the information systems according to a pre-determined synchronization rule.

[0055] The above object three of the present application is achieved by the following technical solutions:

[0056] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the computer program, the steps of the above-mentioned intelligent sample collection management method are implemented.

[0057] The above object four of the present application is achieved by the following technical solutions:

[0058] A computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned intelligent sample collection management method are implemented.

[0059] In summary, the present application includes at least one of the following beneficial technical effects:

[0060] This application obtains a sample transfer request and synchronizes the basic sample data among the Warehouse Management System (WMS), Laboratory Information Management System (LIMS), and mobile terminal devices, enabling real-time updates of information such as sample receipt, outbound, and transfer among systems, and avoiding information inconsistency problems caused by manual entry or system delays. Secondly, this method uses multi-threaded positioning technology to determine the location information of samples in real time and generates a sample transfer log in combination with the basic data, thus ensuring the traceability of sample transfer between the warehouse and the laboratory, no longer relying on manual records or single-point scanning, and reducing management chaos caused by the inability of traditional technologies to track the location of samples in real time. Further, based on the established transfer anomaly recognition model, this method can automatically analyze the transfer log, identify abnormal transfer situations, and timely detect and warn of problems such as missing data, duplicate records, or incorrect status, ensuring data integrity and accuracy. When an anomaly is recognized, the system can automatically trigger anomaly handling or alarm operations, enabling managers to intervene in a timely manner to prevent inventory confusion or experimental arrangement errors caused by untimely information updates. In addition, for normal transfer data, this method adopts preset synchronization rules to ensure the consistency of each log segment of the sample transfer log among WMS, LIMS, and mobile terminals, avoiding information errors caused by asynchronous data updates in different systems in traditional technologies. This intelligent sample receipt management method not only improves the efficiency of sample transfer but also ensures the real-time nature and accuracy of data, fundamentally solving the problems of lagging information updates and poor system interaction in the traditional management mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flowchart of an intelligent sample receipt management method in an embodiment of this application;

[0062] Figure 2 is an implementation flowchart of step S10 in an intelligent sample receipt management method in an embodiment of this application;

[0063] Figure 3 is an implementation flowchart of step S20 in an intelligent sample receipt management method in an embodiment of this application;

[0064] Figure 4 is another implementation flowchart of step S20 in an intelligent sample receipt management method in an embodiment of this application;

[0065] Figure 5 is an implementation flowchart of step S30 in an intelligent sample receipt management method in an embodiment of this application;

[0066] Figure 6 is another implementation flowchart of an intelligent sample receipt management method in an embodiment of this application;

[0067] Figure 7It is a flowchart of the implementation of step S303 in an intelligent sample collection management method according to an embodiment of the present application;

[0068] Figure 8 It is a schematic block diagram of the principle of an intelligent sample collection management device according to an embodiment of the present application;

[0069] Figure 9 It is a schematic diagram of the device according to an embodiment of the present application. Detailed implementation manners

[0070] The present application will be further described in detail below with reference to the accompanying drawings.

[0071] In an embodiment, as Figure 1 shown, the present application discloses an intelligent sample collection management method, which specifically includes the following steps:

[0072] S10. Obtain the transfer request of the sample, and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device; in this embodiment, obtaining the transfer request of the sample means receiving the sample transfer instruction initiated by the user through the interface of the mobile terminal device or the warehouse management system. For example, the laboratory needs to transport a certain batch of samples from the warehouse to the detection table. The transfer request needs to include key information such as the sample number, batch number, and target transfer location. The process of synchronizing the basic data is to perform two-way or one-way data synchronization on the inventory information in the warehouse management system, the detection task data in the laboratory information management system, and the real-time operation records in the mobile terminal device through a predefined data interface protocol (such as REST API or MQTT protocol), so as to ensure that the basic attributes of the sample (such as storage conditions, validity period, current status) in all systems are consistent. For example, when the sample is marked as "out of stock" in the warehouse management system, the laboratory information management system will automatically update the status of the sample to "in transit", and at the same time, the real-time transportation path will be displayed on the mobile terminal device. This cross-system data synchronization mechanism can eliminate information islands and avoid process interruptions caused by data delay or conflict.

[0073] S20. Determine the location data corresponding to the basic data in real time through multi-threaded positioning technology, and generate corresponding sample transfer logs based on the basic data and the location data. In this embodiment, the multi-threaded positioning technology refers to allocating independent computing threads to each mobile terminal device participating in sample transfer (such as the GPS module on a transport vehicle or a handheld scanner), and simultaneously collecting positioning signals from different devices (such as GPS coordinates, Bluetooth beacon distances, or RFID reading and writing locations). For example, when a sample is transported from a warehouse to a laboratory, the GPS module on the transport vehicle continuously sends longitude and latitude data, while the handheld scanner reads the location information of the sample label through RFID during handover. The system fuses multi-source positioning data into accurate real-time location coordinates through timestamp alignment and signal strength weighting algorithms. The generated sample transfer log records the association between the location data and the basic data (such as environmental parameters like temperature and humidity) at each time point. For example, "At 2023-10-01 14:30:00, sample A-001 is located at coordinates (X, Y, Z), and the environmental temperature is 23°C". Such a high-precision log can provide a complete spatio-temporal trajectory evidence chain for subsequent anomaly analysis.

[0074] S30. Analyze the sample transfer log based on the established transfer anomaly recognition model to generate corresponding recognition results. In this embodiment, the transfer anomaly recognition model adopts a hybrid architecture, including a real-time detection module based on a rule engine and a probability prediction module based on machine learning. The rule engine module directly marks abnormal events through predefined logical conditions (such as "transportation time exceeds the threshold" or "location deviates from the preset path by more than 50 meters"). For example, when it is detected that a certain sample has been stagnant for more than 2 hours during transportation, an "transportation delay" alarm is triggered. The machine learning module trains a classification model through historical anomaly data and analyzes the hidden patterns in the log. For example, it predicts the deterioration risk of samples caused by environmental temperature and humidity fluctuations through time series analysis. The input features of the model include timestamp intervals, location movement rates, environmental parameter change gradients, etc., and the output result is an anomaly probability value. When both the rule engine and the machine learning module trigger an anomaly, the system marks the recognition result as "high-confidence anomaly", thereby reducing the false alarm rate.

[0075] S40. If the recognition result is an abnormal result, corresponding abnormal handling operations and / or alarm operations shall be performed according to the abnormal result. In this embodiment, the abnormal handling operations are divided into two categories: automatic intervention and manual intervention. For example, when the system detects that the transportation path of a certain sample deviates from the preset route and the environmental temperature exceeds the standard, the automatically executed operations include: sending an instruction to the transportation vehicle to forcibly turn on the temperature control equipment to cool down, simultaneously pushing an alarm message (such as a text message or an APP notification) to the person in charge through the mobile terminal, and marking the abnormal path segment in red on the map. For abnormalities that require manual handling (such as damaged sample labels that cannot be recognized), the system will generate an emergency task order and allocate it to the nearest patrol personnel, and at the same time freeze the status of the sample in the warehouse management system to prevent misoperation. The alarm operation supports a grading mechanism. For example, "low temperature warning" only records the log, while "location lost for more than 30 minutes" will trigger an audible and visual alarm and notify the superior administrator.

[0076] S50. If the recognition result is a normal result, according to the pre-determined synchronization rules, the corresponding log segments in the sample transfer log shall be synchronized between each information system. In this embodiment, the synchronization rules define the key fields and triggering conditions of the log segments. For example, when the sample completes the laboratory test and is marked as "normal", the system only synchronizes the test time, the tester number, and the result summary to the warehouse management system, while the detailed test data (such as the spectral analysis diagram) remains in the laboratory information management system. The synchronization process adopts an incremental update method and realizes asynchronous transmission through a message queue (such as Kafka) to avoid system blockage. For example, when the mobile terminal uploads the handover completion record, the system will automatically extract fields such as "handover time" and "recipient signature", encrypt them, and synchronize them to the test task dashboard of the laboratory system. This on-demand synchronization mechanism reduces redundant data transmission, and at the same time ensures that each system only obtains the necessary information related to its functions, improving data security and processing efficiency.

[0077] In one embodiment, as Figure 2 shown, in step S10, that is, the step of synchronizing the basic data of the sample between information systems based on the transfer request, includes:

[0078] S101. Extract key fields in the circulation request; in this embodiment, extracting key fields in the circulation request refers to parsing the necessary information for uniquely identifying samples and circulation paths from the circulation application submitted by the user. For example, when a laboratory initiates a test request for a batch of drugs, the circulation request must include fields such as the batch number, production date, storage warehouse number, and target laboratory number of the drug. These fields are structured and extracted using regular expressions or predefined JSON templates, such as automatically identifying "Batch Number: 2023-ABC-001" from unstructured text and mapping it to standardized fields. This process can filter out redundant information (such as the applicant's remarks) to ensure that subsequent processes only process core data, thereby reducing the waste of computing resources and improving data processing efficiency.

[0079] S102. Match the corresponding sample code according to the key field; in this embodiment, matching the sample code means comparing the extracted key field with the database records in the warehouse management system and the laboratory system to generate a globally unique sample identification code. For example, the corresponding sample code "SAMP-2023-09876" is queried in the inventory table of the warehouse management system through the batch number "2023-ABC-001", and it is verified whether the code is bound to the detection task in the laboratory system. The matching process adopts database joint query technology. If it is found that the same batch number has multiple conflicting codes in different systems (such as the warehouse system record is "SAMP-2023-09876", and the laboratory system mistakenly stores it as "SAMP-2023-9876"), the anomaly detection process is triggered. This step ensures the consistency of data across systems and avoids sample mismatch or loss due to coding errors.

[0080] S103, based on the sample code, match the corresponding shelf code in each information system, determine whether the shelf codes are consistent, if not, generate corresponding abnormal identification data; in this embodiment, the shelf code refers to the physical or logical location identification of the sample in a specific system. For example, the shelf code in the warehouse management system is "Area A-3 rows-5 layers" (indicating the shelf location), and the shelf code in the laboratory information management system is "biochemical test bench-02" (indicating the test equipment number). The system queries the associated shelf code in the two systems through the sample code "SAMP-2023-09876". If the warehouse system returns "Area A-3 rows-5 layers", and the laboratory system returns "unassigned", it is determined to be inconsistent and generates abnormal identification data (such as error code "ERR_LOC_MISMATCH"). This mechanism can detect location information conflicts caused by manual operation errors or system synchronization delays in real time, such as when the sample has been shipped out but the laboratory has not allocated testing resources.

[0081] S104. If they are consistent, add a transferred flag to the information system that matches the shelving code, and add a non - transferred flag to the information system that does not match the shelving code; in this embodiment, the transferred flag and the non - transferred flag are used to mark the processing status of each system in this transfer. For example, when the shelving codes of the warehousing system and the laboratory system both match successfully, the system will add a "shipped out" flag to the sample record in the warehousing system and a "pending inspection" flag in the laboratory system; for a non - matched mobile terminal device (such as a barcode scanner that fails to respond to queries), it is marked as the "not ready" state. These flags are implemented through database status fields or log marks. For example, in MySQL, update the "status" field to "TRANSFERRED". This design can clarify the responsibility boundaries of each link. For example, when a sample is lost during transportation, the "not ready" flag of the mobile terminal can be checked to quickly locate the device failure node.

[0082] S105. Integrate the data of the transferred flag, the shelving code, and / or the exception flag to generate corresponding basic data. In this embodiment, the integration process is to merge the status information, location codes, and exception records scattered in different systems into a basic data entity with a unified structure. For example, encapsulate the "shipped out" flag of the warehousing system, the "biochemical detection table - 02" shelving code of the laboratory system, and the "in transit" status reported by the mobile terminal into basic data in JSON format: {"status": "in transit", "warehouse_location": "Area A - Row 3 - Floor 5", "lab_location": "biochemical detection table - 02", "errors": []}. If there is exception flag data (such as ERR_LOC_MISMATCH), add it to the "errors" array and attach the timestamp and source system information. This basic data will be used as the only input source for subsequent transfer logs and exception analysis, ensuring that the full - link data is traceable and the format is standardized. For example, in subsequent processes, an alarm can be directly triggered based on the "errors" field without secondary parsing of the original system logs.

[0083] In one embodiment, as Figure 3 shown, in step S20, that is, in the step of determining the location data corresponding to the basic data in real - time through multi - thread location technology, it includes:

[0084] S2011. Match the corresponding multi-thread management mechanism according to the shelving code in the basic data. In this embodiment, the shelving code is an information code used to uniquely identify the storage location of a sample. Each sample is assigned a shelving code when it is warehoused. This code is usually associated with the bin number, shelf number, storage area number, etc. of the warehouse to accurately locate the storage location of the sample during the sample transfer process. The multi-thread management mechanism is a computing resource management strategy that can automatically allocate computing tasks according to different shelving codes, enabling the positioning calculations of multiple samples to be carried out in parallel, thereby improving the computing efficiency of the system. In the traditional single-thread positioning method, the location information of the samples needs to be processed sequentially, resulting in calculation delays in high-concurrency situations and affecting the real-time nature of the data. Through the multi-thread management mechanism, data processing tasks can be reasonably allocated among multiple computing threads according to different shelving codes, enabling the location information of the samples to be calculated simultaneously and improving the throughput of the positioning calculation. For example, when a batch of samples is warehoused simultaneously and distributed in different storage areas, the system can automatically allocate computing tasks to multiple independent threads according to the shelving codes, enabling each thread to independently calculate the location information of the samples in the corresponding area, avoiding the competition and bottleneck of computing resources, and thus enhancing the overall positioning efficiency.

[0085] S2012. Based on the multi-thread management mechanism, configure independent threads and acquisition adjustment parameters for the corresponding mobile terminal devices. In this embodiment, the mobile terminal devices include but are not limited to PDA handheld devices, RFID scanners, intelligent tag readers / writers, Wi-Fi location detectors, Bluetooth Beacon receivers, etc. These devices are used to collect the location information of the samples during the sample transfer process and upload it to the system for processing through wireless or wired communication methods. The configuration method of the independent threads is dynamically adjusted according to the storage area of the samples, the workload of the devices, and the priority of data acquisition to ensure that each device can obtain reasonable computing resource support during the data acquisition process and avoid acquisition delays caused by resource competition between threads. The acquisition adjustment parameters are used to optimize the data acquisition method of the devices, including adjusting the data acquisition frequency, setting the signal filtering threshold, optimizing the acquisition range, etc. For example, in a high-density warehousing environment, since there are many RFID tags, the system can automatically reduce the scanning frequency to reduce data redundancy caused by repeated scanning. In an environment with strong signal interference, the system can increase the determination threshold of the signal strength to filter out noise data and improve the accuracy of the acquired data.

[0086] S2013. Based on the mobile terminal device with configured independent threads and acquisition adjustment parameters, obtain the corresponding single-thread data, store the single-thread data in the temporary buffer, and record the corresponding data attributes in the temporary buffer. The data attributes include timestamp and signal strength. In this embodiment, the single-thread data refers to the sample location information collected by a certain independent thread alone, such as the tag identification data obtained by an RFID scanner, the sample signal strength information detected by a Wi-Fi AP, the sample reception time recorded by a Bluetooth Beacon device, etc. The temporary buffer is an intermediate storage area for storing real-time collected data, which can ensure that all collected data will not be lost before further calculation, and is also convenient for subsequent data fusion processing. The timestamp is used to record the precise acquisition time of each piece of data to ensure that the data can be correctly sorted in subsequent positioning calculations and avoid data confusion. The signal strength refers to the signal power value received by the device, such as the RSSI (Received Signal Strength Indicator) of the RFID signal, which is used to evaluate the relative distance of the sample from the reading and writing device. For example, when an RFID scanning gun scans a sample, multiple signal data will be returned. The system will store the value with the highest signal strength among them and record the reception time of this signal to improve the positioning accuracy.

[0087] S2014. Substitute each single-thread data into the determined fusion algorithm to generate the corresponding preliminary data. In this embodiment, the fusion algorithm is used to integrate the data collected by multiple devices or multiple threads to improve the calculation accuracy of the sample location. Since there are certain errors in the data collected by different devices, or due to environmental factors (such as metal interference, signal occlusion, etc.), individual data has large deviations. Therefore, it is necessary to optimize and correct the collected single-thread data through a data fusion algorithm. Common data fusion algorithms include, but are not limited to, weighted average method, Kalman filter, particle filter, etc. For example, the weighted average method will give different weights according to the confidence of different sensor data and calculate the final position information; the Kalman filter can perform dynamic correction based on historical data and current observation data to reduce random errors and improve the stability of positioning; the particle filter can be used for positioning optimization in a non-linear environment and is suitable for complex warehouse environments. In a specific application, assume that an RFID scanning gun and a Wi-Fi AP simultaneously obtain the location data of a certain sample. The RFID signal indicates that the sample is on shelf A, while the Wi-Fi signal indicates that the sample is closer to shelf B. Then the fusion algorithm can comprehensively calculate the confidence of these two sets of data and finally obtain the most likely storage location of the sample.

[0088] S2015. Call historical transfer data and determine whether to determine the preliminary data as location data according to the comparison result between the historical transfer data and the preliminary data. In this embodiment, the historical transfer data refers to the past storage locations and transfer trajectories of samples recorded in the system, which are usually stored in a database and can be used to compare whether the currently calculated location information conforms to the normal transfer mode of the samples. The purpose of this step is to verify the accuracy of the preliminary data through historical data and prevent incorrect positioning caused by acquisition errors or signal interference. For example, if the historical record of a certain sample shows that it has been stored in bin A for 90% of the past time, and the current positioning data suddenly shows that it is in bin C, the system will further analyze the reliability of this location information and require additional data verification, such as rescan or comparison with manual operation records. In addition, in scenarios with high-precision requirements such as cold chain management and dangerous goods storage, this step is particularly important and can effectively avoid abnormal sample storage caused by incorrect positioning. For example, in the management of refrigerated samples, if the historical storage location of a certain refrigerated sample has always been in the refrigerated area, but the new positioning data shows that it is stored in the normal temperature area, the system can trigger an alarm to prompt the management staff to check to ensure that the sample has not been misplaced or damaged.

[0089] In one embodiment, as Figure 4 shown, in step S20, that is, the step of generating the corresponding sample transfer log based on the basic data and the location data, includes:

[0090] S2021. Determine the classification rules for the sample transfer log. The classification rules include field standardization rules and field arrangement rules. Among them, the classification rules are determined according to the category of the information system. In this embodiment, the sample transfer log is a detailed data set recording the transfer process of samples among warehousing, laboratories and various links. To ensure the unity and readability of log data, a set of standardization rules need to be established. The field standardization rules are used to perform format conversion on data from different data sources so that they can be compatible with each other among different systems. For example, the sample data fields in the Warehouse Management System (WMS) include "sample number", "storage location", "warehousing time", etc., while the sample data fields in the Laboratory Information Management System (LIMS) include "test number", "experimental progress", "test results", etc. The data formats of the two are different. Without standardization, information will be lost or the data structure will be chaotic when log data is transmitted between different systems. Therefore, the system will convert the data of different systems into a unified data structure according to the field standardization rules. For example, "storage location" is represented as "warehouse location number" in the Warehouse Management System and "laboratory area number" in the Laboratory Information Management System, and is stored in the log with the standard field name "location number" to ensure the consistency of log records. The field arrangement rules are used to specify the organization method when log data is stored, so that the data can be sorted in a certain order for subsequent query and analysis. For example, in the Warehouse Management System, the sample transfer log is sorted by storage location so that managers can quickly view the sample storage situation in a certain storage area, while in the Laboratory Information Management System, the sample transfer log is sorted by experimental batch number so that laboratory personnel can track the detection progress of samples in the same batch. The classification rules are determined according to the category of the information system, which means that the data classification methods between different systems can be different to meet different business needs. For example, the classification in the Warehouse Management System is based on storage areas, shelf numbers, etc., while the classification in the Laboratory Management System is based on experimental tasks, sample categories, etc. When a sample is transferred from the warehousing system to the laboratory, the system will automatically adjust the organization method of the log according to the information category it belongs to, so that operators of different systems can view the log according to their familiar classification methods, thereby improving the convenience of data query.

[0091] S2022. Based on the field standardization processing rules, integrate and standardize the basic data and location data to generate each classification field, as well as the associated fields and / or exception identification fields associated with each classification field. The exception identification fields are generated based on the exception identification data. In this embodiment, the basic data refers to the static information of the sample, including the sample ID, specifications, batch number, manufacturer, etc., while the location data refers to the geographical location information of the sample during the transfer process, including the warehouse location number, laboratory number, storage equipment number, etc. Since the basic data and location data come from different systems and their formats are different, standardization processing is required. For example, the basic data of a certain sample is stored in the warehouse management system, while the location data is the signal data collected by the RFID device in real time. To ensure that the log can accurately record these data, the system will convert the data according to the field standardization processing rules to make it conform to the unified storage format. In addition, during the standardization processing, the system will automatically generate classification fields, such as "sample category", "storage area", etc., so that the log can be organized according to different classification methods. At the same time, to improve the data integrity of the log, the system will also generate associated fields associated with the classification fields. For example, when the classification field of a certain sample is "refrigerated sample", its associated fields include "storage temperature", "storage equipment", etc., to provide more detailed information. The exception identification fields are used to mark the abnormal situations in the log. For example, when the system detects that the storage location of the sample has changed abnormally, or the transfer time is abnormally short, indicating human operation errors or system failures, the system will automatically add an exception identification field to the log to prompt the management personnel to check. For example, if the historical storage location of a certain sample is "refrigerated area", but the latest location information shows that it is stored in the "normal temperature area", the system will automatically add the exception identification "abnormal storage location" to the log for subsequent exception analysis.

[0092] S2023. Rearrange the classification fields, associated fields, and / or exception identification fields based on the field arrangement rule to generate multiple transfer entries arranged according to the classification fields, where the classification fields are arranged at the head of the transfer entries. In this embodiment, the main function of the field arrangement rule is to ensure the readability and traceability of log data, enabling management personnel to view the detailed information of sample transfer in a certain logical order. The classification fields are the main retrieval basis for the logs, so they need to be arranged at the head to quickly locate relevant data when querying the logs. For example, in a warehouse management system, the log data can be sorted according to the sample storage location, enabling management personnel to quickly find all the sample transfer records in a certain storage location. In a laboratory management system, the log data can be sorted according to the experimental batch number, enabling laboratory personnel to quickly find the sample detection progress of a certain batch. The associated fields are used to supplement the information of the classification fields. For example, in the logs sorted by sample category, the associated fields include information such as the supplier and production date of the sample to provide more detailed data support. The exception identification fields are used to mark the abnormal situations in the logs, enabling abnormal records to be quickly filtered out during data query. For example, in a certain laboratory sample management system, the sample transfer logs are preferably classified according to the experimental tasks, the classification field is the experimental batch number, and the associated fields are the sample number and the information of the testing personnel. If an abnormality occurs during the transfer of a certain sample, such as an abnormal change in the storage location, the exception identification field will be automatically added to the log and highlighted for subsequent processing.

[0093] S2024. Determine the transfer entries containing the exception identification fields as abnormal entries, and perform a highlighting prompt operation and a statistical positioning operation. In this embodiment, abnormal entries refer to the log records containing the exception identification fields, and these records usually represent the abnormal situations occurring during the sample transfer process, such as the storage location deviating from the historical storage path, the transfer time of the sample being too short or too long, etc. During the log generation process, the system will automatically detect these abnormal entries and prompt them through highlighting, color marking, etc., enabling management personnel to quickly identify and process the abnormalities. For example, during sample inventory, if the storage location of a certain sample has changed significantly, such as moving from a high-temperature area to a low-temperature area, the system will automatically mark this entry as red and add a detailed abnormality description to the abnormal log to remind management personnel to conduct a review. At the same time, the statistical positioning operation is used to calculate the frequency of abnormality occurrence, the affected range, and the information of the associated samples to help management personnel analyze the causes of the abnormality. For example, if the storage abnormality frequency of a certain batch of samples is relatively high, it indicates that there are management problems in this storage area. The system can automatically generate an abnormality report and suggest optimization measures, such as adjusting the storage rules or adding automatic detection equipment.

[0094] S2025. Integrate multiple transfer entries arranged according to classification fields and / or abnormal entries after performing highlight prompt operations and statistical positioning operations to generate corresponding sample transfer logs. In this embodiment, the final integration of the transfer logs is completed based on the above steps, that is, all transfer entries will be organized according to classification fields, and abnormal entries will be marked separately to ensure the integrity and readability of the log content. The final log includes not only the basic information, location data, and classification information of the sample, but also the abnormal situations that occurred during the transfer process, and provides detailed timestamps, operators, abnormal identifiers, etc. for subsequent analysis and query. For example, in a sample management system, managers can directly open the transfer log to view the historical transfer records of a certain sample and quickly locate abnormal records without manually retrieving multiple databases. At the same time, the system can also generate transfer analysis reports based on the log data to help optimize the sample management process and improve transfer efficiency.

[0095] In one embodiment, as Figure 5 shown, in step S30, that is, in the step of analyzing the sample transfer log based on the established transfer anomaly recognition model to generate corresponding recognition results, the recognition results include abnormal results and normal results, the abnormal results include the first abnormal recognition result and the second abnormal recognition result, and the steps further include:

[0096] S301. Retrieve whether there are abnormal entries in the sample transfer log. If there are, generate corresponding first abnormal recognition results according to the abnormal entries; In this embodiment, the sample transfer log is a comprehensive data set recording information such as the storage locations, transfer links, operators, and operation times of samples. Abnormal entries refer to the entries in the log records that contain abnormal identifier fields, such as abnormal storage locations, abnormal timestamps, abnormal operation steps, etc. When the system performs anomaly analysis, it will first retrieve the log to find out whether there are entries with abnormal identifier fields marked. For example, if the storage location of a certain sample shows as the refrigerated area in the historical records, but the latest transfer record shows that it is stored in the normal temperature area, this change exceeds the allowed transfer path, and the system will automatically identify this log entry as an abnormal entry. If such an anomaly is detected, the system will generate the first abnormal recognition result, which can be directly used to warn managers or as input data for subsequent anomaly analysis. For example, in a cold chain management system, if the transfer records of a batch of vaccine samples show that they frequently move from the refrigerated area to the normal temperature area and then back to the refrigerated area in a short period of time, the system will automatically identify such abnormal transfer situations, generate the first abnormal recognition result, and trigger an alarm mechanism to prevent the vaccines from becoming ineffective due to temperature fluctuations.

[0097] S302. If not, based on the established transfer exception recognition model, select the target fields required by the established transfer exception recognition model from the classification fields and associated fields in each transfer entry; in this embodiment, the target fields refer to the fields that need to be focused on during the exception recognition process, such as timestamps, storage locations, operation types, etc. These fields play a key role in the sample transfer process and can reflect whether the sample transfer is normal. When the system fails to find the marked exception entries in the log, it will further extract the relevant target fields from the log data for more in-depth analysis. For example, in a certain laboratory management system, the classification field of the sample is "experiment number", and the associated fields are "experimenter", "experiment equipment number", etc. The system will extract the fields that can best reflect the abnormal behavior, such as "sample storage location", "experiment start time", "experiment operator", etc., according to the requirements of the exception recognition model, so as to analyze whether there is an abnormal transfer situation later.

[0098] S303. If the target fields are timestamp fields and location coordinate fields, perform logical matching on the target fields according to the predefined exception condition rule library in the established transfer exception recognition model, and output the corresponding exception marks; in this embodiment, the timestamp field refers to the time information recording the transfer of the sample in different links, such as storage time, storage time, outbound time, etc., and the location coordinate field refers to the specific storage location of the sample in the warehouse or laboratory environment, such as warehouse location number, shelf number, experimental bench number, etc. The exception condition rule library is a set of rules summarized from historical exception data, including time conflict rules, location offset rules, transfer path rationality rules, etc. When the system detects abnormal situations in the timestamp field and the location coordinate field, it will perform logical matching according to these rules to determine whether there is an exception. For example, if the transfer log of a certain sample shows that it was stored in bin A at 10:00, but appeared in bin B at 10:02, and the normal movement time between these two bins is at least 10 minutes, the system will judge that the transfer time of this sample is abnormal according to the time conflict rule and output an exception mark. Similarly, in a hazardous chemical management system, if the historical storage location of a certain chemical reagent has always been a specific safety cabinet, and the latest storage location is changed to an ordinary shelf, the system will judge that its storage location does not meet the safety storage standard according to the location offset rule and generate an exception mark to remind the management personnel to check.

[0099] S304. If the target field is the operation type field, call the historical abnormal operation data set, compare features with the operation type field, and output the corresponding abnormal probability value. In this embodiment, the operation type field refers to the specific operations that occur during the transfer process of the sample, such as "warehousing", "outbound", "testing", "destruction", etc. The historical abnormal operation data set is a database that stores past abnormal operations, including information such as operators, operation times, operation types, and abnormal occurrence frequencies. When the system detects the operation type of a certain sample, it will call the historical abnormal operation data set and compare it with the operation to determine whether the operation has abnormal characteristics. For example, if the operation type of a certain sample is "outbound", but the system finds that more than 90% of the outbound operations of this sample are accompanied by abnormal situations (such as sample damage, experiment failure, etc.), the system will calculate the abnormal probability value of this operation and determine whether it needs to be marked as an abnormal operation. Similarly, in a drug management system, if the "destruction" operation of a certain high-value drug is usually performed by a senior administrator, but this destruction operation is performed by a low-privilege user and does not match the historical destruction records, the system will calculate the abnormal probability value based on the abnormal feature comparison and trigger an alarm when the abnormal probability value exceeds the set threshold to prevent the drug from being illegally destroyed or stolen.

[0100] S305. If it is detected that an abnormal mark appears and / or the abnormal probability value exceeds the preset probability threshold, generate the corresponding second abnormal recognition result. In this embodiment, the appearance of an abnormal mark means that there are obvious abnormal situations in the sample transfer log, such as timestamp conflicts, location abnormalities, etc., and the abnormal probability value exceeding the preset threshold means that the abnormality of a certain operation is relatively high and needs further verification. When one of these two conditions is met, the system will generate a second abnormal recognition result and record the abnormal details, such as the abnormal type, abnormal occurrence time, sample ID involved in the abnormality, etc. For example, in a laboratory sample management system, if the storage location of a certain sample is abnormal and the abnormal probability value of the operation type exceeds the set 90% threshold, the system will automatically generate a second abnormal recognition result and push it to the alarm system of the management personnel for timely intervention. In addition, in a high-security warehouse management system, if the transfer path of a certain batch of dangerous goods deviates greatly from the historical data and the probability value of abnormal operations exceeds the preset threshold, the system can further generate an automatic locking command to prevent the batch of dangerous goods from being mistransported or misoperated and ensure the safety of the transfer.

[0101] In one embodiment, as Figure 6 shown, before step S30, it further includes a generation step of an abnormal condition rule base predefined in the established transfer abnormal recognition model, and the generation step includes:

[0102] S60. Obtain the timestamp conflict events and location offset events in the historical abnormal transfer log, and extract the conflict time interval and the offset distance threshold; in this embodiment, the historical abnormal transfer log refers to the set of all abnormal event data recorded by the system during the sample transfer process. These logs contain information such as the unique identifier of the abnormal sample, storage location, timestamp, operator, abnormal type, etc. Timestamp conflict events refer to situations in the log records where there are unreasonable time intervals for the timestamps of the samples. For example, a sample is recorded at two storage locations that are far apart within a short period of time, or it undergoes multiple transfer status changes within an extremely short time, exceeding the reasonable operation time range. Location offset events refer to significant offsets in the storage location of the sample within a short period of time. For example, the normal storage location of a sample is usually in Laboratory A, but the log records show that it is moved to Warehouse B within a short time, and the physical distance between the two storage locations exceeds the normal transfer range. The system will automatically extract these abnormal events, analyze the historical data, calculate the average time interval of timestamp conflicts, and the average threshold of location offsets. For example, in a dangerous goods management system, a certain type of hazardous chemical usually needs to complete the storage confirmation within 10 minutes. If the system finds that there are multiple storage confirmation operations in the historical data with an interval of only 1 minute, the system will extract this time interval and mark it as a timestamp conflict event. Similarly, in a cold chain management system, if the storage location of a batch of refrigerated drugs frequently moves from the refrigerated area to the normal temperature area and then back to the refrigerated area within a short time, and the moving distance exceeds 5 meters, the system will regard this moving distance as a location offset event and record its offset distance threshold.

[0103] S70, generating timestamp conflict rules according to the conflict time interval; In this embodiment, the historical abnormal flow log refers to the collection of all abnormal event data recorded by the system during the sample flow process, and these logs contain information such as the unique identification, storage location, timestamp, operator, and abnormal type of abnormal samples. A timestamp conflict event refers to an unreasonable time interval between the timestamps of the samples in the log records, such as the samples being recorded in two storage locations far apart in a short period of time, or undergoing multiple flow state changes in a very short period of time, which exceeds the reasonable operation time range. A position offset event refers to a significant offset in the storage location of the sample in a short period of time, such as the normal storage location of the sample is usually in laboratory A, and the log record shows that it was moved to warehouse B in a short period of time, and the physical distance between the two storage locations exceeds the normal flow range. The system will automatically extract these abnormal events, analyze historical data, calculate the average time interval of timestamp conflicts, and the average threshold of position offsets. For example, in a hazardous materials management system, a certain type of hazardous chemicals usually needs to complete storage confirmation within 10 minutes, and the system finds that there are multiple storage confirmation operations in the historical data with only 1 minute intervals, then the system will extract the time interval and mark it as a timestamp conflict event. Similarly, in the cold chain management system, if the storage location of a batch of refrigerated medicines is frequently moved from the cold storage area to the normal temperature area and then back to the cold storage area within a short period of time, and the moving distance exceeds 5 meters, the system will treat the moving distance as a position offset event and record its offset distance threshold.

[0104] S80. Generate a position offset rule according to the offset distance threshold; in this embodiment, the position offset rule refers to a reasonable storage position range calculated based on historical data, which is used to determine whether the movement of the sample conforms to the normal circulation mode. After the system analyzes multiple position offset events, it will calculate the average offset distance of the sample in a short time and set an offset threshold. For example, if the system finds that the normal movement range of the sample is usually within 2 meters, and the movement distance of the abnormal storage event is mostly more than 10 meters, the system will set a position offset rule, that is, if the sample moves more than 10 meters in a short time, its storage position is considered abnormal. For example, in a certain intelligent warehousing system, the storage location of some high-value samples is usually fixed on a specific shelf, and the system detects that these samples are moved to another shelf within 5 minutes, and the distance between the shelves exceeds 15 meters, then the system will determine that the movement exceeds the normal circulation range and generate a position offset rule. Similarly, in a laboratory sample management system, samples usually circulate within the laboratory, but if a sample is moved out of the laboratory in a short period of time and appears in the office area or non-laboratory location, and the offset distance exceeds the set threshold, the system will automatically generate a location offset rule and use the rule in subsequent tests to determine whether there is any abnormality in the flow of samples.

[0105] S90. Store the timestamp conflict rule and the location offset rule in the exception condition rule library. In this embodiment, the exception condition rule library is a database within the system for storing and managing all exception detection rules. This rule library is used to support the execution of the exception detection algorithm, enabling the system to detect potential abnormal situations in real time during the sample transfer process. The main purpose of storing the timestamp conflict rule and the location offset rule is to provide a standardized basis for judgment for subsequent exception detection, ensuring that the exception detection logic is consistent for different samples and in different environments. For example, in an intelligent laboratory management system, the system regularly updates the exception condition rule library and adjusts the timestamp conflict threshold and the location offset threshold according to the latest exception events to continuously optimize the accuracy of exception detection. Similarly, in an intelligent warehousing management system, the system can dynamically adjust the rule library based on the logistics data of different storage areas. For example, in a storage area with a high turnover rate, the system relaxes the judgment threshold for timestamp conflicts, while in a sensitive storage area (such as a dangerous goods storage area), the system reduces the judgment threshold for timestamp conflicts to improve the safety management level. In addition, the system can automatically generate exception detection reports based on the data in the rule library and provide visual analysis to help managers understand potential problems in sample transfer and optimize the warehousing and experimental management processes.

[0106] In one embodiment, as Figure 7 shown, in step S303, that is, in the step of performing logical matching on the target field according to the pre-defined exception condition rule library in the established transfer exception recognition model and outputting the corresponding exception flag, the exception flag includes a timestamp conflict flag and a location offset flag, and step S303 further includes:

[0107] S3031. If the target field is a timestamp field, based on the timestamp conflict rule, determine whether the difference between the timestamp field and the timestamp field in the adjacent transfer entry is less than a preset time threshold. If it is less, trigger a timestamp conflict mark. In this embodiment, the timestamp field refers to the time data of the sample recorded in different transfer links, such as the storage time, storage time, outbound time, experiment start time, etc. of the sample. The timestamp conflict rule is used to detect time anomalies during the sample transfer process. Usually, a reasonable time threshold is set to determine whether the two consecutive timestamps conform to the normal transfer logic. For example, the average transfer time for a sample to be transferred from the warehouse to the laboratory usually takes more than 5 minutes. If the system finds that a certain sample has completed the transfer from the warehouse to the laboratory within just 30 seconds, this situation is likely due to data entry errors, duplicate label scanning, or uncompleted transfer operations. Therefore, the system will calculate the time difference between this timestamp and the previous transfer record according to the timestamp conflict rule and determine whether it is less than the set time threshold. If the time difference is less than the preset time threshold, it means that the transfer time of the sample is abnormal, and the system will automatically trigger a timestamp conflict mark to remind the management personnel to review the transfer situation of the sample. For example, in a dangerous goods warehousing management system, a certain high-risk chemical usually requires at least 10 minutes of safety inspection and handover confirmation during the transfer process. If the system detects that the sample is moved from storage location A to laboratory B within 2 minutes, it means that the time interval is much lower than the normal operation time. The system will generate a timestamp conflict mark and record the abnormal event for subsequent review.

[0108] S3032. If the target field is a location coordinate field, based on the location offset rule, determine whether the Euclidean distance between the location coordinate field and the standard coordinate of the preset transfer path exceeds the offset distance threshold. If it exceeds, trigger the location offset mark. In this embodiment, the location coordinate field refers to geographical or warehousing location information used to record the current location of the sample, such as shelf number, laboratory number, storage cabinet number, etc. The location offset rule is used to determine whether the sample is stored according to the normal transfer path. Usually, an offset distance threshold is set to calculate the offset degree between the current location of the sample and the standard storage location. The Euclidean distance is a mathematical calculation method used to measure the straight-line distance between two coordinate points. In sample transfer management, the Euclidean distance can be used to evaluate the storage offset degree of the sample. For example, a batch of refrigerated samples is usually stored on Shelf 1 in Temperature Control Area A, while the system detects that the current location of the sample is on Shelf 10 in Area B, and the straight-line distance between the two exceeds the set offset distance threshold of 10 meters, indicating that the sample has not been transferred according to the specified storage path. The system will trigger the location offset mark and mark the abnormal storage status of the sample. For example, in a laboratory sample management system, a certain high-value biological sample should be strictly stored in a specific low-temperature storage area. However, if the system detects that the sample has been moved to a common laboratory bench within a short period and the location offset exceeds 5 meters, the system will generate a location offset mark to prevent the sample from becoming invalid due to incorrect storage. Similarly, in an intelligent warehousing management system, if the storage location of some valuable equipment suddenly undergoes a large offset, for example, a device is usually stored in a safety cabinet, while the latest transfer log shows that it is located on an open shelf and the offset distance exceeds the set threshold, the system will immediately mark the abnormal location of the device and trigger a security alarm to prevent potential loss or misuse of the equipment.

[0109] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0110] In one embodiment, an intelligent sample receiving management device is provided, and this intelligent sample receiving management device corresponds one-to-one with the intelligent sample receiving management method in the above embodiment. As Figure 8 shown, this intelligent sample receiving management device includes a first acquisition module, a determination module, an analysis module, an execution module, and a synchronization module. The detailed descriptions of each functional module are as follows:

[0111] The first acquisition module is used to acquire the transfer request of the sample and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device. The determination module is used to determine the location data corresponding to the basic data in real time through multi-threaded positioning technology, and generate a corresponding sample transfer log based on the basic data and the location data. The analysis module is used to analyze the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result. The execution module is used to, if the recognition result is an abnormal result, perform corresponding exception handling operations and / or alarm operations according to the abnormal result. The synchronization module is used to, if the recognition result is a normal result, synchronize the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules.

[0112] Optionally, the acquisition module includes: an extraction unit for extracting the key fields in the transfer request; a first matching unit for matching the corresponding sample code according to the key fields; a judgment unit for, based on the sample code, matching the corresponding shelving code in each information system, and judging whether the shelving codes are consistent. If they are not consistent, corresponding abnormal identification data is generated; an addition unit for, if they are consistent, adding a transferred identifier to the information system where the shelving code is matched, and adding an untransferred identifier to the information system where the shelving code is not matched; a first integration unit for integrating the transferred identifier, the shelving code, and / or the abnormal identification data to generate corresponding basic data.

[0113] Optionally, the determination module includes: a matching unit for matching the corresponding multi-threaded management mechanism according to the shelving code in the basic data; a configuration unit for, based on the multi-threaded management mechanism, configuring an independent thread and acquisition adjustment parameters for the corresponding mobile terminal device; an acquisition unit for, based on the mobile terminal device configured with the independent thread and acquisition adjustment parameters, acquiring the corresponding single-thread data, storing the single-thread data in a temporary buffer, and recording the corresponding data attributes in the temporary buffer. The data attributes include a timestamp and a signal strength; a first generation unit for substituting each single-thread data into the determined fusion algorithm to generate corresponding preliminary data; a first determination unit for calling historical transfer data and determining whether to determine the preliminary data as location data according to the comparison result between the historical transfer data and the preliminary data.

[0114] Optionally, the determination module further includes: a second determination unit configured to determine the classification rules for the sample transfer log, where the classification rules include a field standardization processing rule and a field arrangement rule, and wherein the classification rules are determined according to the category of the information system; a second integration unit configured to integrate and standardize the basic data and the location data based on the field standardization processing rule to generate each classification field, and an associated field and / or an exception identification field associated with each classification field, where the exception identification field is generated based on the exception identification data; an arrangement unit configured to rearrange the classification fields, the associated fields, and / or the exception identification fields based on the field arrangement rule to generate a plurality of transfer entries arranged according to the classification fields, where the classification fields are arranged at the beginning of the transfer entries; a third determination unit configured to determine the transfer entries containing the exception identification field as exception entries and perform a highlighting prompt operation and a statistical positioning operation; and a second generation unit configured to integrate a plurality of transfer entries arranged according to the classification fields, and / or the exception entries after performing the highlighting prompt operation and the statistical positioning operation, to generate a corresponding sample transfer log.

[0115] Optionally, the analysis module includes: a retrieval unit configured to retrieve whether there are exception entries in the sample transfer log, and if so, generate a corresponding first exception recognition result according to the exception entries; a selection unit configured to, if not, select, based on a pre-established transfer exception recognition model, target fields required by the pre-established transfer exception recognition model from the classification fields and the associated fields in each transfer entry; a second matching unit configured to, if the target fields are a timestamp field and a location coordinate field, perform a logical match on the target fields according to an exception condition rule library predefined in the pre-established transfer exception recognition model and output a corresponding exception flag; a comparison unit configured to, if the target field is an operation type field, call a historical exception operation data set and perform a feature comparison with the operation type field to output a corresponding exception probability value; and a third generation unit configured to, if it is detected that the exception flag appears and / or the exception probability value exceeds a preset probability threshold, generate a corresponding second exception recognition result.

[0116] Optionally, the intelligent sample collection management device further includes: a second acquisition module, configured to acquire timestamp conflict events and position offset events in the historical abnormal transfer log, and extract the conflict time interval and the offset distance threshold; a first generation module, configured to generate a timestamp conflict rule according to the conflict time interval; a second generation module, configured to generate a position offset rule according to the offset distance threshold, specifically: if the Euclidean distance between the position coordinate field in the current transfer entry and the standard coordinate of the preset transfer path exceeds the offset distance threshold, then trigger a position offset mark; a storage module, configured to store the timestamp conflict rule and the position offset rule into the abnormal condition rule library;

[0117] Optionally, the second matching unit includes: a first judgment subunit, configured to, if the target field is a timestamp field, based on the timestamp conflict rule, judge whether the difference between the timestamp field and the timestamp field in the adjacent transfer entry is less than a preset time threshold, and if so, trigger a timestamp conflict mark; a second judgment subunit, configured to, if the target field is a position coordinate field, based on the position offset rule, judge whether the Euclidean distance between the position coordinate field and the standard coordinate of the preset transfer path exceeds the offset distance threshold, and if so, trigger a position offset mark.

[0118] For the specific limitations of an intelligent sample collection management device, reference may be made to the limitations of an intelligent sample collection management method in the foregoing text, which will not be elaborated herein. Each module in the foregoing intelligent sample collection management device can be implemented in whole or in part by software, hardware, and their combination. The foregoing modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the foregoing modules.

[0119] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 9 shown. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements an intelligent sample collection management method.

[0120] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0121] S10. Obtain a transfer request for a sample, and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device;

[0122] S20. Through multi-threaded positioning technology, determine the location data corresponding to the basic data in real time, and generate a corresponding sample transfer log based on the basic data and the location data;

[0123] S30. Analyze the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result;

[0124] S40. If the recognition result is an abnormal result, perform corresponding abnormal handling operations and / or alarm operations according to the abnormal result;

[0125] S50. If the recognition result is a normal result, synchronize the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0127] S10. Obtain a transfer request for a sample, and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device;

[0128] S20. Through multi-threaded positioning technology, determine the location data corresponding to the basic data in real time, and generate a corresponding sample transfer log based on the basic data and the location data;

[0129] S30. Analyze the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result;

[0130] S40. If the recognition result is an abnormal result, perform corresponding abnormal handling operations and / or alarm operations according to the abnormal result;

[0131] S50. If the recognition result is a normal result, synchronize the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules.

[0132] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0133] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An intelligent sample collection management method, characterized in that, The described intelligent sample collection management method includes: Obtaining a transfer request for a sample, and synchronizing the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device; By using multi-threaded positioning technology, real-time determining the position data corresponding to the basic data, and generating a corresponding sample transfer log based on the basic data and the position data. The multi-threaded positioning technology is a technology for allocating independent computing threads to each mobile terminal device participating in sample transfer and simultaneously collecting positioning signals from different devices; Analyzing the sample transfer log based on the established transfer anomaly recognition model to generate a corresponding recognition result. The established transfer anomaly recognition model adopts a hybrid architecture and includes a real-time detection module based on a rule engine and a probability prediction module based on machine learning; If the recognition result is an abnormal result, performing corresponding abnormal handling operations and / or alarm operations according to the abnormal result; If the recognition result is a normal result, synchronizing the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules; In the step of synchronizing the basic data of the sample between information systems based on the transfer request, it includes: Extracting the key fields in the transfer request; Matching the corresponding sample code according to the key fields; Based on the sample code, matching the corresponding shelf code in each information system, and determining whether each shelf code is consistent. If not, generating corresponding abnormal identification data; If they are consistent, adding a transferred identification to the information system where the shelf code is matched, and adding a non-transferred identification to the information system where the shelf code is not matched; Integrating the transferred identification, the shelf code, and / or the abnormal identification data to generate corresponding basic data; In the step of real-time determining the position data corresponding to the basic data by using multi-threaded positioning technology, it includes: Matching the corresponding multi-thread management mechanism according to the shelf code in the basic data; Based on the multi-thread management mechanism, configuring an independent thread and acquisition adjustment parameters for the corresponding mobile terminal device; Based on the mobile terminal device configured with an independent thread and acquisition adjustment parameters, obtaining the corresponding single-thread data, storing the single-thread data in a temporary buffer, and recording the corresponding data attributes in the temporary buffer. The data attributes include a timestamp and a signal strength, and the signal strength is the signal power value received by the mobile terminal device; Substituting each single-thread data into the determined fusion algorithm to generate corresponding preliminary data. The determined fusion algorithm is the weighted average method; Invoking historical transfer data, and determining whether to determine the preliminary data as position data according to the comparison result between the historical transfer data and the preliminary data; In the step of generating a corresponding sample transfer log based on the basic data and the position data, it includes: Determine the classification rules for the sample transfer log, where the classification rules include field standardization processing rules and field arrangement rules. Among them, the classification rules are determined according to the category of the information system; Based on the field standardization processing rules, integrate and standardize the basic data and the location data to generate each classification field, as well as the associated field and / or the exception identification field associated with each classification field. The exception identification field is generated based on the exception identification data; Based on the field arrangement rules, rearrange the classification fields, the associated fields, and / or the exception identification fields to generate multiple transfer entries arranged according to the classification fields, where the classification fields are arranged at the head of the transfer entries; Determine the transfer entries containing the exception identification field as exception entries, and perform a highlighting prompt operation and a statistical positioning operation; Integrate multiple transfer entries arranged according to the classification fields, and / or the exception entries after performing the highlighting prompt operation and the statistical positioning operation, to generate the corresponding sample transfer log; In the step of analyzing the sample transfer log based on the established transfer exception recognition model to generate the corresponding recognition result, the recognition result includes an exception result and a normal result. The exception result includes a first exception recognition result and a second exception recognition result. The step further includes: Retrieve whether there are exception entries in the sample transfer log. If so, generate the corresponding first exception recognition result according to the exception entries; If not, based on the established transfer exception recognition model, select the target fields required by the established transfer exception recognition model from the classification fields and the associated fields in each transfer entry; If the target fields are the timestamp field and the location coordinate field, perform logical matching on the target fields according to the predefined exception condition rule library in the established transfer exception recognition model, and output the corresponding exception mark; If the target field is the operation type field, call the historical exception operation data set, compare the features with the operation type field, and output the corresponding exception probability value; If it is detected that the exception mark appears and / or the exception probability value exceeds the preset probability threshold, generate the corresponding second exception recognition result.

2. The intelligent sample collection management method according to claim 1, wherein, The generation step of the predefined exception condition rule library in the established transfer exception recognition model includes: Obtain the timestamp conflict event and the location offset event in the historical exception transfer log, and extract the conflict time interval and the offset distance threshold; Generate the timestamp conflict rule according to the conflict time interval; Generate the location offset rule according to the offset distance threshold. Specifically, if the Euclidean distance between the location coordinate field in the current transfer entry and the standard coordinate of the preset transfer path exceeds the offset distance threshold, trigger the location offset mark; Store the timestamp conflict rule and the location offset rule in the exception condition rule library; In the step of performing logical matching on the target field according to the predefined exception condition rule library in the established transfer exception recognition model and outputting corresponding exception marks, the exception marks include a timestamp conflict mark and a position offset mark, and the step further includes: If the target field is a timestamp field, based on the timestamp conflict rule, determine whether the difference between the timestamp field and the timestamp field in the adjacent transfer entry is less than a preset time threshold. If it is less, trigger the timestamp conflict mark; If the target field is a position coordinate field, based on the position offset rule, determine whether the Euclidean distance between the position coordinate field and the standard coordinate of the preset transfer path exceeds the offset distance threshold. If it exceeds, trigger the position offset mark.

3. An intelligent sample collection management device, characterized in that, The intelligent sample receiving management device includes: A first acquisition module, configured to acquire a transfer request of a sample and synchronize the basic data of the sample between information systems based on the transfer request. The information systems include a warehouse management system, a laboratory information management system, and a mobile terminal device; A determination module, configured to determine the position data corresponding to the basic data in real time through a multi-threaded positioning technology, and generate a corresponding sample transfer log based on the basic data and the position data. The multi-threaded positioning technology is a technology for allocating independent calculation threads to each mobile terminal device participating in the sample transfer and simultaneously collecting positioning signals from different devices; An analysis module, configured to analyze the sample transfer log based on the established transfer exception recognition model to generate a corresponding recognition result. The established transfer exception recognition model adopts a hybrid architecture and includes a real-time detection module based on a rule engine and a probability prediction module based on machine learning; An execution module, configured to, if the recognition result is an abnormal result, perform corresponding exception handling operations and / or alarm operations according to the abnormal result; A synchronization module, configured to, if the recognition result is a normal result, synchronize the corresponding log segments in the sample transfer log between each information system according to the pre-determined synchronization rules; The acquisition module includes: An extraction unit, configured to extract key fields in the transfer request; A first matching unit, configured to match corresponding sample codes according to the key fields; A judgment unit, configured to, based on the sample code, match the corresponding shelf code in each information system, and judge whether each shelf code is consistent. If not, generate corresponding exception identification data; An addition unit, configured to, if they are consistent, add a transferred identifier to the information system that matches the shelf code, and add an untransferred identifier to the information system that does not match the shelf code; A first integration unit, configured to integrate the transferred identifier, the shelf code, and / or the exception identification data to generate corresponding basic data; The determination module includes: a matching unit, configured to match a corresponding multi-threaded management mechanism according to the shelf code in the basic data; A configuration unit, configured to configure an independent thread and acquisition adjustment parameters for the corresponding mobile terminal device based on the multi-threaded management mechanism; An acquisition unit, configured to obtain corresponding single-thread data based on a mobile terminal device with an independent thread and acquisition adjustment parameters configured, store the single-thread data in a temporary buffer, and record corresponding data attributes in the temporary buffer, where the data attributes include a timestamp and a signal strength; A first generation unit, configured to substitute each of the single-thread data into a determined fusion algorithm to generate corresponding preliminary data; A first determination unit, configured to call historical transfer data and determine whether to determine the preliminary data as location data according to a comparison result between the historical transfer data and the preliminary data; The determination module further includes: A second determination unit, configured to determine a classification rule for a sample transfer log, where the classification rule includes a field standardization processing rule and a field arrangement rule, and the classification rule is determined according to the category of an information system; A second integration unit, configured to integrate and standardize the basic data and the location data based on the field standardization processing rule to generate each classification field, and an associated field and / or an exception identification field associated with each classification field, where the exception identification field is generated based on the exception identification data; An arrangement unit, configured to rearrange the classification fields, the associated fields, and / or the exception identification fields based on the field arrangement rule to generate a plurality of transfer entries arranged according to the classification fields, where the classification fields are arranged at the head of the transfer entries; A third determination unit, configured to determine a transfer entry including the exception identification field as an exception entry and perform a highlighting prompt operation and a statistical positioning operation; A second generation unit, configured to integrate a plurality of transfer entries arranged according to the classification fields, and / or the exception entries after performing the highlighting prompt operation and the statistical positioning operation, to generate a corresponding sample transfer log; 4. An intelligent sample collection management device according to claim 3, characterized in that, The analysis module includes: A retrieval unit, configured to retrieve whether there is an exception entry in the sample transfer log, and if so, generate a corresponding first exception recognition result according to the exception entry; A selection unit, configured to, if not, select target fields required by a established transfer exception recognition model from the classification fields and the associated fields in each transfer entry based on the established transfer exception recognition model; A second matching unit, configured to, if the target fields are a timestamp field and a location coordinate field, perform logical matching on the target fields according to an exception condition rule library predefined in the established transfer exception recognition model, and output a corresponding exception mark; A comparison unit, configured to, if the target field is an operation type field, call a historical exception operation data set to perform feature comparison with the operation type field, and output a corresponding exception probability value; A third generation unit, configured to, if it is detected that the exception mark appears and / or the exception probability value exceeds a preset probability threshold, generate a corresponding second exception recognition result; The intelligent sample collection management device further includes: A second acquisition module, configured to obtain a timestamp conflict event and a location offset event in a historical exception transfer log, and extract a conflict time interval and an offset distance threshold; A first generation module, configured to generate a timestamp conflict rule according to the conflict time interval; A second generation module, configured to generate a position offset rule according to the offset distance threshold, specifically: if the Euclidean distance between the position coordinate field in the current transfer entry and the standard coordinate of the preset transfer path exceeds the offset distance threshold, a position offset flag is triggered; A storage module, configured to store the timestamp conflict rule and the position offset rule into the exception condition rule library; The second matching unit includes: A first judgment subunit, configured to, if the target field is a timestamp field, based on the timestamp conflict rule, judge whether the difference between the timestamp field and the timestamp field in the adjacent transfer entry is less than a preset time threshold, and if so, trigger a timestamp conflict flag; A second judgment subunit, configured to, if the target field is a position coordinate field, based on the position offset rule, judge whether the Euclidean distance between the position coordinate field and the standard coordinate of the preset transfer path exceeds the offset distance threshold, and if so, trigger a position offset flag.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of an intelligent sample collection management method according to any one of claims 1 to 2 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the steps of an intelligent sample collection management method according to any one of claims 1 to 2 are implemented.

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