Asynchronous Multithreaded Self-Sampling Method, Server Device, System, and Electronic Terminal

Abstract

The present application provides an asynchronous multi-threaded self-service sampling method, server-side equipment, system and electronic terminal, including: obtaining the identity identification information of the person to be tested, thereby obtaining the detection task corresponding to the person to be tested; classifying the detection tasks of the same type into the same detection task queue, and queuing and managing different detection task queues and their corresponding detection tasks through multi-threading; responding to the asynchronous online self-service sampling request information of the person to be tested, supervising and guiding the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread to obtain qualified self-service sampling samples; thereby realizing safe and efficient self-service sampling of asynchronous multi-threading. The present application can realize self-service, parallel multi-person, continuous multi-frequency sampling through asynchronous multi-threading, and the person to be tested can complete the sampling work at home by themselves, thereby reducing the risk of virus transmission.

Description

Technical Field

[0001] The present application relates to the technical field related to medical devices and image processing, and in particular to an asynchronous multi-threaded self-service sampling method, server-side equipment, system and electronic terminal. Background Art

[0002] The rapid development of clinical laboratory medicine has led to an increasing focus on test quality. Obtaining accurate test results usually requires comprehensive quality management and supervision throughout the entire process. Among these, correctly collecting clinical test samples is the most important first step. Therefore, sampling can only be performed by professional medical staff. Limited by the number of professionals and the limitations of the sampling environment, it often leads to a series of problems. For the precise prevention and control of the epidemic, how to achieve rapid and efficient sample collection from large groups of people, eliminate queues, and reduce the scope of transmission has become a huge challenge facing public health experts. How to solve the problems of time-consuming, labor-intensive, and wasteful public resources that come with it; how to get rid of the limitations of fixed sampling sites and improve the environmental comfort of sampling staff and sampled persons; the above are all core practical issues that urgently need to be solved.

[0003] Therefore, there is an urgent need to provide a new self-service sampling method to achieve asynchronous parallel, safe and efficient self-service sampling and ensure the reliability of sampling results. Summary of the Invention

[0004] In view of the shortcomings of the existing technology mentioned above, the purpose of this application is to provide an asynchronous multi-threaded self-service sampling method, server-side equipment, system and electronic terminal to solve any one of the technical problems in the existing technology, such as low sampling efficiency, unsafe sampling process, and limited sampling environment.

[0005] To achieve the above-mentioned purpose and other related purposes, the first aspect of the present application provides an asynchronous multi-threaded self-service sampling method, which is applied to the server; the method includes: obtaining the identity identification information of the person to be tested, and obtaining the detection task corresponding to the person to be tested; classifying the detection tasks of the same type into the same detection task queue, and queuing and managing different detection task queues and their corresponding detection tasks through multiple threads; responding to the asynchronous online self-service sampling request information of the person to be tested, supervising and guiding the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread to obtain qualified self-service sampling samples.

[0006] In some embodiments of the first aspect of the present application, the process of obtaining the detection task based on the identity recognition information specifically includes: obtaining the personal basic information uploaded by the person to be tested, and performing identity recognition by obtaining the facial image and identity document information of the person to be tested; obtaining the detection task corresponding to the person to be tested based on the real-time location information and / or historical test results in the personal basic information; wherein, the identity recognition information includes: any one or more combinations of personal basic information, facial images, and identity document information; the detection tasks include: any one or more combinations of nucleic acid positive, asymptomatic, confirmed COVID-19, and routine physical examinations.

[0007] In some embodiments of the first aspect of the present application, the multithreading includes: several management threads and several working threads; the management threads include: any one or more combinations of a one-to-one video connection thread, a one-to-many video connection thread, a many-to-many video connection thread and a one-to-many online video demonstration thread between the testing personnel and the persons to be tested; the working threads execute each testing task manually or through an AI computer.

[0008] In some embodiments of the first aspect of the present application, the detection tasks of the same type are classified into the same detection task queue, and different detection task queues and their corresponding detection tasks are queued and managed separately through multiple threads, specifically including: each management thread manages one or more detection task queues respectively; each detection task queue corresponds to a working thread queue; each working thread queue includes several working threads; the newly generated detection tasks are sent to the corresponding detection task queue in turn; the detection tasks in each detection task queue are executed by the working threads in the corresponding working thread queue; the working thread at the head of the queue executes the highest priority detection task from the corresponding detection task queue based on polling, and exits the working thread queue after obtaining the detection task, and returns to the working thread queue after executing the corresponding detection task and is listed at the end of the queue; wherein, the detection task queue is a first-in-first-out queue and can be accessed globally.

[0009] In some embodiments of the first aspect of the present application, each management thread manages one or more detection task queues respectively, specifically including: the one-to-one video connection thread manages the detection task queue corresponding to any one or more combinations of nucleic acid positivity, contact degree information, and new crown diagnosis; the one-to-many video connection thread manages the detection task queue corresponding to any one or more combinations of contact degree information and asymptomatic; the many-to-many video connection thread manages the detection task queue corresponding to asymptomatic; the one-to-many online video demonstration thread manages the detection task queue corresponding to daily physical examinations.

[0010] In some embodiments of the first aspect of the present application, in response to the asynchronous online self-service sampling request information of the person to be tested, the online self-service sampling process of the person to be tested is supervised and guided by conducting an online video in the corresponding thread to obtain a qualified self-service sampling sample, specifically including: supervising and guiding the online self-service sampling process of the person to be tested through a video connection with a testing personnel, and answering the questions of the person to be tested during the self-service sampling process; and / or, identifying and supervising the online self-service sampling process of the person to be tested through a video connection with an AI computer; supervising the Whether the person to be tested completes the sampling action, putting in action and sealing action in sequence in the online self-sampling video, so as to determine whether the self-sampling sample of the person to be tested is qualified; when the person to be tested completes the sampling action, putting in action and sealing action in sequence, a qualified self-sampling sample is obtained; wherein, the sampling action is the action of using a sampler to complete the sampling; the putting in action is to put the sampler into the sampling tube, and put the sampling tube into the sample bag; the sealing action is to use a tear-proof seal to seal the sealing part of the sample bag, and paste a bag code consistent with the barcode of the sampling tube.

[0011] In some embodiments of the first aspect of the present application, supervising whether the person to be tested completes the sampling action, placing action and sealing action in sequence in the online self-service sampling video specifically includes: detecting whether there is a sampler image during the online self-service sampling video of the person to be tested; if there is a sampler image, determining whether the person to be tested has completed the sampling action based on the online self-service sampling video; after determining that the person to be tested has completed the sampling action, detecting whether there is a sampling tube image and a sample bag image in the online self-service sampling video; if there is a sampling tube image and a sample bag image, determining whether the person to be tested has completed the placing action based on the online self-service sampling video; after determining that the person to be tested has completed the placing action, detecting whether there is an anti-tear seal image and a bag code image in the online self-service sampling video; if there is an anti-tear seal image and a bag code image, determining whether the person to be tested has completed the sealing action based on the online self-service sampling video.

[0012] To achieve the above-mentioned purpose and other related purposes, the second aspect of the present application provides an asynchronous multi-threaded self-service sampling server, which includes: a user information management module for acquiring and managing the identity identification information of the person to be tested, so as to obtain the detection task corresponding to the person to be tested; a multi-threaded queuing management module for classifying the detection tasks of the same type into the same detection task queue, and queuing different detection task queues and their corresponding detection tasks through multiple threads; a self-service sampling supervision module for responding to the asynchronous online self-service sampling request information of the person to be tested, and supervising and guiding the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread to obtain qualified self-service sampling samples.

[0013] To achieve the above-mentioned purpose and other related purposes, the third aspect of the present application provides an asynchronous multi-threaded self-service sampling system, which includes: a client, used to collect the identity identification information of the person to be tested, and issue an asynchronous online self-service sampling request information of the person to be tested; an asynchronous multi-threaded self-service sampling server as described above, used to communicate with the client, respond to the asynchronous online self-service sampling request information of the person to be tested, and supervise and guide the online self-service sampling process of the person to be tested through a video connection with the testing personnel and / or AI computer.

[0014] To achieve the above-mentioned purpose and other related purposes, the fourth aspect of the present application provides an electronic terminal, including: a memory, a processor and a communicator; the memory is used to store computer programs; the processor is used to execute the computer programs stored in the memory so that the electronic terminal executes the method described above; the communicator is used to communicate with an external device.

[0015] In summary, the asynchronous multi-threaded self-service sampling method, server device, system, and electronic terminal provided by this application have the following beneficial effects:

[0016] 1. The asynchronous multi-threaded self-service sampling method provided in this application can realize self-service, parallel multi-person, continuous multi-frequency sampling. Among them, the management and certification of online video self-service sampling can be carried out by professional medical staff, i.e., background sampling supervisors, to guide the testees to conduct self-service sampling in parallel, or by AI technologies such as image recognition to assist in the parallel supervision and guidance of the self-service sampling process of the testees;

[0017] 2. This application can not only quickly and efficiently collect samples from large groups of people, greatly shortening the time; the sampling process is safer and more efficient; at the same time, it also breaks away from the constraints of a strict sampling environment, greatly improving the environmental comfort of sampling staff and sampled persons;

[0018] 3. This application can realize parallel authentication of self-service sampling, which is not only safe and efficient, but also can ensure the quality of samples collected during the self-service sampling process, thereby ensuring the reliability of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Shown is a flowchart of an asynchronous multi-threaded self-service sampling method in one embodiment of the present application.

[0020] Figure 2A Shown is an overall logic diagram between the management thread and the detection task queue in one embodiment of the present application.

[0021] Figure 2B Shown is a schematic diagram of the specific relationship between the management thread and the detection task queue in one embodiment of the present application.

[0022] Figure 3 Shown is a schematic diagram of the relationship between the detection task queue and the work thread queue in one embodiment of the present application.

[0023] Figure 4 Shown is a module diagram of an asynchronous multi-threaded self-service sampling server in one embodiment of the present application.

[0024] Figure 5 Shown is a structural diagram of an asynchronous multi-threaded self-service sampling system in one embodiment of the present application.

[0025] Figure 6 Shown is a schematic diagram of a specific process for a person to be tested to perform online self-service sampling using a nasal swab in one embodiment of the present application.

[0026] Figure 7 Shown is a schematic diagram of amplification curves obtained through three sampling methods in one embodiment of the present application.

[0027] Figure 8 Shown is a structural schematic diagram of an electronic terminal in one embodiment of the present application. DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0029] It should be noted that in the following description, reference is made to the accompanying drawings, which describe several embodiments of the present application. It should be understood that other embodiments may also be used, and that mechanical composition, structure, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present application is limited only by the claims of the published patents. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0030] Throughout this specification, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," "fixed," and "holding" should be understood broadly. For example, these terms may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this application based on specific circumstances.

[0031] Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise. The terms "first," "second," "third," "fourth," and so on (if any) in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. It should be further understood that the terms "comprising" and "including" indicate the presence of the stated features, operations, elements, components, items, types, and / or groups, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, types, and / or groups. The terms "or" and "and / or" used herein are to be interpreted as inclusive, or to mean any one or any combination. Thus, "A, B, or C" or "A, B, and / or C" means "any of: A; B; C; A and B; A and C; B and C; A, B, and C." Exceptions to this definition occur only when a combination of elements, functions, or operations are inherently mutually exclusive in some manner.

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the following embodiments and the accompanying drawings are used to further describe the technical solutions in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0033] In order to solve the existing problems, this application proposes an asynchronous multi-threaded self-service sampling method, server-side equipment, system and electronic terminal to solve the technical problems of low sampling efficiency, unsafe sampling process and limited sampling environment in the existing technology.

[0034] like Figure 1 FIG. 1 is a flow chart showing an asynchronous multi-threaded self-service sampling method according to an embodiment of the present application, wherein the method comprises the following steps:

[0035] Step S1: Obtain the identity identification information of the person to be tested, and obtain the detection task corresponding to the person to be tested.

[0036] In one embodiment of the present application, the process of obtaining the detection task according to the identity recognition information specifically includes:

[0037] a. Obtain basic personal information uploaded by the person to be tested, and perform identity recognition by obtaining the facial image and identification document information of the person to be tested;

[0038] It should be noted that by obtaining the facial image and identification document information of the person to be tested for identity recognition, it is possible to prevent others from taking the place of others and falsifying the information, thereby ensuring the reliability and accuracy of sampling and testing;

[0039] b. Obtaining a test task corresponding to the person to be tested based on the real-time location information and / or historical test results in the personal basic information;

[0040] Among them, the identity recognition information includes: any one or more combinations of personal basic information, facial images, and identity document information; the detection tasks include: any one or more combinations of nucleic acid positivity, contact degree information, asymptomatic, confirmed COVID-19, and routine physical examinations.

[0041] Step S2: Classify the detection tasks of the same type into the same detection task queue, and perform queue management on different detection task queues and their corresponding detection tasks through multi-threading.

[0042] In one embodiment of the present application, the multithreading includes: a plurality of management threads and a plurality of working threads; wherein,

[0043] The management thread includes: any one or more combinations of a one-to-one video connection thread between a tester and a testee, a one-to-many video connection thread, a many-to-many video connection thread, and a one-to-many online video demonstration thread;

[0044] The worker thread executes each detection task manually or by an AI computer.

[0045] It should be noted that the detection personnel include professional medical staff or professional and technical personnel who have received strict training, etc.; the AI computer is supervised based on deep learning. Specifically, the AI computer can use mature image recognition technologies such as the currently commonly used recognition algorithm based on the entire face image or the algorithm using neural network for recognition for identification and supervision. This application provides a variety of supervision methods that combine human supervision and / or intelligent machines, which can not only ensure the reliability and effectiveness of the sampling results, but also greatly improve the efficiency of supervision and guidance of self-service sampling.

[0046] In one embodiment of the present application, step S2 specifically includes:

[0047] Each management thread manages one or more detection task queues; each detection task queue corresponds to a work thread queue; each work thread queue includes several work threads; Figure 2A As shown;

[0048] Combine Figure 2B It can be seen that when the sample quality requirements are high, the epidemic protection level is high, and the severity of the epidemic is high, in order to prevent the sample quality from not meeting the testing requirements due to insufficient sampling, incorrect sampling methods, incorrect sampling locations, etc., it can be managed through a one-to-one video connection thread; and the one-to-one video connection thread can be supervised by the testing personnel or identified and supervised by the AI computer; and the working threads corresponding to the one-to-one video connection thread and the one-to-many video connection thread can be controlled within 1 minute. When the sampling volume increases, the one-to-one video connection thread can also be switched to a one-to-many video connection thread or a many-to-many video connection thread at any time.

[0049] The newly generated detection tasks are sent to the corresponding detection task queues in sequence; the detection tasks in each detection task queue are executed by the worker threads in the corresponding worker thread queue; the worker thread at the head of the queue executes the detection task with the highest priority from the corresponding detection task queue based on polling, and after obtaining the detection task, it exits the worker thread queue and returns to the worker thread queue after executing the corresponding detection task and is listed at the end of the queue;

[0050] like Figure 3 As shown, the working thread 1 at the head of the working thread queue polls the detection task queue and executes the detection task 2 with the highest priority. After obtaining the detection task 2, the working thread 1 exits the working thread queue and returns to the working thread queue after executing the detection task 2 and is ranked at the end of the queue.

[0051] The detection task queue is a first-in-first-out queue and can be accessed globally.

[0052] In one embodiment of the present application, combined with Figure 2B As shown, each management thread manages one or more detection task queues, specifically including:

[0053] The one-to-one video connection thread management test task is a test task queue corresponding to any one or more combinations of nucleic acid positivity, exposure degree information, and COVID-19 diagnosis;

[0054] The one-to-many video connection thread management detection task is a detection task queue corresponding to any one or more combinations of exposure degree information and asymptomatic;

[0055] The many-to-many video connection thread management detection task is a detection task queue corresponding to no symptoms;

[0056] The one-to-many online video demonstration thread manages the detection task queue corresponding to daily physical examinations and answers questions from the persons to be tested during the sampling process.

[0057] Step S3: In response to the asynchronous online self-sampling request information of the person to be tested, the online self-sampling process of the person to be tested is supervised and guided by conducting online video in the corresponding thread to obtain qualified self-sampling samples.

[0058] In one embodiment of the present application, step S3 specifically includes:

[0059] 1) Supervising and guiding the online self-service sampling process of the person to be tested through a video link with a testing personnel, and answering questions raised by the person to be tested during the self-service sampling process; and / or, identifying and supervising the online self-service sampling process of the person to be tested through a video link with an AI computer;

[0060] 2) Determine whether the self-sampling sample of the person to be tested is qualified by monitoring whether the person to be tested completes the sampling action, the placing action, and the sealing action in sequence in the online self-sampling video;

[0061] In one embodiment of the present application, monitoring whether the person to be tested completes the sampling action, the placing action, and the sealing action in sequence in the online self-service sampling video specifically includes:

[0062] Detect whether there is a sampler image during the online self-service sampling video of the person to be tested;

[0063] If there is a sampler image, determine whether the person to be tested has completed the sampling action based on the online self-service sampling video;

[0064] After determining that the person to be tested has completed the sampling action, detecting whether there is a sampling tube image and a sample bag image in the online self-service sampling video;

[0065] If there are sampling tube images and sample bag images, determine whether the person to be tested has completed the insertion action based on the online self-service sampling video;

[0066] After determining that the person to be tested has completed the placing action, detecting whether there is an anti-tear seal image and a bag code image in the online self-service sampling video;

[0067] If there is an anti-tear seal image and a bag code image, it is determined whether the person to be tested has completed the sealing action based on the online self-service sampling video.

[0068] It should be noted that the sampling action is to complete the sampling using a sampler; the placing action is to place the sampler into the sampling tube, and place the sampling tube into the sample bag; the sealing action is to use a tear-proof seal to seal the sealing part of the sample bag, and paste a bag code consistent with the barcode of the sampling tube.

[0069] 3) When the person to be tested completes the sampling action, the placing action, and the sealing action in sequence, a qualified self-sampling sample is obtained, thereby improving the quality of the self-sampling sample;

[0070] 4) The qualified self-sampling samples are placed in a designated location for unified collection by the testing personnel and subsequent testing; thereby realizing asynchronous multi-threaded safe and efficient self-sampling.

[0071] like Figure 4 FIG. 4 is a block diagram of an asynchronous multi-threaded self-service sampling server in one embodiment of the present application. The asynchronous multi-threaded self-service sampling server 400 includes:

[0072] The user information management module 410 is used to obtain and manage the identity identification information of the person to be tested, so as to obtain the detection task corresponding to the person to be tested;

[0073] The multi-threaded queue management module 420 is used to classify the detection tasks of the same type into the same detection task queue, and to manage the queues of different detection task queues and their corresponding detection tasks through multi-threading;

[0074] The self-service sampling supervision module 430 is used to respond to the asynchronous online self-service sampling request information of the person to be tested, and supervise and guide the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread to obtain qualified self-service sampling samples.

[0075] It should be understood that the division of the various modules of the above system is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by processing elements; they can also all be implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the self-service sampling supervision module 430 can be a separately established processing element, or it can be integrated into a certain chip of the above system for implementation. In addition, it can also be stored in the memory of the above system in the form of program code, and called and executed by a certain processing element of the above system to perform the functions of the above self-service sampling supervision module 430. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or by instructions in the form of software.

[0076] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0077] like Figure 5 FIG. 5 shows a schematic diagram of the structure of an asynchronous multi-threaded self-service sampling system in one embodiment of the present application. The asynchronous multi-threaded self-service sampling system 500 includes:

[0078] The client 510 is used to collect the identity information of the person to be tested, including collecting the facial image of the requester, and issuing an asynchronous online self-service sampling request information of the person to be tested;

[0079] The asynchronous multi-threaded self-service sampling server 400 as described above is used to communicate with the client, respond to the asynchronous online self-service sampling request information of the person to be tested, and supervise and guide the online self-service sampling process of the person to be tested through a video connection with the testing personnel and / or AI computer.

[0080] It should be noted that the asynchronous multi-threaded self-service sampling server 400 can be a local server or a cloud server. When the sample volume is large (for example, a peak of more than 1,000 people), a cloud server model can be adopted. Through blockchain encryption, while improving service response speed, the information of the sampled personnel can also be encrypted to ensure information security.

[0081] Specifically, the self-service sampling process of the asynchronous multi-threaded self-service sampling system 500 includes:

[0082] Step S501: The client collects the identity information of the person to be tested, including collecting the facial image of the requester, and sends an asynchronous online self-service sampling request message to the asynchronous multi-threaded self-service sampling server 400;

[0083] Step S502: The asynchronous multi-threaded self-service sampling server 400 responds to the asynchronous online self-service sampling request information of the person to be tested, and supervises and guides the online self-service sampling process of the person to be tested through a video connection with the testing personnel and / or AI computer.

[0084] like Figure 6 As shown, a schematic diagram of a specific process of a test person using a nasal swab to perform online self-service sampling in one embodiment of the present application is shown, including the following steps:

[0085] 1) Scan the QR code to watch the video: First, obtain the nasal swab self-service sampling box, prepare your personal identification documents and make sanitary preparations; then, read the instructions, scan the QR code on it to watch the standard operation video, and familiarize yourself with the sampling process; among them, watching the operation video before "video connection sampling" and learning in advance will make subsequent self-service sampling more convenient and efficient.

[0086] 2) Check items: According to the instructions in the manual, you need to confirm in advance whether the required items such as sampler, sample storage tube, sample sealing bag and sealing bag code, instruction manual, disinfection items, personal documents, etc. are complete, so as to make subsequent self-service sampling more convenient and efficient.

[0087] 3) Scan the QR code for personal registration: Log in to the self-service sampling system by scanning the QR code on the sampling tube to register personal information and sign the informed consent form for subsequent testing; this application can achieve the "three-certificate unification" of the person to be tested, ID number, and tube code.

[0088] 4) Video connection sampling: The person to be tested uploads personal information on the client and sends an asynchronous online self-service sampling request message, ensuring that the upper body of the person to be tested appears completely in the image acquisition of the client, and opens the camera and microphone permissions, waiting for the video to be connected; after connecting to the server, the person to be tested holds the ID card facing the camera, conducts personal identity recognition, and confirms the sampling material through the testing personnel or AI computer; Open the sampling tube and place it properly, open the sampler package, and do not touch the sampling area of the sampler when taking the sampler; Sampling: Insert the white part of the sampler into the left nostril, gently press the inner wall of the nostril with your left hand, and rotate it at least 5 times before taking it out; Insert the white part of the sampler into the right nostril and repeat the previous sampling step; Place the sampler in the sampling tube, cover the sampling tube cap tightly, and then place the sampling tube in the sample bag.

[0089] Different types of testing tasks require different sample collection procedures. For example, for COVID-19 positive individuals, a one-on-one video call can be used, with medical staff or a deep-learning AI computing system guiding the sampling process. Individuals waiting to be sampled can use both one-on-one and one-to-many video calls, with a deep-learning AI computing system guiding the sampling process. For social screening, one-to-many online video calls and many-to-many video calls can be used, with a deep-learning AI computing system guiding the sampling process. Proper operation should be performed according to the instructions for the self-service sampling box or package and the guidance of medical staff to ensure sample quality before testing.

[0090] 5) Sample Bag Sealing: Press the seal on the sample bag, secure the seal with a tear-resistant seal, and affix the corresponding sampling tube barcode. Deliver the sealed sample bag to the designated location for collection by the sample collector for testing. The collected sample must be sealed in the tear-resistant bag to ensure the authenticity and transportation safety of the sample. The bag code must be affixed to the seal to ensure the consistency between the bag code and the tube code.

[0091] It should be noted that during the self-service sampling operation, both hands, the sampling tube, and the sampler must not exceed the camera's field of view. The test personnel or AI computer on the asynchronous multi-threaded self-service sampling server need to supervise and guide the self-service sampling process of the test personnel to ensure the quality of the collected samples. In addition to ensuring the standardization of the sampling operation, it is also necessary to ensure that the sample tubes are disinfected, cleaned, and bagged and sealed after the test personnel take the sample, so as to ensure the standardization and integrity of the entire process before the sample is sent for inspection and the authenticity and safety of the sample.

[0092] Standardized operation of the above steps can ensure correct self-service sampling, that is, the quality of samples before testing; at the same time, different sample collection operations are adopted according to different testing tasks, which can greatly improve the sampling efficiency while ensuring the quality of samples before testing, and effectively realize the rapid and efficient sample collection of large batches of people; make sampling safer, get rid of the constraints of strict sampling environment, and greatly improve the environmental comfort of sampling staff and sampled persons.

[0093] It should be noted that nasal swab sampling is used as an example. The sampling time and sample quality of 500 people who took nasal swabs offline and online self-sampling were compared. The results are as follows:

[0094] A. Comparison of sampling process and results: Using the self-service sampling method provided in this application, the overall sampling efficiency is greatly improved, the time consumption is significantly reduced, the waiting time is significantly shortened, and medical staff do not need to wear protective equipment. The details are as follows:

[0095] 1) Traditional manual sampling: 1 sampling booth, 2 professional medical staff, wearing protective clothing to collect samples, 500 people sampling time 3.5 hours, 2 queues, each queue is 300 meters long, and the average queue time per person is 44.31 minutes;

[0096] 2) Online self-sampling: 1 office, 2 professional medical staff, not wearing protective clothing to guide sample collection, 500 people sampling time 1.1 hours, no need to queue at home; or, 1 office, 1 AI computer supervises and guides sample collection, 500 people sampling time 4.5 minutes. The specific comparison results are shown in the following table:

[0097]

[0098] B. Sample quality comparison

[0099] The samples collected by the above three methods underwent the same sample processing - nucleic acid extraction, and were tested using the human MNBH nucleic acid detection kit (fluorescence PCR method). It was found that the test results of the samples collected by the three sampling methods were consistent, indicating that the results of the self-service sampling method provided in this application are reliable.

[0100] like Figure 7 As shown, the horizontal axis represents the number of cycles; the vertical axis represents the relative fluorescence value. As the number of amplifications increases, the amount of fluorescence accumulates, which is the amplification curve of the nucleic acid test results, based on which the presence of the new coronavirus is determined. Among them, the black curve represents the test results of the traditional sampling method (control group, n = 10), the red curve represents the test results of the self-sampling-medical guidance group (experimental group 1, n = 16), and the blue curve represents the test results of the self-sampling-computer guidance group (experimental group 2, n = 16). The test results of the three groups are equivalent, with no significant difference.

[0101] In summary, this application provides an asynchronous multi-threaded self-service sampling method, server-side device, system, and electronic terminal. This asynchronous multi-threaded parallel self-service sampling method is targeted at small and medium-sized communities and can control sample collection time to within 1 minute. This method can shift outdoor fixed-point queuing to online queuing, achieving the goal of safe and efficient large-scale sample collection. Self-service sampling certification ensures that the quality of the sample after sampling is consistent with the on-site sampling results of medical staff, meeting the needs of testing.

[0102] Based on the above embodiments, the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a computer, the computer executes the methods described in the embodiments of the present application.

[0103] The computer readable and writable storage medium may include a read-only memory ROM, a random access memory RAM, an EEPROM, a CD-ROM or other optical disk storage device, a magnetic disk storage device or other magnetic storage device, a flash memory, a USB flash drive, a mobile hard disk, or any other medium that can be used to store a desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition, any connection can be appropriately referred to as a computer readable medium. For example, if the instruction is sent from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwaves, the coaxial cable, optical fiber cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwaves are included in the definition of the medium. However, it should be understood that computer readable and writable storage media and data storage media do not include connections, carrier waves, signals or other temporary media, but are intended to be non-temporary, tangible storage media. Disk and disc, as used in this application, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

[0104] For example, the computer-readable storage medium stores a model program, a quantitative analysis method program, and related data.

[0105] like Figure 8 As shown, it is a schematic diagram of the structure of an electronic terminal 800 in one embodiment of the present application. The electronic terminal 800 includes: a memory 810 and a processor 820; the memory 810 is used to store computer instructions; the processor 820 executes the computer instructions to implement the following Figure 1 The method; the communicator 830 is used to communicate with external devices.

[0106] For example, the external device may be a cloud server; or it may be a user's mobile terminal, such as a mobile phone, PC, tablet, etc.

[0107] In some embodiments, the number of the memory 810, the processor 820, and the communicator 830 in the electronic terminal 800 can be one or more. Figure 8 Take one as an example.

[0108] In one embodiment of the present application, the processor 820 in the electronic terminal 800 will follow the following steps: Figure 1 The steps described above load one or more instructions corresponding to the application process into the memory 810, and the processor 820 runs the application stored in the memory 810, thereby achieving the following Figure 1 The method described.

[0109] The memory 810 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. The memory 810 stores an operating system and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof. The operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic services and processing hardware-based tasks.

[0110] The processor 820 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0111] The communicator 830 is used to realize the communication connection between the database access device and other devices (such as clients, read-write libraries and read-only libraries). The communicator 830 may include one or more modules of different communication modes, for example, a CAN communication module connected to a CAN bus. The communication connection may be one or more wired / wireless communication modes and combinations thereof. The communication modes include: any one or more of the Internet, CAN, intranet, wide area network (WAN), local area network (LAN), wireless network, digital subscriber line (DSL) network, frame relay network, asynchronous transfer mode (ATM) network, virtual private network (VPN) and / or any other suitable communication network. For example: any one or more of WIFI, Bluetooth, NFC, GPRS, GSM, and Ethernet and any combination thereof.

[0112] In some specific applications, the various components of the electronic terminal 800 are coupled together through a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 8 In Chinese, all kinds of buses are called bus systems.

[0113] Based on the above embodiments, the present application further provides a chip, which is used to read a computer program stored in a memory to implement the various methods described in the embodiments of the present application.

[0114] Based on the above embodiments, the present application provides a chip system, which includes a processor for supporting a computer device to implement the various methods described in the embodiments of the present application. In one possible design, the chip system also includes a memory for storing the necessary programs and data for the computer device. The chip system can be composed of a chip or can include a chip and other discrete devices.

[0115] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0116] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] In summary, the present application provides an asynchronous multi-threaded self-service sampling method, server-side device, system and electronic terminal, including: obtaining the identity identification information of the person to be tested, and obtaining the detection task corresponding to the person to be tested; classifying the detection tasks of the same type into the same detection task queue, and queuing and managing different detection task queues and their corresponding detection tasks through multiple threads; responding to the asynchronous online self-service sampling request information of the person to be tested, supervising and guiding the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread, so as to obtain qualified self-service sampling samples.

[0120] The asynchronous multi-threaded self-service sampling method provided by this application can realize self-service, parallel multi-person, and continuous multi-frequency sampling. Among them, the management and certification of online video self-service sampling can be carried out by professional medical staff, i.e., background sampling supervisors, to guide the testees to conduct self-service sampling in parallel, or can be assisted by AI technologies such as image recognition to realize parallel supervision and guidance of the self-service sampling process of the testees. This application can not only quickly realize the rapid and efficient sample collection of large batches of people, greatly shorten the sampling process time, and make it safer and more efficient; at the same time, it also breaks away from the constraints of a strict sampling environment, and greatly improves the environmental comfort of sampling staff and sampled persons.

[0121] This application effectively overcomes various shortcomings of the prior art and has high industrial utilization value.

[0122] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. An asynchronous multi-threaded self-service sampling method, characterized in that: Applied to the server; the method includes: Obtaining the identity identification information of the person to be tested, and obtaining the detection task corresponding to the person to be tested; Classify the same type of detection tasks into the same detection task queue, and manage the queues of different detection task queues and their corresponding detection tasks through multiple threads; wherein the multiple threads include: a plurality of management threads and a plurality of working threads; and wherein the management threads include: any one or more combinations of a one-to-one video connection thread between the detection personnel and the persons to be detected, a one-to-many video connection thread, a many-to-many video connection thread, and a one-to-many online video demonstration thread; the working threads execute each detection task manually or through an AI computer; The method of classifying detection tasks of the same type into the same detection task queue, and queuing and managing different detection task queues and their corresponding detection tasks through multiple threads specifically includes: each management thread manages one or more detection task queues respectively; each detection task queue corresponds to a working thread queue; each working thread queue includes several working threads; newly generated detection tasks are sequentially sent to the corresponding detection task queue; the detection tasks in each detection task queue are executed by the working threads in the corresponding working thread queue; the working thread at the head of the queue executes the detection task with the highest priority from the corresponding detection task queue based on a polling method, and exits the working thread queue after obtaining the detection task, and returns to the working thread queue after executing the corresponding detection task and is listed at the end of the queue; and wherein, the detection task queue is a first-in-first-out queue and can be globally accessed; Each management thread manages one or more detection task queues respectively, specifically including: the one-to-one video connection thread manages the detection task queue corresponding to any one or more combinations of nucleic acid positivity, exposure degree information, and COVID-19 diagnosis; the one-to-many video connection thread manages the detection task queue corresponding to any one or more combinations of exposure degree information and asymptomatic; the many-to-many video connection thread manages the detection task queue corresponding to asymptomatic; the one-to-many online video demonstration thread manages the detection task queue corresponding to daily physical examinations; In response to the asynchronous online self-sampling request information of the person to be tested, the online self-sampling process of the person to be tested is supervised and guided by conducting online video in the corresponding thread to obtain qualified self-sampling samples.

2. The asynchronous multi-threaded self-service sampling method according to claim 1, characterized in that: The process of obtaining the detection task according to the identity identification information specifically includes: Obtaining basic personal information uploaded by the person to be tested, and performing identity recognition by obtaining a facial image and identification document information of the person to be tested; Obtaining a test task corresponding to the person to be tested based on the real-time location information and / or historical test results in the personal basic information; Among them, the identity recognition information includes: any one or more combinations of personal basic information, facial images, and identity document information; the detection tasks include: any one or more combinations of nucleic acid positive, asymptomatic, confirmed COVID-19, and routine physical examinations.

3. The asynchronous multi-threaded self-service sampling method according to claim 1, characterized in that: In response to the asynchronous online self-sampling request information of the test subject, the online self-sampling process of the test subject is supervised and guided by online video in the corresponding thread to obtain qualified self-sampling samples, specifically including: Through video connection, the testing personnel supervise and guide the online self-service sampling process of the person to be tested, and answer the questions of the person to be tested during the self-service sampling process; and / or, through video connection, the AI computer identifies and supervises the online self-service sampling process of the person to be tested; By monitoring whether the person to be tested completes the sampling action, the placing action and the sealing action in sequence in the online self-sampling video, it is determined whether the self-sampling sample of the person to be tested is qualified; When the person to be tested completes the sampling action, the placing action and the sealing action in sequence, a qualified self-sampling sample is obtained; Among them, the sampling action is to complete the sampling using a sampler; the placing action is to place the sampler into the sampling tube, and place the sampling tube into the sample bag; the sealing action is to use a tear-proof seal to seal the sealing part of the sample bag, and paste a bag code consistent with the barcode of the sampling tube.

4. The asynchronous multi-threaded self-service sampling method according to claim 3, characterized in that: Supervise whether the person to be tested completes the sampling, placing and sealing actions in sequence in the online self-service sampling video, specifically including: Detect whether there is a sampler image during the online self-service sampling video of the person to be tested; If there is a sampler image, determine whether the person to be tested has completed the sampling action based on the online self-service sampling video; After determining that the person to be tested has completed the sampling action, detecting whether there is a sampling tube image and a sample bag image in the online self-service sampling video; If there are sampling tube images and sample bag images, determine whether the person to be tested has completed the insertion action based on the online self-service sampling video; After determining that the person to be tested has completed the placing action, detecting whether there is an anti-tear seal image and a bag code image in the online self-service sampling video; If there is an anti-tear seal image and a bag code image, it is determined whether the person to be tested has completed the sealing action based on the online self-service sampling video.

5. An asynchronous multi-threaded self-service sampling server, characterized in that: The server includes: The user information management module is used to obtain and manage the identity identification information of the person to be tested, so as to obtain the detection task corresponding to the person to be tested; A multi-threaded queue management module is used to classify detection tasks of the same type into the same detection task queue, and to manage the queues of different detection task queues and their corresponding detection tasks through multiple threads; wherein the multiple threads include: a plurality of management threads and a plurality of working threads; and wherein the management threads include: any one or more combinations of one-to-one video connection threads, one-to-many video connection threads, many-to-many video connection threads, and one-to-many online video demonstration threads between the detection personnel and the persons to be detected; the working threads execute each detection task manually or through AI computers; The method of classifying detection tasks of the same type into the same detection task queue, and queuing and managing different detection task queues and their corresponding detection tasks through multiple threads specifically includes: each management thread manages one or more detection task queues respectively; each detection task queue corresponds to a working thread queue; each working thread queue includes several working threads; newly generated detection tasks are sequentially sent to the corresponding detection task queue; the detection tasks in each detection task queue are executed by the working threads in the corresponding working thread queue; the working thread at the head of the queue executes the detection task with the highest priority from the corresponding detection task queue based on a polling method, and exits the working thread queue after obtaining the detection task, and returns to the working thread queue after executing the corresponding detection task and is listed at the end of the queue; and wherein, the detection task queue is a first-in-first-out queue and can be globally accessed; Each management thread manages one or more detection task queues respectively, specifically including: the one-to-one video connection thread manages the detection task queue corresponding to any one or more combinations of nucleic acid positivity, exposure degree information, and COVID-19 diagnosis; the one-to-many video connection thread manages the detection task queue corresponding to any one or more combinations of exposure degree information and asymptomatic; the many-to-many video connection thread manages the detection task queue corresponding to asymptomatic; the one-to-many online video demonstration thread manages the detection task queue corresponding to daily physical examinations; The self-service sampling supervision module is used to respond to the asynchronous online self-service sampling request information of the person to be tested, and supervise and guide the online self-service sampling process of the person to be tested by conducting online video in the corresponding thread to obtain qualified self-service sampling samples.

6. An asynchronous multi-threaded self-service sampling system, characterized in that: The system comprises: The client is used to collect the identity information of the person to be tested and send asynchronous online self-service sampling request information of the person to be tested; An asynchronous multi-threaded self-service sampling server as described in claim 5 is used to communicate with the client, respond to the asynchronous online self-service sampling request information of the person to be tested, and supervise and guide the online self-service sampling process of the person to be tested through a video connection with the testing personnel and / or AI computer.

7. An electronic terminal, characterized in that: The electronic terminal includes: a memory, a processor and a communicator; The memory is used to store computer programs; the processor is used to execute the computer programs stored in the memory, so that the electronic terminal executes the method according to any one of claims 1 to 4; and the communicator is used to communicate with an external device.

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