CN112768017A

CN112768017A - Pipeline system, time prediction method thereof and storage medium

Info

Publication number: CN112768017A
Application number: CN201911061144.7A
Authority: CN
Inventors: 王鑫润
Original assignee: Shenzhen Mindray Bio Medical Electronics Co Ltd
Current assignee: Shenzhen Mindray Bio Medical Electronics Co Ltd
Priority date: 2019-11-01
Filing date: 2019-11-01
Publication date: 2021-05-07

Abstract

The application discloses a production line system, a time prediction method and a storage medium thereof, wherein when sample detection is carried out, a sample is received in an input area at first, a sample identifier of the sample is identified, then sample detection is carried out on the sample, the residual detection time required by the sample to finish detection is predicted, and when the residual time updating condition is reached, the residual time required by the sample to finish detection is updated according to the current test condition, so that the residual detection time of the sample can be ensured to be updated according to the actual detection sequence of the sample, and accurate detection time information is provided for a user.

Description

Pipeline system, time prediction method thereof and storage medium

Technical Field

The application relates to but is not limited to the field of sample detection, and relates to a pipeline system, a time prediction method thereof and a storage medium.

Background

With the rapid development of medical technology, the demand of sample detection is also higher and higher. To meet sample testing requirements and reduce testing time, pipeline systems have emerged that pipeline samples. When a pipeline system is used for testing samples, the Turn-Around Time (TAT) of the samples is a very interesting factor for clinical laboratories, and the International Organization for Standardization (ISO) 15189 "quality and competence approval criteria for medical laboratories" explicitly uses the Turn-Around Time as a quality index of the laboratories, and each clinical laboratory needs to determine the Turn-Around Time reflecting clinical requirements for each test and periodically review whether the Turn-Around Time meets the requirements.

In the related technology, the real-time statistical calculation and TAT overtime early warning are generally performed only on TAT time of each sample, TAT time prediction is not given, even if the prediction is only given at most in a project analysis link, sample result time prediction is not traced back to a sample on-line time, and the time or the remaining time of the sample after being processed on the on-line time is not predicted, so that the time of the sample result is difficult to estimate by a worker, accurate preparation work in advance cannot be made for corresponding work such as manual result auditing and report after the result is obtained, and the worker needs to spend more time and energy to pay attention to the TAT time of sample processing.

Disclosure of Invention

In view of the above, embodiments of the present application provide a pipeline system, a time prediction method thereof, and a storage medium to solve the problems in the prior art.

An embodiment of the present application provides a pipeline system, including:

an input module for receiving a sample at an input area thereof;

the sample identification module is used for identifying a sample identifier;

the pretreatment module is used for pretreating a sample; the pretreatment module comprises one or more of a centrifugation module, a serum detection module, a decapping module and a dispensing module; the centrifugal module is used for centrifuging a sample to be centrifuged; the serum detection module is used for detecting whether the serum amount of the sample is enough and/or whether the serum quality of the sample is qualified; the decapping module is used for decapping the centrifuged sample; the separate injection module is used for separating samples;

one or more analysis modules for testing the sample;

the system comprises a track, a scheduling device and a controller, wherein the track is connected with each module, and the scheduling device is used for scheduling samples among the modules through the track; wherein:

the controller is configured to predict a remaining detection time required for the sample to complete detection; and when the updating condition of the residual detection time length is reached, the controller updates the residual detection time length required by the sample to finish detection.

In the above-described aspect of the present invention,

when the controller obtains the sample identification of the new online sample, determining the updating condition of the residual detection time; in a corresponding manner, the first and second electrodes are,

when the updating condition of the residual detection time length is reached, the controller updates the residual detection time length required by the sample to finish the detection, and the updating comprises the following steps:

the controller acquires a test item of the new online sample based on the new sample identification;

and the controller updates the residual detection time required by the sample to finish detection based on the test item of the new online sample.

In the above solution, the updating, by the controller, the remaining detection duration required for completing the detection of the sample based on the test item of the new online sample includes:

the controller determines load information of each module for processing the new online sample in the production line based on each test item;

the controller determines processing path information of the new online sample based on the load information of each analysis module;

and the controller updates the residual detection time required by the sample to finish detection based on the processing path information of the new online sample.

In the above solution, the load information of the analysis module includes a first processing time length for processing the arrived samples, a first queuing number of each queued sample, and a first start processing time of each queued sample, and correspondingly,

the controller determines processing path information of the new online sample based on the load information of each module, and the processing path information comprises the following steps:

the controller determines the processing sequence of the new online sample based on the first processing duration of each module;

and determining a second queuing sequence number and a second processing starting time of the new online sample in each module based on the processing sequence and the first processing starting time of each queued sample.

In the foregoing solution, the updating, by the controller, the remaining detection time required for completing the detection of the sample based on the processing path information of the new online sample includes:

the controller updates the first processing starting time of each queuing sample after the second queuing sequence number in each module according to the second queuing sequence number and the second processing starting time of the new online sample in each module to obtain a third processing starting time;

and the controller updates the remaining detection duration of each queuing sample after the second queuing number according to the first processing starting time, the third processing starting time and the remaining detection duration of each queuing sample after the second queuing number.

In the above solution, the controller is further configured to,

and determining the processing time of the new online sample in each module according to the actual arrival time of the new online sample in each module and the second processing starting time.

In the above solution, the controller is further configured to,

acquiring the transmission time length of the new online sample on the track and the unfinished program of the new online sample according to the current position and the processing path information of the new online sample;

the controller obtains the processing time of the new online sample in each module of the uncompleted program; and determining the residual detection time length of the new online sample based on the transmission time length and the processing time length of the new online sample in each module of the incomplete program.

In the above-described aspect of the present invention,

and the controller updates the queuing sample information of each module according to the second queuing sequence number and the second processing starting time of the new online sample in each module.

In the above-described aspect of the present invention,

when the controller detects that the sample reaches a preset path node, determining an updating condition of the residual detection time; in a corresponding manner, the first and second electrodes are,

the controller acquires the position information of the path node;

and the controller updates the residual detection time required by the sample to finish detection according to the position information.

In the above-described aspect of the present invention,

and the controller determines the processing ending time and the working time of the analysis module according to the queuing sample information of each module.

In the above-described aspect of the present invention,

the controller acquires the processing end time of all modules in the pipeline system;

and determining the working end time and the working duration of the pipeline system based on the processing end time of all the modules.

In the above scheme, the controller is further configured to determine a detection completion time of the sample according to the current time and the remaining detection duration of the sample;

the production line further comprises a display module for displaying the remaining detection time required by the sample to finish the detection and the detection finish time of the sample.

In the above scheme, the display module is further configured to display a processing duration of the new sample in each analysis module.

In the foregoing solution, the pipeline system further includes:

and the prompting module is used for outputting prompting information to prompt a user that the sample detection is about to be completed when the residual detection duration is less than the first duration threshold.

In the above scheme, the controller is further configured to obtain a used detection duration of the sample; determining the predicted detection time length of the sample according to the used detection time length and the residual detection time length of the sample; and/or

The controller is further configured to obtain an actual detection duration of the detected sample.

In the above scheme, the controller is further configured to acquire a time duration passing threshold of the detection time duration, and determine a first detection time duration passing rate according to the predicted detection time duration, the actual detection time duration and the time duration passing threshold; and/or

And the controller determines a second detection duration passing rate according to the actual detection duration and the duration passing threshold of the detected sample.

In the above scheme, the controller is further configured to determine a first average detection duration according to the predicted detection duration and the actual detection duration; and/or

The controller is further configured to determine a second average detection duration based on an actual detection duration for which detection of the sample has been completed.

In the above scheme, the controller is further configured to determine a median of the first detection duration according to the predicted detection duration and the actual detection duration; and/or

The controller is further configured to determine a median of the second detection duration based on an actual detection duration for which the detection of the sample has been completed.

In the foregoing solution, the controller is further configured to determine a longest detection duration of the predicted detection duration and the actual detection duration; and/or

The controller is further configured to determine a longest actual detection time period of the actual detection time periods.

In the above scheme, the pipeline system further includes an early warning module, and the controller is further configured to obtain a first early warning threshold and a second early warning threshold; and when the used detection time length is greater than a first early warning threshold value and/or the predicted detection time length is greater than a second early warning threshold value, controlling the early warning module to output first early warning information and/or second early warning information.

In the above scheme, the controller is further configured to obtain a working time length of each analysis module having the same test function, and determine a target analysis module to be adjusted based on the working time length of each analysis module; and adjusting the authorized test items of the target analysis module.

In the above solution, the adjusting, by the controller, the authorized test items of the target analysis module includes:

the controller acquires the authorized test items of each target analysis module and the item test duration of each authorized item;

and the controller adjusts the authorized test items of the target analysis module based on the item test duration of each authorized test item and the module working duration of each target analysis module.

In the above scheme, the pipeline system further includes an early warning module, and the controller is further configured to determine a shortest operating time length based on the operating time lengths of the analysis modules, and determine a time length difference and/or a difference percentage between each operating time length and the shortest operating time length;

the controller is further used for controlling the early warning module to output third early warning information and/or fourth early warning information when the fact that the time length difference is larger than the difference threshold and/or the difference percentage is larger than the percentage threshold is detected.

The embodiment of the application provides a time prediction method based on a pipeline system, and the method comprises the following steps:

receiving a sample at an input area, and identifying a sample identification of the sample;

carrying out sample detection on the sample, and predicting the residual detection time required by the sample to finish detection;

and when the updating condition of the residual detection time length is reached, updating the residual detection time length required by the sample to finish the detection.

In the scheme, when the controller acquires the sample identifier of the new online sample, the updating condition of the residual detection time length is determined; in a corresponding manner, the first and second electrodes are,

when the condition for updating the residual detection time length is met, updating the residual detection time length required by the sample to finish the detection, wherein the updating comprises the following steps:

acquiring a test item of the new online sample based on the new sample identification;

and updating the residual detection time length required by the sample to finish detection based on the test item of the new online sample.

In the scheme, when the sample is detected to reach the preset path node, the updating condition of the residual detection time length is determined; in a corresponding manner, the first and second electrodes are,

acquiring the position information of the path node;

and updating the residual detection duration required by the sample to finish detection according to the position information.

In the above aspect, the method further includes:

determining the detection completion time of the sample according to the current time and the residual detection duration of the sample;

and displaying the remaining detection time required by the sample to finish the detection and the detection finishing time of the sample.

In the above aspect, the method further includes:

obtaining a used detection duration of the sample; determining the predicted detection time length of the sample according to the used detection time length and the residual detection time length of the sample; and/or

And acquiring the actual detection time length of the detected sample.

In the above aspect, the method further includes:

acquiring a first early warning threshold value and a second early warning threshold value;

and when the used detection time length is greater than a first early warning threshold value and/or the predicted detection time length is greater than a second early warning threshold value, outputting first early warning information and/or second early warning information.

In the above scheme, updating the remaining detection duration required for completing the detection of the sample based on the test item of the new online sample includes:

determining load information of each module for processing the new online sample in the production line based on each test item;

determining processing path information of the new online sample based on load information of each module;

and updating the residual detection time length required by the sample to finish detection based on the processing path information of the new online sample.

In the above solution, the load information of the module includes a first processing time length for processing the arrived samples, a first queuing number of each queued sample, and a first start processing time of each queued sample, and correspondingly,

determining processing path information of the new online sample based on the load information of each module, wherein the processing path information comprises the following steps:

determining the processing sequence of the new online sample based on the first processing duration of each module;

In the above solution, updating the remaining detection duration required for completing the detection of the sample based on the processing path information of the new online sample includes:

updating the first starting processing time of each queuing sample after the second queuing sequence number in each module according to the second queuing sequence number and the second starting processing time of the new online sample in each module to obtain a third starting processing time;

and updating the remaining detection duration of each queuing sample after the second queuing number according to the first processing starting time, the third processing starting time and the remaining detection duration of each queuing sample after the second queuing number.

In the above aspect, the method further includes:

acquiring the processing time of the new online sample in each module of the unfinished program;

and determining the residual detection time length of the new online sample based on the transmission time length and the processing time length of the new online sample in each module of the incomplete program.

In the above aspect, the method further includes:

and updating the queuing sample information of each module according to the second queuing sequence number and the second processing starting time of the new online sample in each module.

In the above aspect, the method further includes:

and determining the processing ending time and the module working time of each module according to the queuing sample information of each module.

In the above aspect, the method further includes:

acquiring the processing end time of all modules in the pipeline system;

In the above aspect, the method further includes:

and displaying the processing time of the new sample in each analysis module.

In the above aspect, the method further includes:

and when the residual detection duration is smaller than a first duration threshold, outputting prompt information to prompt a user that sample detection is about to be completed.

In the above aspect, the method further includes:

acquiring a time passing threshold of the detection time, and determining a first detection time passing rate according to the predicted detection time, the actual detection time and the time passing threshold; and/or

And determining the second detection duration passing rate according to the actual detection duration and the duration passing threshold of the detected sample.

In the above aspect, the method further includes:

determining a first average detection duration according to the predicted detection duration and the actual detection duration; and/or

And determining the second average detection time length according to the actual detection time length of the detected sample.

In the above aspect, the method further includes:

determining a median of the first detection duration according to the predicted detection duration and the actual detection duration; and/or

And determining the median of the second detection time length according to the actual detection time length of the detected sample.

In the above aspect, the method further includes:

determining the longest detection duration in the predicted detection duration and the actual detection duration; and/or

Determining the longest actual detection duration of the actual detection durations.

In the above aspect, the method further includes:

acquiring the working time of each analysis module with the same test function;

determining a target analysis module to be adjusted based on the working time of each analysis module;

and adjusting the authorized test items of the target analysis module.

In the above solution, the adjusting the authorized test items of the target analysis module includes:

obtaining the authorized test items of each target analysis module and the item test duration of each authorized item;

and adjusting the authorized test items of the target analysis module based on the item test duration of each authorized test item and the module working duration of each target analysis module.

In the above aspect, the method further includes:

determining the shortest working time length based on the working time lengths of the analysis modules;

determining a time length difference and/or a difference percentage between each working time length and the shortest working time length;

and when the detected time length difference is greater than the difference threshold and/or the difference percentage is greater than the percentage threshold, outputting third early warning information and/or fourth early warning information.

The embodiment of the application provides a storage medium, which stores executable instructions and is used for causing a processor to execute, so that the time prediction method based on a pipeline system provided by the embodiment of the application is realized.

The embodiment of the application provides a pipeline system, a time prediction method and a storage medium thereof, wherein when sample detection is carried out, a sample is received in an input area at first, a sample identifier of the sample is identified, then the sample is detected, the residual detection time required by the sample to finish detection is predicted, and when the residual time reaches an updating condition, the residual time required by the sample to finish detection is updated according to the current testing condition, so that the residual detection time of the sample can be ensured to be updated according to the actual detection sequence of the sample, and accurate detection time information is provided for a user.

Drawings

Fig. 1 is a schematic structural diagram of a pipeline system according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a preprocessing module according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of an aftertreatment module according to an embodiment of the present disclosure;

FIG. 4 is a schematic structural diagram of another pipeline system provided in an embodiment of the present application;

fig. 5 is a schematic flow chart of an implementation of the time prediction method according to the embodiment of the present application.

Detailed Description

To make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the accompanying drawings, the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

Where a similar description of "first/second" appears in the specification, and the description below is added where the terms "first/second/third" are used merely to distinguish between similar objects and do not denote a particular order or importance to the objects, it will be appreciated that "first/second/third" may, where permissible, be interchanged in a particular order or sequence to enable embodiments of the application described herein to be practiced in other than the order shown or described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

For a better understanding of the embodiments of the present application, a pipeline system, a related time prediction method and existing shortcomings in the related art will be described first.

The laboratory automation production line of the clinical laboratory receives a large number of patient samples every day, and carries out pretreatment, project analysis and post-treatment on the patient samples, for the department, TAT time for treating the patient samples and time for which the system needs to work are the key points of concern, TAT time for treating the patient samples needs to meet the requirements of a hospital on the clinical laboratory, and the shorter the working time required by the production line system for treating all the samples is, the earlier the department staff can get off the office; due to numerous sample processing links of the pipeline system, a plurality of sample batches and a large sample amount are obtained, so that the fluctuation of the TAT time of sample processing is large, the time of a sample result is difficult to estimate by a worker in a department, and the worker cannot accurately prepare for corresponding work such as manual result auditing and report sending after the result is obtained, so that the worker needs to spend more time and energy to pay attention to the TAT time of sample processing.

At present, each pipeline only carries out real-time statistical calculation and TAT overtime early warning on TAT time of each sample, TAT time prediction is not given, sample result time prediction is only given at most in a project analysis link, the time of the sample on the pipeline is not traced back, the time or the residual time of the sample after being processed on the pipeline is not predicted, the TAT based on the real-time statistical calculation and the corresponding overtime early warning can remind workers of processing overtime samples, but the TAT is relatively passive for the workers, the workers can process other work tasks when the samples are overtime, and the overtime samples cannot be processed in time even if the TAT gives an alarm, so that the requirements of the pipeline cannot be met obviously only by predicting the sample result time in the project analysis stage.

Based on this, the pipeline system provided in the embodiment of the present application can provide the remaining time of each sample test and the corresponding early warning function thereof, the remaining time of all samples after each service module is processed, and the remaining time difference function of different modules of the same type, so as to facilitate a user to know the test efficiency of the system sample in real time.

Referring to fig. 1, an embodiment of a pipeline system includes an input module 10, a preprocessing module 20, one or more analysis modules 30, a postprocessing module 40, a track 50, a scheduling device 60, and a controller 70. The track 50 is used to connect the modules, for example, the input module 10, the pre-processing module 20, the one or more analysis modules 30, the post-processing module 40, and the like, and the dispatching device 60 dispatches the samples to the corresponding modules through the track. It should be noted that the post-processing module 40 is not necessary in some pipeline systems and is an optional module.

The input module 10 is used for receiving samples put by users. The input module 10 in the pipeline system is generally the area where the user puts the sample, and when the pipeline system is in operation, the input module 10 can automatically scan the sample put therein, sort the sample, and the like, so as to be processed by the next module, such as the preprocessing module 20. In one embodiment the input module 10 has an input area for a user to place a sample. In one embodiment, the input module 10 can also identify the sample identifier, and the specific implementation manner is various. In an embodiment, after the input module 10 receives a sample from a user, a sample identification module (not shown in the figure) may scan a barcode on a container, such as a test tube, containing the sample, so as to obtain information of a sample identifier. In the embodiment of the present application, the sample identifier may be an IC tag, a barcode tag, or an ID of an RFID. The sample identification module sends the sample identification Information to the controller 70 after acquiring the sample identification Information, and the controller 70 may acquire the detection item Information of the sample from a Laboratory Information Management System (LIS) based on the sample identification so as to perform sample detection based on the detection item Information.

In the embodiment of the present application, the sample identification module may be a sub-module in the input module 10, or may be independent from the input module 10.

The preprocessing module 20 is used for completing the preprocessing of the sample. In one embodiment, referring to fig. 2, the pre-processing module 20 may include one or more of a centrifuge module 21, a serum detection module 22, a decapping module 23, and a dispensing module 24. The centrifuge modules 21 are used for centrifuging the sample to be centrifuged, and the number of the centrifuge modules 21 may be one or more. The serum test module 22 is used to determine whether the serum amount of the sample is sufficient and/or whether the serum quality of the sample is acceptable, so as to determine whether the centrifuged sample can be used for subsequent determination. The decapping module 23 is used for decapping the centrifuged sample — as will be understood, capping, coating, decapping, and decapping the sample herein, it refers to capping, coating, decapping, and decapping the sample tube containing the sample; typically, the sample is uncapped after centrifugation for subsequent dispensing or pipetting in the dispensing module 24 or in the analysis module. The dispensing module 24 is used to dispense a sample, for example, a sample is divided into a plurality of samples, which are sent to different analysis modules 30 for measurement. The preprocessing module 20 generally has a preprocessing flow: the centrifugal module 21 receives the sample scheduled by the input module 10 and centrifuges the sample; the serum detection module 22 detects serum of the centrifuged sample, and judges whether the serum can be used for subsequent measurement, and if the serum is insufficient in amount or unqualified in quality, the serum cannot be used for subsequent measurement; if the detection is passed, the sample is dispatched to the decapping module 23, the decapping module 23 removes the cap of the sample, if the dispensing module 24 exists, the dispensing module 24 performs the sample splitting on the removed sample, then the sample after the sample splitting is dispatched to the corresponding analysis module 30 for measurement, and if the dispensing module 24 does not exist, the sample is dispatched from the decapping module 23 to the corresponding analysis module 30 for measurement.

The analysis module 30 is used to test the centrifuged and decapped samples. To improve efficiency and test throughput, typically, a pipeline system will have multiple analysis modules 30, and these analysis modules 30 may be the same kind of analysis module, i.e., analysis module for determining the same item, or different kinds of analysis modules, i.e., analysis modules for determining different items, which may be configured according to the needs of users and departments.

The post-processing module 40 is used to complete post-processing of the sample. In one embodiment, referring to fig. 3, the post-treatment module 40 includes one or more of a capping/filming module 41, a refrigerated storage module 42, and a decapping/decapping module 43. The film adding/capping module is used for adding films or caps to the samples; the refrigerated storage module 42 is used for storing samples; the stripping/decapping module is used for stripping or decapping a sample. One typical post-processing flow for post-processing module 40 is: after the sample is aspirated by the analysis module 30, the sample is dispatched to the membrane/capping module 41, and the membrane/capping module 41 performs membrane or capping on the sample after the measurement is completed, and then the sample is dispatched to the cold storage module 42 for storage. If the sample requires retesting, the sample is dispatched from the refrigerated storage module 42, stripped or decapped in a stripping/decapping module 43, and then dispatched to the analysis module 30 for testing.

Referring to fig. 4, as an example of the pipeline system, each module further includes a module buffer, for example, the centrifugal module 21 has a buffer; the track 50 also has a track buffer (a zigzag track in the upper right corner of the figure), and the whole track can be a circular track. It should be noted that there are only one module in many types shown in the figures, but those skilled in the art will understand that there is no limitation on the number, for example, there may be more than one centrifuge module 21 and more than one analysis module 30.

In the embodiment of the present application, when a user puts a sample into the input area of the input module 10, the input module 10 receives the sample put into the input area and identifies the sample identifier through the sample identification module in the input module (or a sample identification module outside the input module). After acquiring the sample identification information, the sample identification module sends the sample identification information to the controller 70, and the controller 70 may acquire the detection item information of the sample from the LIS system based on the sample identification, so as to perform sample detection based on the detection item information.

After the sample identifier is identified, the sample is transmitted to the preprocessing module 20 through the rail 50 to preprocess the sample, and after the preprocessing is completed, the sample is transmitted to the analysis module 30 through the rail 50, and the sample is detected based on the acquired detection item information.

In this embodiment, the sample recognition module may be multiple and may be disposed at some fixed position on the track of the pipeline system, and when the sample passes through the track where the sample recognition module is disposed, the sample recognition module may scan and recognize the sample identifier, thereby determining the position information of the scanned sample, and transmitting the position information of the sample to the controller 70.

After the sample is on-line, the controller 70 may plan processing path information of the sample according to the detection item information of the sample and load information of each module in the pipeline that processes the new on-line sample, and further predict a remaining detection time required for completing detection of the sample. And when the update condition of the remaining detection time length is reached, the controller 70 updates the remaining detection time length required by the sample to complete the detection, so that the remaining detection time length of the sample can be ensured to be updated according to the actual detection sequence of the sample, and an accurate remaining detection time length is provided for a user.

In some embodiments, the controller 70 may determine whether the update condition of the remaining detection period is reached by two ways:

in the first mode, the controller 70 determines whether the sample identifier of the new online sample is obtained.

When the controller 70 acquires the sample identifier of the new online sample, it indicates that the new sample is online tested at this time, and the test of the new online sample may affect the test of the online sample, so that the update condition of the remaining test duration is determined to be reached at this time.

Second, the controller 70 detects whether the sample reaches a predetermined path node.

Here, the preset path node may be a position node of a plurality of sample recognition modules previously disposed on the track, and in some embodiments, a pressure sensor may be further disposed at a position of the track where the sample recognition module is disposed to detect whether the sample reaches the preset path node.

The controller 70 may detect whether the sample reaches a preset path node by detecting whether a sample identifier and position information of the sample sent by a sample identification module preset on the track are received. When the controller 70 receives the sample identifier sent by the sample identification module, it indicates that the sample corresponding to the sample identifier reaches the sample identification module, that is, reaches the preset path node. When the sample reaches the path node, the time for reaching the path node, the remaining test items and the remaining paths can be determined, so that the remaining detection time can be predicted more accurately, and the updating condition for reaching the remaining detection time is determined at the moment.

In some embodiments, when it is determined that the condition of reaching the remaining detection time period is due to an online test of a new sample, and thus the controller 70 needs to update the remaining detection time period required by the sample to complete the detection, it may be:

the controller 70 acquires a test item of the new online sample based on the new sample identifier identified by the sample identification module; and the controller 70 updates the remaining detection duration required for completing the detection of the sample based on the test item of the new online sample.

The samples may be all samples that have not been tested on the pipeline system, i.e. include new on-line samples. And updating the residual detection time of the new online sample, wherein the residual detection time of the new online sample can be predicted according to the test items of the new online sample and the test conditions of all analysis modules in the current pipeline system. In some embodiments, the samples that need to be updated for the remaining detection duration may also be other samples in the pipeline system that do not include the new on-line samples.

In some embodiments, the controller 70 updates the remaining detection time required for the sample to complete the detection based on the test item of the new online sample, which may be implemented as: the controller 70 first determines load information for each module in the pipeline that processes the new inline sample based on each test item (which may include queued sample information queued at each module and the processing time required to process samples that have reached a module); the controller 70 determines processing path information of the new online sample based on the load information of each module, wherein the processing path information includes a processing sequence, a queuing number at each module, and a processing start time at each module. For example, a new online sample needs to be detected by the analysis module a, the analysis module B, and the analysis module C, and the finally determined processing path information may be, for example, the centrifugation module 1 (queue number 4, start processing time 8: 20) — the decapping module 1 (queue number 4, start processing time 8: 40) — the dispensing module 4 (queue number 2, start processing time 8: 50) — the analysis module B (queue number 2, start processing time 9: 00) — the analysis module a (queue number 3, start processing time 9: 30) — the analysis module C (queue number 4, start processing time 10: 10).

After the controller 70 determines the processing path information of the new online sample, the remaining detection time required for completing the detection of the sample may be updated based on the processing path information of the new online sample.

In some embodiments, the load information of the module includes a first processing time length for processing the arrived sample, a first queuing number of each queued sample, and a first start processing time of each queued sample, where, in implementation, each module may correspond to a queuing list, where a sample identifier for requesting each module to process in the current pipeline system is stored in the queuing list, and the sample requested to process by each sample may include a sample that has arrived at the module for testing, and a sample that has not arrived at the module and is requested to process by the analysis module. The arrived samples refer to the samples currently arrived at the module waiting for detection, and the queued samples refer to the samples corresponding to all the sample identifications in the queuing list.

Based on the above further description of the load information, the controller 70 determines the processing path information of the new online sample based on the load information of each module, and when implemented, the processing path information may be:

the controller 70 first determines the processing sequence of the new online sample based on the first processing time of each module, and further, the controller 70 sorts the first processing time of each analysis module from small to large, thereby determining the processing sequence of the new online sample according to the sorting result. For example, when the first processing time of the analysis module a is 40 minutes, the first processing time of the analysis module B is 15 minutes, and the first processing time of the analysis module C is 80 minutes, the first processing times of the analysis modules are sorted from small to large, and the obtained processing sequence is the analysis module B- > the analysis module a- > the analysis module C.

After the processing sequence is obtained, the queuing number of the new online sample in each analysis module and the second processing start time need to be further determined, and at this time, the control module 70 determines the second queuing number and the second processing start time of the new online sample in each analysis module based on the processing sequence and the first processing start time of each queued sample. In an actual implementation process, the controller 70 determines a first arrival time at which a new online sample arrives at a first module based on the processing sequence, and then determines a second processing start time and a second queuing number of the new sample according to the first processing start time of each queued sample of the first module, where the second processing start time may be between the first processing start times of some two queued samples (that is, the new online sample is inserted between two queued samples), may be before the first processing start time of the first queued sample (that is, the new online sample is inserted before the first queued sample), or may be after the first processing start time of the last queued sample (that is, the new online sample is not inserted and is queued after the last queued sample); correspondingly, the second queuing number may be a first queuing number of a later queuing sample among the two queuing samples, and at this time, the first queuing numbers of the later queuing sample to the last queuing sample are respectively added with 1; the second queuing number may also be 1; the second queuing number may also be the first queuing number of the last queued sample plus 1.

In some embodiments, based on the determined processing path information of the new online sample, the controller 70 updates the remaining detection time length required for the sample to complete the detection, and first, according to the second queuing number and the second starting processing time of the new online sample in each module, the controller 70 updates the first starting processing time of each queued sample after the second queuing number in each module to obtain a third starting processing time, that is, only in the case of the new online sample being queued, the new online sample may possibly affect the remaining detection time length of other queued samples after the new online sample.

While updating the first start-of-processing time of each queued sample after the second queue number, the controller 70 may determine a second ending processing time of the new online sample based on the second starting processing time and the average test duration of the module, it is then further determined whether the second end-processing time is after the first start-processing time of the next queued sample, if the second end-processing time is after the first start-processing time of the next queued sample, then illustratively, when the first starting processing time of the next queuing sample is reached, the analysis module needs to continue waiting for the new online sample to be tested, and therefore, updating the first starting processing time of the next queuing sample to obtain a third starting processing time, the third processing start time may be the second processing end time, or may be a time after a preset duration of the second processing end time. If the second processing ending time is before the first starting processing time of the next queued sample, it indicates that the module has finished testing the new online sample when the first starting processing time of the next queued sample is reached, and the next queued sample can be tested according to the original first starting processing time without waiting.

For example, the second start processing time of the new online sample is 9: 00, queue number 2, average processing time of the module is 15 minutes, and then the second processing end time is 9: 15; if the first start-of-processing time of the queued sample with queue number 3 is 9:10, since the ratio in 9:10 is testing for a new online sample, the first starting processing time of the queued sample with queue number 3 may be updated to the third starting processing time (assume 9: 15). Whereas if the first start-of-processing time of a queued sample with queue number 3 is 9: 20, then at 9: 20 has finished the test on the new online sample, the first starting processing moment of the queued sample with queuing number 3 is kept unchanged, i.e. the third starting processing moment is equal to the first starting processing moment.

After determining the third starting processing time of each queued sample after the second queuing number, the controller 70 may refer to the initial first starting processing time to determine whether the starting processing time is delayed, and when the third starting processing time is after the first starting processing time, it indicates that the starting processing time is delayed, at this time, the controller 70 updates the remaining detection time length of each queued sample after the second queuing number according to the first starting processing time, the third starting processing time, and the remaining detection time length of each queued sample after the second queuing number, further, the controller 70 calculates a difference value between the third starting processing time and the first starting processing time, and then adds the difference value to the remaining detection time length of each queued sample to obtain the updated remaining detection time length.

For example, when the first start processing time of a sample with queue number 3 is 9:10, the remaining detection time before updating is 2 hours, and the third starting processing time is 9:15, the difference between the third processing start time and the first processing start time is 5 minutes, that is, the sample with the queuing number of 3 needs to be delayed for 5 minutes, and then the remaining detection time before the update with the queuing number of 3 needs to be added by 5 minutes to obtain the updated remaining detection time, that is, 2 hours and 5 minutes.

In some embodiments, the controller 70 is further configured to determine a processing duration of the new online sample at each module according to the arrival time of the new online sample at each module and the second processing start time. When the method is realized, the waiting time of the new online sample can be determined according to the difference value between the second processing starting time of the new online sample and the arrival time of the new online sample reaching each module, and then the processing time of the new online sample at the analysis module is determined according to the waiting time and the actual processing time of each module. It should be noted that the actual processing time of each module refers to the time between the beginning of processing and the end of processing of one sample.

For example, the arrival time of the new online sample at the analysis module a is 9:10, the second start processing time is 9:15, and the actual processing time of the analysis module a is 20 minutes, so that the processing time of the new online sample at the analysis module a is 20+ (9:15-9:10) ═ 25 minutes.

In some embodiments, the controller 70 may also determine the processing time duration of each sample in the pipeline system at each corresponding module through a similar implementation manner as described above, that is, the controller 70 determines the processing time duration of each sample at each corresponding module according to the arrival time of each sample at each module and the first processing start time (or the third processing start time if the processing start time is updated).

In some embodiments, the controller 70 is further configured to obtain a transmission duration of the new online sample on the track and an incomplete procedure of the new online sample according to the current position and the processing path information of the new online sample; the controller obtains the processing time of the new online sample in each module of the uncompleted program; and determining the residual detection time length of the new online sample based on the transmission time length and the processing time length of the new online sample in each module of the incomplete program.

Here, the transmission duration of the new online sample on the track refers to the running duration and the waiting duration of the new online sample on the track from the current position of the new online sample to the end of the test. When the method is realized, the transmission time length is added with the processing time length of each module of each incomplete program of the new online sample, and the residual detection time length of the new online sample can be obtained.

Further, the controller 70 determines each incomplete procedure of the new online sample and the transmission time length of the new online sample on the track when the incomplete procedure is completed, based on the current position and the processing path information of the new online sample, and obtains the processing time length of the new online sample on the module of each incomplete procedure; the incomplete procedure of the new online sample can be determined according to the position information of the sample or according to the completed procedure. For example, it may be determined from the position information of the sample that the centrifugation procedure has just been performed on the sample, and the incomplete procedure may include: serum detection, decapping, dispensing, individual detection procedures, membrane addition and capping, and the like. For another example, when it is determined that the sample has just executed a certain detection procedure according to the position information of the sample, the incomplete procedure may include: the detection procedure is not completed, and the membrane is added and covered. After the incomplete procedure is determined, the processing time of the new online sample in each module of the incomplete procedure can be further determined.

In some embodiments, the controller 70 updates the queuing sample information of each module according to the new online sample at the second queuing number and the second starting processing time of each module. Here, the second queuing number in the queuing sample information before updating and the first queuing number and the first start processing time of each subsequent queuing sample are mainly updated. Assuming that the queuing number of the new online sample in the analysis module a is 3, the queuing sample information of the analysis module a before the new online sample is online is as shown in table 1:

table 1 queuing sample information for analysis module a

Sample identification	Queue number	Time of starting treatment
			001	1	9:00
002	2	9:15
			003	3	9:40
004	4	10:00

Assuming that the second start processing time of the new online sample is 9:30 and the second processing end time is 9:45, so that the new online sample can be inserted before the sample 003 with queue number 3 in table 1, that is, the second queue number of the new online sample is 3, then the samples 003 and 004 in table 1 have queue numbers 4 and 5 in the queue sample information after updating, and the first start processing time of the two samples determines whether updating is needed based on the first processing end time of the last sample, when the first processing end time of the last sample is before the first test start time before the sample updating, updating is not needed, and when the first processing end time of the last sample is after the first test start time before the sample updating, the first start processing time of the sample needs updating.

The following description will be given by taking the queuing sample information in table 1 as an example. Since the new online sample 005 is queued with sequence number 3 in table 1, the second processing end time is 9:45, the queue number of the sample 003 is updated to 4, the first start processing time is 9:40, and since the second processing end time of the new online sample 005 with queue number 3 is 9:45, the first start processing time of the sample 003 is updated to 9: based on the updated first processing start time, the first processing end time of obtaining the sample 003 is 10:00, the first processing starting time of the next sample 004 is 10:00, and is not before the first processing ending time of the sample 003, then the first processing starting time of the sample 004 is not changed, the queuing number is updated to 5, and the queuing sample information updated by the analysis module a is shown in table 2:

table 2 analysis of updated queuing sample information by module a

Sample identification	Queue number	Time of starting treatment
			001	1	9:00
002	2	9:15
			005	3	9:30
003	4	9:45
			004	5	10:00

In some embodiments, when the controller detects that the sample reaches the preset path node and determines that the update condition of the remaining detection time period is reached, so that the controller 70 needs to update the remaining detection time period required by the sample to complete the detection, the update condition may be:

the controller 70 obtains the position information of the path node, and updates the remaining detection duration required for completing the detection of the sample according to the position information. Because the position information of the path node, that is, the current position information of the sample, the transmission duration of each unfinished program of the sample and the transmission duration of the sample on the track when the unfinished program is finished can be determined based on the position information of the path node, the test item of the sample and the processing path information, and the processing duration of the new online sample in the module of each unfinished program can be obtained; the incomplete procedure of the new online sample can be determined according to the position information of the sample or according to the completed procedure. After the incomplete procedure is determined, the processing time of the new online sample in each module of the incomplete procedure can be further determined.

The remaining detection time required for completing the detection of the sample, that is, the transmission time plus the processing time of the sample of each uncompleted program.

In some embodiments, the controller 70 is further configured to obtain queued sample information of each module in the pipeline system, and determine a processing end time and an operating time length of each module based on the queued sample information of each module, where the queued sample information includes at least a first start processing time of each queued sample. In implementation, the controller 70 may obtain the first starting processing time of the last queued sample according to the queued sample information of each module, and further determine the first processing ending time of the last queued sample, where the first processing ending time of the last queued sample is also the processing ending time of the module.

The working time of the module is also the time from the starting time of the module to the processing ending time, for example, the starting time of the module is 8:00, the processing ending time is 17:00, and the working time of the module is 9 hours.

It should be noted that the starting time of the general module, that is, the starting time of the pipeline system, is.

In some embodiments, the controller 70 is further configured to obtain the processing end time of all modules in the pipeline system; and determining the working end time and the working duration of the pipeline system based on the processing end time of all the modules. In implementation, the latest processing end time, that is, the work end time of the pipeline system, needs to be determined from the processing end times of all the modules.

In some embodiments, the controller 70 is further configured to determine a detection completion time of the sample according to the current time and the remaining detection duration of the sample; when the detection is implemented, the detection completion time of the sample is also the time obtained by adding the remaining detection time to the current time, for example, the current time is 10:00, and the remaining detection time is 1 hour, then the detection completion time is 11: 00.

The pipeline further includes a display module 80 for displaying the remaining detection time required for completing the detection of the sample and the detection completion time of the sample.

In some embodiments, the display module 80 is further configured to display the processing time of the new sample in each analysis module.

In some embodiments, the pipeline system further comprises:

and the prompting module is used for outputting prompting information to prompt a user that sample detection is about to be completed when the residual detection time length is less than a first time length threshold value, and the user can prepare result auditing and issue reporting matters in advance after receiving the prompting information.

In implementation, the prompt information may be output in a manner of displaying the sample information to be tested in a highlighted manner on the display module (for example, displaying the sample information to be tested in a highlighted manner in a highlighted color, a red, orange, yellow or other font or background, highlighting, or displaying the related information of the sample to be tested in other forms such as different fonts), a prompt box to be tested may be output on the display module, or a prompt sound such as a voice or a buzzer may be output.

In some embodiments, the controller 70 is further configured to obtain a used detection duration of the sample (where the sample refers to a sample that is not currently detected yet); determining a predicted detection time length of the unfinished detection sample according to the used detection time length and the residual detection time length of the sample (the predicted detection time length is the used detection time length and the residual detection time length); and/or

The controller 70 is further configured to obtain an actual testing duration of the tested sample.

The completed test sample refers to a sample in which all test items have been completed.

In some embodiments, the display module 80 is further configured to display a predicted detection duration of the sample and/or display an actual detection duration of the completed detection sample.

In some embodiments, the controller 70 is further configured to obtain a time duration passing threshold of the detection time duration, and determine a first detection time duration passing rate according to the predicted detection time duration, the actual detection time duration, and the time duration passing threshold; and/or the controller 70 determines the second detection duration passing rate according to the actual detection duration and duration passing threshold of the detected sample.

Here, in determining the first detection period passage rate, the controller 70 determines the number of passages of the predicted detection period and the actual detection period, which are less than or equal to the passage threshold value, and then divides the number of passages by the total number of the predicted detection period and the actual detection period, to obtain the first detection period passage rate.

For example, if the predicted detection periods are 3, respectively 70 minutes, 60 minutes, and 65 minutes, the actual detection periods are 3, respectively 60 minutes, and 65 minutes, and the period passage threshold is 65 minutes, then of these 6 detection periods, the number of times less than or equal to 65 minutes is 5, and therefore the first detection period passage rate is 83.3%.

When determining the second detection duration passing rate, the controller 70 determines the number of passes in the actual detection duration, which is less than or equal to the duration passing threshold, and then divides the number of passes by the total number of the actual detection duration, so as to obtain the second detection duration passing rate.

In connection with the above example, the number of passes in the actual detection time period is 3, and the total number is also 3, so that the pass rate of the second detection time period is 100%.

In some embodiments, the controller 70 is further configured to determine a first average detection duration based on the predicted detection duration and the actual detection duration; and/or the controller 70 is further configured to determine a second average detection duration based on an actual detection duration of the detected sample.

In line with the above example, the first average detection time period is (70+60+65+60+65+60)/6 ═ 63.3 minutes, and the second average detection time period is (60+65+60)/3 ═ 61.7 minutes.

In some embodiments, the controller 70 is further configured to determine a median first detection duration based on the predicted detection duration and the actual detection duration; and/or the controller 70 is further configured to determine a median of the second detection duration based on an actual detection duration for which the detection of the sample has been completed.

Taking the above example, the predicted detection duration and the actual detection duration are sorted from small to large to obtain 60 minutes, 65 minutes and 70 minutes, then the median of the first detection duration is 62.5 minutes, the actual detection duration is sorted from small to large to obtain 60 minutes, 60 minutes and 65 minutes, and then the median of the second detection duration is 60 minutes.

In some embodiments, the controller 70 is further configured to determine the longest detection duration of the predicted detection duration and the actual detection duration; and/or the controller 70 is further configured to determine the longest actual detection period among the actual detection periods.

In some embodiments, the pipeline system further comprises an early warning module, and the controller 70 is further configured to obtain a first early warning threshold and a second early warning threshold; and when the used detection time length is greater than a first early warning threshold value and/or the predicted detection time length is greater than a second early warning threshold value, controlling the early warning module to output first early warning information and/or second early warning information.

Here, the used detection time length may be a time length from the online time to the current time of the uncompleted detected sample; the used detection time length may also be a time length from the time of the sample completing the detection to the time of completing the detection, and at this time, the used detection time length is also the actual detection time length in other embodiments. In the embodiment of the application, the used detection time length may be greater than the first early warning threshold value, the predicted detection time length may be greater than the second early warning threshold value, and if one of the used detection time length and the predicted detection time length is greater than the second early warning threshold value, the corresponding early warning information is output, or if both the used detection time length and the predicted detection time length are satisfied, the early warning information is output.

The first pre-warning threshold is not greater than the second pre-warning threshold. The manner of outputting the first warning information and the manner of outputting the second warning information may be different, for example, the first warning information may be implemented by outputting a warning prompt box, and the second warning information may be implemented by outputting a buzzer warning sound.

In some embodiments, when it is determined that the used detection time is longer than the first warning threshold and/or the predicted detection time is longer than the second warning threshold, it is indicated that the TAT of the sample is too long at this time, and there is an overtime risk, so the processing strategy adopted may be that the controller controls to perform warning, and the user may select whether to call out the warning sample, and perform the priority processing of manual offline single-computer or online single-computer sample loading.

In some embodiments, the controller 70 is further configured to obtain the working time lengths of the analysis modules having the same test function, and determine a target analysis module to be adjusted based on the working time lengths of the analysis modules; and adjusting the authorized test items of the target analysis module.

Here, the analysis modules having the same test function refer to the analysis modules having the same function of detecting items, but items that the analysis modules can test in the pipeline system need to be configured by a worker, and the worker configures authorized test items of the analysis modules in combination with the item test duration of each item.

For example, the analysis module a1 and the analysis module a2 both have functions of detecting item 1, item 2, and item 3, the item test duration of item 1 is 20 minutes, and the item test durations of item 2 and item 3 are 10 minutes, respectively, so that the staff configures the authorized detection item of the analysis module a1 as item 1, and the authorized detection items of the analysis module a2 as item 2 and item 3, so that the analysis module a1 can only test item 1, and the analysis module a2 can only test item 2 and item 3.

However, even though the staff member may distribute the authorized test items of the analysis modules according to the item detection duration, in the work engineering of the pipeline system, the working duration of each analysis module is closely related to the test item of the sample for which detection is applied. For example, the authorized test items of the analysis module A1 are item 1, and the authorized test items of the analysis module A2 are item 2 and item 3, but there are few samples required to test item 1, and most of the samples are required to test item 2 and item 3, which results in a short operation time of the analysis module A1 and a long operation time of the analysis module A2, resulting in a load imbalance. Therefore, it is necessary to determine a target analysis module of load imbalance according to the operating time of the analysis modules having the same test function; thereby adjusting the authorized test items of the target analysis module.

In the embodiment of the application, the target analysis module is an analysis module with too long or too short working time. In practical implementation, the average operating time length may be determined according to the operating time lengths of the analysis modules having the same test function, and the target analysis module may be determined based on the average operating time length, for example, the target analysis module is an analysis module in which an absolute value of a difference between the operating time length and the average operating time length is greater than a time length difference threshold.

When the controller 70 adjusts the authorized test items of the target analysis modules, the authorized test items of each target analysis module and the item test duration of each authorized item may be obtained first; the controller 70 adjusts the authorized test items of the target analysis module based on the item test duration of each authorized test item and the module operating duration of each target analysis module.

In order to distinguish between the target analysis module and the analysis module with too long working time, in the embodiment of the present application, the analysis module with too short working time is referred to as a first target analysis module, and the analysis module with too long working time is referred to as a second target analysis module.

When the controller 70 adjusts the authorized test items of the target analysis module, there are two implementation manners:

the first implementation mode comprises the following steps: and authorizing the authorized test items in the first target analysis module to the second target analysis module.

That is, both the first target analysis module and the second target analysis module have the right to test a certain item. The first implementation manner is generally applied to a scenario where only 1 authorized test item is included in the first target analysis module, or the working time of the first target analysis module is not much different from that of the second target analysis module.

The second implementation mode comprises the following steps: and authorizing a part of authorized test items in the first target analysis module to the second target analysis module, and canceling the test permission of the part of items of the first target analysis module.

The second implementation manner is generally applied to a scenario where at least two authorized test items are in the first target analysis module, or the working time of the first target analysis module is greatly different from that of the second target analysis module.

In some embodiments, the pipeline system further comprises an early warning module, and the controller 70 is further configured to determine a shortest operating time based on operating times of the analysis modules having the same function of detecting the project, and determine a time difference and/or a difference percentage between each operating time and the shortest operating time; the controller 70 is further configured to control the early warning module to output third early warning information and/or fourth early warning information when it is detected that the duration difference is greater than the difference threshold and/or the difference percentage is greater than the percentage threshold.

The time length difference between each operating time length and the shortest operating time length is obtained by subtracting the shortest operating time length from each operating time length, and the difference percentage can be the ratio of each time length difference to the shortest operating time length. When the detected time length difference is greater than the difference threshold and/or the difference percentage is greater than the percentage threshold, the controller 70 controls the early warning module to output the early warning information, so that the user can see the working condition of the module in real time, and confirm which module has a larger load and which module has a smaller load, and at this time, if the user wants to test part of the sample on line, the user can select to sample the sample on an instrument with a smaller load based on the early warning information to process the sample.

An embodiment of the present application further provides a method for predicting sample remaining detection time of a pipeline system, which is hereinafter referred to as a time prediction method for short, and is applied to the pipeline system shown in fig. 1, fig. 5 is a schematic diagram of an implementation flow of the time prediction method according to the embodiment of the present application, and as shown in fig. 5, the time prediction method includes the following steps:

step S501, receiving a sample in an input area, and identifying a sample identifier of the sample.

Here, when the pipeline system is in operation, the samples are automatically scanned and sorted at the input area. The sample identification of the sample can be identified by automatically scanning the code of the sample, and further, a container containing the sample, such as a bar code on a test tube, can be scanned by the sample identification module (or a sample identification module outside the input area) in the input area, so that the sample identification is acquired. The sample identifier may be a barcode number in the embodiment of the present application.

In some embodiments, the sample identifier for identifying the sample may be not limited to the input area, but may be multiple, because the sample identification module may be disposed at a fixed position on the track of the pipeline system, and the sample identifier may be identified every time the sample passes through the sample identification module.

Step S502, sample detection is carried out on the sample, and the remaining detection time required by the sample to finish detection is predicted.

Here, in the embodiment of the present application, after the sample identifier of the sample is identified, the pipeline system (controller in the system: e.g. middleware) may obtain the detection item information of the sample from the data management center (e.g. LIS system) through the sample identifier, so that the sample can be sequentially detected according to the detection item information of the sample. And in the detection process, predicting the residual detection time required by the sample to finish detection.

Step S503, when the condition for updating the remaining detection time length is reached, updating the remaining detection time length required for completing the detection of the sample.

In some embodiments, whether the update condition of the remaining detection period is reached may be determined in two ways:

in the first mode, whether the sample identifier of the new online sample is acquired is judged.

When the sample identifier of the new online sample is obtained, it is indicated that the new sample is online tested at this time, and the test of the new online sample can influence the test of the online sample, so that the update condition of the remaining detection time is determined to be reached at this time.

In the second mode, whether the sample reaches a preset path node is detected.

Whether the sample reaches the preset path node can be detected by detecting whether the sample identifier and the sample position information sent by a sample identification module preset on the track are received. When the sample identifier sent by the sample identification module is received, it is indicated that the sample corresponding to the sample identifier reaches the sample identification module, that is, reaches the preset path node. When the sample reaches the path node, the time for reaching the path node, the remaining test items and the remaining paths can be determined, so that the remaining detection time can be predicted more accurately, and the updating condition for reaching the remaining detection time is determined at the moment.

In some embodiments, when it is determined that the condition of the remaining detection time period is reached due to the on-line test with the new sample, the step S503 may be implemented by:

step S5031a, obtaining the test item of the new online sample based on the new sample identifier.

Here, the pipeline system (controller in the system: e.g., middleware) may obtain the test items of the new online sample from the data management center (e.g., LIS system) through the new sample identification.

Step S5032a, based on the test item of the new online sample, updating the remaining detection time length required for the sample to complete the detection.

The samples may be samples including new online samples in the pipeline system, or samples not including new online samples in the pipeline system. And updating the residual detection time of the new online sample, wherein the residual detection time of the new online sample can be predicted according to the test items of the new online sample and the test conditions of all analysis modules in the current pipeline system.

In some embodiments, step S5032a may be implemented by:

step 321, determining load information of each module in the pipeline, which processes the new online sample, based on each test item.

Here, the modules in the pipeline for processing the new online sample may include a preprocessing module and an analysis module, and may further include a post-processing module. The load information of the module includes a first processing time duration for processing the arrived samples, a first queuing number for each queued sample, and a first start processing time for each queued sample.

In implementation, each module may correspond to a queuing list, where the queuing list stores a sample identifier that requests each module to process in the current pipeline system, and the samples that request each module to process may include samples that have already arrived at the module for processing, and also include samples that have not arrived at the module and request the module to process. The arrived sample refers to the sample that has currently arrived at the module waiting for processing, and the queued sample refers to the sample corresponding to all sample identifications in the queuing list.

Step 322, determining processing path information of the new online sample based on the load information of each analysis module;

here, the processing path information may include a processing order, a queuing number at each analysis module, and a start processing time at each analysis module. Correspondingly, step 322 may be implemented by:

step 3221, determining a processing sequence of the new online sample based on the first processing duration of each module.

When the step S3221 is implemented, the first processing durations of the modules are sorted from small to large, so that the processing order of the new online sample is determined according to the sorting result. For example, when the first processing time of the analysis module a is 40 minutes, the first processing time of the analysis module B is 15 minutes, and the first processing time of the analysis module C is 80 minutes, the first processing times of the analysis modules are sorted from small to large, and the obtained processing sequence is the analysis module B- > the analysis module a- > the analysis module C.

Step 3222, based on the processing sequence and the first processing start time of each queued sample, determining a second queuing number and a second processing start time of the new online sample at each module.

In an actual implementation process, a first arrival time of a new online sample reaching a first module is determined based on a processing sequence, and then a second processing start time and a second queuing number of the new sample are determined according to a first processing start time of each queued sample of the first module, where the second processing start time may be between the first processing start times of some two queued samples (that is, the new online sample is inserted between two queued samples), may be before the first processing start time of the first queued sample (that is, the new online sample is inserted before the first queued sample), or may be after the first processing start time of the last queued sample (that is, the new online sample is not inserted and is queued after the last queued sample); correspondingly, the second queuing number may be a first queuing number of a later queuing sample among the two queuing samples, and at this time, the first queuing numbers of the later queuing sample to the last queuing sample are respectively added with 1; the second queuing number may also be 1; the second queuing number may also be the first queuing number of the last queued sample plus 1.

Step 323, based on the processing path information of the new online sample, updating the remaining detection duration required by the sample to complete the detection.

Here, step S323 may be implemented by:

step 3231, according to the second queuing sequence number and the second processing start time of the new online sample in each module, updating the first processing start time of each queuing sample after the second queuing sequence number in each module, and obtaining a third processing start time.

Here, step 3231, when implemented, may determine that the new online sample is at the second processing end time of the module according to the second start processing time and the average test duration of the module, and then further determine whether the second processing end time is after the first start processing time of the next queued sample. If the second processing ending time is after the first starting processing time of the next queued sample, it indicates that when the first starting processing time of the next queued sample is reached, the module needs to continue waiting for processing the new online sample, and therefore, the first starting processing time of the next queued sample is updated to obtain the third starting processing time. If the second processing ending time is before the first starting processing time of the next queued sample, it indicates that the module has finished testing the new online sample when the first starting processing time of the next queued sample is reached, and the next queued sample can be tested according to the original first starting processing time without waiting.

For example, the second start processing time of the new online sample is 9: 00, queue number 2, average test duration of the module is 15 minutes, then the second processing end time is 9: 15; if the first start-of-processing time of the queued sample with queue number 3 is 9:10, since the ratio in 9:10 is processing a new online sample, the first starting processing time of the queued sample with queue number 3 may be updated to the third starting processing time (assume 9: 15). Whereas if the first start-of-processing time of a queued sample with queue number 3 is 9: 20, then at 9: 20 has finished processing the new online sample, the first starting processing time of the queued sample with queuing number 3 is kept unchanged, i.e. the third starting processing time is equal to the first starting processing time.

Step 3232, updating the remaining detection duration of each queued sample after the second queuing number according to the first processing start time, the third processing start time, and the remaining detection duration of each queued sample after the second queuing number.

After the third processing start time of each queued sample after the second queuing number is determined, the initial first processing start time may be referred to determine whether the processing start time is delayed, and when the third processing start time is after the first processing start time, the processing start time is described to be delayed, at this time, the remaining detection time length of each queued sample after the second queuing number is updated according to the first processing start time, the third processing start time, and the remaining detection time length of each queued sample after the second queuing number, further, a difference between the third processing start time and the first processing start time is calculated, and then the remaining detection time length of each queued sample is added to the difference, so as to obtain the updated remaining detection time length.

In some embodiments, when it is determined that the controller detects that the sample reaches the preset path node and the update condition of the remaining detection time length is reached, step S503 may be implemented by:

step S5031b, obtaining the location information of the path node.

Step S5032b, updating the remaining detection time required for the sample to complete the detection according to the location information.

Because the position information of the path node, that is, the current position information of the sample, the transmission duration of each unfinished program of the sample and the transmission duration of the sample on the track when the unfinished program is finished can be determined based on the position information of the path node, the test item of the sample and the processing path information, and the processing duration of the new online sample in the module of each unfinished program can be obtained; the incomplete procedure of the new online sample can be determined according to the position information of the sample or according to the completed procedure. After the incomplete procedure is determined, the processing time of the new online sample in each module of the incomplete procedure can be further determined.

In some embodiments, the method further comprises:

and step S61, determining the detection completion time of the sample according to the current time and the residual detection duration of the sample.

When the detection is implemented, the detection completion time of the sample is also the time obtained by adding the remaining detection time to the current time, for example, the current time is 10:00, and the remaining detection time is 1 hour, then the detection completion time is 11: 00.

And step S62, displaying the remaining detection time required by the sample to finish the detection and the detection finish time of the sample.

Here, after the remaining test duration and the test completion time of the sample are displayed in a centralized manner, the clinical laboratory staff can know the remaining duration and the time point of the completion of the test of the sample (i.e., the result of the sample) in real time through the pipeline software, and based on the predicted test completion time, the staff can prepare the result audit and issue the report matters in advance.

In some embodiments, the predicted detection time length (TAT) of the sample may also be predicted based on the used detection time length of the sample and the predicted remaining detection time length, and the actual detection time length of the completed detection sample may be obtained by:

step S63, obtaining the used detection duration of the sample.

Here, the sample refers to a sample of incomplete detection, and the used detection time length of the sample of incomplete detection may be calculated according to a difference between the current time and the on-line time of the sample.

Step S64, determining the predicted detection time length of the sample according to the used detection time length and the residual detection time length of the sample.

Here, the predicted detection period is the used detection period + the remaining detection period.

In step S65, the actual detection time length of the sample that has been detected is obtained.

Here, the actual detection time length of the completed detection sample refers to the actual TAT of the completed detection sample. In some embodiments, steps S63 through S64 and S65 may be performed alternatively or both.

In some embodiments, the user may also set a first warning threshold and a second warning threshold to determine whether to warn of the used detection duration and the predicted detection duration, and the method further includes:

and step S71, acquiring a first early warning threshold value and a second early warning threshold value.

Here, the first warning threshold is a criterion for warning a used detection time, and the second warning threshold is a criterion for warning a predicted detection time, and therefore the first warning threshold is not greater than the second warning threshold.

And step S72, when the used detection time is longer than a first early warning threshold value and/or the predicted detection time is longer than a second early warning threshold value, outputting first early warning information and/or second early warning information.

In the embodiment of the application, the used detection time length may be greater than the first early warning threshold value, the predicted detection time length may be greater than the second early warning threshold value, and if one of the used detection time length and the predicted detection time length is greater than the second early warning threshold value, the corresponding early warning information is output, or if both the used detection time length and the predicted detection time length are satisfied, the early warning information is output.

The manner of outputting the first warning information and the manner of outputting the second warning information may be different, for example, the first warning information may be implemented by outputting a warning prompt box, and the second warning information may be implemented by outputting a buzzer warning sound.

In some embodiments, the processing duration of the new online sample in each analysis module may be determined according to the arrival time of the new online sample at each analysis module and the second start processing time, and the processing duration of the new online sample in each analysis module may be displayed.

When the method is implemented, the waiting time of the new online sample can be determined according to the difference value between the second processing starting time of the new online sample and the arrival time of the new online sample reaching each analysis module, and then the processing time of the new online sample at each analysis module is determined according to the waiting time and the actual processing time of each analysis module. It should be noted that the actual processing time of each analysis module refers to the time between the beginning of the test and the end of the test of one sample.

In some embodiments, the remaining detection duration of the new online sample may be predicted by:

step 71, obtaining the transmission time length of the new online sample on the track and the unfinished program of the new online sample according to the current position and the processing path information of the new online sample;

here, the transmission duration of the new online sample on the track refers to the running duration and the waiting duration of the new online sample on the track from the current position of the new online sample to the end of the test.

The incomplete procedure for a new online sample may be determined from the location information of the sample or from the completed procedure. For example, it may be determined from the position information of the sample that the centrifugation procedure has just been performed on the sample, and the incomplete procedure may include: serum detection, decapping, dispensing, individual detection procedures, membrane addition and capping, and the like. For another example, when it is determined that the sample has just executed a certain detection procedure according to the position information of the sample, the incomplete procedure may include: the detection procedure is not completed, and the membrane is added and covered.

And 72, acquiring the processing time of the new online sample in each module of the incomplete program.

Here, in the step 72, when the processing is performed, the time when the new online sample reaches each module of the incomplete program may be predicted, then the second processing start time of the new online sample in each module of the incomplete program may be obtained, then the waiting time of the new online sample in each module of the incomplete program may be determined according to the reached time and the second processing start time, and then the processing time of the new online sample in each module of the incomplete program may be obtained by adding the waiting time to the actual testing time of the new online sample in the module.

And 73, determining the residual detection time of the new online sample based on the transmission time and the processing time of the new online sample in each module of the incomplete program.

When the method is realized, the transmission time length is added with the processing time length of each module of each incomplete program of the new online sample, and the residual detection time length of the new online sample can be obtained.

In some embodiments, since the new online sample may affect the testing of some samples in the pipeline system, the queued sample information of each analysis module needs to be updated, and the method correspondingly further includes:

and updating the queuing sample information of each analysis module according to the second queuing sequence number and the second processing starting time of the new online sample in each analysis module.

The queuing sample information includes a queuing number, a processing start time, a sample identifier, and the like of each queuing sample. Here, the second queuing number in the queuing sample information before updating and the first queuing number and the first start processing time of each subsequent queuing sample are mainly updated. Assuming that the queuing number of the new online sample in the analysis module a is 3, the queuing sample information of the analysis module a before the new online sample is online is as shown in table 1:

table 1 queuing sample information for analysis module a

table 2 analysis of updated queuing sample information by module a

In some embodiments, the processing end time and the working time of each module can be further determined according to the queuing sample information of each module.

Wherein the queued sample information includes at least a first start processing time of each queued sample. During implementation, the first processing start time of the last queued sample may be obtained according to the queued sample information of each module, and then the first processing end time of the last queued sample is determined, which is also the processing end time of the module.

In some embodiments, the method further comprises:

step 81, acquiring the processing end time of all modules in the pipeline system;

and step 82, determining the working end time and the pipeline working time of the pipeline system based on the processing end time of all the modules.

In implementation, the latest processing end time, that is, the work end time of the pipeline system, needs to be determined from the processing end times of all the modules. After the working end time of the assembly line system is determined, the assembly line system is convenient to be subjected to unified maintenance or shutdown power-off and other processing.

In some embodiments, when the remaining detection duration is less than the first duration threshold, a prompt message may be output to prompt the user that the sample detection is about to be completed.

After receiving the prompt message, the user can prepare result auditing and report matters in advance.

In some embodiments, after the predicted detection duration and/or the actual detection duration of each sample are obtained, a detection duration passing rate, an average detection duration, a median of the detection durations, a longest detection duration, and the like of the pipeline system may also be obtained.

In some embodiments, the detection duration passing rate may be determined by:

step 91a, acquiring a time length passing threshold of the detection time length, and determining a first detection time length passing rate according to the predicted detection time length, the actual detection time length and the time length passing threshold; and step 92a, determining a second detection duration passing rate according to the actual detection duration and the duration passing threshold of the detected sample.

In some embodiments, the average detection time period may be determined by:

step 91b, determining a first average detection duration according to the predicted detection duration and the actual detection duration;

and step 92b, determining a second average detection time length according to the actual detection time length of the detected sample.

In some embodiments, the median number of detection durations may be determined by:

step 91c, determining a median of the first detection duration according to the predicted detection duration and the actual detection duration;

and step 92c, determining the median of the second detection time length according to the actual detection time length of the detected sample.

In some embodiments, the longest detection duration may be determined by:

step 91d, determining the longest detection duration of the predicted detection duration and the actual detection duration;

and step 92d, determining the longest actual detection time length in the actual detection time lengths.

The specific process of determining the detection time passing rate, the average detection time and the median of the detection time of the pipeline system can refer to the description of the related implementation in the pipeline system.

In some embodiments, when at least two analysis modules with the same test function are configured in the pipeline system, the load of the analysis module can be determined according to the working time of the analysis module through the following steps, and the analysis module with unbalanced load is subjected to adjustment of authorized test items:

step 101, acquiring the working time of each analysis module with the same test function;

102, determining a target analysis module to be adjusted based on the working time of each analysis module;

Step 103, adjusting the authorized test items of the target analysis module.

Here, when step 103 is implemented, firstly, the authorized test items of the target analysis modules and the item test duration of each authorized item may be obtained; and then, based on the project testing time length of each authorized testing project and the module working time length of each target analysis module, the authorized testing projects of the target analysis modules are adjusted.

When the authorized test items of the target analysis module are adjusted, two implementation modes can be provided:

The second implementation manner is generally applied to a scenario where at least two authorized test items are in the first target analysis module, or the working durations of the first target analysis module and the second target analysis module are different greatly.

In some embodiments, the method further comprises:

step 201, determining the shortest working time length based on the working time lengths of the analysis modules with the same detection project function.

Step 202, determining the time length difference and/or the difference percentage between each working time length and the shortest working time length.

Here, the time length difference between each operation time length and the shortest operation time length is obtained by subtracting the shortest operation time length from each operation time length, and the difference percentage may be a ratio between each time length difference and the shortest operation time length.

And 203, outputting third early warning information and/or fourth early warning information when the fact that the time length difference is larger than the difference threshold and/or the difference percentage is larger than the percentage threshold is detected.

When the fact that the time length difference is larger than the difference threshold value and/or the difference percentage is larger than the percentage threshold value is detected, the early warning module is controlled to output early warning information, so that a user can see the working condition of the module in real time, and confirms which module is large in load and which module is small in load, and at the moment, if the user needs to test a part of samples on line, the samples can be selected to be loaded on an instrument with small load to be processed based on the early warning information.

By the time prediction method, the residual detection time of each sample and the corresponding early warning function thereof, the residual time of all samples after the treatment of each analysis module and the working time of different modules with the same test function can be counted, a user can know the test efficiency condition of the system sample in real time conveniently, the early-warning sample can be called out in time, the prior treatment of manual offline single machine or online single machine sample loading is carried out, the user can conveniently carry out the preparation treatment work such as manual examination and verification of sample results, sample sending and reporting, module consumable replacement and addition, module maintenance and the like and the subsequent instrument project configuration optimization work, the time and the energy of manual attention of staff in an inspection department are greatly reduced, and the problem that the samples and the system modules are not processed timely is avoided to a certain extent.

Next, an exemplary application of the embodiment of the present application in a practical application scenario will be described.

In order to solve the problem that the pipeline system cannot predict the remaining time of the sample test and the remaining working time of each processing module of the system well, the embodiment of the present application provides a pipeline system, which can provide the remaining time of each sample test and the corresponding early warning function in real time, and can also provide the remaining time of all samples processed by each service module and the remaining time difference function of different modules of the same type in real time, which will be described in detail below.

1. Real-time provision of each sample test remaining time and corresponding early warning function

After bar code scanning is carried out on the samples of each on-line system to obtain the pre-and-post processing service, the project analysis service and the detailed project test work order which need to be carried out, the on-line system calculates the paths of the pre-and-post processing and the project analysis of the samples in the on-line system based on the sample loading time points, the pre-and-post processing which need to be carried out, the project analysis service, the test projects which need to be carried out, the current states of all the modules of the system and the sample conditions which are being processed.

Because all modules in the pipeline system work in parallel, when a new sample comes on line, the sequence and time of other samples to be dispatched to a part of modules can be influenced, and at the moment, the test path of the new sample and the test path of the planned sample need to be synthesized, the processing time of the sample influenced by the test path of the new sample on line in each service module is calculated, and the predicted remaining time of the influenced sample is further adjusted.

And meanwhile, calculating the processing time of the new online sample in each service module and the corresponding residual test time of the new online sample, and displaying the predicted residual test time of the sample on the pipeline software in a centralized manner.

After the pipeline system predicts and centrally displays the residual sample testing time, the staff of the clinical laboratory can know the residual time and the time point of the completion of the sample testing (namely the sample output result) and the completion of the processing (namely the completion of the filing or the output to the output module for waiting for the processing of the user) in real time through pipeline software, based on the predicted sample testing completion time, the staff can prepare the result auditing and the report affairs in advance, and based on the processing completion time, the staff can prepare the sample filing in advance or carry out manual processing affairs on the sample.

In addition, on the basis of real-time prediction of all on-line sample TATs, the system counts the TAT passing rate (corresponding to the first detection duration passing rate in other embodiments) of all samples (including predicted samples) and the TAT passing rate (corresponding to the second detection duration passing rate in other embodiments) of samples which have already been tested in real time, the satisfying time (corresponding to the duration passing threshold in other embodiments) of the TAT can be set by the user, the system calculates the TAT passing rates of the samples which include the predicted samples and do not include the predicted samples based on the TAT satisfying time set by the user, the TAT (corresponding to the predicted detection duration in other embodiments) of all samples which are predicted in real time and the TAT (corresponding to the actual detection duration in other embodiments) of the samples which have already been tested, and simultaneously calculates the average TAT, the TAT median and the longest TAT time of the samples which include the predicted samples and do not include the predicted samples.

The user may also set a TAT early warning value (corresponding to the first early warning threshold in other embodiments) for actual statistics and a predicted TAT early warning value (corresponding to the second early warning threshold in other embodiments), and the pipeline system performs early warning based on the used detection time of the sample and the TAT early warning value for actual statistics set by the user, for example, the used detection time is set to 95% of the TAT, early warning is performed if the used detection time exceeds 95%, and warning is performed if the used detection time exceeds 100%.

Early warning is carried out based on the TAT (corresponding to the predicted detection duration in other embodiments) predicted in real time and a predicted TAT early warning value set by a user, for example, the TAT early warning value can be set to be 100% of the TAT, and early warning is carried out if the TAT early warning value exceeds the predicted TAT early warning value.

The alarm early warning is carried out based on the TAT of the actually completed task, the follow-up task which is possibly carried out is not considered, but the alarm is carried out based on the actually consumed time, and the alarm is more accurate. The tasks needing to be carried out subsequently are considered for carrying out early warning based on the TAT prediction, and the early warning is carried out based on the prediction time and the actual time consumption of the tasks needing to be carried out, so that the TAT prediction method has prediction significance, but has certain deviation. The system provides early warning and alarming judgment under two conditions to remind a user to carry out corresponding processing, and meanwhile, the early warning and alarming functions under two conditions can ensure that the user can select whether to start the corresponding function or not under actual conditions; based on the early warning, the user can select whether to call out the early warning sample or not, and carry out the priority processing of manual online single machine unloading or online single machine sample loading.

2. And providing the residual time of all samples processed by each service module and the residual time difference function of different modules of the same type in real time.

For each sample entering the pipeline system and subjected to bar code scanning, the pipeline system determines the pre-and-post processing, project analysis service and detailed project testing task required by the sample. Like the analysis, the system can predict the time of each sample needing to be processed in each processing link, so that each module in the pipeline system integrates the time of all samples needing to be processed in the module, and the time point of the module needed to process all samples can be obtained.

Similarly, when the sample reaches the actual path detection node, the system needs to update the time point predicted before based on the time point when the sample reaches the actual path detection node; if a new batch of samples enters the system through bar code scanning during the test, the original last sample is no longer the last sample processed by each module, and time prediction needs to be performed based on the processing time of the new last sample in the module. At the same time, the system calculates the maximum working time of the system based on the maximum working time of all samples in the module.

Based on the functions, after a batch of samples are loaded, the clinical laboratory staff can know the time of finishing processing all samples of the batch by each module in an aspect so as to add/replace consumables or maintain and other processing operations on each module; based on the longest working time of the module, the system is convenient to be uniformly maintained or shut down and power off.

In addition, if the pipeline system has more than two modules for processing the same service, the pipeline system can compare the residual working time of the modules and calculate the value and percentage of the time consumed by the modules with more time consumption than the least time-consumed module.

The user can set the early warning values of the excess consumed time and the excess consumed time percentage, and if the early warning values are exceeded, the early warning is carried out on instruments which are more consumed in time. The user can select whether to start the early warning function, and based on the early warning function, the user can see the working conditions of the modules in real time, the load of which module is larger, and the load of which module is smaller, and at the moment, if the user needs to test part of samples on line, the samples can be loaded on an instrument with smaller load for processing based on the prediction and early warning selection.

The assembly line system can also draw working time differences and difference value percentages of different modules of the same type in real time, and a user can know the difference conditions of the different modules of the same type in the whole working process of the system in real time, so that the user can be guided to subsequently perform configuration optimization of instrument projects.

The assembly line system in the embodiment of the application provides residual time of each sample test and a corresponding early warning function, residual time of all samples after each service module is processed and residual time difference functions of different modules of the same type, a user can conveniently know the test efficiency condition of the system sample in real time, early-warning samples can be called out in time, prior processing of manual offline single machines or online single machines for sample loading is carried out, the user can conveniently carry out sample result manual examination and verification, sample sending reports, module consumable replacement and addition, module maintenance and other preparation processing work and subsequent instrument project configuration optimization work, time and energy of manual attention of staff in an inspection department are greatly reduced, and the problem that the samples and the system module are not processed timely is avoided to a certain extent.

Accordingly, an embodiment of the present application further provides a computer storage medium, on which computer-executable instructions are stored, and when executed, the computer-executable instructions implement the steps of the time prediction method provided by the foregoing embodiment.

The above description of the pipeline system and computer storage medium embodiments, similar to the description of the method embodiments above, has similar beneficial effects as the method embodiments. For technical details not disclosed in the present application pipeline system and computer storage medium embodiments, reference is made to the description of the method embodiments of the present application for understanding.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.

Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A pipeline system, comprising:

an input module for receiving a sample at an input area thereof;

the sample identification module is used for identifying a sample identifier;

one or more analysis modules for testing the sample;

2. The pipeline system of claim 1,

3. The pipeline system of claim 2, wherein the controller updates the remaining test duration required for the sample to complete the test based on the test item of the new online sample, comprising:

the controller determines processing path information of the new online sample based on load information of each module;

4. The pipeline system of claim 3, wherein the load information for a module includes a first processing time duration for processing the arrived samples, a first queuing number for each queued sample, and a first start-of-processing time for each queued sample, and, correspondingly,

the controller determines the processing trial sequence of the new online sample based on the first processing duration of each module;

5. The pipeline system of claim 4, wherein the controller updates the remaining detection time required for the sample to complete detection based on the processing path information of the new online sample, comprising:

6. The pipeline system of claim 4, wherein the controller is further configured to,

and determining the processing time of the new online sample in each module according to the arrival time of the new online sample at each module and the second processing starting time.

7. The pipeline system of claim 6, wherein the controller is further configured to,

the controller obtains the processing time of the new online sample in each module of the uncompleted program;

and determining the residual detection time of the new online sample based on the transmission time of the new online sample on the track and the processing time of the new online sample in each module of the incomplete program.

8. The pipeline system of claim 4,

9. The pipeline system of claim 1,

the controller acquires the position information of the path node;

10. The pipeline system of any of claims 1 to 9,

the controller is further configured to obtain queuing sample information of each module in the pipeline system, and determine a processing end time and a working time of each module based on the queuing sample information of each module, where the queuing sample information at least includes a first start processing time of each queuing sample.

11. The pipeline system of claim 10,

the controller is further used for acquiring the processing end time of all the modules in the pipeline system;

12. The pipeline according to any of claims 1 to 9, wherein the controller is further configured to determine a detection completion time of the sample according to a current time and a remaining detection time period of the sample;

13. The pipeline system of claim 12, wherein the display module is further configured to display a processing duration of the new sample at each analysis module.

14. The pipeline system according to any of claims 1 to 9, further comprising:

15. The pipeline system of any of claims 1 to 9, wherein the controller is further configured to obtain a used detection time duration for the sample; determining the predicted detection time length of the sample according to the used detection time length and the residual detection time length of the sample; and/or

16. The pipeline system of claim 15, wherein the controller is further configured to obtain a time duration passing threshold for the detection time duration, and determine a first detection time duration passing rate based on the predicted detection time duration, the actual detection time duration, and the time duration passing threshold; and/or

17. The pipeline system of claim 15, wherein the controller is further configured to determine a first average detection duration based on the predicted detection duration and the actual detection duration; and/or

18. The pipeline system of claim 15, wherein the controller is further configured to determine a median first detection duration based on the predicted detection duration and the actual detection duration; and/or

19. The pipeline system of claim 15, wherein the controller is further configured to determine a longest detection duration of the predicted detection duration and the actual detection duration; and/or

20. The pipeline system of claim 15, further comprising an early warning module, wherein the controller is further configured to obtain a first early warning threshold and a second early warning threshold; and when the used detection time length is greater than a first early warning threshold value and/or the predicted detection time length is greater than a second early warning threshold value, controlling the early warning module to output first early warning information and/or second early warning information.

21. The pipeline system of claim 10, wherein the controller is further configured to obtain an operating time duration of each analysis module having the same test function, and determine a target analysis module to be adjusted based on the operating time duration of each analysis module; and adjusting the authorized test items of the target analysis module.

22. The pipeline system of claim 21, the controller to adjust authorized test items of the target analysis module, comprising:

23. The pipeline system of claim 21, wherein the pipeline system further comprises an early warning module, and wherein the controller is further configured to determine a shortest operating time based on operating times of the respective analysis modules, and determine a time difference and/or a difference percentage between the respective operating times and the shortest operating time;

24. A method for predicting time based on a pipeline system, the method comprising:

25. The method of claim 24,

26. The method according to claim 24, wherein when it is detected that the sample reaches a preset path node, an update condition that the remaining detection time period is reached is determined; in a corresponding manner, the first and second electrodes are,

acquiring the position information of the path node;

27. The method of any one of claims 24 to 26, further comprising:

28. The method of any one of claims 24 to 26, further comprising:

And acquiring the actual detection time length of the detected sample.

29. The method as recited in claim 28, wherein said method further comprises:

30. A storage medium having stored thereon executable instructions for causing a processor to perform the method of claims 24 to 29 when executed.