A multi-condition determination scheduling processing method, system, platform and storage medium suitable for laboratory samples

Abstract

The application discloses a multi-condition judgment scheduling processing method, system, platform and storage medium suitable for laboratory samples; at least two first modules corresponding to laboratory samples to be scheduled and processed are created through the method; wherein the first module is a laboratory sample task process; based on the first module, the laboratory samples are scheduled and processed in real time in combination with a multi-condition judgment mode, and the system, platform and storage medium corresponding to the method can realize multi-condition judgment scheduling processing of the laboratory samples, realize parallel scheduling of multiple modules, accurately calculate the processing time of the samples through the modules, and have high processing efficiency. In addition, the Mermaid language is used in the application scheme to further encapsulate and link the modules to form new functional module units, so that the processing capacity of the experimental platform can be quickly customized and changed according to the needs of users; that is, users can easily adjust the process logic to meet different scene requirements.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of laboratory sample processing, and particularly relates to a multi-condition determination scheduling processing method, system, platform and storage medium suitable for laboratory samples. BACKGROUND

[0002] At present, there is no specific and feasible processing method to realize parallel scheduling of multiple modules in the process of processing experimental samples, that is, in the prior art, the processing time of samples through the module is not accurately calculated, the processing efficiency is low, and the experimental sample scheduling processing mode is single, and multi-condition determination scheduling processing cannot be realized.

[0003] In addition, in the existing experimental sample processing platform technology, the design, control and delivery efficiency of the platform is low because the free editing and definition of module processing logic are not realized.

[0004] Therefore, in view of the above technical problems and defects that there is no specific and feasible processing method to realize parallel scheduling of multiple modules, that is, the processing time of samples through the module is not accurately calculated, the processing efficiency is low, and the experimental sample scheduling processing mode is single, and multi-condition determination scheduling processing cannot be realized, it is urgent to design and develop a multi-condition determination scheduling processing method, system, platform and storage medium suitable for laboratory samples. SUMMARY

[0005] To overcome the deficiencies and difficulties of the prior art, the purpose of the present application is to provide a multi-condition determination scheduling processing method, system, platform and storage medium suitable for laboratory samples to solve the design and scheduling problems of the flow laboratory sample pretreatment platform.

[0006] The first purpose of the present application is to provide a multi-condition determination scheduling processing method suitable for laboratory samples; the second purpose of the present application is to provide a multi-condition determination scheduling processing system suitable for laboratory samples; the third purpose of the present application is to provide a multi-condition determination scheduling processing platform suitable for laboratory samples; and the fourth purpose of the present application is to provide a computer readable storage medium.

[0007] The first purpose of the present application is achieved by the method comprising the following steps:

[0008] Creating at least two first modules corresponding to the laboratory samples to be scheduled and processed; wherein the first module is a laboratory sample task process;

[0009] Based on the first module and in combination with a multi-condition determination mode, the laboratory samples are scheduled and processed in real time.

[0010] Further, the creating the at least two first modules corresponding to the laboratory samples to be processed further comprises:

[0011] generating and obtaining first data corresponding to the laboratory samples, and initializing processing the first data; wherein the first data is information data of the laboratory samples to be processed;

[0012] generating second data corresponding to the laboratory samples to be processed based on the first data; wherein the second data is time data required for all the laboratory samples to be processed.

[0013] Further, the creating the at least two first modules corresponding to the laboratory samples to be processed further comprises:

[0014] generating and obtaining third data corresponding to the first modules, and pre-processing the laboratory samples to be processed based on the third data; wherein the third data is state data of a test tube state machine.

[0015] Further, the real-time scheduling processing the laboratory samples based on the first modules and in combination with the multi-condition determination mode further comprises:

[0016] generating and obtaining first prompt data corresponding to the laboratory samples to be processed, and determining whether to execute the next step based on the first prompt data; wherein the first prompt data is state prompt data in the processing of the laboratory samples;

[0017] generating and obtaining first control data corresponding to the laboratory samples to be processed, and real-time scheduling processing the laboratory samples based on the first control data; wherein the first control data is control instruction data of the laboratory samples to be processed.

[0018] Further, the generating and obtaining first prompt data corresponding to the laboratory samples to be processed, and determining whether to execute the next step based on the first prompt data further comprises:

[0019] generating and obtaining second prompt data corresponding to the laboratory samples, and determining whether to execute the next step based on the second prompt data; wherein the second prompt data is preparation state prompt data of the laboratory samples;

[0020] generating and obtaining third prompt data corresponding to the laboratory sample processing equipment, and determining whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment operation state prompt data;

[0021] Generate and acquire the fourth prompt data corresponding to the laboratory sample processing process, and determine whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is the prompt data of the temperature state of the laboratory sample processing process.

[0022] Further, the generation and acquisition of the fourth prompt data corresponding to the laboratory sample processing process, and the determination of whether to execute the next step based on the fourth prompt data, further comprises:

[0023] Generate and acquire the fourth data corresponding to the laboratory sample processing process; wherein the fourth data is the temperature threshold data of the laboratory sample processing process;

[0024] Based on the fourth prompt data and in combination with the fourth data, real-time fifth data is generated; wherein the fifth data is the real-time temperature state data of the laboratory sample processing process, including suitable temperature state data, overheating temperature state data and overcooling temperature state data;

[0025] According to the fifth data, the laboratory sample in the heating treatment or cooling treatment or no treatment module is processed.

[0026] The second object of the application is achieved in that the system is used to realize the multi-condition determination scheduling processing method for laboratory samples; the system comprises:

[0027] The data module creation unit is used to create at least two first modules corresponding to the laboratory samples to be scheduled for processing; wherein the first module is a laboratory sample task process;

[0028] The sample scheduling processing unit is used to schedule and process the laboratory samples in real time based on the first module and in combination with the multi-condition determination mode.

[0029] Further, the data module creation unit further comprises:

[0030] The first processing module is used to generate and acquire the first data corresponding to the laboratory samples, and to initialize the processing of the first data; wherein the first data is the information data of the laboratory samples to be processed;

[0031] The first generation module is used to generate the second data corresponding to the laboratory samples to be scheduled for processing based on the first data; wherein the second data is the time data required for all laboratory samples to be processed;

[0032] The second processing module is used to generate and acquire the third data corresponding to the first module, and to pre-process the laboratory samples to be scheduled for processing based on the third data; wherein the third data is the state data of the test tube state machine.

[0033] And / or, the sample scheduling processing unit further comprises:

[0034] A first determination module is configured to generate and acquire first prompt data corresponding to the laboratory sample to be processed, and determine whether to execute the next step based on the first prompt data; wherein the first prompt data is state prompt data in the laboratory sample processing process;

[0035] A third processing module is configured to generate and acquire first control data corresponding to the laboratory sample to be processed, and schedule the laboratory sample in real time based on the first control data; wherein the first control data is control instruction data of the laboratory sample scheduling processing job;

[0036] And / or, the first determination module further comprises:

[0037] A second determination module is configured to generate and acquire second prompt data corresponding to the laboratory sample, and determine whether to execute the next step based on the second prompt data; wherein the second prompt data is laboratory sample preparation state prompt data;

[0038] A third determination module is configured to generate and acquire third prompt data corresponding to the laboratory sample processing equipment, and determine whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment job state prompt data;

[0039] A fourth determination module is configured to generate and acquire fourth prompt data corresponding to the laboratory sample processing process, and determine whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is temperature state prompt data of the laboratory sample processing process;

[0040] And / or, the fourth determination module further comprises:

[0041] A second generation module is configured to generate and acquire fourth data corresponding to the laboratory sample processing process; wherein the fourth data is temperature threshold data of the laboratory sample processing process;

[0042] A third generation module is configured to generate corresponding fifth data in real time based on the fourth prompt data and in combination with the fourth data; wherein the fifth data is real-time temperature state data of the laboratory sample processing process, including suitable temperature state data, overheating temperature state data and overcooling temperature state data;

[0043] A fourth processing module is configured to heat, cool or not process the laboratory sample in the module according to the fifth data.

[0044] A third object of the present application is achieved by comprising a processor, a memory, and a multi-condition determination scheduling processing platform control program suitable for laboratory samples; wherein the processor executes the multi-condition determination scheduling processing platform control program suitable for laboratory samples, the multi-condition determination scheduling processing platform control program suitable for laboratory samples is stored in the memory, and the multi-condition determination scheduling processing platform control program suitable for laboratory samples implements the multi-condition determination scheduling processing method suitable for laboratory samples.

[0045] A fourth object of the present application is achieved by storing a multi-condition determination scheduling processing platform control program suitable for laboratory samples in the computer-readable storage medium, wherein the multi-condition determination scheduling processing platform control program suitable for laboratory samples implements the multi-condition determination scheduling processing method suitable for laboratory samples.

[0046] The present application creates at least two first modules corresponding to laboratory samples to be scheduled for processing by the method; wherein the first module is a laboratory sample task process; based on the first module and in combination with a multi-condition determination mode, the laboratory samples are processed in real time, and the system, platform and storage medium corresponding to the method can realize multi-condition determination scheduling processing of laboratory samples, and realize parallel scheduling of multiple modules, and the processing time of the samples through the module is accurate and the processing efficiency is high.

[0047] In addition, the present application scheme further encapsulates and links the modules by means of the Mermaid language to form new functional module units, so that the processing capacity of the experimental platform can be quickly customized and changed according to user needs; that is, the user can easily adjust the process logic to meet different scene requirements. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0049] Figure 1 A multi-condition determination scheduling processing method suitable for laboratory samples according to the present application is shown in the flowchart.

[0050] Figure 2 A module structure diagram of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application is shown in the flowchart.

[0051] Figure 3Basic linear flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0052] Figure 4 Flowchart with conditional branching of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0053] Figure 5 Complex flowchart with subgraphs of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0054] Figure 6 Multi-condition determination flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0055] Figure 7 Loop processing flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0056] Figure 8 Page display flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0057] Figure 9 Node configuration flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0058] Figure 10 Triggering subsequent steps flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0059] Figure 11 Pausing operation flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0060] Figure 12 Stopping operation flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0061] Figure 13 Clearing operation flowchart of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0062] Figure 14 System design architecture one of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0063] Figure 15 System design architecture two of a multi-condition determination scheduling processing method for laboratory samples of the present application;

[0064] Figure 16Figure 3 is a schematic diagram of a three-embodiment flow of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0065] Figure 17 Figure 4 is a schematic diagram of a multi-condition determination scheduling processing system architecture suitable for laboratory samples according to the present application;

[0066] Figure 18 Figure 5 is a schematic diagram of a multi-condition determination scheduling processing platform architecture suitable for laboratory samples according to the present application;

[0067] Figure 19 Figure 6 is a schematic diagram of a computer-readable storage medium architecture according to an embodiment of the present application;

[0068] Figure 20 Figure 7 is a schematic diagram of a first-embodiment flow of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0069] Figure 21 Figure 8 is a schematic diagram of a second-embodiment flow of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0070] Figure 22 Figure 9 is a schematic diagram of a third-embodiment flow of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0071] Figure 23 Figure 10 is a schematic diagram of a flow supporting a chart type of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0072] Figure 24 Figure 11 is a schematic diagram of a timing supporting a chart type of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0073] Figure 25 Figure 12 is a schematic diagram of an embodiment module action flow of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0074] Figure 26 Figure 13 is a schematic diagram of an embodiment application solution directory structure of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0075] Figure 27 Figure 14 is a schematic diagram of an embodiment application Mermaid language project structure of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0076] Figure 28 Figure 15 is a schematic diagram of an embodiment application ConsoleApplication architecture of a multi-condition determination scheduling processing method suitable for laboratory samples according to the present application;

[0077] Figure 29 For an embodiment of the application, a Mermaid language is used to parse the core function diagram of a multi-condition judgment scheduling processing method for laboratory samples.

[0078] Figure 30 For an embodiment of the application, a Mermaid language is used to parse the core function diagram of a multi-condition judgment scheduling processing method for laboratory samples. DETAILED DESCRIPTION

[0079] In order to better understand the purpose, technical solutions and advantages of the present application, the present application will be further described below in conjunction with the drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the present specification.

[0080] The present application can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in the present specification based on different views and applications without departing from the spirit of the present application.

[0081] It should be noted that if the present application embodiments involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative positional relationship, movement condition, etc. between components in a certain specific posture (as shown in the drawings), if the specific posture changes, the directional indications will also change accordingly.

[0082] In addition, if the present application embodiments involve descriptions of "first", "second", etc., the descriptions of "first", "second", etc. are only for descriptive purposes, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. Secondly, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection claimed by the present application.

[0083] Preferably, the multi-condition determination scheduling processing method for laboratory samples is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0084] The terminal can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal can interact with a user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, and the like.

[0085] The present application discloses a multi-condition determination scheduling processing method, system, platform and storage medium for laboratory samples.

[0086] As shown in Figure 1 FIG. 1 is a flowchart of a multi-condition determination scheduling processing method for laboratory samples according to an embodiment of the present application.

[0087] In the embodiment, the multi-condition determination scheduling processing method for laboratory samples can be applied in a terminal with a display function or a fixed terminal, and the terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer with a camera, etc.

[0088] The multi-condition determination scheduling processing method for laboratory samples can also be applied in a hardware environment composed of a terminal and a server connected to the terminal through a network. The network includes but is not limited to a wide area network, a metropolitan area network or a local area network. The multi-condition determination scheduling processing method for laboratory samples according to the embodiment of the present application can be executed by the server, or can be executed by the terminal, or can be executed by the server and the terminal together.

[0089] For example, for a terminal that needs to perform multi-condition determination scheduling processing suitable for laboratory samples, the multi-condition determination scheduling processing function suitable for laboratory samples provided by the method of the present application can be integrated directly on the terminal, or a client for implementing the method of the present application can be installed. For another example, the method provided by the present application can also run on a device such as a server in the form of a software development kit (SDK), and the interface of the multi-condition determination scheduling processing function suitable for laboratory samples is provided in the form of the SDK, so that the terminal or other devices can implement the multi-condition determination scheduling processing function suitable for laboratory samples through the provided interface. The present application is further described below with reference to the accompanying drawings.

[0090] As shown in Figure 1 The present application provides a multi-condition determination scheduling processing method suitable for laboratory samples, which comprises the following steps:

[0091] S1, creating at least two first modules corresponding to laboratory samples to be scheduled for processing; wherein the first module is a laboratory sample task process;

[0092] S2, based on the first module and in combination with a multi-condition determination mode, scheduling processing the laboratory samples in real time.

[0093] The creating of at least two first modules corresponding to laboratory samples to be scheduled for processing further comprises:

[0094] S11, generating and obtaining first data corresponding to laboratory samples and initializing processing the first data; wherein the first data is information data of laboratory samples to be processed;

[0095] S12, based on the first data, generating second data corresponding to laboratory samples to be scheduled for processing; wherein the second data is time data required for all laboratory samples to be processed.

[0096] The creating of at least two first modules corresponding to laboratory samples to be scheduled for processing further comprises:

[0097] S13, generating and obtaining third data corresponding to the first module and pre-processing the laboratory samples to be scheduled for processing based on the third data; wherein the third data is state data of a test tube state machine.

[0098] The scheduling processing the laboratory samples in real time based on the first module and in combination with a multi-condition determination mode further comprises:

[0099] S21, generating and acquiring first prompt data corresponding to the laboratory sample to be scheduled for processing, and determining whether to execute the next step based on the first prompt data; wherein the first prompt data is state prompt data in the laboratory sample processing process;

[0100] S22, generating and acquiring first control data corresponding to the laboratory sample scheduled for processing, and scheduling the laboratory sample for processing in real time based on the first control data; wherein the first control data is control instruction data for laboratory sample scheduling processing job.

[0101] The generating and acquiring first prompt data corresponding to the laboratory sample to be scheduled for processing, and determining whether to execute the next step based on the first prompt data, further comprises:

[0102] S211, generating and acquiring second prompt data corresponding to the laboratory sample, and determining whether to execute the next step based on the second prompt data; wherein the second prompt data is laboratory sample preparation state prompt data;

[0103] S212, generating and acquiring third prompt data corresponding to the laboratory sample processing equipment, and determining whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment job state prompt data;

[0104] S213, generating and acquiring fourth prompt data corresponding to the laboratory sample processing process, and determining whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is temperature state prompt data of the laboratory sample processing process.

[0105] The generating and acquiring fourth prompt data corresponding to the laboratory sample processing process, and determining whether to execute the next step based on the fourth prompt data, further comprises:

[0106] S2131, generating and acquiring fourth data corresponding to the laboratory sample processing process; wherein the fourth data is temperature threshold data of the laboratory sample processing process;

[0107] S2132, generating corresponding fifth data in real time based on the fourth prompt data and in combination with the fourth data; wherein the fifth data is real-time temperature state data of the laboratory sample processing process, including suitable temperature state data, overheating temperature state data and overcooling temperature state data;

[0108] S2133, according to the fifth data, the laboratory sample in the heating processing or cooling processing or no processing module is processed in real time.

[0109] Specifically, in the embodiments of the present application, the provided scheme is a system for customizing and parallel scheduling of module processing logic of a pre-processing platform. It is mainly applied to a sample processing system of an automated laboratory. The entire system contains processing logic customization of multiple processing modules (Module). Each module performs a specific processing task, and samples are processed in sequence through the modules in the platform. The system needs to freely define the module processing logic and efficiently parallel schedule the work of these modules, so that the experimental samples (i.e. samples) complete the required experimental processing steps in sequence.

[0110] As shown in Figure 2 , it is assumed that the platform has n modules: {M1, M2, M3, … M n} (the execution order is M1→M n →…→Mn, from left to right). The time length of n modules processing a group of samples (the current channel is 2 test tubes) is: {t1, t2, t3, … tn n}.

[0111] The algorithm is as follows:

[0112] Module time length normalization (the time unit can be unified into seconds):

[0113] Example: [10, 15, 20 , 2, 4, 6, 10 , 4, 8 ]; denoted as →{t1, t2, t3, … tn n};

[0114] Normalization: [20, 20, 20 , 10, 10, 10, 10 , 8, 8 ]; denoted as →{T1, T2, T3, … TN n};

[0115] Algorithm steps: select the maximum value in all module processing time lengths, and set the time length from the first time length to the time length at the position of the time length to the maximum value; repeat the above algorithm for the remaining array until all time length array data is processed. For example:

[0116] Original data: [10, 15, 20 , 2, 4, 6, 10 , 4, 8 ];

[0117] → Transformation step 1: [20, 20, 20 , 2, 4, 6, 10 , 4, 8 ];

[0118] → Transformation step 2: [20, 20, 20,10,10,10, 10 ,4, 8 ];

[0119] →Transformation step 3: [20,20, 20 ,10,10,10, 10 ,8, 8 ];

[0120] If there are m groups of samples that need to be pre-processed, the total time T is calculated as:

[0121] <1> When m==1 (no blocking and beat adjustment)

[0122]

[0123] <2> When m==2 (considering beat adjustment)

[0124]

[0125] <3> When m>=3 (consider beat adjustment)

[0126]

[0127] Task scheduling execution:

[0128] ① For each group of test tubes, set the following marker matrix (m rows, xn columns); m groups of samples (each group of samples

[0129] 2 test tubes), n modules:

[0130]

[0131] Among them, f ij The processing status flag for the i-th sample group in the j-th module. The status bits can be granularized based on your business needs, but at least the following three states are required: {Initialize, Running, Finish}. These three states indicate the processing stage of a group of tubes in a particular module, facilitating task scheduling, EBR recording, error recovery, and emergency stop.

[0132] ② For each module, set the flag to indicate whether the module is occupied:

[0133] {s1,s2,s3,……s n}(5)

[0134] Among them, s i ==false means the i-th module is idle, s i ==true indicates that the i-th module is occupied.

[0135] Algorithm steps: create n task processes (corresponding to n modules) and run the following scheduling algorithm:

[0136] 1. Initialize all sample information (m groups);

[0137] 2. Calculate the time T required for all samples (m groups) to be completely processed (algorithm see previous page);

[0138] 3. Create n parallel tasks (corresponding to each module), and start executing the following operations:

[0139] {Change the state of the test tube state machine in n parallel tasks;

[0140] Let the m group samples pass through the n modules to complete the sample pretreatment;}

[0141] 4. When the n parallel tasks end, complete the scheduling of the pretreatment task.

[0142] Wherein, in the above algorithm, T i > = t i , when T i > t i , in order to realize the uniformization of the experimental process, T wait = T i -t i , whether to wait first or later depends on the needs of the process.

[0143] Because the reagent processing of the laboratory involves multi-level industrial processes, the processing process is divided into multiple independent modules (continue to subdivide the experimental sample processing function), and each module is responsible for performing its own related operations. The interaction logic between the modules is directly encapsulated together, which results in high maintenance cost, insufficient flexibility, and code redundancy. Importing the Mermaid flow description language can allow users to freely define module functions and experimental execution order.

[0144] Mermaid grammar definition (including parsing and calling of gRPC hardware control logic):

[0145] grammar MermaidFlowchart;

[0146] flowchart

[0147] : FLOWCHART_HEADER direction? statement+EOF ;

[0149] direction

[0150] : TD | LR | RL | BT ;

[0152] statement

[0153] :node_definition

[0154] |edge_statement

[0155] |subgraph_statement ;

[0157] node_definition

[0158] :node_id node_shape ;

[0160] node_shape

[0161] :ROUND_NODE

[0162] |SQUARE_NODE

[0163] |DIAMOND_NODE ;

[0165] edge_statement

[0166] :node_id ARROW (EDGE_LABEL)? node_id ;

[0168] subgraph_statement

[0169] :SUBGRAPH ID? NEWLINE node_definition+ END ;

[0171] node_id

[0172] :ID ;

[0174] / / LexerRules

[0175] FLOWCHART_HEADER:'flowchart'|'graph';

[0176] SUBGRAPH:'subgraph';

[0177] END:'end';

[0178] TD:'TD';

[0179] LR:'LR';

[0180] RL:'RL';

[0181] BT:'BT';

[0182] ARROW:'-->';

[0183] EDGE_LABEL:'|'~('|')*'|';

[0184] ROUND_NODE:'(('~(')')+'))';

[0185] SQUARE_NODE:'['~']'+']';

[0186] DIAMOND_NODE:'{'~'}'+'}';

[0187] ID:[a-zA-Z_][a-zA-Z0-9_]*;

[0188] NEWLINE:'\r'?'\n'->skip;

[0189] WS:[\t]+->skip;

[0190] COMMENT:'%%'.*? NEWLINE->skip;

[0191] Customization of various module processing logic

[0192] like Figure 3 As shown, the basic linear process example (LinearProcessFlow):

[0193] flowchartLR

[0194] Start

[0195] Step 1 [Sample preparation]

[0196] Step 2 [Sample Uncapping_gRPC]

[0197] Step 3 [Sample Processing_gRPC]

[0198] Step 4 [Sample Cover_gRPC]

[0199] End((end));

[0200] like Figure 4 As shown, an example of a process with conditional branches (ConditionalProcessFlow):

[0201] flowchartTD

[0202] Start

[0203] Init[Initialize_gRPC]

[0204] Check1{Dosage Match?}

[0205] Check 2 {Is the temperature appropriate?}

[0206] Process1[standard processing flow_gRPC]

[0207] Process2[Special processing flow_gRPC]

[0208] End

[0209] Start-->Init

[0210] Init-->Check1

[0211] Check1-->|Yes|Check2

[0212] Check1-->|No|End

[0213] Check2-->|Yes|Process1

[0214] Check2-->|No|Process2

[0215] Process1-->End

[0216] Process2-->End

[0217] like Figure 5 As shown, an example of a complex process with subgraphs (ComplexProcesswithSubgraphs):

[0218] flowchartTD

[0219] Start

[0220] End

[0221] Subgraph sample preparation

[0222] Prep1[Device Preparation_gRPC]

[0223] Prep2[Load sample_gRPC]

[0224] Prep3 [System Initialization_gRPC]

[0225] end

[0226] subgraph Main Flow

[0227] Main1 [Sample Uncovering_gRPC]

[0228] Main2 [Sample Processing_gRPC]

[0229] Check {Status OK?}

[0230] Retry [Retry Processing_gRPC]

[0231] end

[0232] subgraph Post Processing

[0233] Post1 [Sample Covering_gRPC]

[0234] Post2 [Experiment Cleanup_gRPC]

[0235] end

[0236] Start --> Prep1

[0237] Prep1 --> Prep2

[0238] Prep2 --> Prep3

[0239] Prep3 --> Main1

[0240] Main1 --> Main2

[0241] Main2 --> Check

[0242] Check --> |Yes| Post1

[0243] Check --> |No| Retry

[0244] Retry --> Main2

[0245] Post1 --> Post2

[0246] Post2 --> End

[0247] As Figure 6 shown, a multi-condition process flow example (Multi-Condition Process Flow):

[0248] flowchart TD

[0249] Start((start))

[0250] Init [system initialization_gRPC]

[0251] Check1 {is sample ready?}

[0252] Check2 {is equipment ready?}

[0253] Check3 {is temperature OK?}

[0254] Process1 [sample capping_gRPC]

[0255] Process2 [heating_gRPC]

[0256] Process3 [cooling_gRPC]

[0257] Process4 [experiment processing_gRPC]

[0258] Process5 [sample uncapping_gRPC]

[0259] Error [error handling_gRPC]

[0260] End ((end))

[0261] Start --> Init

[0262] Init --> Check1

[0263] Check1 --> |Yes| Check2

[0264] Check1 --> |No| Error

[0265] Check2 --> |Yes|

[0266] Check3 Check2 --> |No| Error

[0267] Check3 --> |Perfect| Process1

[0268] Check3 --> |Undercooling| Process2

[0269] Check3 --> |Overheating| Process3

[0270] Process2 --> Process1

[0271] Process3 --> Process1

[0272] Process1-->Process4

[0273] Process4-->Process5

[0274] Process5-->End

[0275] Error-->End

[0276] like Figure 7 As shown, the loop processing flow example (LoopProcessFlow):

[0277] flowchartTD

[0278] Start

[0279] Init[Initialize batch processing_gRPC]

[0280] CheckMore{Are there more samples?}

[0281] Process1[Sample Uncapping_gRPC]

[0282] Process2[sample processing_gRPC]

[0283] Check{Is the processing OK?}

[0284] Process3[sample capping_gRPC]

[0285] Retry[retry processing_gRPC]

[0286] End

[0287] Start-->Init

[0288] Init-->CheckMore

[0289] CheckMore-->|Yes|Process1

[0290] CheckMore-->|No|End

[0291] Process1-->Process2

[0292] Process2-->Check

[0293] Check-->|Yes|Process3

[0294] Check-->|No|Retry

[0295] Retry-->Process2

[0296] Process3-->Init

[0297] Module action logic for parsing Mermaid descriptions using ANTLR: ANTLR (Another Tool for Language Recognition) v4 is a powerful grammar analyzer generator that can be used to read, process, execute, and convert structured text or binary files. It is widely used in building languages, tools, and frameworks. ANTLR grammar analyzer can automatically build a syntax analysis tree - a data structure that represents how the grammar matches the input. ANTLR can also automatically generate tree walkers that you can use to access the nodes of those trees to execute specific code.

[0298] Group function definition and parsing execution: Demo details, as shown in Figure 8 , page display, function introduction.

[0299] Mermaid editing and generation: as shown in Figure 9 , the left box is the Mermaid editing box, and the right box is the real-time visualization area of the flowchart. By editing the Mermaid code in the left box, you can dynamically update the flowchart on the right and preview the design effect in real time.

[0300] If you need to implement node interaction functions, you can add the following statements to the last part of the Mermaid code: click(node ID), call(function name); Through this setting, users can click the corresponding node to trigger the specified interaction function to achieve dynamic interaction operation.

[0301] Node configuration: Users can bind interaction functions to nodes in the Mermaid code, and dynamically pass in parameters or set the behavior logic of the nodes through these functions.

[0302] Run waiting time: This configuration can be used to set the waiting time before the function runs. For example: if a step needs to wait for 10 seconds after adding liquid before adding acid, you can configure the waiting time to be 10 seconds in the node that adds acid. In this way, when the process executes to this node, it will first wait for the set time before running the corresponding function logic.

[0303] Parameter setting: By dynamically obtaining the corresponding parameters from the nodes, you can flexibly support reading parameters from static files (such as JSON files) or dynamically obtaining parameters from pre-node. The following are the implementation rules and configuration descriptions:

[0304] Static file parameter acquisition: If the parameter comes from a JSON file, the first layer node name of the JSON file must be consistent with the node name in the Mermaid flowchart (remove the "grpc_" prefix).

[0305] Dynamic parameter acquisition: If the parameter comes from a front node, it can be configured by the source node and source field to dynamically obtain the output result of the front node.

[0306] Source field: Whether static or dynamic, the parameter path is specified using the Parent.Child format for hierarchical division.

[0307] Save: Save the configured parameters to achieve persistent storage of parameters and send these parameters to the background during runtime to ensure that the execution process conforms to the preset logic.

[0308] Button function: Run button, start the entire process, trigger subsequent steps from the Start node, as shown in Figure 10 .

[0309] Pause button: temporarily pause during process running, used for debugging or interrupting operation, and resume running by clicking again, as shown in Figure 11 .

[0310] Stop button: terminate the current process running, as shown in Figure 12 .

[0311] Clear running process button: clear the current process state, ready for the next run, as shown in Figure 13 .

[0312] In summary, the present application successfully achieves the following key goals by introducing the Mermaid solution:

[0313] Flexible scheduling: By dynamically defining the relationship between multiple modules, users can easily adjust the process logic to meet different scenario requirements.

[0314] Unified architecture: With the help of Mermaid's visual design tool, complex multi-module interactions are integrated into an intuitive unified architecture, improving system readability and maintenance efficiency.

[0315] Low coupling and high expansion: Modules are decoupled through clear rules, avoiding code redundancy while providing strong expandability for quick integration of new modules in future feature expansion.

[0316] Through this solution, not only is the modular design of the system optimized, but also the development and maintenance costs are significantly reduced, laying a solid foundation for the flexibility, maintainability and scalability of the system.

[0317] Extension: Currently, the use of Mermaid's syntax structure for flowchart drawing and logical operation has successfully achieved dynamic scheduling and visual design. However, this solution is not the only feasible path. It can be further expanded and innovated according to project needs to achieve more flexible and efficient process management.

[0318] Flexible adaptation of multiple flowchart syntaxes:

[0319] In addition to Mermaid, other code writing syntaxes that support flowcharts (such as LiteFlow, BPMN, etc.) can be introduced to parse and adapt different graphical languages. This not only enhances the system's compatibility, but also allows for the selection of the most suitable syntax structure according to different business needs. LiteFlow: By supporting low-code, configurable graphical flow design, LiteFlow provides the advantages of rapid deployment and operation, making it suitable for building efficient process automation applications.

[0320] BPMN: As an industry standard, BPMN can handle more complex business processes and workflow management, especially suitable for cross-team collaboration and enterprise-level process modeling.

[0321] Construction and innovation of custom flowchart syntax:

[0322] A unique set of flowchart syntax rules can also be designed and written according to individual needs, fully meeting specific business scenarios and operational needs. Through customized syntax, more efficient and more practical process design can be achieved, making the system more marketable and unique. Custom rules: For example, custom node types, event triggering mechanisms, and process execution conditions can be introduced to further enhance the expressiveness of flowcharts.

[0323] Graphical and interactive enhancements: By defining specialized interaction logic, users can design and schedule processes in a more intuitive way, with real-time adjustments and visual feedback.

[0324] The module scheduling algorithm of the system meets the requirements of concurrency, efficiency, and uniformity for laboratory task execution. The action execution logic of the module is customizable, avoiding the impact of hardware design changes on experimental process execution. The system standardizes the module construction and process definition of the experimental platform, greatly reducing code duplication and improving project delivery stability and timeliness. Module parallel scheduling algorithm, Mermaid language parsing, and hardware module gRPC interface linkage are all core technologies of the system.

[0325] The module action logic definition of the experimental platform of the present invention can be described in a different language than Mermaid. Instead, a DSP (Domain Special Language) language can be defined independently, and then parsed and translated using the ANTLR tool for lexical analysis and syntax analysis. Using Mermaid is convenient for existing systems, utilizing existing Mermaid display controls to accelerate system implementation and make it easier for general laboratory operators to understand the module action logic.

[0326] Preferably, in the embodiment of the present invention, the Mermaid language definition of the module action, the Mermaid language description and technical features are:

[0327] Mermaid is a simple yet powerful diagramming language that allows users to create various diagrams using text descriptions. Through simple text syntax, you can generate beautiful and professional diagrams that describe the logic of module actions.

[0328] Core features: Declarative syntax: Use intuitive text descriptions to define chart structures; Real-time rendering: Write and view, and instantly preview the effect; Multi-platform support: Can be integrated into multiple platforms such as GitHub, GitLab, Notion, etc.; Strong scalability: Supports custom styles and themes.

[0329] Supported chart types: Figure 23 As shown, Flowchart is one of the most commonly used chart types, used to show processes, decisions, and workflows.

[0330] The flowchart supports a variety of node shapes and connection styles: Node shape: square [], rounded corner (), diamond {}, circle (); Connection type: solid line -->, dashed line -.-, thick line ==>; Direction: TD (top to bottom), LR (left to right), RL (right to left), BT (bottom to top).

[0331] like Figure 24 As shown, Sequence Diagram: Sequence diagram is used to show the interaction process and message passing order between objects.

[0332] like Figure 25 As shown, Mermaid description of module actions: Create a detailed state diagram to show the custom process of module actions in the laboratory automation platform.

[0333] This diagram includes the following main components: Basic state flow: The system starts from initialization, enters the standby state, can perform action configuration, and enters the running state after execution preparation.

[0334] Action configuration sub-state: Parameter setting: set specific parameters of the module, action arrangement: design action sequence, verification check: ensure configuration effectiveness.

[0335] Execution preparation sub-state: safety check, resource check, position calibration.

[0336] Running state sub-flow: support single-step execution, can pause monitoring, support continue execution until completion.

[0337] Exception handling mechanism: pause operation, error diagnosis, resume flow.

[0338] ANTLR 4 translation parsing tool: ANTLR 4 (ANother Tool for Language Recognition) is a powerful parser generator specifically designed for creating parsers for programming languages, domain-specific languages (DSLs), and other structured text. Developed by Professor Terence Parr, it is the fourth generation of the ANTLR series.

[0339] Core function details: Lexical analysis.

[0340] Lexical analysis (Lexer) is responsible for converting the input character stream into a token stream. It can: identify and classify input characters, handle comments and white space characters, support lexical mode switching, and handle Unicode character sets.

[0341] Syntax analysis (Parsing): The parser (Parser) processes the token stream and constructs a syntax tree. Main features include: support for LL(*) parsing strategy, handle left-recursive grammar rules, automatically eliminate syntax ambiguity, and provide powerful error recovery mechanism.

[0342] Tree traversal mechanism: ANTLR 4 provides two main tree traversal mechanisms: Visitor pattern (Visitor), explicit control of traversal process; can return values; suitable for complex tree operations; Listener pattern (Listener); automatically traverse the syntax tree; simple and easy to use; suitable for simple tree operations.

[0343] Development tool support: ANTLRWorks: integrated development environment, syntax debugger, syntax visualization tool, IDE plug-ins (IntelliJ IDEA, VS Code, etc.).

[0344] Target language support: ANTLR 4 can generate parser code in multiple programming languages: Java (primary target language); C# (the target language for this project); Python 2 and 3; JavaScript / TypeScript; Go; C++; Swift;

[0345] Detailed development process: Write grammar files (.g4); define lexical rules; define syntax rules; specify rule attributes and options;

[0346] Generate parser code: Use ANTLR tool to generate target language code; compile generated code.

[0347] Integration into application: Create instances of lexical analyzer and parser, configure error handling strategy, implement tree traversal logic.

[0348] Testing and debugging: Use test cases to verify grammar; debug parsing process; optimize performance; performance considerations; fast parsing speed suitable for real-time processing; controllable memory usage; support streaming large files; provide performance optimization options.

[0349] Best practices: Modular grammar design; reasonable use of syntax rule options; appropriate error recovery; pay attention to the separation of lexical analyzer and parser; ANTLR 4 is not just a parser generator, it provides a complete language recognition solution, capable of meeting various language processing needs from simple to complex.

[0350] Apply ANTLR 4 to translate and parse Mermaid language, as shown in Figure 26-30 .

[0351] To achieve the above purpose, the application further provides a multi-condition judgment scheduling processing system suitable for laboratory samples, which is used to realize the multi-condition judgment scheduling processing method suitable for laboratory samples; as shown in Figure 17 , the system specifically comprises:

[0352] A data module creation unit is configured to create at least two first modules corresponding to the laboratory samples to be processed; wherein the first module is a laboratory sample task process.

[0353] A sample scheduling processing unit is configured to schedule and process the laboratory samples in real time based on the first module and in combination with a multi-condition judgment mode.

[0354] The data module creation unit further comprises:

[0355] The first processing module is configured to generate and acquire first data corresponding to the laboratory samples, and initialize processing of the first data; wherein the first data is information data of the laboratory samples to be processed.

[0356] The first generating module is configured to generate second data corresponding to the laboratory samples to be processed based on the first data; wherein the second data is time data required for processing of all the laboratory samples.

[0357] The second processing module is configured to generate and acquire third data corresponding to the first module, and pre-process the laboratory samples to be processed based on the third data; wherein the third data is state data of a test tube state machine.

[0358] And / or, the sample scheduling processing unit further comprises:

[0359] The first determining module is configured to generate and acquire first prompt data corresponding to the laboratory samples to be processed, and determine whether to execute the next step based on the first prompt data; wherein the first prompt data is state prompt data in the process of processing the laboratory samples.

[0360] The third processing module is configured to generate and acquire first control data corresponding to the laboratory samples to be processed, and schedule processing of the laboratory samples in real time based on the first control data; wherein the first control data is control instruction data of the laboratory sample scheduling processing task.

[0361] And / or, the first determining module further comprises:

[0362] The second determining module is configured to generate and acquire second prompt data corresponding to the laboratory samples, and determine whether to execute the next step based on the second prompt data; wherein the second prompt data is preparation state prompt data of the laboratory samples.

[0363] The third determining module is configured to generate and acquire third prompt data corresponding to the laboratory sample processing equipment, and determine whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment operation state prompt data.

[0364] The fourth determining module is configured to generate and acquire fourth prompt data corresponding to the process of processing the laboratory samples, and determine whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is temperature state prompt data of the process of processing the laboratory samples.

[0365] And / or, the fourth determining module further comprises:

[0366] A second generation module is configured to generate and acquire fourth data corresponding to the laboratory sample processing process, wherein the fourth data is temperature threshold data of the laboratory sample processing process;

[0367] A third generation module is configured to generate fifth data in real time based on the fourth prompt data and in combination with the fourth data, wherein the fifth data is real-time temperature state data of the laboratory sample processing process, including suitable temperature state data, overheated temperature state data and overcooled temperature state data.

[0368] A fourth processing module is configured to perform real-time heating processing or cooling processing or no processing on the laboratory sample in the module according to the fifth data.

[0369] In the system scheme embodiment of the present application, the method steps involved in the multi-condition judgment scheduling processing suitable for laboratory samples have been described in detail above, that is, the function modules in the system are used to realize the steps or sub-steps in the above method embodiment, which will not be described here.

[0370] To achieve the above object, the present application further provides a multi-condition judgment scheduling processing platform suitable for laboratory samples, as shown in Figure 18 The processor executes the multi-condition judgment scheduling processing platform control program suitable for laboratory samples, the multi-condition judgment scheduling processing platform control program suitable for laboratory samples is stored in the memory, and the multi-condition judgment scheduling processing platform control program suitable for laboratory samples realizes the multi-condition judgment scheduling processing method steps. For example:

[0371] S1, creating at least two first modules corresponding to the laboratory samples to be scheduled for processing, wherein the first module is a laboratory sample task process;

[0372] S2, scheduling the laboratory samples in real time based on the first module and in combination with the multi-condition judgment mode.

[0373] The specific steps have been described above, and will not be described here.

[0374] In the embodiment of the present application, the built-in processor of the multi-condition determination scheduling processing platform for laboratory samples can be composed of integrated circuits, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more combinations of central processing units (CPU), microprocessors, digital processing chips, graphics processors and various control chips. The processor connects various components through various interfaces and lines, executes programs or units stored in the memory, and calls data stored in the memory to perform various functions and process data of the multi-condition determination scheduling processing platform for laboratory samples.

[0375] The memory is used to store program codes and various data, is installed in the multi-condition determination scheduling processing platform for laboratory samples, and realizes high-speed and automatic access to programs or data during operation.

[0376] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memory, magnetic disk memory, magnetic tape memory, or any other computer-readable medium capable of carrying or storing data.

[0377] To achieve the above-mentioned purpose, the present application also provides a computer readable storage medium, such as Figure 19 As shown in the figure, the computer readable storage medium stores a multi-condition determination scheduling processing platform control program for laboratory samples, which realizes the steps of the multi-condition determination scheduling processing method for laboratory samples, for example:

[0378] S1, creating at least two first modules corresponding to laboratory sample to be scheduled;

[0379] S2, based on the first module, and combined with multi-condition judgment mode, real-time scheduling processing the laboratory sample.

[0380] The specific details of the steps have been described above, and will not be repeated here.

[0381] In the description of the embodiments of the present application, it should be noted that any process or method described in the flowchart or otherwise described herein can be understood as representing a module, a fragment or a part of code including one or more executable instructions for implementing a specific logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations in which the functions can be performed in an order other than that shown or discussed, including in a substantially simultaneous manner or in reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0382] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a list of ordered executable instructions for implementing a logical function, which can be specifically implemented in any computer readable medium for use by or in conjunction with an instruction execution system, device or apparatus, such as a computer-based system, a system including a processing module or other system that can fetch and execute instructions from the instruction execution system, device or apparatus. For the purpose of this specification, "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in conjunction with an instruction execution system, device or apparatus. More specific examples (non-exhaustive list) of computer readable medium include the following: electrical connections having one or more wires (electronic devices), portable computer disk boxes (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROMs).

[0383] In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be obtained electronically, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or otherwise processing, if necessary, in other suitable ways, to be stored in the computer memory.

[0384] In the embodiments of the present application, in order to achieve the above-mentioned purpose, the present application also provides a chip system, which comprises at least one processor, and when program instructions are executed in the at least one processor, the chip system executes the steps of the multi-condition judgment scheduling processing method suitable for laboratory samples, for example:

[0385] S1, creating at least two first modules corresponding to laboratory samples to be scheduled for processing; wherein the first module is a laboratory sample task process;

[0386] S2, based on the first module, and combined with a multi-condition judgment mode, the laboratory samples are scheduled for processing in real time.

[0387] The specific details of the steps have been described above, and will not be repeated here.

[0388] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0389] The present application creates at least two first modules corresponding to laboratory samples to be scheduled for processing by the method; wherein the first module is a laboratory sample task process; based on the first module, and combined with a multi-condition judgment mode, the laboratory samples are scheduled for processing in real time, and the system, platform and storage medium corresponding to the method can realize multi-condition judgment scheduling processing of laboratory samples, and realize parallel scheduling of multiple modules. The processing time of the sample through the module is accurate, and the processing efficiency is high.

[0390] In addition, the present application scheme further encapsulates and links the modules by means of Mermaid language to form new functional module units, so that the processing capacity of the experimental platform can be quickly customized and changed according to user needs; that is, users can easily adjust the process logic to meet different scene requirements.

[0391] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A multi-condition determination scheduling processing method suitable for laboratory samples, characterized in that: The method comprises: Creating at least two first modules corresponding to laboratory samples to be scheduled for processing; wherein the first modules are laboratory sample task processes; further comprising: generating and acquiring first data corresponding to the laboratory samples, and initiating processing of the first data; wherein the first data is information data of the laboratory samples to be processed; generating second data corresponding to the laboratory samples to be scheduled for processing based on the first data; wherein the second data is data on the time required to complete processing of all laboratory samples; Based on the first module and in combination with the multi-condition judgment mode, the laboratory samples are scheduled for processing in real time; wherein, it also includes: generating and obtaining first prompt data corresponding to the laboratory samples to be scheduled for processing, and determining whether to execute the next step based on the first prompt data; wherein, the first prompt data is status prompt data during the laboratory sample processing process; generating and obtaining first control data corresponding to the scheduled processing of the laboratory samples, and based on the first control data, scheduling the processing of the laboratory samples in real time; wherein, the first control data is control instruction data for the laboratory sample scheduling processing operation.

2. A multi-condition determination scheduling processing method applicable to laboratory samples according to claim 1, characterized in that: The step of creating at least two first modules corresponding to the laboratory samples to be scheduled for processing further includes: Generate and obtain third data corresponding to the first module, and pre-process the laboratory sample to be scheduled for processing based on the third data; wherein the third data is state data of the test tube state machine.

3. A multi-condition determination scheduling processing method applicable to laboratory samples according to claim 1, characterized in that: The generating and obtaining first prompt data corresponding to the laboratory sample to be scheduled for processing, and determining whether to execute the next step based on the first prompt data, further includes: Generate and obtain second prompt data corresponding to the laboratory sample, and determine whether to execute the next step based on the second prompt data; wherein the second prompt data is laboratory sample preparation status prompt data; Generate and obtain third prompt data corresponding to the laboratory sample processing equipment, and determine whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment operation status prompt data; Generate and obtain fourth prompt data corresponding to the laboratory sample processing process, and determine whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is prompt data of the temperature status of the laboratory sample processing process.

4. A multi-condition determination scheduling processing method applicable to laboratory samples according to claim 3, characterized in that: The generating and obtaining fourth prompt data corresponding to the laboratory sample processing process, and determining whether to execute the next step based on the fourth prompt data, further includes: Generate and obtain fourth data corresponding to the laboratory sample processing process; wherein the fourth data is temperature threshold data of the laboratory sample processing process; Based on the fourth prompt data and in combination with the fourth data, corresponding fifth data is generated in real time; wherein the fifth data is real-time temperature status data of the laboratory sample processing process, including appropriate temperature status data, overheating temperature status data, and undercooling temperature status data; According to the fifth data, the laboratory sample in the module is heated, cooled or not heated in real time.

5. A multi-condition determination scheduling processing system suitable for laboratory samples, characterized in that: The system is applied to a multi-condition determination scheduling processing method applicable to laboratory samples as claimed in any one of claims 1 to 4; the system comprises: A data module creation unit, configured to create at least two first modules corresponding to laboratory samples to be scheduled for processing; wherein the first modules are laboratory sample task processes; The sample scheduling processing unit is used to schedule and process the laboratory samples in real time based on the first module and in combination with a multi-condition judgment mode.

6. A multi-condition determination scheduling processing system suitable for laboratory samples according to claim 5, characterized in that: The data module creation unit further includes: A first processing module is configured to generate and obtain first data corresponding to a laboratory sample and to initiate processing of the first data; wherein the first data is information data of the laboratory sample to be processed; A first generating module is configured to generate second data corresponding to the laboratory samples to be scheduled for processing based on the first data; wherein the second data is data on the time required for all laboratory samples to be processed; a second processing module, configured to generate and obtain third data corresponding to the first module, and pre-process the laboratory sample to be scheduled for processing based on the third data; wherein the third data is state data of a test tube state machine; And / or, the sample scheduling processing unit further includes: A first determination module is configured to generate and obtain first prompt data corresponding to the laboratory sample to be scheduled for processing, and determine whether to execute the next step based on the first prompt data; wherein the first prompt data is status prompt data during the laboratory sample processing process; a third processing module, configured to generate and obtain first control data corresponding to the scheduling and processing of laboratory samples, and to schedule and process the laboratory samples in real time based on the first control data; wherein the first control data is control instruction data for the scheduling and processing of laboratory samples; And / or, the first determination module further includes: A second determination module is configured to generate and obtain second prompt data corresponding to the laboratory sample, and determine whether to execute the next step based on the second prompt data; wherein the second prompt data is laboratory sample preparation status prompt data; a third determination module, configured to generate and obtain third prompt data corresponding to the laboratory sample processing equipment, and determine whether to execute the next step based on the third prompt data; wherein the third prompt data is equipment operation status prompt data; a fourth determination module, configured to generate and obtain fourth prompt data corresponding to the laboratory sample processing process, and determine whether to execute the next step based on the fourth prompt data; wherein the fourth prompt data is prompt data of the temperature status of the laboratory sample processing process; And / or, the fourth determination module further includes: A second generating module is configured to generate and obtain fourth data corresponding to the laboratory sample processing process; wherein the fourth data is temperature threshold data of the laboratory sample processing process; a third generating module, configured to determine and generate corresponding fifth data in real time based on the fourth prompt data and in combination with the fourth data; wherein the fifth data is real-time temperature status data of the laboratory sample processing process, including appropriate temperature status data, overheating temperature status data, and undercooling temperature status data; The fourth processing module is used to perform real-time heating treatment, cooling treatment or no treatment on the laboratory samples in the module according to the fifth data.

7. A multi-condition determination scheduling processing platform suitable for laboratory samples, characterized by: The invention comprises a processor, a memory and a multi-condition determination scheduling processing platform control program applicable to laboratory samples; wherein the multi-condition determination scheduling processing platform control program applicable to laboratory samples is executed by the processor, the multi-condition determination scheduling processing platform control program applicable to laboratory samples is stored in the memory, and the multi-condition determination scheduling processing platform control program applicable to laboratory samples implements the multi-condition determination scheduling processing method applicable to laboratory samples as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a multi-condition determination scheduling processing platform control program applicable to laboratory samples, and the multi-condition determination scheduling processing platform control program applicable to laboratory samples implements the multi-condition determination scheduling processing method applicable to laboratory samples as described in any one of claims 1 to 4.

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