A heating control method and system for a heating plate used in a nucleic acid detector

Abstract

The present invention provides a heating plate heating control method and system applied to a nucleic acid detector. The first-level processing idea will identify the first heating control element and use the identified first heating control element to select a heating plate control task request, and only input the initial heating plate control task request obtained after the selection into the second-level processing idea for second heating control element identification, which significantly saves the overhead of the second-level processing idea using the second heating control element to pair the heating plate control task request, and ensures the timeliness of the heating plate control task request analysis. After the heating plate control task request is selected using the first heating control element, the second heating control element will be used to pair the heating plate control task request. A heating control execution list for the heating plate control task request to be processed can be generated based on the quickly and accurately obtained heating plate control reference information, thereby improving the intelligence level of the heating control of the heating plate applied to the nucleic acid detector.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a heating control method and system for a heating plate applied to a nucleic acid detector. Background Art

[0002] The nucleic acid detector is used to find the nucleic acid of foreign invading viruses in human respiratory specimens, blood or feces to determine whether the patient is infected with the new coronavirus. Therefore, once the nucleic acid test is positive, it can be proved that the patient has the virus in his body. Therefore, in the laboratory, the heating control of the heating plate used in the nucleic acid detector is crucial to the test results. The heating plate can effectively convert electrical energy into thermal energy to heat the object evenly. It has high heating efficiency and fast heating speed. It is a way to utilize electrical energy. Compared with ordinary fuels, it has a higher temperature and can reach a very high temperature. With the continuous promotion of the application scope of the heating plate, the heating control of the heating plate has also become diversified. Therefore, how to improve the intelligence of the heating control of the heating plate is one of the technical problems that need to be solved at present. Summary of the invention

[0003] In order to improve the technical problems existing in the related art, the present invention provides a heating control method and system for a heating plate applied to a nucleic acid detector.

[0004] An embodiment of the present invention provides a heating control method for a heating plate applied to a nucleic acid detector, which is applied to an artificial intelligence system. The method includes:

[0005] Obtaining a pending heating plate control task request, and identifying a heating control element for the pending heating plate control task request according to a first heating control element identification strategy to obtain a first heating control element, wherein the first heating control element identification strategy is an identification strategy using a first attention rule;

[0006] In combination with the first heating control element of the pending heating plate control task request, at least one initial heating plate control task request paired with the pending heating plate control task request is determined, and heating control element identification is performed on the pending heating plate control task request and each initial heating plate control task request according to a second heating control element identification strategy to obtain a second heating control element, wherein the second heating control element identification strategy is an identification strategy using a second attention rule;

[0007] Using the identified second heating control element, the pending heating plate control task request is sequentially paired with each initial heating plate control task request for heating control links, a first heating plate control task request in the at least one initial heating plate control task request is determined as heating plate control reference information, and the number of heating control links paired between the first heating plate control task request and the pending heating plate control task request is greater than a number threshold;

[0008] A heating control execution list for the to-be-processed heating plate control task request is generated through the heating plate control reference information.

[0009] Preferably, the step of identifying the heating control elements of the pending heating plate control task request according to the first heating control element identification strategy includes:

[0010] A residual network is used to identify heating control elements for the pending heating plate control task request, and the residual network is debugged using a first debugging sample set and a second debugging sample set; wherein the first debugging sample set is a sample set carrying training annotations, and the second debugging sample set is a sample set without carrying training annotations.

[0011] Preferably, the residual network includes a sliding average model, a clustering component and a discriminant component, and the debugging process of the residual network includes:

[0012] inputting the heating plate control task request samples included in the first debugging sample set and the second debugging sample set into the sliding average model;

[0013] The heating control factor data output by the sliding average model are cyclically clustered through the clustering layer of the clustering component, and the obtained clustering results are output to the discrimination component as training interference annotations;

[0014] A debugging cost index combined with the training interference annotation is obtained through a discriminant kernel of the discriminant component, and a model variable of the sliding average model is cyclically optimized according to the debugging cost index.

[0015] Preferably, the obtaining of the debugging cost indicator combined with the training interference annotation includes: using the training interference annotation, the heating control factor data output by the sliding average model, and the discrimination result of the discrimination check on the heating control factor data output by the sliding average model as the debugging cost indicator combined with the training interference annotation.

[0016] Preferably, the method further comprises:

[0017] Obtaining basic correlation data of two consecutive clustering results, wherein the basic correlation data indicates the jumpiness of the residual network, and a larger value of the basic correlation data indicates that the residual network is more stable;

[0018] The stable state of the residual network is determined according to the basic association data, and the network debugging process is completed after the residual network reaches the stable state, wherein the stability requirement of the residual network is that the value of the basic association data reaches a set value.

[0019] Preferably, the determining of at least one initial heating plate control task request paired with the to-be-processed heating plate control task request in combination with the first heating control element of the to-be-processed heating plate control task request comprises:

[0020] Obtaining a heating control element difference between the first heating control element of the to-be-processed heating plate control task request and the first heating control element recorded in the heating control element space, and sorting the target heating plate control task requests corresponding to the recorded first heating control element according to a rule of descending order of heating control element differences;

[0021] A set number of target heating plate control task requests that are in front of the target heating plate control task request after the sorting is completed are determined as the at least one initial heating plate control task request.

[0022] Preferably, performing second heating control factor identification on each initial heating plate control task request includes:

[0023] Obtaining a first heating control element of each initial heating plate control task request;

[0024] The heating control factor difference between the first heating control factor of each initial heating plate control task request and the first heating control factor of the heating plate control task request to be processed is obtained respectively, and the second heating control factor is identified for the second heating plate control task request in the at least one initial heating plate control task request; wherein the heating control factor difference between the second heating plate control task request and the heating plate control task request to be processed is less than the first difference limit.

[0025] Preferably, the obtaining of the pending heating plate control task request comprises: receiving a heating plate control task report sent by a heating plate control system, and obtaining the pending heating plate control task request from the heating plate control task report;

[0026] After obtaining the heating plate control reference information, the method further includes: sending the heating plate control reference information to the heating plate control system.

[0027] Preferably, the step of identifying heating control elements for the pending heating plate control task request and each initial heating plate control task request according to the second heating control element identification strategy includes:

[0028] For any heating plate control task request to be subjected to heating control element identification, the heating control link is located in the heating plate control task request to obtain a plurality of heating control links;

[0029] A characterization vector is created for each located heating control link, wherein the characterization vector is outputted via a description vector of a set scale.

[0030] Preferably, the use of the identified second heating control element to sequentially pair the pending heating plate control task request with each initial heating plate control task request for heating control link processing includes:

[0031] Obtain a first heating control link representation carrier set of the pending heating plate control task request and a second heating control link representation carrier set of any initial heating plate control task request;

[0032] Determine a difference in heating control elements between two random heating control link representation carriers, one of the two random heating control link representation carriers is from the first heating control link representation carrier set, and the other heating control link representation carrier is from the second heating control link representation carrier set;

[0033] Determine paired heating control links in the pending heating plate control task request and the initial heating plate control task request by using the obtained heating control element difference, and the heating control element difference corresponding to any pair of paired heating control links is less than a second difference limit value;

[0034] The paired heating control links are screened to obtain the final paired heating control links.

[0035] An embodiment of the present invention also provides an artificial intelligence system, including a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the above method.

[0036] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program implements the above method when running.

[0037] The embodiment of the present invention combines the first heating control element and the second heating control element to collaboratively perform heating plate control task request analysis. The first heating control element is a heating control element identified based on the first heating control element identification strategy, and the second heating control element is a heating control element identified based on the second heating control element identification strategy, wherein the first heating control element identification strategy is an identification strategy utilizing the first attention rule, and the second heating control element identification strategy is an identification strategy utilizing the second attention rule. The embodiment of the present invention combines the strengths of different heating control elements, wherein the overhead of using the first heating control element to perform heating plate control task request analysis is small, and the pairing accuracy of using the second heating control element to perform heating plate control task request analysis is guaranteed. An embodiment of the present invention includes a two-level processing idea. The first-level processing idea will identify the first heating control element and use the identified first heating control element to select the heating plate control task request, and only input the initial heating plate control task request obtained after the selection into the second-level processing idea for second heating control element identification, which significantly saves the overhead of the second-level processing idea using the second heating control element to pair the heating plate control task request, ensures the timeliness of the heating plate control task request analysis, and after the first heating control element is used to select the heating plate control task request, the second heating control element will be used again to pair the heating plate control task request, ensures the accuracy of the heating plate control task request analysis, so that a heating control execution list for the heating plate control task request to be processed can be generated based on the quickly and accurately obtained heating plate control reference information, thereby improving the intelligence level of the heating control of the heating plate applied to the nucleic acid detector. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0039] Figure 1 It is a flow chart of a heating control method for a heating plate applied to a nucleic acid detector provided in an embodiment of the present invention. Implementation

[0040] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0042] The method embodiments provided in the embodiments of the present invention may be executed in an artificial intelligence system, a computer device or a similar computing device. Taking running on an artificial intelligence system as an example, the artificial intelligence system may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory for storing data. Optionally, the artificial intelligence system may also include a transmission device for communication functions.

[0043] The memory can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a heating control method for a heating plate applied to a nucleic acid detector in an embodiment of the present invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implementing the above method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the artificial intelligence system via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0044] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the artificial intelligence system. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0045] Based on this, see Figure 1 , Figure 1 It is a flow chart of a heating control method for a heating plate applied to a nucleic acid detector provided in an embodiment of the present invention. The method is applied to an artificial intelligence system. Further, the method may at least include the technical solutions recorded in the following steps 10 to 40.

[0046] Step 10, obtain a pending heating plate control task request, identify the heating control element of the pending heating plate control task request according to a first heating control element identification strategy, and obtain a first heating control element, wherein the first heating control element identification strategy is an identification strategy using a first attention rule.

[0047] Among them, the pending heating plate control task request records a series of heating control requirements for the heating plate in text form, such as heating period, heating method, heating temperature and heating trigger conditions. Heating control factor identification can be understood as mining the heating control features of the pending heating plate control task request, and the heating control factor can be understood as the task feature of the pending heating plate control task request. Furthermore, the heating control factor identification strategy can be understood as a feature extraction strategy, and the first attention rule focuses on the timeliness and resource conservation of heating control factor mining.

[0048] Step 20, in combination with the first heating control element of the pending heating plate control task request, determine at least one initial heating plate control task request paired with the pending heating plate control task request, and identify the heating control elements of the pending heating plate control task request and each initial heating plate control task request according to the second heating control element identification strategy to obtain the second heating control element, wherein the second heating control element identification strategy is an identification strategy using the second attention rule.

[0049] Among them, the initial heating plate control task request can be understood as an alternative heating plate control task request that matches the pending heating plate control task request, and the second attention rule focuses on the accuracy of heating control element mining.

[0050] Step 30, using the identified second heating control element, the pending heating plate control task request is paired with each initial heating plate control task request in sequence for heating control link processing, and the first heating plate control task request in the at least one initial heating plate control task request is determined as the heating plate control reference information, and the number of heating control links paired between the first heating plate control task request and the pending heating plate control task request is greater than the number threshold.

[0051] Among them, the heating control link can be understood as the control event or control item in the corresponding heating plate control task request. Based on this, the determined heating plate control reference information can be used as a reference for generating a heating control execution list.

[0052] Step 40: Generate a heating control execution list for the pending heating plate control task request using the heating plate control reference information.

[0053] Among them, a heating control execution list for a pending heating plate control task request can be generated based on the heating control reference list corresponding to the heating plate control reference information. For example, the heating control execution list can be obtained by fine-tuning the heating plate control reference information based on the pending heating plate control task request. Since the heating plate control reference information and the pending heating plate control task request have a high degree of match, the degree of fit between the heating plate control reference information and the pending heating plate control task request can also be guaranteed, so that the heating control execution list for the pending heating plate control task request can be generated quickly and accurately, thereby improving the accuracy and timeliness of the heating control of the heating plate applied to the nucleic acid detector.

[0054] In some possible embodiments, the step 10 of identifying the heating control elements of the pending heating plate control task request according to the first heating control element identification strategy includes step 100 .

[0055] Step 100: using a residual network to identify heating control elements for the pending heating plate control task request, wherein the residual network is debugged using a first debugging sample set and a second debugging sample set.

[0056] In the embodiment of the present invention, the first debugging sample set is a sample set carrying training annotations, and the second debugging sample set is a sample set not carrying training annotations.

[0057] In some optional design ideas, the residual network includes a sliding average model, a clustering component and a discrimination component. Based on this, the debugging process of the residual network includes steps 1001 to 1003.

[0058] Step 1001: Input the heating plate control task request samples included in the first debugging sample set and the second debugging sample set into the sliding average model.

[0059] Step 1002: cyclically cluster the heating control factor data output by the sliding average model through the clustering layer of the clustering component, and output the obtained clustering results as training interference annotations to the discrimination component.

[0060] Step 1003: Obtain a debugging cost index combined with the training interference annotation through the discriminant kernel of the discriminant component, and cyclically optimize the model variables of the sliding average model according to the debugging cost index.

[0061] Furthermore, obtaining the debugging cost indicator combined with the training interference annotation in step 1003 includes: using the training interference annotation, the heating control factor data output by the sliding average model, and the discrimination result of the heating control factor data output by the discrimination check on the sliding average model as the debugging cost indicator combined with the training interference annotation.

[0062] For example, training perturbation annotations can be understood as pseudo-annotations.

[0063] Based on the above content, the method further includes step 1004 and step 1005.

[0064] Step 1004: Obtain basic correlation data of two consecutive clustering results, wherein the basic correlation data indicates the jumpiness of the residual network. A larger value of the basic correlation data indicates that the residual network is more stable.

[0065] Step 1005: determine the stable state of the residual network according to the basic association data, and complete the network debugging process after the residual network reaches the stable state, wherein the stability requirement of the residual network is that the value of the basic association data reaches a set value.

[0066] In another possible embodiment, the first heating control element described in step 20 combined with the pending heating plate control task request to determine at least one initial heating plate control task request paired with the pending heating plate control task request may include steps 21 and 22.

[0067] Step 21, obtain the heating control element difference between the first heating control element of the to-be-processed heating plate control task request and the first heating control element recorded in the heating control element space, and sort the target heating plate control task requests corresponding to the recorded first heating control element according to the rule of descending order of heating control element differences.

[0068] Step 22: determining the first set number of target heating plate control task requests after sorting as the at least one initial heating plate control task request.

[0069] By applying step 21 and step 22, the target heating plate control task requests can be sorted based on the heating control factor differences, so that a set number of the first target heating plate control task requests after sorting are selected as the initial heating plate control task requests.

[0070] In some exemplary embodiments, the step 30 of identifying the second heating control element for each initial heating plate control task request includes steps 31 and 32 .

[0071] Step 31: Obtain the first heating control element of each initial heating plate control task request.

[0072] Step 32, respectively obtain the heating control element difference between the first heating control element of each initial heating plate control task request and the first heating control element of the to-be-processed heating plate control task request, and identify the second heating control element of the second heating plate control task request in the at least one initial heating plate control task request.

[0073] In an embodiment of the present invention, a difference in heating control factors between the second heating plate control task request and the to-be-processed heating plate control task request is less than a first difference limit.

[0074] In some possible design ideas, obtaining the pending heating plate control task request in step 10 includes: receiving a heating plate control task report sent by a heating plate control system, and obtaining the pending heating plate control task request from the heating plate control task report. Based on this, after obtaining the heating plate control reference information, the method further includes: sending the heating plate control reference information to the heating plate control system.

[0075] In some possible examples, step 20 includes identifying heating control elements for the pending heating plate control task request and each initial heating plate control task request according to a second heating control element identification strategy, and includes sub-step 201 and sub-step 202 .

[0076] Sub-step 201: for any heating plate control task request to be subjected to heating control element identification, locate the heating control link in the heating plate control task request to obtain a plurality of heating control links.

[0077] Sub-step 202: creating a characterization vector for each located heating control link, wherein the characterization vector is outputted via a description vector of a set scale.

[0078] Therefore, the representation carrier can also be understood as a description field.

[0079] On the basis of sub-steps 201 and 202, step 30 utilizes the identified second heating control element to pair the pending heating plate control task request with each initial heating plate control task request in turn for heating control link processing, including sub-steps 301 to 304.

[0080] Sub-step 301, obtaining a first heating control link representation carrier set of the to-be-processed heating plate control task request and a second heating control link representation carrier set of any initial heating plate control task request.

[0081] Sub-step 302: determining the difference in heating control elements between two random heating control link representation carriers, wherein one of the two random heating control link representation carriers is from the first heating control link representation carrier set, and the other heating control link representation carrier is from the second heating control link representation carrier set.

[0082] Sub-step 303, using the obtained heating control element difference, determine the paired heating control links in the pending heating plate control task request and the initial heating plate control task request, and the heating control element difference corresponding to any pair of paired heating control links is less than the second difference limit.

[0083] Sub-step 304: screening the paired heating control links to obtain the final paired heating control links.

[0084] Applied to sub-steps 301 to 304, the pairing of the heating control links can be achieved accurately and reliably.

[0085] An embodiment of the present invention also provides an artificial intelligence system, including a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the above method.

[0086] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program implements the above method when running.

[0087] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0088] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0089] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A heating control method for a heating plate used in a nucleic acid detector, characterized in that: Applied to an artificial intelligence system, the method comprises: Obtaining a pending heating plate control task request, and identifying a heating control element for the pending heating plate control task request according to a first heating control element identification strategy to obtain a first heating control element, wherein the first heating control element identification strategy is an identification strategy using a first attention rule; In combination with the first heating control element of the pending heating plate control task request, at least one initial heating plate control task request paired with the pending heating plate control task request is determined, and heating control element identification is performed on the pending heating plate control task request and each initial heating plate control task request according to a second heating control element identification strategy to obtain a second heating control element, wherein the second heating control element identification strategy is an identification strategy using a second attention rule; Using the identified second heating control element, the pending heating plate control task request is sequentially paired with each initial heating plate control task request for heating control links, a first heating plate control task request in the at least one initial heating plate control task request is determined as heating plate control reference information, and the number of heating control links paired between the first heating plate control task request and the pending heating plate control task request is greater than a number threshold; generating a heating control execution list for the pending heating plate control task request through the heating plate control reference information; The step of identifying heating control elements for the to-be-processed heating plate control task request and each initial heating plate control task request according to the second heating control element identification strategy comprises: for any heating plate control task request to be identified with heating control elements, locating a heating control link in the heating plate control task request to obtain a plurality of heating control links; creating a representation carrier for each located heating control link, wherein the representation carrier is outputted through a description vector of a set scale; Among them, the use of the identified second heating control element to pair the pending heating plate control task request with each initial heating plate control task request in turn for heating control link pairing processing, including: obtaining the first heating control link representation carrier set of the pending heating plate control task request and the second heating control link representation carrier set of any initial heating plate control task request; determining the heating control element difference between two random heating control link representation carriers, one of the two random heating control link representation carriers is from the first heating control link representation carrier set, and the other heating control link representation carrier is from the second heating control link representation carrier set; using the obtained heating control element difference, determining the paired heating control link in the pending heating plate control task request and the initial heating plate control task request, the difference in heating control elements corresponding to any pair of paired heating control links is less than the second difference limit; screening the paired heating control links to obtain the final paired heating control links.

2. The method according to claim 1, characterized in that The step of identifying the heating control elements of the pending heating plate control task request according to the first heating control element identification strategy includes: A residual network is used to identify heating control elements for the pending heating plate control task request, and the residual network is debugged using a first debugging sample set and a second debugging sample set; wherein the first debugging sample set is a sample set carrying training annotations, and the second debugging sample set is a sample set without carrying training annotations.

3. The method according to claim 2, characterized in that The residual network includes a sliding average model, a clustering component and a discrimination component. The debugging process of the residual network includes: inputting the heating plate control task request samples included in the first debugging sample set and the second debugging sample set into the sliding average model; The heating control factor data output by the sliding average model are cyclically clustered through the clustering layer of the clustering component, and the obtained clustering results are output to the discrimination component as training interference annotations; Obtaining a debugging cost index combined with the training interference annotation through the discrimination check of the discrimination component, and cyclically optimizing the model variables of the sliding average model according to the debugging cost index; Among them, the obtaining of the debugging cost indicator combined with the training interference annotation includes: using the training interference annotation, the heating control factor data output by the sliding average model, and the discrimination result of the heating control factor data output by the sliding average model as the debugging cost indicator combined with the training interference annotation.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining basic correlation data of two consecutive clustering results, wherein the basic correlation data indicates the jumpiness of the residual network, and a larger value of the basic correlation data indicates that the residual network is more stable; The stable state of the residual network is determined according to the basic association data, and the network debugging process is completed after the residual network reaches the stable state, wherein the stability requirement of the residual network is that the value of the basic association data reaches a set value.

5. The method according to claim 1, characterized in that The determining, in combination with the first heating control element of the pending heating plate control task request, at least one initial heating plate control task request paired with the pending heating plate control task request comprises: Obtaining a heating control element difference between the first heating control element of the to-be-processed heating plate control task request and the first heating control element recorded in the heating control element space, and sorting the target heating plate control task requests corresponding to the recorded first heating control element according to a rule of descending order of heating control element differences; A set number of target heating plate control task requests that are in front of the target heating plate control task request after the sorting is completed are determined as the at least one initial heating plate control task request.

6. The method according to claim 1, characterized in that The second heating control element identification is performed for each initial heating plate control task request, including: Obtaining a first heating control element of each initial heating plate control task request; The heating control factor difference between the first heating control factor of each initial heating plate control task request and the first heating control factor of the heating plate control task request to be processed is obtained respectively, and the second heating control factor is identified for the second heating plate control task request in the at least one initial heating plate control task request; wherein the heating control factor difference between the second heating plate control task request and the heating plate control task request to be processed is less than the first difference limit.

7. The method according to claim 1, characterized in that The obtaining of the pending heating plate control task request comprises: receiving a heating plate control task report sent by a heating plate control system, and obtaining the pending heating plate control task request from the heating plate control task report; After obtaining the heating plate control reference information, the method further includes: sending the heating plate control reference information to the heating plate control system.

8. An artificial intelligence system, characterized in that: It comprises a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the method described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program implements the method described in any one of claims 1 to 7 when running.

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