VR ecological simulation module control processing method and system

Abstract

The VR ecological simulation module control processing method and system of the present invention can not only be compatible with the module operation data of various module operation data states for control element analysis, but also can ensure the accuracy and credibility of the control element analysis results, thereby realizing accurate and reliable module operation data updates, so as to achieve targeted environmental control of the target VR ecological simulation scene and improve the intelligence level of simulation module control technology.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and in particular to a VR ecological simulation module control processing method and system. Background Art

[0002] With the continuous advancement of virtual reality technology, users have higher and higher requirements for the realism of virtual scenes. The realism of virtual scenes can usually be reflected from the perspective of scene ecology, and scene ecology is usually realized through a series of simulation modules. For example, simulation modules operate by receiving control instructions to achieve ecological simulation of virtual scenes. However, the intelligence level of related simulation module control technology is low, making it difficult to achieve targeted VR ecological environment control. Summary of the invention

[0003] In order to improve the technical problems existing in the related technologies, the present invention provides a VR ecological simulation module control processing method and system.

[0004] In a first aspect, an embodiment of the present invention provides a VR ecological simulation module control processing method, which is applied to a module control processing system, and the method includes:

[0005] Determine the module operation data to be processed corresponding to the target VR ecological simulation scene; obtain the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data;

[0006] Based on the identification record of the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, a joint analysis of the control element description of the module operation data to be processed and the historical module operation data is performed to obtain a control element analysis result about the target VR ecological simulation scene;

[0007] Based on the control element analysis result, the operation data of the module to be processed is updated to achieve environmental control of the target VR ecological simulation scene.

[0008] In a possible embodiment, the module operation data state of the module operation data to be processed is different from the state of the historical module operation data; the identification record based on the module operation data state of the module operation data to be processed and the module operation data state of the historical module operation data performs a joint analysis of the control element description of the module operation data to be processed and the historical module operation data to obtain a control element analysis result about the target VR ecological simulation scene, including:

[0009] Determine the module operation data whose module operation data state is a first state among the module operation data to be processed and the historical module operation data as first module operation data, and determine the module operation data whose module operation data state is a second state among the module operation data to be processed and the historical module operation data as second module operation data;

[0010] Adjusting the state of the first module operation data to obtain third module operation data;

[0011] The third module operation data and the second module operation data are jointly analyzed for control element description to obtain a control element analysis result for the target VR ecological simulation scene.

[0012] In a possible embodiment, the module operation data state of the module operation data to be processed is the same as the module operation data state of the historical module operation data;

[0013] The identification record of the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, performing a joint analysis of the control element description of the module operation data to be processed and the historical module operation data, and obtaining a control element analysis result about the target VR ecological simulation scene, includes:

[0014] The control element description of the module operation data to be processed and the historical module operation data are jointly analyzed to obtain the control element analysis results of the target VR ecological simulation scene.

[0015] In a possible embodiment, the module operation data status based on the module operation data to be processed and the module operation data status of the historical module operation data are realized through an AI model, and a joint analysis of the control element description of the module operation data to be processed and the historical module operation data is performed to obtain a control element analysis result about the target VR ecological simulation scene;

[0016] The method also includes: optimizing the AI ​​model based on a previously obtained debugging data set, the debugging data set including a plurality of first authenticated debugging information sets, a plurality of second authenticated debugging information sets, and a plurality of third authenticated debugging information sets, wherein the first authenticated debugging information set includes a first example module operation data of a first state and a second example module operation data of a first state, and boot information of the first example module operation data; the second authenticated debugging information set includes a third example module operation data of a second state and a fourth example module operation data of a second state, and boot information of the third example module operation data; the third authenticated debugging information set includes a fifth example module operation data of a first state and a sixth example module operation data of a second state, and boot information of the fifth example module operation data.

[0017] In a possible embodiment, optimizing the AI ​​model based on a previously obtained debugging data set includes:

[0018] Performing a first optimization on the AI ​​model based on the multiple first authenticated debugging information sets;

[0019] The plurality of first authenticated debugging information sets and the plurality of second authenticated debugging information sets form a first example queue, and perform a second optimization on the first optimized AI model based on the first example queue;

[0020] Add multiple third authenticated debugging information sets to the first example queue to obtain a second example queue, and perform a third optimization on the second optimized AI model based on the second example queue.

[0021] In a possible embodiment, under the condition that the first optimization is performed on the AI ​​model, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of a first confidence value;

[0022] Under the condition that the AI ​​model is subjected to the second optimization, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of the first confidence value and a second quality inspection variable of a second confidence value, the second confidence value changes from an initial confidence value to the first confidence value, and the initial confidence value is less than the first confidence value;

[0023] Under the condition of performing the third optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a third quality inspection variable of the third confidence value.

[0024] In a possible embodiment, performing a third optimization on the second optimized AI model based on the second example queue includes:

[0025] Adjusting the state of the fifth example module operation data to obtain seventh example module operation data;

[0026] Inputting the seventh example module operation data and the sixth example module operation data into the AI ​​model to obtain a first control element analysis result on the fifth example module operation data;

[0027] Based on the first control element analysis result of the fifth example module operation data, a third optimization is performed on the AI ​​model after the second optimization.

[0028] In a possible embodiment, before performing the third optimization on the AI ​​model, the method further includes:

[0029] Inputting the fifth example module operation data and the sixth example module operation data into the AI ​​model to obtain a second control element analysis result of the fifth example module operation data;

[0030] Based on the second control element analysis result of the fifth example module operation data, performing a fourth optimization on the AI ​​model after the second optimization;

[0031] Wherein, under the condition that the AI ​​model is subjected to the fourth optimization, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a fourth quality inspection variable of a fourth confidence value, and the method further includes:

[0032] Under the condition that the quality inspection variable of the AI ​​model is determined based on the fourth quality inspection variable of the fourth confidence value, the fourth confidence value is updated.

[0033] In a second aspect, the present invention further provides a module control processing system, comprising 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 above.

[0034] In a third aspect, the present invention further provides a readable storage medium having a program stored thereon, which implements the above method when executed by a processor.

[0035] It is understandable that the module operation data to be processed corresponding to the target VR ecological simulation scene can be determined, and the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data can be obtained. Based on the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, the module operation data to be processed and the historical module operation data are jointly analyzed for the control element description to obtain the control element analysis results of the target VR ecological simulation scene, and the module operation data to be processed is updated based on the control element analysis results to achieve environmental control of the target VR ecological simulation scene. In this way, not only can the module operation data of various module operation data states be compatible with the control element analysis, but also the accuracy and credibility of the control element analysis results can be guaranteed, thereby achieving accurate and reliable module operation data updates, so as to achieve targeted environmental control of the target VR ecological simulation scene and improve the intelligence of the simulation module control technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] 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.

[0037] Figure 1 It is a hardware structure diagram of a module control processing system provided by an embodiment of the present invention.

[0038] Figure 2 It is a flowchart of a VR ecological simulation module control processing method provided by an embodiment of the present invention.

[0039] Figure 3 It is a communication architecture diagram of an application environment of a VR ecological simulation module control processing method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[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 embodiment provided in the embodiment of the present invention can be executed in a module control processing system, a computer device or a similar computing device. Taking the operation on the module control processing system as an example, Figure 1 1 is a hardware structure block diagram of a module control processing system for implementing a VR ecological simulation module control processing method according to an embodiment of the present invention. Figure 1 As shown, the module control processing system 10 may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the module control processing system may also include a transmission device 106 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the module control processing system. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0043] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a VR ecological simulation module control processing method in an embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 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 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the module control processing system 10 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 106 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 module control processing system 10. In one example, the transmission device 106 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 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0045] Based on this, see Figure 2 , Figure 2It is a flow chart of a VR ecological simulation module control processing method provided in an embodiment of the present invention. The method is applied to a module control processing system. Further, the method may at least include the technical solutions recorded in the following steps.

[0046] S11. Determine the module operation data to be processed corresponding to the target VR ecological simulation scene; obtain the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data.

[0047] S12. Based on the identification records of the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, a joint analysis of the control element descriptions of the module operation data to be processed and the historical module operation data is performed to obtain a control element analysis result for the target VR ecological simulation scene.

[0048] For example, control elements can be understood as control characteristics or control descriptions of module operation data, involving various simulation environment parameters of the target VR ecological simulation scene.

[0049] In a possible embodiment, the module operation data status of the module operation data to be processed is different from the status of the historical module operation data. Based on this, the identification record based on the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data recorded in S12 performs a joint analysis of the control element description of the module operation data to be processed and the historical module operation data to obtain the control element analysis result of the target VR ecological simulation scene, which may include the contents recorded in S121-S123.

[0050] S121. Determine the module operation data whose module operation data status is a first state among the module operation data to be processed and the historical module operation data as first module operation data, and determine the module operation data whose module operation data status is a second state among the module operation data to be processed and the historical module operation data as second module operation data.

[0051] S122: Adjust the state of the first module operation data to obtain third module operation data.

[0052] S123: Perform a joint analysis of control element descriptions on the third module operation data and the second module operation data to obtain a control element analysis result for the target VR ecological simulation scene.

[0053] In this way, under the premise that the module operation data status of the module operation data to be processed is different from the status of the historical module operation data, the obtained third module operation data and the second module operation data are jointly analyzed for control element description, which can ensure the accuracy and credibility of the control element analysis results of the target VR ecological simulation scene.

[0054] In a possible embodiment, the module operation data status of the module operation data to be processed is the same as the module operation data status of the historical module operation data. Based on this, the identification record based on the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data recorded in S12 performs a joint analysis of the control element description of the module operation data to be processed and the historical module operation data to obtain the control element analysis result about the target VR ecological simulation scene, which may exemplarily include the following content: performing a joint analysis of the control element description of the module operation data to be processed and the historical module operation data to obtain the control element analysis result about the target VR ecological simulation scene. In this way, under the premise that the module operation data status of the module operation data to be processed is the same as the module operation data status of the historical module operation data, the control element description of the module operation data to be processed and the historical module operation data can be directly jointly analyzed, which can improve the data analysis efficiency.

[0055] In a possible embodiment, the module operation data status based on the module operation data to be processed and the module operation data status of the historical module operation data is realized through an AI model, and a joint analysis of the control element description of the module operation data to be processed and the historical module operation data is performed to obtain a control element analysis result about the target VR ecological simulation scene.

[0056] On the basis of the above content, the method may also include the following content: optimizing the AI ​​model based on a previously obtained debugging data set, the debugging data set including multiple first authenticated debugging information sets, multiple second authenticated debugging information sets, and multiple third authenticated debugging information sets, wherein the first authenticated debugging information set includes the first example module operation data of the first state and the second example module operation data of the first state, and the boot information of the first example module operation data; the second authenticated debugging information set includes the third example module operation data of the second state and the fourth example module operation data of the second state, and the boot information of the third example module operation data; the third authenticated debugging information set includes the fifth example module operation data of the first state and the sixth example module operation data of the second state, and the boot information of the fifth example module operation data.

[0057] In a possible embodiment, the optimization of the AI ​​model based on the previously obtained debugging data set may exemplarily include the technical solutions recorded in S21-S23.

[0058] S21. Perform a first optimization on the AI ​​model based on the multiple first authenticated debugging information sets.

[0059] S22. Group the multiple first authenticated debugging information sets and the multiple second authenticated debugging information sets into a first example queue, and perform a second optimization on the first optimized AI model based on the first example queue.

[0060] S23. Add multiple third authenticated debugging information sets to the first example queue to obtain a second example queue, and perform a third optimization on the second optimized AI model based on the second example queue.

[0061] Implementing the technical solutions recorded in S21-S23 and continuously optimizing the AI ​​model through iterative cycles can ensure the performance of the AI ​​model.

[0062] In a possible embodiment, under the condition of performing the first optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of a first confidence value; under the condition of performing the second optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of the first confidence value and a second quality inspection variable of a second confidence value, the second confidence value changes from an initial confidence value to the first confidence value, and the initial confidence value is less than the first confidence value; under the condition of performing the third optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a third quality inspection variable of a third confidence value.

[0063] In a possible embodiment, the third optimization of the second optimized AI model based on the second example queue recorded in S23 may exemplarily include the following contents: adjusting the state of the fifth example module operation data to obtain the seventh example module operation data; inputting the seventh example module operation data and the sixth example module operation data into the AI ​​model to obtain a first control element analysis result of the fifth example module operation data; and performing a third optimization on the AI ​​model after the second optimization based on the first control element analysis result of the fifth example module operation data.

[0064] In a possible embodiment, before performing the third optimization on the AI ​​model, the method may also include the following contents: inputting the fifth example module operation data and the sixth example module operation data into the AI ​​model to obtain a second control element analysis result of the fifth example module operation data; based on the second control element analysis result of the fifth example module operation data, performing a fourth optimization on the AI ​​model after the second optimization.

[0065] In a possible embodiment, under the condition of performing the fourth optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a fourth quality inspection variable of a fourth confidence value, and the method further includes: updating the fourth confidence value under the condition of determining the quality inspection variables of the AI ​​model based on the fourth quality inspection variable of the fourth confidence value.

[0066] S13. Based on the control element analysis result, the operation data of the module to be processed is updated to achieve environmental control of the target VR ecological simulation scene.

[0067] In summary, by implementing the contents recorded in S11-S13, the module operation data to be processed corresponding to the target VR ecological simulation scene can be determined, and the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data can be obtained. Based on the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, the control element description of the module operation data to be processed and the historical module operation data can be jointly analyzed to obtain the control element analysis results of the target VR ecological simulation scene, and the module operation data to be processed can be updated based on the control element analysis results to achieve environmental control of the target VR ecological simulation scene. In this way, not only can the control element analysis of the module operation data of various module operation data states be compatible, but also the accuracy and credibility of the control element analysis results can be guaranteed, thereby achieving accurate and reliable module operation data updates, so as to achieve targeted environmental control of the target VR ecological simulation scene and improve the intelligence of the simulation module control technology.

[0068] Based on the same or similar inventive concepts mentioned above, an embodiment of the present invention also provides an architectural diagram of an application environment 30 of a VR ecological simulation module control processing method, including a module control processing system 10 and a VR ecological simulation module 20 that communicate with each other. The module control processing system 10 and the VR ecological simulation module 20 implement or partially implement the technical solution described in the above method embodiment during operation.

[0069] Furthermore, a readable storage medium is provided, on which a program is stored, and the program implements the above method when executed by a processor.

[0070] 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.

[0071] 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.

[0072] 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, a media service server 10, 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 terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a 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 a 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.

[0073] 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 VR ecological simulation module control processing method, characterized in that: Applied to a module control processing system, the method comprises: Determine the module operation data to be processed corresponding to the target VR ecological simulation scene; obtain the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data; Based on the identification record of the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, a joint analysis of the control element description of the module operation data to be processed and the historical module operation data is performed to obtain a control element analysis result about the target VR ecological simulation scene; Based on the control element analysis result, the operation data of the module to be processed is updated to achieve environmental control of the target VR ecological simulation scene; The module operation data state of the module operation data to be processed is different from the state of the historical module operation data; the identification record based on the module operation data state of the module operation data to be processed and the module operation data state of the historical module operation data, the joint analysis of the control element description of the module operation data to be processed and the historical module operation data is performed to obtain the control element analysis result about the target VR ecological simulation scene, including: Determine the module operation data whose module operation data state is a first state among the module operation data to be processed and the historical module operation data as first module operation data, and determine the module operation data whose module operation data state is a second state among the module operation data to be processed and the historical module operation data as second module operation data; Adjusting the state of the first module operation data to obtain third module operation data; Performing a joint analysis of control element description on the third module operation data and the second module operation data to obtain a control element analysis result on the target VR ecological simulation scene; The module operation data state of the module operation data to be processed is the same as the module operation data state of the historical module operation data; The identification record of the module operation data status of the module operation data to be processed and the module operation data status of the historical module operation data, performing a joint analysis of the control element description of the module operation data to be processed and the historical module operation data, and obtaining a control element analysis result about the target VR ecological simulation scene, includes: The control element description of the module operation data to be processed and the historical module operation data are jointly analyzed to obtain the control element analysis results of the target VR ecological simulation scene.

2. The method according to claim 1, characterized in that The module operation data status based on the module operation data to be processed and the module operation data status of the historical module operation data are realized through the AI ​​model, and the control element description of the module operation data to be processed and the historical module operation data are jointly analyzed to obtain the control element analysis result of the target VR ecological simulation scene; The method also includes: optimizing the AI ​​model based on a previously obtained debugging data set, the debugging data set including a plurality of first authenticated debugging information sets, a plurality of second authenticated debugging information sets, and a plurality of third authenticated debugging information sets, wherein the first authenticated debugging information set includes a first example module operation data of a first state and a second example module operation data of a first state, and boot information of the first example module operation data; the second authenticated debugging information set includes a third example module operation data of a second state and a fourth example module operation data of a second state, and boot information of the third example module operation data; the third authenticated debugging information set includes a fifth example module operation data of a first state and a sixth example module operation data of a second state, and boot information of the fifth example module operation data.

3. The method according to claim 2, characterized in that The step of optimizing the AI ​​model based on a previously obtained debugging data set includes: Performing a first optimization on the AI ​​model based on the multiple first authenticated debugging information sets; The plurality of first authenticated debugging information sets and the plurality of second authenticated debugging information sets form a first example queue, and perform a second optimization on the first optimized AI model based on the first example queue; Add multiple third authenticated debugging information sets to the first example queue to obtain a second example queue, and perform a third optimization on the second optimized AI model based on the second example queue.

4. The method according to claim 3, characterized in that Under the condition that the AI ​​model is subjected to the first optimization, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of a first confidence value; Under the condition that the AI ​​model is subjected to the second optimization, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of the first confidence value and a second quality inspection variable of a second confidence value, the second confidence value changes from an initial confidence value to the first confidence value, and the initial confidence value is less than the first confidence value; Under the condition of performing the third optimization on the AI ​​model, the model quality inspection variables corresponding to the AI ​​model include a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a third quality inspection variable of the third confidence value.

5. The method according to claim 4, characterized in that Performing a third optimization on the second optimized AI model based on the second example queue includes: Adjusting the state of the fifth example module operation data to obtain seventh example module operation data; Inputting the seventh example module operation data and the sixth example module operation data into the AI ​​model to obtain a first control element analysis result on the fifth example module operation data; Based on the first control element analysis result of the fifth example module operation data, a third optimization is performed on the AI ​​model after the second optimization.

6. The method according to claim 5, characterized in that Before performing the third optimization on the AI ​​model, the method further includes: Inputting the fifth example module operation data and the sixth example module operation data into the AI ​​model to obtain a second control element analysis result of the fifth example module operation data; Based on the second control element analysis result of the fifth example module operation data, performing a fourth optimization on the AI ​​model after the second optimization; Wherein, under the condition that the AI ​​model is subjected to the fourth optimization, the model quality inspection variable corresponding to the AI ​​model includes a first quality inspection variable of the first confidence value, a second quality inspection variable of the second confidence value, and a fourth quality inspection variable of a fourth confidence value, and the method further includes: Under the condition that the quality inspection variable of the AI ​​model is determined based on the fourth quality inspection variable of the fourth confidence value, the fourth confidence value is updated.

7. A module control processing 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 6.

8. A readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the method described in any one of claims 1 to 6 is implemented.

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