Method and device for collecting expert information on structural reliability of liquid rocket engine based on fuzzy theory

Abstract

The application provides a liquid rocket engine structure reliability expert information collection method and device based on fuzzy theory, relates to the technical field of expert information collection, and comprises the following steps: acquiring an influence factor set, an expert semantic set and a preset score deviation threshold value which influence the reliability of a liquid rocket engine structure; wherein the influence factor set contains at least two types of influence factors; the expert semantic set contains semantic information of each expert for any type of influence factor; based on the semantic information and a preset semantic information and score comparison table, the score of each expert for each type of influence factor is obtained; based on the score of each expert for each type of influence factor and the score deviation threshold value, the fuzzy score number of each expert for each type of influence factor and the weight value of the corresponding influence factor are obtained; and based on the obtained weight value of each type of influence factor, a weight set of the influence factor set is constructed. The application realizes reliable quantitative description of expert semantics, and is more applicable to the field of liquid rocket engine structure reliability factors.

Description

Technical Field

[0001] This application relates to the field of expert information collection technology, and more specifically, to a method and apparatus for collecting expert information on the structural reliability of liquid rocket engines based on fuzzy theory. Background Technology

[0002] Numerous factors influence engine structural reliability, and their weights are not only difficult to determine but also become very small due to the requirement of "normalization," making it difficult to reflect the role of the main influencing factors of structural reliability and thus affecting the rationality of fuzzy decision-making. Expert information, as an important source of prior information on engine reliability, has significant practical application value.

[0003] However, existing expert information collection methods are mostly subjective probability collection methods, including direct methods (including direct estimation of quantities, giving probability ratios, and using graphs), indirect methods, and hybrid methods. However, due to the diverse forms of expert information description and the large differences in accuracy, existing expert information collection methods cannot quantitatively describe expert information and fail to consider the influence of fuzzy semantics in expert information, making them unsuitable for the field of liquid rocket engine structural reliability. Summary of the Invention

[0004] The purpose of this application is to provide a method and apparatus for collecting expert information on the structural reliability of liquid rocket engines based on fuzzy theory, so as to solve the above-mentioned problems existing in the prior art, and to obtain expert information that can be quantitatively described and applied to the field of structural reliability of liquid rocket engines.

[0005] Firstly, a method for collecting expert information on the structural reliability of liquid rocket engines based on fuzzy theory is provided, which may include:

[0006] The system acquires a set of factors affecting the structural reliability of liquid rocket engines, an expert semantic set, and a preset scoring deviation threshold; wherein the set of factors includes at least two categories of factors; and the expert semantic set includes semantic information of each expert for any category of factors.

[0007] Based on the semantic information and the preset semantic information and scoring comparison table, the scores of each expert on various influencing factors are obtained;

[0008] Based on the scores given by each expert for various influencing factors and the aforementioned score deviation threshold, the fuzzy score number of each expert for each influencing factor and the corresponding weight value of the influencing factor are obtained.

[0009] Based on the obtained weight values ​​of various influencing factors, a weight set of the influencing factor set is constructed.

[0010] In an optional implementation, the scoring deviation threshold includes: a first deviation threshold and a second deviation threshold;

[0011] Based on the experts' scores for various influencing factors and the aforementioned score deviation threshold, the fuzzy score numbers for each expert on various influencing factors are obtained, including:

[0012] For any expert's score on any type of influencing factor, the first deviation threshold is subtracted from the score to obtain the first deviation value;

[0013] Subtract the score from the second deviation threshold to obtain the second deviation value;

[0014] Based on the score, the first deviation value, and the second deviation value, the expert's fuzzy score for the influencing factors of this category is obtained.

[0015] For any category of influencing factors, the fuzzy scores of each expert for the influencing factors of that category are summed and averaged to obtain the fuzzy weight of the influencing factors of that category.

[0016] Based on the fuzzy weights of the influencing factors of the category, the weight values ​​of the influencing factors of the category are obtained.

[0017] In an optional implementation, the weight values ​​of the influencing factors of the category are obtained based on the fuzzy weights of the influencing factors of the category, including:

[0018] The fuzzy weights of the influencing factors of the category are defuzzified to obtain the initial weight values ​​of the influencing factors of the category;

[0019] The initial weight values ​​of the influencing factors of the category are normalized to obtain the weight values ​​of the influencing factors of the category.

[0020] In an optional implementation, after obtaining the scores from various experts for each type of influencing factor, the method further includes:

[0021] The triangular membership function was used to process the scores of each expert on various influencing factors.

[0022] In an optional implementation, the triangular membership function is as follows:

[0023]

[0024] Where x represents any expert's score for any type of influencing factor; μ(x) represents the membership degree of x to any type of influencing factor's fuzzy score; a represents the first bias threshold; m represents any expert's score for any type of influencing factor; and b represents the second bias threshold.

[0025] Secondly, a liquid rocket engine structural reliability expert information collection device based on fuzzy theory is provided, which may include:

[0026] The acquisition unit is used to acquire a set of influencing factors affecting the structural reliability of liquid rocket engines, an expert semantic set, and a preset scoring deviation threshold; wherein, the set of influencing factors includes at least two types of influencing factors; and the expert semantic set includes semantic information of each expert for any type of influencing factor.

[0027] The determining unit is used to obtain the scores of each expert for various influencing factors based on the semantic information and a preset semantic information and scoring comparison table; and to obtain the fuzzy score number of each expert for various influencing factors and the weight value of the corresponding influencing factor based on the scores of each expert for various influencing factors and the scoring deviation threshold.

[0028] The construction unit is used to construct a weight set of the influencing factor set based on the obtained weight values ​​of various influencing factors.

[0029] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0030] Memory, used to store computer programs;

[0031] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0032] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0033] Based on the establishment of a set of factors affecting engine reliability, this application converts expert semantics into influencing factor scores, thereby achieving a quantitative description of expert semantics with higher accuracy. Based on the triangular membership function, it considers the fuzzy meaning of expert scores, making the results more reliable and more applicable to the field of structural reliability factors of liquid rocket engines. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating an expert information collection method for the structural reliability of a liquid rocket engine based on fuzzy theory, provided for embodiments of this application;

[0036] Figure 2 A schematic diagram of the triangular membership function provided in the embodiments of this application;

[0037] Figure 3 A schematic diagram of a liquid rocket engine structural reliability expert information collection device based on fuzzy theory, provided for an embodiment of this application;

[0038] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0040] The method for collecting expert information on the structural reliability of liquid rocket engines based on fuzzy theory provided in this application can be applied to servers or terminals with strong computing capabilities. The server can be a physical server, a server cluster composed of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be user equipment (UE) such as mobile phones, smartphones, laptops, digital radio receivers, personal digital assistants (PDAs), and tablet computers (PADs), handheld devices, in-vehicle devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, mobile stations (MS), and mobile terminals. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.

[0041] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0042] Figure 1 This is a flowchart illustrating a method for collecting expert information on the structural reliability of a liquid rocket engine based on fuzzy theory, provided as an embodiment of this application. Figure 1 As shown, the method may include:

[0043] Step S110: Obtain the set of factors affecting the structural reliability of liquid rocket engines, the expert semantic set, and the preset scoring deviation threshold.

[0044] In this embodiment of the application, the influencing factor set includes at least two types of influencing factors; the expert semantic set includes the semantic information of each expert for any type of influencing factor.

[0045] Specifically, given the inherent properties (complexity and technological maturity, etc.) and usage properties (operating conditions and missions, etc.) of liquid rocket engines, the factors affecting the structural reliability of liquid rocket engines are divided into three categories: engine design factors, engine testing factors, and engine manufacturing factors, resulting in a set of factors U, where U = {engine design, engine testing, engine manufacturing} = {U1, U2, U3}.

[0046] In this embodiment of the application, the scoring deviation threshold includes: a first deviation threshold and a second deviation threshold.

[0047] Step S120: Based on semantic information and a preset semantic information and scoring comparison table, obtain the scores of each expert for various influencing factors.

[0048] In this embodiment of the application, the semantic information and scoring comparison table is shown in Table 1:

[0049] Table 1. Comparison of Semantic Information and Scoring

[0050]

[0051] Step S130: Based on the scores and score deviation thresholds of each expert for each type of influencing factor, obtain the fuzzy score of each expert for each type of influencing factor and the corresponding weight value of the influencing factor; based on the obtained weight values ​​of each type of influencing factor, construct the weight set of the influencing factor set.

[0052] In this embodiment of the application, based on the scores given by each expert for various influencing factors and the score deviation threshold, the fuzzy score number of each expert for each influencing factor and the weight value of the corresponding influencing factor are obtained, including:

[0053] For any expert's score on any type of influencing factor, subtract the first deviation threshold from the score to obtain the first deviation value; subtract the score from the second deviation threshold to obtain the second deviation value; based on the score, the first deviation value, and the second deviation value, obtain the expert's fuzzy score for that type of influencing factor.

[0054] For any category of influencing factors, the fuzzy scores of each expert for that category of influencing factors are summed and averaged to obtain the fuzzy weight of that category of influencing factors; the fuzzy weights are then defuzzified to obtain the initial weight values ​​of that category of influencing factors; the initial weight values ​​of that category of influencing factors are then normalized to obtain the final weight values ​​of that category of influencing factors.

[0055] In this embodiment of the application, the calculation formulas for the first deviation value WL (also known as the left deviation) and the second deviation value WR (also known as the right deviation) are as follows:

[0056] WL = ma;

[0057] WR = bm;

[0058] Where m represents the score given by any expert to any type of influencing factor; a represents the first deviation threshold (i.e., the lower limit of the score); b represents the second deviation threshold (i.e., the upper limit of the score); both the first and second deviation thresholds are preset by the experts.

[0059] In this embodiment of the application, the formula for calculating the fuzzy score of any expert for any category of influencing factors is as follows:

[0060]

[0061] in, This indicates that the j-th expert's opinion on the i-th type of influencing factor U i The fuzzy score number; i and j are both positive integers. Specifically, if the influencing factors of the structural reliability of a liquid rocket engine are divided into three categories, then i = 1, 2, 3; for example, the fuzzy score number of the second expert for the first category of influencing factors. It is based on the score given by the second expert for the first type of influencing factor and the corresponding first and second deviation thresholds.

[0062] In the embodiments of this application, the fuzzy score of an expert for any category of influencing factors can be represented by a triple (m, WL, WR), as shown in Table 2; where, the larger WL and WR are, the greater the degree of fuzzy uncertainty.

[0063] Table 2. Experts' fuzzy scores for various influencing factors

[0064]

[0065] In this embodiment of the application, the fuzzy weight calculation formula for any category of influencing factors is as follows:

[0066]

[0067] Where n represents the number of experts participating in the scoring; This indicates that the j-th expert's opinion on the i-th type of influencing factor U i Fuzzy score; This represents the fuzzy weight of the i-th type of influencing factor.

[0068] In this embodiment of the application, the fuzzy weights are defuzzified to obtain the initial weight values ​​of the influencing factors of that category; the initial weight values ​​of the influencing factors of that category are then normalized to obtain the weight values ​​of the influencing factors of that category, including:

[0069] The initial weight values ​​of the influencing factors in this category are obtained by defuzzifying the center value of the fuzzy weights. The initial weight values ​​of the influencing factors in this category are normalized by converting them into standardized weights whose sum is 1, thus obtaining the weight values ​​of the influencing factors in this category.

[0070] In one embodiment of this application, the fuzzy weights of the three types of influencing factors, calculated using the fuzzy scores given by experts to the three types of influencing factors in Table 2, and the corresponding initial weight values ​​are as follows:

[0071]

[0072] U1 = 7.6664;

[0073]

[0074] U2 = 6.6829;

[0075]

[0076] U3 = 7.4661.

[0077] In one embodiment of this application, the initial weight values ​​U1, U2, and U3 of the three types of influencing factors obtained above are normalized to obtain the following weight values ​​for the three types of influencing factors:

[0078]

[0079]

[0080]

[0081] Based on the above weight values, the weight set of the influencing factor set is constructed as follows:

[0082]

[0083] In this embodiment of the application, after obtaining the scores from various experts for different influencing factors, the method further includes:

[0084] The triangular membership function is used to process the experts' scores on various influencing factors; specifically, such as... Figure 2 The triangular membership function is shown below:

[0085]

[0086] Where x represents the score given by any expert for any type of influencing factor; μ(x) represents the membership degree of x to a certain fuzzy set; a represents the lower limit of the score, which is the first deviation threshold in the score deviation threshold; m represents the most likely value of the score, which is the specific score given by the expert; b represents the upper limit of the score, which is the second deviation threshold in the score deviation threshold; the calculated result μ(x) represents the membership degree of the expert score to the fuzzy score of a certain type of influencing factor. The membership degree of the expert score to the fuzzy score of a certain type of influencing factor can be used in subsequent fuzzy comprehensive evaluation, fuzzy decision-making or fuzzy weight calculation, and can also be used to further analyze the reliability and consistency of the expert score.

[0087] Corresponding to the above method, this application also provides a liquid rocket engine structural reliability expert information collection device based on fuzzy theory, such as... Figure 3 As shown, the expert information collection device for the structural reliability of liquid rocket engines based on fuzzy theory includes:

[0088] The acquisition unit 310 is used to acquire a set of influencing factors affecting the structural reliability of liquid rocket engines, an expert semantic set, and a preset scoring deviation threshold; wherein, the influencing factor set contains at least two types of influencing factors; the expert semantic set contains semantic information of each expert for any type of influencing factor;

[0089] The determination unit 320 is used to obtain the scores of each expert on various influencing factors based on semantic information and a preset semantic information and scoring comparison table; based on the scores of each expert on various influencing factors and the scoring deviation threshold, the fuzzy score number of each expert on various influencing factors and the weight value of the corresponding influencing factor are obtained.

[0090] Construction unit 330 is used to construct a weight set of the influencing factor set based on the obtained weight values ​​of various influencing factors.

[0091] The functions of each functional unit of the expert information collection device for the structural reliability of liquid rocket engines based on fuzzy theory provided in the above embodiments of this application can be realized through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the expert information collection device for the structural reliability of liquid rocket engines based on fuzzy theory provided in the embodiments of this application will not be repeated here.

[0092] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0093] Memory 430 is used to store computer programs;

[0094] When the processor 410 executes the program stored in the memory 430, it performs the following steps:

[0095] The system acquires a set of factors affecting the structural reliability of liquid rocket engines, an expert semantic set, and a preset scoring deviation threshold. The set of factors includes at least two categories of factors, and the expert semantic set includes semantic information from each expert for any category of factors.

[0096] Based on semantic information and a pre-defined semantic information and scoring comparison table, the scores of each expert on various influencing factors are obtained; based on the scores of each expert on various influencing factors and the scoring deviation threshold, the fuzzy scores of each expert on various influencing factors and the weight values ​​of the corresponding influencing factors are obtained.

[0097] Based on the obtained weight values ​​of various influencing factors, a weight set of the influencing factor set is constructed.

[0098] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0099] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0100] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0101] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0102] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0103] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the above embodiments of the expert information collection method for the structural reliability of liquid rocket engines based on fuzzy theory.

[0104] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the above embodiments of the expert information collection method for the structural reliability of liquid rocket engines based on fuzzy theory.

[0105] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0110] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for collecting expert information on the structural reliability of liquid rocket engines based on fuzzy theory, characterized in that, The method includes: The system acquires a set of influencing factors affecting the structural reliability of a liquid rocket engine, an expert semantic set, and a preset scoring deviation threshold. The influencing factor set includes at least two categories of influencing factors; the expert semantic set includes semantic information from each expert regarding any one category of influencing factors; and the scoring deviation threshold includes a first deviation threshold and a second deviation threshold. Based on the semantic information and the preset semantic information and scoring comparison table, the scores of each expert on various influencing factors are obtained; Based on the experts' scores for various influencing factors and the score deviation threshold, the fuzzy score counts for each expert on each influencing factor and the corresponding weight values ​​of the influencing factors are obtained, including: for any expert's score on any type of influencing factor, subtracting the first deviation threshold from the score to obtain a first deviation value; subtracting the score from the second deviation threshold to obtain a second deviation value; based on the score, the first deviation value, and the second deviation value, obtaining the experts' fuzzy score counts for that type of influencing factor; for any type of influencing factor, summing and averaging the fuzzy score counts for each expert on that type of influencing factor to obtain the fuzzy weight of that type of influencing factor; and based on the fuzzy weight of that type of influencing factor, obtaining the weight value of that type of influencing factor. Based on the obtained weight values ​​of various influencing factors, a weight set of the influencing factor set is constructed; After obtaining the scores from various experts for each influencing factor, the method further includes: The triangular membership function is used to process the experts' scores on various influencing factors; the triangular membership function is as follows: ； Where x represents any expert's score for any type of influencing factor; denoted by , x represents the membership degree of the fuzzy score of any class of influencing factors; a represents the first bias threshold; m represents the score of any expert for any class of influencing factors; b represents the second bias threshold.

2. The method as described in claim 1, characterized in that, Based on the fuzzy weights of the influencing factors of the aforementioned category, the weight values ​​of the influencing factors of the aforementioned category are obtained, including: The fuzzy weights of the influencing factors of the category are defuzzified to obtain the initial weight values ​​of the influencing factors of the category; The initial weight values ​​of the influencing factors of the category are normalized to obtain the weight values ​​of the influencing factors of the category.

3. A liquid rocket engine structural reliability expert information collection device based on fuzzy theory, characterized in that, The device includes: The acquisition unit is used to acquire a set of influencing factors affecting the structural reliability of liquid rocket engines, an expert semantic set, and a preset scoring deviation threshold; wherein, the set of influencing factors includes at least two types of influencing factors; the expert semantic set includes semantic information of each expert for any type of influencing factor; and the scoring deviation threshold includes: a first deviation threshold and a second deviation threshold. The determining unit is configured to: obtain the scores of each expert for various influencing factors based on the semantic information and a preset semantic information and scoring comparison table; and obtain the fuzzy score count of each expert for each influencing factor and the weight value of the corresponding influencing factor based on the scores of each expert for each influencing factor and the scoring deviation threshold, including: for any expert's score for any type of influencing factor, subtracting the first deviation threshold from the score to obtain a first deviation value; subtracting the score from the second deviation threshold to obtain a second deviation value; obtaining the fuzzy score count of the expert for that type of influencing factor based on the score, the first deviation value, and the second deviation value; summing and averaging the fuzzy score counts of each expert for that type of influencing factor to obtain the fuzzy weight of that type of influencing factor; and obtaining the weight value of that type of influencing factor based on the fuzzy weight of that type of influencing factor. A construction unit is used to construct a weight set for the set of influencing factors based on the obtained weight values ​​of various influencing factors; The processing unit is used to process the scores given by each expert for various influencing factors using a triangular membership function; the triangular membership function is as follows: ； Where x represents any expert's score for any type of influencing factor; denoted by , x represents the membership degree of the fuzzy score of any class of influencing factors; a represents the first bias threshold; m represents the score of any expert for any class of influencing factors; b represents the second bias threshold.

4. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-2.

Images