A target threat assessment method and related device

Abstract

The present invention provides a target threat assessment method and related apparatus. The target threat assessment method comprises: obtaining attribute assessment information of a target; calculating the reliability of the attribute and calculating the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute; and assessing the threat of the target based on the attribute assessment information, the reliability, and the importance. This application simultaneously considers both the reliability and importance of the attribute, resulting in a more accurate threat assessment result.

Description

Technical Field

[0001] The present invention belongs to the field of early warning system behavior analysis and relates to a target threat assessment method and related devices. Background Art

[0002] In real-time, highly competitive battlefield environments, such as those associated with early warning systems, the increasing variety and lethality of targets necessitates the rapid detection, identification, and threat assessment of incoming targets to support decision-making systems. Due to the diverse and concealed nature of threats and the advanced nature of countermeasures, information provided by a single sensor cannot meet operational requirements. It has been recognized that only by effectively combining multi-sensor detection information with artificial intelligence (AI) can a more comprehensive and reliable threat assessment be achieved, enhancing system robustness.

[0003] The ultimate task of the early warning system is to assess the threat of incoming targets, thereby providing a basis for interception decisions and target allocation. The early warning center system integrates threat assessments of the target's location, category, and intent, based on multiple sensor systems and artificial intelligence, to determine its overall threat level. This process can be abstracted as a typical multi-attribute decision fusion problem. Currently, existing threat assessment algorithms struggle to accurately assess the threat level of a target. Summary of the Invention

[0004] The present application provides a target threat assessment method and related devices, which can accurately assess the threat of a target.

[0005] In a first aspect, the present application provides a target threat assessment method, the method comprising: obtaining attribute assessment information of the target; calculating the reliability of the attribute, and calculating the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute; and assessing the threat of the target based on the attribute assessment information, the reliability and the importance.

[0006] The step of obtaining the target's attribute evaluation information includes: obtaining the target's attributes and the parameters corresponding to the attributes; and obtaining the target's attribute evaluation information based on the target's attributes and the parameters corresponding to the attributes using a fuzzy mapping model.

[0007] The step of calculating the reliability of the attribute includes: calculating the reliability of the attribute using a monotonically decreasing function.

[0008] Among them, the step of calculating the reliability of the attribute using a monotonically decreasing function includes: calculating the reliability of the attribute using a monotonically decreasing function based on the estimation error and the fuzzy interval range; the estimation error is negatively correlated with the reliability; wherein the estimated difference is determined based on the detection system of the target, and the fuzzy interval range is determined by a fuzzy mapping model.

[0009] The step of calculating the importance of the attribute includes: using a hierarchical analysis method to compare the attributes in pairs to obtain a judgment matrix describing the relative importance between two attributes, and determining the importance based on the judgment matrix.

[0010] The step of calculating the judgment matrix to obtain the importance includes: calculating the principal eigenvector of the judgment matrix to obtain a calculation result; and normalizing the calculation result to obtain the importance.

[0011] Among them, the step of evaluating the threat of the target based on the attribute evaluation information, the reliability and the importance includes: performing discount calculation using the attribute evaluation information, the reliability and the importance to obtain multiple discounted mass functions; fusing the multiple discounted mass functions to obtain a fusion result; and normalizing the fusion result to obtain the threat of the target.

[0012] In a second aspect, the present application provides a target threat assessment device, comprising: an acquisition module for acquiring attribute assessment information of a target; a calculation module for calculating the reliability of the attribute, and calculating the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute; an assessment module for assessing the threat of the target based on the attribute assessment information, the reliability and the importance.

[0013] In a third aspect, the present application provides an electronic device comprising a processor and a memory coupled to each other, wherein the memory is used to store program instructions for implementing any of the methods described above; and the processor is used to execute the program instructions stored in the memory.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing a program file, wherein the program file can be executed to implement any of the methods described above.

[0015] The beneficial effects of the present invention are different from those of the prior art. The target threat assessment method of the present invention includes: obtaining attribute evaluation information of the target; calculating the reliability of the attribute and calculating the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute; and assessing the threat of the target based on the attribute evaluation information, the reliability, and the importance. The present application considers both the reliability and importance of the attribute, resulting in a more accurate threat assessment result. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a first embodiment of a target threat assessment method according to the present invention;

[0017] Figure 2 Schematic diagram of the structure of the first embodiment of the target threat assessment device of the present invention;

[0018] Figure 3 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention;

[0019] Figure 4 It is a structural diagram of an embodiment of a computer-readable storage medium of the present invention. Specific implementation methods

[0020] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The aforementioned and other technical contents, features and effects of the present invention can be clearly presented in the following detailed description of the specific embodiments with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The attached drawings are only for reference and illustration purposes and are not intended to limit the technical solutions of the present invention.

[0021] See Figure 1 , which is a flowchart of a first embodiment of a target threat assessment method of the present invention, specifically comprising:

[0022] Step S11: Obtaining the attribute evaluation information of the target.

[0023] In one embodiment, obtaining the target's attribute evaluation information includes: obtaining the target's attributes and parameters corresponding to the attributes; and obtaining the target's attribute evaluation information based on the target's attributes and parameters corresponding to the attributes using a fuzzy mapping model.

[0024] Specifically, one of the core tasks of an early warning system is to assess the threat of incoming targets based on target identification and other intelligence information. This involves ranking the threat levels of all incoming ballistic missiles, providing a basis for interception decisions and target allocation. According to relevant research, numerous factors influence the threat level of ballistic missiles, including missile type, launch point location, range, shutoff velocity, reentry velocity, radar cross-section, maneuverability, hit accuracy, remaining flight time, and the importance of the attack area. Some of these quantitative and qualitative factors are directly detected by early warning equipment such as ground-based radars and space-based infrared satellites; some are estimated using target tracking and recognition technologies based on early warning equipment measurements; and still others may require the assistance of external intelligence systems. Therefore, when selecting these influencing factors, it is important to comprehensively consider the ease of obtaining information and its importance to threat assessment. Based on these principles, we select four key factors for assessing the threat of incoming targets: range, shutoff velocity, remaining flight time, and the importance of the attack area.

[0025] Range (e1): Range refers to the distance between a missile's launch point and its impact point. Ballistic missiles range from tactical missiles with a range of several hundred kilometers to intercontinental missiles with a range of tens of thousands of kilometers. Shorter-range ballistic missiles are generally used for tactical strikes, resulting in less destructive power and a lower threat level. Longer-range ballistic missiles, on the other hand, are generally used for strategic strikes, with greater destructive power and a higher threat level.

[0026] Shutdown Speed ​​(e2): Shutdown speed refers to the speed at which a ballistic missile's engine shuts down. It determines the reentry velocity and attack power of an incoming ballistic missile. Generally speaking, the higher the shutdown speed, the more difficult it is to intercept and the greater the damage, thus increasing the threat level.

[0027] Remaining flight time (e3): Remaining flight time refers to the time it takes an incoming ballistic missile to travel from its position at the moment it is detected by early warning equipment to its impact point. The shorter the remaining time, the less time the missile defense system has to make decisions and intercept, thus increasing the threat level.

[0028] Importance of the attack area (e4): The importance of the attack area refers to the political, military and economic impact that may be caused after the area is attacked. It is an important factor in determining whether the ballistic missile target has threatening intentions and the degree of threat.

[0029] Furthermore, the threat level of the target is divided into five levels, thus forming an evaluation level set A fuzzy mapping model is used to map the value space of each basic attribute to a fuzzy level set. It should be noted that the fuzzy mapping model is a pre-trained model. Among them, the values ​​of the first three basic attributes (range, shutdown speed, and remaining flight time) are directly given by the early warning system, while the attack area importance is given by experts through comprehensive evaluation based on the target landing point predicted by the early warning system (0 represents the lowest attack area importance, and 10 represents the highest attack area importance). For example, for an incoming ballistic missile target, assuming that the target range reported by the early warning system is 1000km, the shutdown speed is 2.8km / s, and the remaining flight time is 450s, that is, the early warning system gives the attributes and the parameters corresponding to the attributes. The attack area importance given by the expert based on the target landing point predicted by the early warning system is 5, then based on the fuzzy mapping model, the attribute evaluation information of each attribute of the following target can be obtained:

[0030] Range: S(e1) = {(H1, 0.42), (H2, 0.58)};

[0031] Shutoff point speed: S(e2)={(H2,0.20),(H3,0.80)};

[0032] Remaining flight time: S(e3) = {(H4, 0.50), (H5, 0.50)};

[0033] Attack area importance: S(e4) = {(H3, 1.00)}.

[0034] It should be noted that the fuzzy mapping model may be a triangular fuzzy partitioning model.

[0035] Step S12: Calculate the reliability of the attribute and calculate the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute.

[0036] Specifically, in one embodiment, the objective error of the attribute depends on the ability of the early warning system to provide correct attribute evaluation values.

[0037] In one embodiment, the step of calculating the reliability of the attribute includes: calculating the reliability of the attribute using a monotonically decreasing function. Specifically, the reliability of the attribute is calculated using the monotonically decreasing function based on an estimation error and a fuzzy interval range; the estimation error is negatively correlated with the reliability; the estimation difference is determined based on the detection system of the target, and the fuzzy interval range is determined by a fuzzy mapping model.

[0038] Specifically, the reliability of an attribute reflects the ability of the early warning system to provide correct attribute evaluation values. Therefore, we can calculate the corresponding reliability based on the estimation error of the early warning system in each attribute. The larger the estimation error, the lower the reliability of the attribute evaluation value. Therefore, reliability should decrease with the estimation error. We use the following monotonically decreasing function to calculate the reliability of each attribute α i , α i ∈(0,1]:

[0039] α i =exp(-∈ i / u i ),i=1,2,3,4. (1)

[0040] where ∈ i is the attribute e i The estimation error, u i is the attribute e i A single fuzzy interval range. When the attribute value estimation error ∈ i = 0, the corresponding reliability α i =1; and when the attribute value estimation error ∈ i →∞, the corresponding reliability α i →0.

[0041] The estimated errors of the early warning system in various attributes usually depend on the detection performance of the early warning equipment and the corresponding data processing performance. Assuming that the estimated errors of the various attribute values ​​reported by the early warning system are as shown in the second column of Table 1, and the single fuzzy interval range obtained by the fuzzy mapping model is as shown in the third column of Table 1, the reliability shown in the fourth column of Table 1 can be calculated based on formula (1).

[0042] Table 1

[0043]

[0044] Furthermore, the step of calculating the importance of the attribute includes: using a hierarchical analysis method to compare the attributes pairwise to obtain a judgment matrix describing the relative importance of two attributes, and determining the importance based on the judgment matrix. Specifically, the principal eigenvector of the judgment matrix is ​​calculated to obtain a calculation result, and the calculation result is normalized to obtain the importance.

[0045] Attribute importance reflects the relative importance of four basic attributes for target threat assessment: range, shutdown speed, remaining flight time, and attack zone importance. This is typically determined through expert consultation. The Analytic Hierarchy Process (AHP) is a practical model for quantifying decision makers' empirical judgments. This method consists of two main steps: first, constructing a judgment matrix based on pairwise comparisons between attributes. Then, the principal eigenvectors of this judgment matrix are calculated and normalized to determine the importance of each attribute.

[0046] Assume that the decision maker compares each of the four attributes considered, resulting in the judgment matrix shown in the first five columns of Table 2. Each element in the matrix represents the relative importance of the corresponding row attribute to the corresponding column attribute. Solving for the principal eigenvector of this judgment matrix and normalizing it yields the importance scores shown in the sixth column of Table 2.

[0047] Table 2

[0048] <![CDATA[Attribute (e i )]]> <![CDATA[e1]]> <![CDATA[e2]]> <![CDATA[e3]]> <![CDATA[e4]]> <![CDATA[Importance (β i )]]> <![CDATA[e1]]> 1 1 1 / 3 1 / 2 0.31 <![CDATA[e2]]> 1 1 1 / 3 1 / 2 0.31 <![CDATA[e3]]> 3 3 1 2 1 <![CDATA[e4]]> 2 2 1 / 2 1 0.58

[0049] The second row of the second column represents the relative importance between attribute e1 and attribute e2, the second row of the third column represents the relative importance between attribute e1 and attribute e2, and the data in other cells are similar and will not be repeated here.

[0050] Step S13: Evaluate the threat of the target based on the attribute evaluation information, the reliability and the importance.

[0051] In one embodiment, the threat of the target is evaluated based on the attribute evaluation information, the reliability, and the importance using a GER algorithm (Generalized Evidential Reasoning algorithm).

[0052] Specifically, the step of evaluating the threat of the target based on the attribute evaluation information, the reliability and the importance includes: performing discount calculation using the attribute evaluation information, the reliability and the importance to obtain multiple discounted mass functions; fusing the multiple discounted mass functions to obtain a fusion result; and normalizing the fusion result to obtain the threat of the target.

[0053] In one embodiment, the discount calculation is performed based on the attribute evaluation information, the reliability, and the importance using the following formula (2):

[0054]

[0055] Among them, α i represents reliability, β i Indicates importance, m i (A) represents attribute evaluation information, represents the mass function after the i-th discount.

[0056] Furthermore, the following formula (3) is used to fuse multiple discounted mass functions to obtain a fusion result:

[0057]

[0058]

[0059]

[0060]

[0061] in represents the fusion result of the first i discounted mass functions. When i reaches L-1, the fusion result of all L discounted mass functions is obtained.

[0062] Furthermore, the above fusion results are assigned to the recognition framework Confidence Assignment of Power Sets Redistribute it to other focal elements in proportion to obtain the evaluation result of the target threat level y, and the normalization method is as follows:

[0063]

[0064]

[0065] Existing target threat assessment methods based on multi-attribute decision-making fusion only consider the importance of attributes while ignoring their reliability, thus failing to achieve comprehensive and accurate target threat assessment. This paper proposes a target threat assessment method for early warning systems that comprehensively considers both attribute reliability and importance. First, a multi-attribute decision-making fusion target threat assessment model is constructed within the framework of evidence theory. Then, a reliability-importance discounting algorithm for evidence is proposed. Finally, a generalized evidential reasoning method that comprehensively considers both attribute reliability and importance is developed to achieve comprehensive and accurate target threat assessment.

[0066] See Figure 2 , is a structural diagram of an embodiment of a target threat assessment device of the present invention, which specifically includes: an acquisition module 21, a calculation module 22, and an assessment module 23.

[0067] The acquisition module 21 is used to obtain attribute evaluation information of the target. The calculation module 22 is used to calculate the reliability of the attribute and the importance of the attribute; the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute. The evaluation module 23 is used to evaluate the threat of the target based on the attribute evaluation information, the reliability, and the importance.

[0068] In one embodiment, the acquisition module 21 is configured to acquire the attributes of the target and the parameters corresponding to the attributes; and obtain the attribute evaluation information of the target based on the attributes of the target and the parameters corresponding to the attributes using a fuzzy mapping model.

[0069] In one embodiment, the calculation module 22 calculates the reliability of the attribute using a monotonically decreasing function. Specifically, the calculation module 22 calculates the reliability of the attribute using a monotonically decreasing function based on an estimation error and a fuzzy interval range; the estimation error is negatively correlated with the reliability; the estimation error is determined based on the detection system of the target, and the fuzzy interval range is determined by a fuzzy mapping model.

[0070] In one embodiment, the calculation module 22 uses the analytic hierarchy process to perform a pairwise comparison of the attributes to obtain a judgment matrix describing the relative importance of the two attributes, and determines the importance based on the judgment matrix. Specifically, the principal eigenvector of the judgment matrix is ​​calculated to obtain a calculation result, and the calculation result is normalized to obtain the importance.

[0071] In one embodiment, the evaluation module 23 performs discount calculation using the attribute evaluation information, the reliability, and the importance to obtain multiple discounted mass functions; fuses the multiple discounted mass functions to obtain a fusion result; and normalizes the fusion result to obtain the threat level of the target.

[0072] See Figure 3 , which is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device includes a memory 82 and a processor 81 connected to each other.

[0073] The memory 82 is used to store program instructions for implementing any one of the above methods.

[0074] The processor 81 is configured to execute program instructions stored in the memory 82 .

[0075] The processor 81 may also be referred to as a CPU (Central Processing Unit). The processor 81 may be an integrated circuit chip having signal processing capabilities. The processor 81 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0076] The memory 82 can be a memory stick, a TF card, etc., which can store all the information in the electronic device, including the input raw data, computer programs, intermediate operation results and final operation results. It is stored in the memory. It stores and retrieves information according to the location specified by the controller. Only with the memory can the electronic device have a memory function and ensure normal operation. The memory of the electronic device can be divided into main memory (internal memory) and auxiliary memory (external memory) according to its purpose. There is also a classification method of dividing it into external memory and internal memory. External memory is usually a magnetic medium or an optical disk, etc., which can store information for a long time. Memory refers to the storage component on the motherboard, which is used to store the data and programs currently being executed, but is only used to temporarily store programs and data. If the power is turned off or the power is cut off, the data will be lost.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented by other methods. For example, the device implementation method described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the objectives of this embodiment as needed.

[0079] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit 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 application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, system server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application.

[0081] See also Figure 4 , which is a structural diagram of the computer-readable storage medium of the present invention. The storage medium of the present application stores a program file 91 that can implement all the above methods, wherein the program file 91 can be stored in the above storage medium in the form of a software product, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each implementation method of the present application. The aforementioned storage device includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0082] The above is only an implementation method of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A target threat assessment method, characterized in that: include: Acquiring attribute evaluation information of a target, wherein the attribute evaluation information includes a danger level of each attribute of the target; Calculating the reliability of the attribute and calculating the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the decision maker's subjective evaluation of the attribute; evaluating the threat of the target based on the attribute evaluation information, the reliability, and the importance; The step of obtaining target attribute evaluation information includes: Obtaining attributes of the target and parameters corresponding to the attributes, wherein the attributes of the target include the target's range, shutdown speed, remaining flight time, and attack area importance. The values ​​corresponding to the range, shutdown speed, and remaining flight time are directly provided by the early warning system, and the attack area importance is obtained by expert evaluation based on the values ​​corresponding to the range, shutdown speed, and remaining flight time. Obtaining attribute evaluation information of the target based on the attributes of the target and parameters corresponding to the attributes using a fuzzy mapping model, wherein the fuzzy mapping model is a triangular fuzzy partitioning model; The step of calculating the reliability of the attribute includes: The reliability of the attribute is calculated using a monotonically decreasing function, wherein the monotonically decreasing function satisfies the following formula: in, For attributes reliability, For the attribute The estimation error, For the attribute A single fuzzy interval range; The step of calculating the importance of the attribute includes: Using the hierarchical analysis method to compare the attributes in pairs, and obtain a judgment matrix describing the relative importance between the two attributes; The importance is determined based on the judgment matrix.

2. The method according to claim 1, characterized in that The step of calculating the reliability of the attribute using a monotonically decreasing function comprises: The reliability of the attribute is calculated using a monotonically decreasing function based on the estimation error and the fuzzy interval range; the estimation error is negatively correlated with the reliability; The estimation error is determined based on a detection system of the target, and the fuzzy interval range is determined by a fuzzy mapping model.

3. The method according to claim 1, characterized in that The step of determining the importance based on the judgment matrix includes: Calculating the principal eigenvector of the judgment matrix to obtain a calculation result; The calculation result is normalized to obtain the importance.

4. The method according to claim 1, wherein The step of evaluating the threat of the target based on the attribute evaluation information, the reliability, and the importance includes: A discount calculation is performed using the attribute evaluation information, the reliability, and the importance to obtain a plurality of discounted mass functions, wherein the discounted mass functions satisfy the following formula: in, For attributes reliability, For the attribute The importance of For the attribute Attribute evaluation information, Indicates the A discounted mass function; is the set of evaluation levels, Indicates the nth evaluation level; Fusing the plurality of discounted mass functions to obtain a fusion result; The fusion result is normalized to obtain the threat level of the target.

5. A target threat assessment device, characterized in that: include: An acquisition module, configured to acquire attribute evaluation information of a target, wherein the attribute evaluation information includes a danger level of each attribute of the target; a calculation module, configured to calculate the reliability of the attribute and the importance of the attribute; wherein the reliability of the attribute represents the objective error of the attribute, and the importance of the attribute represents the subjective evaluation of the attribute by the decision maker; an evaluation module, configured to evaluate the threat of the target based on the attribute evaluation information, the reliability, and the importance; The acquisition module is specifically used for: Obtaining attributes of the target and parameters corresponding to the attributes, wherein the attributes of the target include the target's range, shutdown speed, remaining flight time, and attack area importance. The values ​​corresponding to the range, shutdown speed, and remaining flight time are directly provided by the early warning system, and the attack area importance is obtained by expert evaluation based on the values ​​corresponding to the range, shutdown speed, and remaining flight time. Obtaining attribute evaluation information of the target based on the attributes of the target and parameters corresponding to the attributes using a fuzzy mapping model, wherein the fuzzy mapping model is a triangular fuzzy partitioning model; The calculation module is specifically used for: The reliability of the attribute is calculated using a monotonically decreasing function, wherein the monotonically decreasing function satisfies the following formula: in, For attributes reliability, For the attribute The estimation error, For the attribute A single fuzzy interval range; The calculation module is further configured to: Using the hierarchical analysis method to compare the attributes in pairs, and obtain a judgment matrix describing the relative importance between the two attributes; The importance is determined based on the judgment matrix.

6. An electronic device, characterized in that: It includes a processor and a memory coupled to each other, wherein: The memory is used to store program instructions for implementing the method according to any one of claims 1 to 4; The processor is configured to execute the program instructions stored in the memory.

7. A computer-readable storage medium, characterized in that A program file is stored, and the program file can be executed to implement the method according to any one of claims 1 to 4.

Images