Decision object policy statement analysis method, system, device and storage medium

Abstract

A decision object policy statement analysis method, system, device and storage medium. The method determines the decision makers of the conflicting parties of the game. The decision makers output the policy statement according to the requirements of the policy statement; construct a fuzzy priority model, process the policy statement through the fuzzy priority model, and output the fuzzy priority vector of the policy statement; determine whether the policy statement state matches the policy statement, and calculate the incremental score of each policy statement state through the fuzzy priority vector of the policy statement to obtain the incremental score of each policy statement state by the policy statement; perform improved fuzzy score calculation on each policy statement state to obtain the improved fuzzy score of each policy statement state, and perform preference sorting; obtain the real preference of the decision maker according to the preference sorting. The present invention obtains the real preference of the decision maker by calculating the improved fuzzy score of the state and performing preference sorting on it.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a decision object policy statement analysis method, system, device and storage medium. Background Art

[0002] Conflict analysis is a method for modeling and analyzing real conflicts developed on the basis of classical game theory and partial game theory. In the process of conflict analysis, it mainly involves modeling using data such as decision makers, feasible states, and decision makers' preference rankings for states. Then, by analyzing the stability of each state under different solution concept types, the individual stable state and global equilibrium state of the conflict event are determined, thereby helping decision makers to effectively deal with conflicts. Specifically, each decision maker controls one or more strategies. When all decision makers in the conflict determine their own strategy selection schemes (strategy combinations), a possible conflict state is constituted. Decision makers judge whether to achieve a new state by changing their strategy choices based on their own preferences. When all decision makers have no motivation to leave the current state, the state is called an equilibrium state. Obviously, preference is the most important factor in determining the equilibrium result of conflict analysis. Therefore, obtaining decision preferences that meet actual conditions is particularly critical for conflict analysis methods.

[0003] At present, the method for obtaining the preferences of decision makers in conflict events mainly adopts the strategy priority ranking method. When using the strategy priority ranking method to obtain a simple preference sequence, for each decision maker, the conflict analyst needs to give an ordered set consisting of some statements, which are composed of some strategy numbers and logical relationship symbols. Depending on whether the state satisfies the strategy statement, the state will have a truth value T or F in the statement. In the strategy statement, the statement with a higher ranking is considered to have a higher priority. For the decision maker, these states are sorted in order from large to small by setting a score for each state.

[0004] In real conflict problems, considering the uncertainty of whether a state satisfies the policy statement, the fuzzy truth value processing method is often used to calculate the fuzzy score of each state.

[0005] Therefore, since the ranking of policy statements itself has a certain degree of uncertainty, decision makers can often only identify the most important or least important policy statements. Determining the priority of other options is a challenge that cannot be ignored and a problem that needs to be solved. Summary of the invention

[0006] To this end, the present invention provides a decision object policy statement analysis method, system, device and storage medium, which obtains the decision maker's real preference by calculating the improved fuzzy score of the state and performing preference sorting on it.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a decision object policy statement analysis method, comprising:

[0008] Determine the decision makers of the parties to the game conflict, and the decision makers output the policy statement according to the requirements of the policy statement;

[0009] Constructing a fuzzy priority model, processing the policy statement through the fuzzy priority model, and outputting a fuzzy priority vector of the policy statement;

[0010] Determine whether the policy declaration state matches the policy declaration statement, and calculate the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement;

[0011] A modified fuzzy score is calculated for each of the policy declaration states to obtain a modified fuzzy score for each of the policy declaration states, and preference ranking is performed; and the real preference of the decision maker is obtained according to the preference ranking.

[0012] As a preferred solution of the decision object policy statement analysis method, the policy statement statement is a logical statement related to the policy, and the logical statement is expressed by a combination of the policy and logical connectives; the logical statement includes unconditional logical statements, conditional logical statements and bi-conditional logical statements.

[0013] As a preferred solution of the decision object policy statement analysis method, the incremental score calculation formula of the policy statement for each policy statement state is:

[0014]

[0015] In the formula, ψ t (s) is the incremental score; Ω is the policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions.

[0016] As a preferred solution of the decision object policy statement analysis method, the improved fuzzy score calculation formula of the policy statement state is:

[0017]

[0018] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; is the policy statement; t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0019] According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement, the improved fuzzy score calculation formula of the policy statement state is transformed:

[0020] First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to:

[0021]

[0022] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; for a policy statement;

[0023] Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to:

[0024]

[0025] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; σ t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0026] Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to:

[0027]

[0028] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; ψ t (s) is the incremental score.

[0029] The present invention also provides a decision object policy statement analysis system, which adopts the above-mentioned decision object policy statement analysis method and includes:

[0030] A strategy statement acquisition module is used to determine the decision makers of the parties to the game conflict, and the decision makers output the strategy statement according to the requirements of the strategy statement;

[0031] A fuzzy priority vector acquisition module, used to construct a fuzzy priority model, process the policy declaration statement through the fuzzy priority model, and output a fuzzy priority vector of the policy declaration statement;

[0032] An incremental score calculation module is used to determine whether the policy declaration state matches the policy declaration statement, and calculate the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement;

[0033] The improved fuzzy score calculation and preference ranking module is used to perform improved fuzzy score calculation on each of the policy declaration states, obtain the improved fuzzy score of each of the policy declaration states, and perform preference ranking; and obtain the real preference of the decision maker according to the preference ranking.

[0034] As a preferred solution for the decision object policy declaration analysis system, in the policy declaration statement acquisition module, the policy declaration statement is a logical statement related to the policy, and the logical statement is expressed by a combination of a policy and a logical conjunction; the logical statement includes an unconditional logical statement, a conditional logical statement and a bi-conditional logical statement.

[0035] As a preferred solution of the decision object policy statement analysis system, in the incremental score calculation module, the incremental score calculation formula of the policy statement for each policy statement state is:

[0036]

[0037] In the formula, ψ t (s) is the incremental score; Ω is the policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions.

[0038] As a preferred solution of the decision object policy statement analysis system, in the improved fuzzy score calculation and preference ranking module, the improved fuzzy score calculation formula of the policy statement state is:

[0039]

[0040] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; is the policy statement; t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0041] According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement, the improved fuzzy score calculation formula of the policy statement state is transformed:

[0042] First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to:

[0043]

[0044] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; for a policy statement;

[0045] Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to:

[0046]

[0047] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; σ t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0048] Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to:

[0049]

[0050] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; ψ t (s) is the incremental score.

[0051] The present invention also provides an electronic device, which includes a controller, a memory, a computer program stored in the memory and executable by the controller, and a data bus for realizing connection and communication between the controller and the memory, wherein when the computer program is executed by the controller, the steps of the decision object policy statement analysis method are implemented.

[0052] The present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the decision object policy statement analysis method.

[0053] The present invention has the following advantages: determining the decision makers of the parties in the game conflict, and the decision makers output the policy statement according to the requirements of the policy statement; constructing a fuzzy priority model, processing the policy statement through the fuzzy priority model, and outputting the fuzzy priority vector of the policy statement; judging whether the policy statement state matches the policy statement, and calculating the incremental score of each policy statement state through the fuzzy priority vector of the policy statement, and obtaining the incremental score of each policy statement state by the policy statement; calculating the improved fuzzy score for each policy statement state, obtaining the improved fuzzy score of each policy statement state, and performing preference sorting; obtaining the real preference of the decision maker according to the preference sorting. The policy statement is a logical statement related to the strategy, and the logical statement is expressed by a combination of strategy and logical conjunctions; the logical statement includes unconditional logical statements, conditional logical statements and bi-conditional logical statements. The present invention obtains the real preference of the decision maker by calculating the improved fuzzy score of the state and performing preference sorting on it. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0055] Figure 1 This is a flow chart of the decision object policy statement analysis method provided in Example 1 of the present invention;

[0056] Figure 2 This is an architecture diagram of the decision object policy declaration analysis system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0057] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] See also Figure 1 Embodiment 1 of the present invention provides a decision object policy statement analysis method, comprising the following steps:

[0060] S1. Determine the decision makers of the parties to the game conflict, and the decision makers output the policy statement according to the requirements of the policy statement;

[0061] S2, constructing a fuzzy priority model, processing the policy statement through the fuzzy priority model, and outputting a fuzzy priority vector of the policy statement;

[0062] S3, determining whether the policy declaration state matches the policy declaration statement, and calculating the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement;

[0063] S4. Calculate the improved fuzzy score for each of the policy declaration states to obtain the improved fuzzy score for each of the policy declaration states, and perform preference sorting; obtain the real preference of the decision maker according to the preference sorting.

[0064] In this embodiment, in step S1, the policy declaration statement is a logic statement related to the policy, and the logic statement is expressed by a combination of a policy and a logical conjunction; the logic statement includes an unconditional logic statement, a conditional logic statement, and a bi-conditional logic statement.

[0065] Specifically, when using the policy statement ranking method based on the improved fuzzy priority, the decision maker needs to first clarify all policy statement statements Ω according to the specifications of the policy statement statements;

[0066] Each policy statement is a logical statement about the policy, which can be expressed as a combination of policy and logical connectives, and can be unconditional, conditional, or biconditional. When describing preferences, logical words are usually used, such as logical AND ("and" and "&"), logical OR ("or" and "|"), and logical NOT ("not" and "-"). Two unconditional statements connected by the connectives if-then ("IF") and if and only if ("IFF") can form a conditional statement and a biconditional statement respectively;

[0067] Suppose the set of policy statements of decision maker i is φ i ={Ω1,Ω2,…,Ω q}, and from Ω1 to Ω q From most important to least important statement.

[0068] In this embodiment, in step S3, it is determined whether the state matches the policy statement, and the incremental score of the policy statement for the state is calculated by the fuzzy priority vector of the policy statement to obtain the incremental score of the policy statement for each state;

[0069] Specifically, the policy statement Ω t (1≤t≤q) In each state s∈S there is a truth value Ω t (s), either "true" (T) or "false" (F), depending on whether the state matches the claim.

[0070] σ t (s) is the policy statementΩ t (1≤t≤q) is the fuzzy truth value of state s∈S, ranging from [0,1], which is used in situations where it is difficult to determine whether the state matches the policy statement.

[0071] The decision maker's preference for a state depends on whether the state is consistent with the decision maker's policy statement. If the state is consistent with the policy statement, the decision maker's preference for the state will increase, otherwise it will not;

[0072] The specific degree of improvement depends on the importance of the policy statement it meets to the decision maker. The position of the importance ranking reflects the relative importance of the policy statement to the decision maker. Since the statement with high importance can give the state a higher incremental score, the higher the statement is ranked, the greater the influence of whether the state meets the statement on the state preference;

[0073] In the real world, decision makers are often able to identify the most and least important options among many policy statements, but are confused about the ranking of other options. Due to the limitations of knowledge and available information, they are uncertain about the priority ranking of policy statements.

[0074] Policy StatementΩ t The fuzzy priority Φ of (1≤t≤q) t is a q-dimensional vector, written as in Represents a policy statementΩ t The truth value at priority j, And the smaller j is, the higher the priority of the policy statement is.

[0075] ψ t (s) is the policy statementΩ t The incremental score of state s∈S (1≤t≤q) is calculated as:

[0076]

[0077] In the formula, ψ t (s) is the incremental score; Ω is the policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions.

[0078] In this embodiment, in step S4, the improved fuzzy score calculation formula of the policy statement state is:

[0079]

[0080] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; is the policy statement; t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0081] According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement, the improved fuzzy score calculation formula of the policy statement state is transformed:

[0082] First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to:

[0083]

[0084] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; for a policy statement;

[0085] Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to:

[0086]

[0087] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; σ t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0088] Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to:

[0089]

[0090] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; ψ t (s) is the incremental score.

[0091] In a possible embodiment, the following specific examples are provided:

[0092] T1. Each decision maker gives all policy declaration statements.

[0093] In the example of technology selection for a cutting-edge technology system, in order to accurately represent the model of the conflict game, the decision makers in the conflict are first determined, that is, the three capability domains in the technology system: A, B, and C. Among them, the technologies that can be developed in capability domain A are A1, A2, and A3; the technologies that can be developed in capability domain B are B1 and B2; and the technologies that can be developed in capability domain C are C1, C2, and C3. The technologies that can be developed in each capability domain are the optional strategies of the decision makers. Therefore, this conflict event contains a total of 8 strategies. Each decision maker makes a binary choice for his or her own strategy, so there are 2 8 = 256 states. Considering the actual constraints, we finally get 32 ​​feasible states, such as

[0094] As shown in Table 1. Tables 2, 3, and 4 summarize the policy statements of A, B, and C, respectively.

[0095]

[0096]

[0097] Table 1 Feasible technology combinations in technology selection

[0098]

[0099] Table 2 Policy statement of A

[0100]

[0101] Table 3 B's policy statement

[0102]

[0103] Table 4 C's policy statement

[0104] T2. Model each policy statement using fuzzy priority.

[0105] In this example, a fuzzy ranking vector is used for the fuzzy priority of the policy statement. Build. Represents a policy statementΩ t The truth value at priority j, And the smaller j is, the higher the priority of the policy statement is.

[0106] In this example, the policy statements that B, A, and C consider to be the most important and least important are clarified respectively, and then the fuzzy priorities of other policy statements with uncertain priorities are constructed. The specific contents are shown in Tables 5, 6, and 7.

[0107]

[0108] Table 5 Fuzzy priority of B policy statement

[0109]

[0110] Table 6 A Fuzzy priority of policy statement

[0111]

[0112] Table 7 Fuzzy priority of C policy statement

[0113] T3. Calculate the incremental score of the policy statement for each state.

[0114] In this example, ψ t (s) is the policy statementΩ t The incremental score of state s∈S (1≤t≤q) is calculated as:

[0115]

[0116] Taking B as an example, it is most inclined to develop B1. According to the fuzzy priority of the build (1,0,0,0,0,0,0,0), the state matching this statement is given an incremental score For the fuzzy ordering (0, 0.4, 0.5, 0.1, 0, 0, 0, 0) of B's ​​second policy statement, the incremental score of each state that matches it is

[0117] T4. Calculate the improved fuzzy score value of each state and perform preference ranking.

[0118] In this example, the improved fuzzy score ψ(s) of state s is defined as:

[0119]

[0120] Considering that the applicability of each state to the policy statement is clear, the calculation formula of ψ(s) can be transformed into:

[0121]

[0122] According to the formula in step 4, the improved fuzzy score value of each state can be calculated, and the preference ranking can be obtained by comparing the scores, as shown in Table 8.

[0123]

[0124] Table 8 Preference ranking of decision makers in technology selection

[0125] In order to verify the effectiveness of the present invention, the simple preferences are compared with the preferences obtained in the actual situation. The simple preferences cannot accurately reflect the fuzzy psychology of decision makers in real life when dealing with conflicts due to the fixed nature of the declaration statements. For example, there is uncertainty about B's preference to choose B2 and if B2 has been chosen, B hopes that A will choose A3; A most wants B to develop B1, but is not sure whether he prefers B to choose B2 or C to choose C1. The present invention simulates the fuzzy psychology of decision makers in conflict events, models the priority of strategies based on fuzzy truth values, and the resulting state ranking is more in line with the actual situation of decision makers, which is convenient for analyzing conflict events.

[0126] In summary, the present invention determines the decision makers of the conflicting parties of the game, and the decision makers output the policy statement according to the requirements of the policy statement; constructs a fuzzy priority model, processes the policy statement through the fuzzy priority model, and outputs the fuzzy priority vector of the policy statement; determines whether the policy statement state matches the policy statement, and calculates the incremental score of each policy statement state through the fuzzy priority vector of the policy statement to obtain the incremental score of each policy statement state by the policy statement; calculates the improved fuzzy score for each policy statement state to obtain the improved fuzzy score of each policy statement state, and performs preference sorting; obtains the real preference of the decision maker according to the preference sorting. The policy statement is a logical statement related to the strategy, and the logical statement is expressed by a combination of strategy and logical conjunctions; the logical statement includes unconditional logical statements, conditional logical statements, and bi-conditional logical statements. The present invention obtains the real preference of the decision maker by calculating the improved fuzzy score of the state and performing preference sorting on it.

[0127] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0128] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] Example 2

[0130] See also Figure 2 Embodiment 2 of the present invention further provides a decision object policy statement analysis system, which adopts the decision object policy statement analysis method of embodiment 1 above, including:

[0131] The strategy statement acquisition module 001 is used to determine the decision makers of the parties to the game conflict, and the decision makers output the strategy statement according to the requirements of the strategy statement;

[0132] A fuzzy priority vector acquisition module 002 is used to construct a fuzzy priority model, process the policy statement through the fuzzy priority model, and output the fuzzy priority vector of the policy statement;

[0133] The incremental score calculation module 003 is used to determine whether the policy declaration state matches the policy declaration statement, and calculate the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement;

[0134] The improved fuzzy score calculation and preference ranking module 004 is used to perform improved fuzzy score calculation on each of the policy declaration states, obtain the improved fuzzy score of each of the policy declaration states, and perform preference ranking; and obtain the real preference of the decision maker according to the preference ranking.

[0135] In this embodiment, in the policy declaration statement acquisition module 001, the policy declaration statement is a logical statement related to the policy, and the logical statement is expressed by a combination of a policy and a logical conjunction; the logical statement includes an unconditional logical statement, a conditional logical statement and a bi-conditional logical statement.

[0136] In this embodiment, in the incremental score calculation module 003, the incremental score calculation formula of the policy declaration statement for each policy declaration state is:

[0137]

[0138] In the formula, ψ t (s) is the incremental score; Ω is the policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions.

[0139] In this embodiment, in the improved fuzzy score calculation and preference ranking module 004, the improved fuzzy score calculation formula of the policy statement state is:

[0140]

[0141] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; is the policy statement; t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0142] According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement, the improved fuzzy score calculation formula of the policy statement state is transformed:

[0143] First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to:

[0144]

[0145] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; for a policy statement;

[0146] Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to:

[0147]

[0148] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; σ t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S;

[0149] Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to:

[0150]

[0151] In the formula, ψ(s) is the improved fuzzy score; j is the variable; t is the variable; ψ t (s) is the incremental score.

[0152] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0153] Example 3

[0154] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a decision object policy declaration analysis method is stored, and the program code includes instructions for executing the decision object policy declaration analysis method of embodiment 1 or any possible implementation thereof.

[0155] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0156] Example 4

[0157] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0158] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the decision object policy statement analysis method of embodiment 1 or any possible implementation thereof.

[0159] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0160] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0161] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0162] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A decision object policy statement analysis method, characterized in that: include: Determine the decision makers of the parties to the game conflict, and the decision makers output the policy statement according to the requirements of the policy statement; Constructing a fuzzy priority model, processing the policy statement through the fuzzy priority model, and outputting a fuzzy priority vector of the policy statement; Determine whether the policy declaration state matches the policy declaration statement, and calculate the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement; Calculating the improved fuzzy score for each of the policy declaration states to obtain the improved fuzzy score for each of the policy declaration states, and performing preference sorting; Obtaining the decision maker's real preference according to the preference ranking; The policy declaration statement is a logical statement related to the policy, and the logical statement is expressed by a combination of the policy and a logical conjunction; The logic statements include unconditional logic statements, conditional logic statements and bi-conditional logic statements; The incremental score calculation formula of the policy statement for each policy statement state is: In the formula, ψ t (s) is the incremental score; Ω is a policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions; Ω t (s) is the policy statement Ω t (1≤t≤q) is the truth value in state s∈S, T is true and F is false; The improved fuzzy score calculation formula of the policy statement status is: Where, ψ(s) is the improved fuzzy score; j is a variable; t is a variable; For the policy statement Ω t The truth value at priority j; s t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S; According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement statements, the improved fuzzy score calculation formula of the policy statement state is transformed: First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to: Where, ψ(s) is the improved fuzzy score; j is a variable; t is a variable; For the policy statement Ω t The truth value at priority j; Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to: Where, ψ(s) is the improved fuzzy score; j is a variable; t is a variable; s t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S; Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to: Where, ψ(s) is the improved fuzzy score; j is a variable; t is a variable; ψ t (s) is the incremental score.

2. A decision object policy statement analysis system, using the decision object policy statement analysis method according to claim 1, characterized in that: include: A strategy statement acquisition module is used to determine the decision makers of the parties to the game conflict, and the decision makers output the strategy statement according to the requirements of the strategy statement; A fuzzy priority vector acquisition module, used to construct a fuzzy priority model, process the policy declaration statement through the fuzzy priority model, and output the fuzzy priority vector of the policy declaration statement; An incremental score calculation module is used to determine whether the policy declaration state matches the policy declaration statement, and calculate the incremental score of each policy declaration state through the fuzzy priority vector of the policy declaration statement to obtain the incremental score of each policy declaration state by the policy declaration statement; An improved fuzzy score calculation and preference sorting module, used to calculate the improved fuzzy score for each of the policy declaration states, obtain the improved fuzzy score of each of the policy declaration states, and perform preference sorting; Obtaining the decision maker's real preference according to the preference ranking; In the policy statement acquisition module, the policy statement is a logical statement related to the policy, and the logical statement is represented by a combination of a policy and a logical conjunction; The logic statements include unconditional logic statements, conditional logic statements and bi-conditional logic statements; In the incremental score calculation module, the incremental score calculation formula of the policy declaration statement for each policy declaration state is: In the formula, ψ t (s) is the incremental score; Ω is a policy statement; For the policy statement Ω t The truth value at priority j; q is the number of vector dimensions; Ω t (s) is the policy statement Ω t (1≤t≤q) is the truth value in state s∈S, T is true and F is false; In the improved fuzzy score calculation and preference ranking module, the improved fuzzy score calculation formula of the policy statement status is: Where ψ(s) is the improved fuzzy score; j is a variable; t is a variable; For the policy statement Ω t The truth value at priority j; s t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S; According to the applicability of the policy statement to the policy statement state and the uncertainty of the order of the policy statement statements, the improved fuzzy score calculation formula of the policy statement state is transformed: First, if the applicability of each policy statement state to the policy statement is clear, then the improved fuzzy score calculation formula of the policy statement state is converted to: Where ψ(s) is the improved fuzzy score; j is a variable; t is a variable; For the policy statement Ω t The truth value at priority j; Second, if the priority ranking of the policy statement is clear, the improved fuzzy score calculation formula of the policy statement status is converted to: Where ψ(s) is the improved fuzzy score; j is a variable; t is a variable; s t (s) is the policy statement Ω t (1≤t≤q) fuzzy truth value in state s∈S; Third, if the priority ranking of the policy statement and the applicability of each state to the policy statement are clear, the improved fuzzy score calculation formula of the policy statement state is converted to: Where ψ(s) is the improved fuzzy score; j is a variable; t is a variable; ψ t (s) is the incremental score.

3. An electronic device, characterized in that: The electronic device includes a controller, a memory, a computer program stored in the memory and executable by the controller, and a data bus for realizing connection and communication between the controller and the memory, wherein when the computer program is executed by the controller, the steps of the decision object policy statement analysis method as described in claim 1 are implemented.

4. A storage medium for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the decision object policy statement analysis method as claimed in claim 1.

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