Method, system and device for evaluating mission task demand satisfaction degree of equipment system

Abstract

The invention provides an equipment system mission task demand satisfaction degree evaluation method, system and device, and relates to the technical field of task demand satisfaction degree evaluation.The method mainly comprises the steps that a mission task is decomposed into subtasks, and the subtask demand importance degree # imgabs0 # is calculated; the subtasks are decomposed into meta-tasks, and a first relation matrix # imgabs1 # is constructed; decomposing the meta-task into equipment capability requirements, and constructing a second relation matrix # imgabs2; constructing a reliability rule base; inputting the equipment capability index data of the meta-task demand into a reliability rule base, and calculating the reliability of the capability demand satisfaction degree of the equipment system; based on the reliability and the equipment capability demand, calculating an equipment system underlying capability demand satisfaction degree # imgabs3 #; based on the # imgabs4 # and the # imgabs5 #, calculating a meta-task demand satisfaction degree # imgabs6 #; based on the # imgabs7 # and the # imgabs8 #, the sub-task requirement satisfaction degree # imgabs9 # is calculated; and based on the # imgabs 10 # and the # imgabs 11 #, calculating a mission task demand satisfaction degree # imgabs 12 #. According to the scheme, complex and uncertain data can be dynamically, effectively and comprehensively processed, and the accuracy and reliability of satisfaction degree evaluation can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mission requirement satisfaction evaluation, and in particular to a method, system and device for evaluating the mission requirement satisfaction of an equipment system. Background Art

[0002] The modern equipment system itself is highly complex and uncertain. With the rapid growth of the proportion of intelligent equipment deployment, the evaluation of the satisfaction of the mission requirements of the equipment system will face more challenges. How to accurately handle multiple uncertain data, systematically evaluate the satisfaction of the requirements of each task and capability, and clarify the complex relationship between the requirements at all levels are important issues that need to be solved in the evaluation of the satisfaction of the mission requirements of the equipment system.

[0003] Du Zhanlong et al. (Reference: Equipment System Mission Requirements Satisfaction Assessment Method Based on QFD [J]. Measurement and Control Technology, 2022, 41(03): 33-37+67.) used the QFD method to decompose the mission requirements layer by layer into specific equipment capability requirements. By building a layer-by-layer mapping relationship, the logical consistency and relevance between the requirements at each level were ensured, but there are still limitations when dealing with uncertain data and fuzzy information. The core of the QFD method is to build a House of Quality (HOQ), through which customer requirements can be decomposed step by step into product engineering and technical characteristics, and by evaluating the importance of each product engineering and technical characteristics, the key factors affecting the product can be determined, so as to take targeted measures to meet customer needs to the greatest extent.

[0004] Yu Minggang et al. (Reference: Task-oriented and ANP-based network information system capability requirement satisfaction analysis method [J]. Systems Engineering - Theory & Practice, 2020, 40(03): 795-806.) studied the ANP-based network information system capability requirement satisfaction analysis method. This method performed well in terms of systematicity and hierarchy, but it still has shortcomings when dealing with uncertain data.

[0005] After evaluating the existing technology, the specific problems are as follows: 1) Lack of dynamic mission assessment: Most existing technologies focus on the assessment of static equipment system capabilities, without fully considering the diversity and abstractness of missions. Static capability assessment results cannot fully reflect the satisfaction of mission requirements; 2) Insufficient processing capabilities for complex and uncertain data: Existing methods are relatively weak in processing complex and uncertain data in equipment system evaluation, and often rely on deterministic or simple fuzzy processing; 3) The comprehensive index aggregation method is single: When dealing with the serial and parallel aggregation problems of indicators in complex equipment systems, the existing technical methods are single and it is difficult to fully reflect the relationship between the various indicators. Summary of the invention

[0006] The purpose of the present invention is to provide a method, system and device for evaluating the degree of satisfaction of mission requirements of an equipment system, so as to solve at least one of the above-mentioned technical problems existing in the prior art.

[0007] In a first aspect, in order to solve the above technical problems, the present invention provides a method for evaluating the satisfaction of mission requirements of an equipment system, comprising the following steps: Step 1: Use the QFD method to decompose the mission into subtasks and calculate the importance of subtask requirements ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; Build a credibility rule base based on the equipment capability indicator data involved in the mission and the equipment expert opinion scores; The credibility rule base can be expressed as: ; in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; Preferably, the representation form of the rules in the credibility rule base can be: ; in, Indicates When the premise is met, the conclusion is The reliability of ; Indicates the total number of rules; Indicates The total number of preconditions in the rule; For example, the task planning capability is decomposed into two capability indicators: planning speed and flexibility. When the planning speed is 5 minutes and the flexibility is strong, the conclusion is: the reliability of high task planning capability is 1, the reliability of medium task planning capability is 0, and the reliability of low task planning capability is 0. The specific rules are expressed as follows: ; Step 2: Input the equipment capability index data of the meta-task requirement into the credibility rule base; obtain the prerequisites that match the equipment capability index data in the credibility rule base through condition matching calculation; obtain the rules that match the equipment capability index data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability index data based on the prerequisites and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; Step 3: Based on the reliability and equipment capability requirements, calculate the satisfaction of the equipment system's underlying capability requirements through weighted average. ; Step 4: Based on the second relationship matrix And the satisfaction of the underlying capability requirements of the equipment system , calculate the meta-task requirement satisfaction ; Step 5: Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Step 6: Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction .

[0008] Through the above process, in the evaluation of the underlying equipment capability, the credibility rule base is used to effectively handle various types of uncertain data; through the QFD method, the high-level mission tasks are decomposed layer by layer into specific equipment capability requirements, and a clear mapping relationship is established; this method can solve complex and uncertain problems and effectively improve the accuracy and reliability of evaluation in this field.

[0009] In a feasible implementation, in step 1, the importance of the subtask requirement is calculated. The method is the Decision-making Trial and Evaluation Laboratory (DEMATEL) method, which is an existing technology. It mainly calculates the influence and influence of each element on other elements through the logical relationship and direct influence matrix between the elements in the system, thereby calculating the causal degree and centrality of each element as the basis for constructing the model, and then determining the causal relationship between the elements and the importance of each element in the system.

[0010] In a feasible implementation, in step 1, a first relationship matrix is ​​constructed. And the second relationship matrix The method includes: scoring the opinions of equipment experts through hesitant fuzzy number scoring; integrating the information of hesitant fuzzy numbers through the OWA operator based on normal distribution to minimize the influence of opinion scoring on decision conclusions; calculating the corresponding relationship matrix through row sum normalization (NRA); in this way, the situation that the equipment experts have hesitant fuzzy information when judging and scoring can be taken into account, and information loss can be avoided. The specific expression can be: ; ; in, Indicates The subtasks and the corresponding The correlation between the meta-tasks; Indicates Item task and corresponding The correlation between the equipment capability requirements; the correlation adopts the 0.1~0.9 scaling method as the quantitative standard for value selection, and the larger the value, the higher the correlation; Indicates the total number of subtasks; represents the total number of meta-tasks; Indicates the total number of equipment capacity requirements.

[0011] In a feasible implementation manner, the formula for condition matching calculation in step 2 includes: ; in, Indicates Equipment capability indicators The set of matched preconditions after conversion; Indicates equipment capability index reliability; Indicates the total number of equipment capability indicators; Indicates Equipment capability indicators and corresponding Value The degree of match between , the specific calculation formula can be: ; in, Represents a similarity function, used to characterize and The degree of similarity between them.

[0012] In a feasible implementation manner, the similarity function includes: When the attribute value of the premise is a discrete value, the similarity function can be expressed as: ; in, The changing trend of is monotonically increasing; When the attribute value of the premise is a single point, the similarity function can be expressed as: ; in, Indicates The corresponding values ​​of equipment capability indicators; express The lower limit of the value range; express The upper limit of the value range of ; When the attribute value of the prerequisite is the subjective judgment value of the equipment expert, the subjective judgment value is directly used as the value of the equipment capability index, that is, the equipment capability index With the corresponding Value The degree of match between them is equal to the equipment capability index reliability.

[0013] Through the above three situations, the comprehensiveness and rationality of equipment capability indicator data are ensured.

[0014] In a feasible implementation manner, the formula for rule matching calculation in step 2 includes: The calculation formula for calculating the matching degree between equipment capability index data and rules is: ; in, Indicates Equipment capability index data and The matching degree between the rules, and ; Indicates The equipment capability index data is in The relative weight of the following premises; express The correction weight of ; Indicates equipment capability index data and The matching degree between the rules, and ; Based on the The weight of the rule , through data normalization calculation, the activation degree is obtained , the specific formula can be: ; Among them, when When , indicating the rules are not activated.

[0015] In a feasible implementation manner, the specific formula for constructing the basic credible number in step 2 includes: ; in, Indicates Rule No. The basic credibility number of the conclusion assignment; Indicates The rule has no basic confidence number assigned to any conclusion; Representation and The relevant uncertainty component; Indicates The uncertainty part related to the rules and their conclusions; in this way, the basic credibility number can be constructed based on the degree of activation and credibility of the rules by the equipment capability indicator data, which will help the subsequent data aggregation based on evidence reasoning and deduce the upper-level demand satisfaction from the lower-level demand satisfaction.

[0016] In a feasible implementation, the specific calculation formula for constructing the evidence combination in step 2 includes: ; in, It represents the conflict coefficient, which represents the degree of conflict between multiple pieces of evidence; Indicates The rule for The basic credibility of the conclusion; Indicates The set of rules for various conclusions The uncertainty of Indicates The set of rules for various conclusions The fuzzy reliability of Indicates the number of activated rules; Indicates the reliability of the conclusion drawn from the equipment capability index data; Indicates the uncertainty of the conclusion; Indicates The basic credibility of the conclusion; Represents a collection of various conclusions The uncertainty of Represents a collection of various conclusions The fuzzy reliability of In this way, the basic credibility numbers of multiple rules can be combined to obtain the final confidence distribution.

[0017] In a feasible implementation, in step 3, the satisfaction degree of the underlying capability requirements of the equipment system is calculated. The specific formula can be: ; in, Represents the equipment capability confidence vector matrix, Indicates Equipment capability indicators, Indicates confidence vector values, Can be taken from ; Represents the weight vector, and the specific expression can be: , where 1, 0.5 and 0 correspond to the confidence values ​​of satisfaction, almost satisfaction and dissatisfaction respectively; Represents the weight vector The transposed vector of ; In this way, the weighted average method can be used to aggregate and describe the satisfaction of the underlying capability requirements of the equipment system.

[0018] In a feasible implementation manner, in step 4, the meta-task requirement satisfaction is calculated. The specific formula can be: ; in, Indicates the satisfaction of the underlying capability requirements of the equipment system The transposed vector of .

[0019] In a feasible implementation manner, in step 5, the subtask requirement satisfaction is calculated. The specific formula can be: ; in, Represents the degree of satisfaction of meta-task requirements The transposed vector of .

[0020] In a feasible implementation, in step 6, the mission requirement satisfaction is calculated. The specific formula can be: ; in, Indicates the satisfaction of subtask requirements The transposed vector of .

[0021] In the second aspect, based on the same inventive concept, the present application also provides an equipment system mission task requirement satisfaction evaluation system, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive equipment capability indicator data required by the meta-task; The data processing module includes a task decomposition unit, a credibility rule base unit and a satisfaction calculation unit; The task decomposition unit decomposes the mission into subtasks and calculates the importance of subtask requirements through the QFD method. ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; The credibility rule base unit stores a credibility rule base, which is used to obtain the preconditions matching the equipment capability indicator data in the credibility rule base through condition matching calculation for the equipment capability indicator data of the meta-task requirement; obtain the rules matching the equipment capability indicator data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability indicator data based on the preconditions and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; the credibility rule base is constructed based on the equipment capability indicator data involved in the mission task and the equipment expert opinion score, etc., and can be expressed as: ; in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; The representation of rules in the credibility rule base can be: ; in, Indicates When the premise is met, the conclusion is The reliability of ; Indicates the total number of rules; Indicates The total number of preconditions in the rule; The satisfaction calculation unit calculates the satisfaction of the equipment system bottom capability requirement through weighted average based on the credibility and equipment capability requirement. ; Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction ; The result generation module is used to calculate the mission task requirement satisfaction Outgoing.

[0022] On the third aspect, based on the same inventive concept, the present application also provides an equipment system mission task requirement satisfaction assessment device, including a processor, a memory and a bus, the memory stores instructions and data that can be read by the processor, the processor is used to call the instructions and data in the memory to execute the equipment system mission task requirement satisfaction assessment method as described above, and the bus connects the functional components for transmitting information.

[0023] By adopting the above technical solution, the present invention has the following beneficial effects: The present invention provides a method, system and device for evaluating the satisfaction of equipment system mission requirements. The high-level mission requirements are decomposed layer by layer into specific equipment capability requirements through the QFD method, and a clear mapping relationship is established. The importance of each sub-task requirement is determined through the DEMATEL method, and the hesitant fuzzy number method is used to improve the traditional relationship matrix determination method, so as to ensure that the fuzziness and uncertainty of the equipment expert evaluation are taken into account in the evaluation process. In the evaluation of the bottom-level equipment capability, various types of uncertainty data are quantitatively processed by building a credibility rule library. Through reverse deduction, the satisfaction evaluation of the requirements from the bottom-level equipment capability to the mission requirements is realized. The scheme can dynamically, effectively and comprehensively process complex and uncertain data, and can significantly improve the accuracy and reliability of the satisfaction evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flow chart of a method for evaluating the degree of satisfaction of mission requirements of an equipment system provided by an embodiment of the present invention; Figure 2 Mission task decomposition flow chart provided for the embodiment of the present invention; Figure 3 A diagram of a mission requirement satisfaction evaluation system for an equipment system provided by an embodiment of the present invention; Figure 4 A diagram of the urban intelligent disaster relief task requirements provided by an embodiment of the present invention; Figure 5 A subtask requirement impact relationship diagram provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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.

[0027] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0028] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] The present invention is further explained below in conjunction with specific implementation modes.

[0030] It should also be noted that the following specific embodiments or specific implementations are a series of optimized settings listed in the present invention to further explain the specific content of the invention, and these settings can be used in combination or in association with each other.

[0031] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for evaluating the satisfaction of equipment system mission requirements, including the following steps: Step 1: Through QFD method, such as Figure 2 As shown, the mission task is decomposed into subtasks and the importance of subtask requirements is calculated. ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; Build a credibility rule base based on the equipment capability indicator data involved in the mission and the equipment expert opinion scores; The equipment capability index data may include equipment capability, capability value, capability index and index value, etc.; The credibility rule base can be expressed as: ; in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; The representation of rules in the credibility rule base can be: ; in, Indicates When the premise is met, the conclusion is The reliability of ; Indicates the total number of rules; Indicates The total number of preconditions in the rule; For example, the task planning capability is decomposed into two capability indicators: planning speed and flexibility. When the planning speed is 5 minutes and the flexibility is strong, the conclusion is: the reliability of high task planning capability is 1, the reliability of medium task planning capability is 0, and the reliability of low task planning capability is 0. The specific rules are expressed as follows: ; Step 2: Input the equipment capability index data of the meta-task requirement into the credibility rule base; obtain the prerequisites that match the equipment capability index data in the credibility rule base through condition matching calculation; obtain the rules that match the equipment capability index data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability index data based on the prerequisites and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; Step 3: Based on the reliability and equipment capability requirements, calculate the satisfaction of the equipment system's underlying capability requirements through weighted average. ; Step 4: Based on the second relationship matrix And the satisfaction of the underlying capability requirements of the equipment system , calculate the meta-task requirement satisfaction ; Step 5: Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Step 6: Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction .

[0032] Through the above process, in the evaluation of the underlying equipment capability, the credibility rule base is used to effectively handle various types of uncertain data; through the QFD method, the high-level mission tasks are decomposed layer by layer into specific equipment capability requirements, and a clear mapping relationship is established; this method can solve complex and uncertain problems and effectively improve the accuracy and reliability of evaluation in this field.

[0033] Furthermore, in step 1, the importance of subtask requirements is calculated The method is the Decision-making Trial and Evaluation Laboratory (DEMATEL) method, which is an existing technology. It mainly calculates the influence and influence of each element on other elements through the logical relationship and direct influence matrix between the elements in the system, thereby calculating the causal degree and centrality of each element as the basis for constructing the model, and then determining the causal relationship between the elements and the importance of each element in the system.

[0034] Furthermore, in step 1, a first relationship matrix is ​​constructed And the second relationship matrix The method includes scoring the opinions of equipment experts through hesitant fuzzy number scoring; integrating the hesitant fuzzy number information through the OWA operator based on normal distribution to minimize the impact of opinion scoring on decision conclusions; and calculating the corresponding relationship matrix through row and normalization (NRA); in this way, the situation that the equipment experts have hesitant fuzzy information when judging and scoring can be taken into account, and information loss can be avoided. The specific expression can be: ; ; in, Indicates The subtasks and the corresponding The correlation between the meta-tasks; Indicates Item task and corresponding The correlation between the equipment capability requirements; the correlation adopts the 0.1~0.9 scaling method as the quantitative standard for value selection, and the larger the value, the higher the correlation; Indicates the total number of subtasks; represents the total number of meta-tasks; Indicates the total number of equipment capacity requirements.

[0035] Furthermore, the formula for condition matching calculation in step 2 includes: ; in, Indicates Equipment capability indicators The set of matched preconditions after conversion; Indicates equipment capability index reliability; Indicates the total number of equipment capability indicators; Indicates Equipment capability indicators and corresponding Value The degree of match between , the specific calculation formula can be: ; in, Represents a similarity function, used to characterize and The degree of similarity between them.

[0036] Furthermore, the similarity function includes: When the attribute value of the premise is a discrete value, the similarity function can be expressed as: ; in, The changing trend of is monotonically increasing; When the attribute value of the premise is a single point, the similarity function can be expressed as: ; in, Indicates The corresponding values ​​of equipment capability indicators; express The lower limit of the value range; express The upper limit of the value range of ; When the attribute value of the prerequisite is the subjective judgment value of the equipment expert, the subjective judgment value is directly used as the value of the equipment capability index, that is, the equipment capability index With the corresponding Value The degree of match between them is equal to the equipment capability index reliability.

[0037] Through the above three situations, the comprehensiveness and rationality of equipment capability indicator data are ensured.

[0038] Furthermore, the formula for rule matching calculation in step 2 includes: The calculation formula for calculating the matching degree between equipment capability index data and rules is: ; in, Indicates Equipment capability index data and The matching degree between the rules, and ; Indicates The equipment capability index data is in The relative weight of the following premises; express The correction weight of ; Indicates equipment capability index data and The matching degree between the rules, and ; Based on the The weight of the rule , through data normalization calculation, the activation degree is obtained , the specific formula can be: ; Among them, when When , indicating the rules are not activated.

[0039] Furthermore, the specific formula for constructing the basic credible number in step 2 includes: ; in, Indicates Rule No. The basic credibility number of the conclusion assignment; Indicates The rule has no basic confidence number assigned to any conclusion; Representation and The relevant uncertainty component; Indicates The uncertainty part related to the rules and their conclusions; in this way, the basic credibility number can be constructed based on the degree of activation and credibility of the rules by the equipment capability indicator data, which will help the subsequent data aggregation based on evidence reasoning and deduce the upper-level demand satisfaction from the lower-level demand satisfaction.

[0040] Furthermore, the specific calculation formula for constructing the evidence combination in step 2 includes: ; in, It represents the conflict coefficient, which represents the degree of conflict between multiple pieces of evidence; Indicates The rule for The basic credibility of the conclusion; Indicates The set of rules for various conclusions The uncertainty of Indicates The set of rules for various conclusions The fuzzy reliability of Indicates the number of activated rules; Indicates the reliability of the conclusion drawn from the equipment capability index data; Indicates the uncertainty of the conclusion; Indicates The basic credibility of the conclusion; Represents a collection of various conclusions The uncertainty of Represents a collection of various conclusions The fuzzy reliability of In this way, the basic credibility numbers of multiple rules can be combined to obtain the final confidence distribution.

[0041] Furthermore, in step 3, the satisfaction degree of the underlying capability requirements of the equipment system is calculated. The specific formula can be: ; in, Represents the equipment capability confidence vector matrix, Indicates Equipment capability indicators, Indicates confidence vector values, Can be taken from ; Represents the weight vector, and the specific expression can be: , where 1, 0.5 and 0 correspond to the confidence values ​​of satisfaction, almost satisfaction and dissatisfaction respectively; Represents the weight vector The transposed vector of ; In this way, the weighted average method can be used to aggregate and describe the satisfaction of the underlying capability requirements of the equipment system.

[0042] Furthermore, in step 4, the meta-task requirement satisfaction is calculated The specific formula can be: ; in, Indicates the satisfaction of the underlying capability requirements of the equipment system The transposed vector of .

[0043] Furthermore, in step 5, the subtask requirement satisfaction is calculated The specific formula can be: ; in, Represents the degree of satisfaction of meta-task requirements The transposed vector of .

[0044] Furthermore, in step 6, the mission requirement satisfaction is calculated The specific formula can be: ; in, Indicates the satisfaction of subtask requirements The transposed vector of .

[0045] Embodiment 2: like Figure 3 As shown, this embodiment provides an equipment system mission task requirement satisfaction evaluation system, including a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive equipment capability indicator data required by the meta-task; The data processing module includes a task decomposition unit, a credibility rule base unit and a satisfaction calculation unit; The task decomposition unit decomposes the mission into subtasks and calculates the importance of subtask requirements through the QFD method. ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; The credibility rule base unit stores a credibility rule base, which is used to obtain the preconditions matching the equipment capability indicator data in the credibility rule base through condition matching calculation for the equipment capability indicator data of the meta-task requirement; obtain the rules matching the equipment capability indicator data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability indicator data based on the preconditions and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; the credibility rule base is constructed based on the equipment capability indicator data involved in the mission task and the equipment expert opinion score, etc., and can be expressed as: ; in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; The representation of rules in the credibility rule base can be: ; in, Indicates When the premise is met, the conclusion is The reliability of ; Indicates the total number of rules; Indicates The total number of preconditions in the rule; The satisfaction calculation unit calculates the satisfaction of the equipment system bottom capability requirement through weighted average based on the credibility and equipment capability requirement. ; Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction ; The result generation module is used to calculate the mission task requirement satisfaction Outgoing.

[0046] Embodiment three: The present embodiment provides an equipment system mission task requirement satisfaction assessment device, including a processor, a memory and a bus, the memory stores instructions and data that can be read by the processor, the processor is used to call the instructions and data in the memory to execute the equipment system mission task requirement satisfaction assessment method as described above, and the bus connects the functional components for transmitting information.

[0047] In another embodiment, the present solution can be implemented by an integrated device, which may include corresponding modules for performing each or several steps in the above-mentioned embodiments. The module may be one or more hardware modules specially configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination.

[0048] The processor performs the various methods and processes described above. For example, the method implementation in the present solution can be implemented as a software program, which is tangibly contained in a machine-readable medium, such as a memory. In some embodiments, part or all of the software program can be loaded and / or installed via a memory and / or a communication interface. When the software program is loaded into the memory and executed by the processor, one or more steps in the method described above can be executed. Alternatively, in other embodiments, the processor can be configured to perform one of the above methods by any other appropriate means (for example, by means of firmware).

[0049] The device can be implemented using a bus architecture. The bus architecture can include any number of interconnecting buses and bridges, depending on the specific application and overall design constraints of the hardware. The bus connects various circuits including one or more processors, memories, and / or hardware modules together. The bus can also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, etc.

[0050] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0051] Embodiment 4: The method in Example 1 is applied to a case study of an urban intelligent disaster relief mission.

[0052] like Figure 4 As shown in the figure, the urban intelligent disaster relief task is taken as the mission task, and the mission task satisfaction evaluation of the equipment system is carried out. Specifically: In step 1, the equipment capability index data and relevant equipment expert opinion scores involved in the urban intelligent disaster relief mission are first collected to build a credibility rule base; the equipment expert opinion score avoids the influence of equipment expert subjectivity on the results through multi-level expert participation, anonymous scoring and multiple rounds of feedback; The mission is then broken down into reconnaissance and early warning. , Communication Network , Command and Control Rescue and emergency There are 4 subtasks in total; the importance of subtask requirements is calculated using the DEMATEL method The specific calculation results (influence, influence, centrality, cause and weight) are shown in Table 1:

[0053] From this we can get, , the influence relationship between subtask requirements is as follows Figure 5 As shown; The four subtasks are decomposed into 12 meta-tasks, namely satellite early warning , radar warning , air warning , Satellite Communications , air communication , ground communication , Situation Processing , Mission Planning , Action Control , Moving Target Rescue , Cluster Target Rescue and hidden target rescue , the evaluation object is scored by hesitant fuzzy numbers. The larger the score value, the higher the influence or correlation. The specific scoring results are shown in Table 2:

[0054] Through the OWA operator based on normal distribution, the above hesitant fuzzy numbers are integrated and normalized by row and get the first relationship matrix , the specific results are: ; Due to space limitations, the command and control Meta-task in situation processing , Mission Planning and motion control ) as an example, calculate its second relationship matrix ; The corresponding scoring results are shown in Table 3:

[0055] Similarly, the OWA operator based on normal distribution is used to integrate the above hesitant fuzzy numbers and normalize them by row and get the second relation matrix of the corresponding meta-task: , the specific expression is: ; Assuming that the self-weights of the rules in the credibility rule base are equal, the relative weights of the preconditions in the rules are also equal; Command and Control The equipment capability index data involved are listed in Table 4:

[0056] Exemplarily, build conflict resolution The relevant credibility rule base is shown in Table 5:

[0057] in, Represents satisfaction, Represents basic satisfaction. Represents dissatisfaction.

[0058] In step 2, assume that command and control Equipment capabilities required by meta-tasks in (conflict resolution ) The indicator data are: , and ; Based on Table 5, , and Substitute the similarity functions respectively and calculate: , , ; , , ; , , ; , ; Through condition matching calculation and rule matching calculation, the rules with ID numbers 1, 2, 4, 6, 7 and 8 in the credibility rule library are activated. The matching degree of each rule is as follows: , , , , and ; Calculate the activation degree and get the activation degree of each rule as follows: , , , , and ; The corresponding correction factors are all 1, which can be expressed as: , , , , and ; Construct a basic credible number, which is specifically expressed as:

[0059] Construct evidence combination, which is specifically expressed as: , , ; , that is, the conflict is resolved The reliability of the satisfaction conclusion is ; The above calculation results prove that if the equipment capability index data input into the credibility rule base is complete, that is, ; then the output information is also complete, that is , and the uncertainty of the conclusion .

[0060] In step 3, the weight vector is constructed , calculate the corresponding conflict resolution The satisfaction degree of the basic capability requirements of the equipment system is 0.89.

[0061] Similarly, we can get situation fusion , Situation Generation , Situation Sharing , Task Management , Task Allocation , Status Monitoring , Intelligent Collaboration and manual control The satisfaction degree of the underlying capability requirements of the equipment system is calculated, so as to obtain the overall satisfaction degree of the underlying capability requirements of the equipment system. The specific results are: .

[0062] In step 4, the command and control Meta-task requirement satisfaction , the specific results are: ; Similarly, get early warning of detection , Communication Network Rescue and emergency The meta-task requirement satisfaction is calculated to obtain the overall meta-task requirement satisfaction. The specific results are: .

[0063] In step 5, the subtask requirement satisfaction is calculated, and the specific result is: ; Then according to the importance of subtask requirements ; The mission requirement satisfaction of the urban intelligent disaster relief mission is calculated, and the specific results are as follows: .

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the satisfaction of equipment system mission requirements, characterized in that: include: Step 1: Use the QFD method to decompose the mission into subtasks and calculate the importance of subtask requirements ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; Build a credibility rule base based on the equipment capability indicator data involved in the mission and the equipment expert opinion scores; The credibility rule base is expressed as: ； in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; Step 2: Input the equipment capability index data of the meta-task requirement into the credibility rule base; obtain the prerequisites that match the equipment capability index data in the credibility rule base through condition matching calculation; obtain the rules that match the equipment capability index data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability index data based on the prerequisites and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; Step 3: Based on the reliability and equipment capability requirements, calculate the satisfaction of the equipment system's underlying capability requirements through weighted average. ; Step 4: Based on the second relationship matrix And the satisfaction of the underlying capability requirements of the equipment system , calculate the meta-task requirement satisfaction ; Step 5: Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Step 6: Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction .

2. The method according to claim 1, characterized in that The representation of rules in the credibility rule base is: ； in, Indicates When the premise is met, the conclusion is The reliability of ; Indicates the total number of rules; Indicates The total number of preconditions in the rule.

3. The method according to claim 2, characterized in that In step 1, a first relationship matrix is ​​constructed. And the second relationship matrix The method includes: scoring the opinions of equipment experts through hesitant fuzzy number scoring; integrating the information of hesitant fuzzy numbers through OWA operator based on normal distribution; calculating the corresponding relationship matrix through row and normalization; The specific expression is: ； ； in, Indicates The subtasks and the corresponding The correlation between the meta-tasks; Indicates Item task and corresponding The correlation between the equipment capability requirements; the correlation adopts the 0.1~0.9 scaling method as the quantitative standard for value selection, and the larger the value, the higher the correlation; Indicates the total number of subtasks; represents the total number of meta-tasks; Indicates the total number of equipment capacity requirements.

4. The method according to claim 3, characterized in that The formula for condition matching calculation in step 2 includes: ； in, Indicates Equipment capability indicators The set of matched preconditions after conversion; Indicates equipment capability index reliability; Indicates the total number of equipment capability indicators; Indicates Equipment capability indicators and corresponding Value The degree of match between , the specific calculation formula is: ； in, Represents a similarity function, used to characterize and The degree of similarity between The similarity function includes: When the attribute value of the premise is a discrete value, the similarity function is expressed as: ； in, The changing trend of is monotonically increasing; When the attribute value of the premise is a single point, the similarity function is expressed as: ； in, Indicates The corresponding values ​​of equipment capability indicators; express The lower limit of the value range; express The upper limit of the value range of ; When the attribute value of the prerequisite is the subjective judgment value of the equipment expert, the subjective judgment value is directly used as the value of the equipment capability index, that is, the equipment capability index With the corresponding Value The degree of match between them is equal to the equipment capability index reliability; The formula for rule matching calculation in step 2 includes: The calculation formula for calculating the matching degree between equipment capability index data and rules is: ； in, Indicates Equipment capability index data and The matching degree between the rules, and ; Indicates The equipment capability index data is in The relative weight of the following premises; express The correction weight of ; Indicates equipment capability index data and The matching degree between the rules, and ; Based on the The weight of the rule , through data normalization calculation, the activation degree is obtained , the specific formula is: ； Among them, when When , indicating the rules are not activated; The specific formula for constructing the basic credible number in step 2 includes: ； in, Indicates Rule No. The basic credibility number of the conclusion assignment; Indicates The rule has no basic confidence number assigned to any conclusion; Representation and The relevant uncertainty component; Indicates The uncertainty associated with the rule and its conclusions; The specific calculation formula for constructing the evidence combination in step 2 includes: ； in, It represents the conflict coefficient, which represents the degree of conflict between multiple pieces of evidence; Indicates The rule for The basic credibility of the conclusion; Indicates The set of rules for various conclusions The uncertainty of Indicates The set of rules for various conclusions The fuzzy reliability of Indicates the number of activated rules; Indicates the reliability of the conclusion drawn from the equipment capability index data; Indicates the uncertainty of the conclusion; Indicates The basic credibility of the conclusion; Represents a collection of various conclusions The uncertainty of Represents a collection of various conclusions fuzzy reliability.

5. The method according to claim 4, characterized in that In step 3, the satisfaction degree of the underlying capability requirements of the equipment system is calculated. The specific formula is: ； in, Represents the equipment capability confidence vector matrix, Indicates Equipment capability indicators, Indicates confidence vector values, Value taken from ; Represents the weight vector, and the specific expression is: , where 1, 0.5 and 0 correspond to: satisfied, basically satisfied and unsatisfied confidence values ​​respectively; Represents the weight vector The transposed vector of .

6. The method according to claim 5, characterized in that In step 4, the meta-task requirement satisfaction is calculated The specific formula is: ； in, Indicates the satisfaction of the underlying capability requirements of the equipment system The transposed vector of .

7. The method according to claim 6, characterized in that In step 5, the subtask requirement satisfaction is calculated The specific formula is: ； in, Represents the degree of satisfaction of meta-task requirements The transposed vector of .

8. The method according to claim 7, characterized in that In step 6, the mission requirement satisfaction is calculated The specific formula is: ； in, Indicates the satisfaction of subtask requirements The transposed vector of .

9. An equipment system mission requirement satisfaction evaluation system, characterized in that: It includes a data receiving module, a data processing module and a result generating module; The data receiving module is used to receive equipment capability indicator data required by the meta-task; The data processing module includes a task decomposition unit, a credibility rule base unit and a satisfaction calculation unit; The task decomposition unit decomposes the mission into subtasks and calculates the importance of subtask requirements through the QFD method. ; Decompose subtasks into metatasks and construct the first relationship matrix between subtask requirements and metatask requirements ; Decompose the meta-task into equipment capability requirements and construct the second relationship matrix between the meta-task requirements and equipment capability requirements ; The credibility rule base unit stores a credibility rule base, which is used to obtain the preconditions matching the equipment capability indicator data in the credibility rule base through condition matching calculation for the equipment capability indicator data of the meta-task requirement; obtain the rules matching the equipment capability indicator data in the credibility rule base through rule matching calculation; perform data conversion on the equipment capability indicator data based on the preconditions and the rules; construct a basic credibility number and evidence combination to calculate the credibility of the equipment system capability requirement satisfaction; the credibility rule base is constructed based on the equipment capability indicator data involved in the mission task and the equipment expert opinion score, and is specifically expressed as follows: ； in, Represents the credibility rule base; Represents the collection of various equipment capabilities, specifically expressed as: ,in, Indicates the total number of equipment capabilities; It represents the corresponding value set of the credibility rule base and each equipment capability, which is specifically expressed as: ,in, The confidence rule base and The corresponding value subset of the equipment capability is expressed as ,in, Representing a sub-collection The number of elements in ; Represents a collection of various conclusions, specifically expressed as: ,in, Representing a collection The total number of conclusions; A mapping function that represents the premise and conclusion in a rule; The satisfaction calculation unit calculates the satisfaction of the equipment system bottom capability requirement through weighted average based on the credibility and equipment capability requirement. ; Based on meta-task requirement satisfaction And the first relationship matrix , calculate the subtask requirement satisfaction ; Based on the satisfaction of subtask requirements and importance of subtask requirements , calculate the mission task requirement satisfaction ; The result generation module is used to calculate the mission task requirement satisfaction Outgoing.

10. A device for evaluating the satisfaction of equipment system mission requirements, characterized in that: It includes a processor, a memory and a bus, wherein the memory stores instructions and data read by the processor, the processor is used to call the instructions and data in the memory to execute any method as claimed in claim 1-8, and the bus connects the functional components for transmitting information.

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