Efficiency evaluation method and device for early warning aircraft system

Abstract

The invention relates to an early warning aircraft system efficiency evaluation method, which comprises the steps of analyzing the composition and the structure of a multi-model early warning aircraft system and a corresponding subsystem, and generating early warning aircraft efficiency factors and early warning aircraft efficiency influence factors; based on the early warning aircraft efficiency factors, establishing a multi-model early warning aircraft system efficiency evaluation index system; adopting a target method to distribute corresponding index weights of subsystem level indexes corresponding to the subsystem levels and performance element level indexes corresponding to the performance element levels during early warning aircraft performance evaluation; performing comprehensive efficiency evaluation on the multi-model early warning aircraft system by adopting a cloud model comprehensive cloud evaluation method based on the efficiency evaluation index system of the multi-model early warning aircraft system and the index weight, generating an evaluation result, and establishing an evaluation grade and an evaluation rule of the evaluation result; and generating an evaluation result analysis report based on the efficiency evaluation index system of the multi-model early warning aircraft system and the evaluation result. The method can provide data support for prediction of the comprehensive efficiency of the early warning aircraft.

Description

Technical Field

[0001] The present application relates to the technical field of system effectiveness evaluation, and in particular to a method and device for evaluating the effectiveness of an early warning aircraft system. Background Art

[0002] The main technology of early warning aircraft effectiveness evaluation lies in the construction of early warning aircraft system effectiveness index system. Under normal circumstances, it is necessary to design a research plan for the comprehensive effectiveness evaluation of a certain type of early warning aircraft system, analyze the effectiveness evaluation process, and select a suitable evaluation method; then, on the basis of studying the tasks, composition and functions of each subsystem of the early warning aircraft system, clarify the various components of the early warning aircraft system capability index system, and establish the evaluation index system of the early warning aircraft system based on the establishment principles of the index system and the construction process of the system effectiveness index system; then, use the analytic hierarchy process to calculate the relative weights of each layer of indicators in the early warning aircraft system index system; according to the relevant theories of fuzzy comprehensive evaluation method, ranking method of approximating ideal solution and cloud model method, combined with the system capability index system, evaluate the performance of each subsystem and the comprehensive effectiveness of the system of this type of early warning aircraft, understand the capabilities of each subsystem of the early warning aircraft based on the evaluation results, and find the weak links in the system performance, so as to provide scientific decisions for the design, development, use and upgrade of the early warning aircraft system, so as to improve the overall effectiveness of the early warning aircraft system, thereby avoiding unnecessary material, human and financial losses, and ensuring that the early warning aircraft system completes its mission.

[0003] However, the current AWACS performance evaluation index system is only for a certain type of AWACS, and the index system has not been standardized. When calculating the relative weights of indicators at each level in the index system, only the analytic hierarchy process is used to qualitatively process the weights of indicators at each level, and the specific performance parameters of each subsystem are not used to quantify the reference data. The index weight values ​​are different from the actual application. In addition, when using the cloud model to determine the comprehensive effectiveness of the system, the maximum membership principle and the maximum similarity principle are usually used. The decision-making process is relatively simple and it is easy to get wrong judgment results.

[0004] In view of this, how to provide a mission system effectiveness evaluation method that can evaluate single and multiple missions of multiple types of early warning aircraft to provide support for the integrated support assessment and maintenance auxiliary decision-making of early warning aircraft has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide an early warning aircraft system effectiveness evaluation method, an early warning aircraft system effectiveness evaluation device, an electronic device and a computer storage medium, which are used to solve the problem of how to provide support for integrated support evaluation and maintenance auxiliary decision-making for early warning aircraft.

[0006] In a first aspect of an embodiment of the present application, a method for evaluating the effectiveness of an early warning aircraft system is provided, comprising:

[0007] Analyze the composition and structure of various AWACS systems and corresponding subsystems, generate AWACS effectiveness factors and factors affecting AWACS effectiveness;

[0008] Based on the effectiveness factors of the AWACS, a multi-model AWACS system effectiveness evaluation index system is established, wherein the multi-model AWACS system effectiveness evaluation index system carries the system level, subsystem level and performance factor level;

[0009] Using a target method, assigning the corresponding indicator weights of the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level in the early warning aircraft effectiveness evaluation, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator;

[0010] Using the comprehensive cloud evaluation method of the cloud model, based on the effectiveness evaluation index system of the multi-type early warning aircraft system and the index weights, a comprehensive effectiveness evaluation is performed on the multi-type early warning aircraft system, an evaluation result is generated, and an evaluation grade and an evaluation rule for the evaluation result are established;

[0011] Based on the multi-model early warning aircraft system effectiveness evaluation index system and the evaluation results, an evaluation result analysis report is generated.

[0012] In a second aspect of an embodiment of the present application, there is provided an early warning aircraft system effectiveness evaluation device, comprising:

[0013] The first generation module is configured to analyze the composition and structure of multiple types of AWACS systems and corresponding subsystems, and generate AWACS effectiveness factors and AWACS effectiveness influencing factors;

[0014] An establishing module is configured to establish a multi-model AWACS system effectiveness evaluation index system based on the AWACS effectiveness elements, wherein the multi-model AWACS system effectiveness evaluation index system carries a system level, a subsystem level, and a performance element level;

[0015] An allocation module is configured to use a target method to allocate the indicator weights corresponding to the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level when evaluating the effectiveness of the early warning aircraft, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator;

[0016] An evaluation module is configured to adopt a comprehensive cloud evaluation method of a cloud model, based on the multi-model early warning aircraft system effectiveness evaluation index system and the index weights, to conduct a comprehensive effectiveness evaluation on the multi-model early warning aircraft system, generate an evaluation result, and establish an evaluation grade and an evaluation rule for the evaluation result;

[0017] The second generating module is configured to generate an evaluation result analysis report based on the multi-type early warning aircraft system effectiveness evaluation index system and the evaluation results.

[0018] In a third aspect of an embodiment of the present application, a computing device is provided, including:

[0019] Memory and processor;

[0020] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned early warning aircraft system effectiveness evaluation method are implemented.

[0021] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned early warning aircraft system effectiveness evaluation method are implemented.

[0022] The present application provides an early warning aircraft system effectiveness evaluation method, comprising: firstly analyzing the composition and structure of a multi-model early warning aircraft system and a corresponding subsystem, generating early warning aircraft effectiveness elements and early warning aircraft effectiveness influencing elements; then establishing a multi-model early warning aircraft system effectiveness evaluation index system based on the early warning aircraft effectiveness elements, wherein the multi-model early warning aircraft system effectiveness evaluation index system carries a system level, a subsystem level and a performance element level; secondly adopting a target method to allocate the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level when evaluating the effectiveness of the early warning aircraft, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator; thirdly adopting a comprehensive cloud evaluation method of a cloud model, based on the multi-model early warning aircraft system effectiveness evaluation index system and the indicator weight, performing a comprehensive effectiveness evaluation on the multi-model early warning aircraft system, generating an evaluation result, and establishing an evaluation grade and an evaluation rule for the evaluation result; finally, based on the multi-model early warning aircraft system effectiveness evaluation index system and the evaluation result, generating an evaluation result analysis report.

[0023] By applying the AWACS system effectiveness evaluation method provided in the embodiment of the present application, the comprehensive effectiveness of the single-task and multi-task mission systems of multiple types of AWACS aircraft can be evaluated, which can support scientific researchers in grasping the performance efficiency status trend of AWACS aircraft and provide data support for the comprehensive effectiveness prediction of AWACS aircraft.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0026] Figure 1 A flowchart of a method for evaluating the effectiveness of an early warning aircraft system provided in an embodiment of the present application;

[0027] Figure 2 A flowchart of another early warning aircraft system effectiveness evaluation method provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of an early warning aircraft system effectiveness evaluation device provided in an embodiment of the present application;

[0029] Figure 4 A structural block diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0031] See also Figure 1 , Figure 1 A flowchart of a method for evaluating the effectiveness of an early warning aircraft system provided in an embodiment of the present application is shown below. Figure 1 As shown, the specific steps include the following steps.

[0032] Step S102: Analyze the composition and structure of the multi-type AWACS systems and corresponding subsystems, and generate AWACS efficiency factors and factors affecting AWACS efficiency.

[0033] In practical applications, the composition and structure of various types of AWACS systems are analyzed, and the effectiveness elements of AWACS are summarized according to the subsystems such as AWACS radar, secondary radar, identification friend or foe, electronic reconnaissance, mission navigation, and communication. Based on the working description, composition, functional analysis, and performance understanding of the AWACS system, the factors affecting the effectiveness of the AWACS system are summarized from a functional perspective.

[0034] Step S104: Based on the AWACS performance factors, a multi-model AWACS system performance evaluation index system is established, wherein the multi-model AWACS system performance evaluation index system includes system level, subsystem level and performance factor level.

[0035] In practical applications, the AWACS effectiveness factors are combined into a three-layer structure, namely, system level, subsystem level, and performance factor level, in order to construct an evaluation index system for multi-model AWACS systems.

[0036] Step S106: Using a target method, assign indicator weights corresponding to the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level when evaluating the effectiveness of the early warning aircraft, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator.

[0037] In an embodiment of the present application, the target method is used to allocate the indicator weights corresponding to the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level when evaluating the effectiveness of the early warning aircraft, including: determining the subsystem-level indicators corresponding to the subsystem level; and using a qualitative hierarchical analysis method to determine the first indicator weight corresponding to the subsystem indicators when evaluating the effectiveness of the early warning aircraft.

[0038] It also includes: determining a performance factor level indicator corresponding to a performance factor level, and performing dimensionless normalization processing on data corresponding to the performance factor level indicator to obtain an indicator entropy value, wherein the data corresponding to the performance factor level indicator includes original data, intermediate data and structural data; using a quantitative entropy weight method, based on the indicator entropy value, determining a second indicator weight corresponding to the performance factor level indicator corresponding to the performance factor level when evaluating the effectiveness of the early warning aircraft.

[0039] In practical applications, different indicators have different degrees of influence on the comprehensive effectiveness of early warning aircraft, and a certain method should be used to allocate the weight of each indicator in the evaluation of early warning aircraft effectiveness.

[0040] On the one hand, it is necessary to determine the weights of subsystem-level indicators.

[0041] Taking the radar subsystem as an example, the performance factors of the radar can be divided into four items: track maintenance rate, maximum detection distance, detection accuracy, and track tracking accuracy. These four indicators themselves do not contain obvious quantitative information. Therefore, for the subsystem-level indicators, the qualitative hierarchical analysis method is used to determine the weights.

[0042] The other aspect is to determine the weights of performance factor-level indicators.

[0043] Taking the radar subsystem as an example, for the performance factor of track maintenance rate, the ratio data of the number of target tracking times and the number of scanning times of the radar are collected, and the data samples are dimensionlessly normalized to obtain the index entropy value, and the weight of the corresponding index is calculated based on the entropy value of the index. At the performance factor level, the specific performance parameters of each function have actual measured data available, so the original data, intermediate data, and result data on the aircraft can be collected for compilation and storage and standardized management, and the quantitative entropy weight method can be used to determine the index weight under the unified data specification.

[0044] Step S108: Using the comprehensive cloud evaluation method of the cloud model, based on the multi-type early warning aircraft system effectiveness evaluation index system and the index weights, a comprehensive effectiveness evaluation is performed on the multi-type early warning aircraft system, an evaluation result is generated, and an evaluation grade and evaluation rules for the evaluation results are established.

[0045] In an embodiment of the present application, the comprehensive cloud evaluation method using a cloud model performs a comprehensive performance evaluation on the multi-type early warning aircraft system based on the multi-type early warning aircraft system performance evaluation index system and the index weights, and generates an evaluation result, including: a performance aggregation algorithm based on matrix operations, performing performance aggregation evaluation on any two indicators in the performance factor level indicators; in response to any two indicators in the performance factor level indicators being a constraint relationship, a data matrix product calculation is adopted to obtain a first evaluation result; in response to any two indicators in the performance factor level indicators being a cooperative relationship, a data matrix addition calculation is adopted to obtain a second evaluation result.

[0046] It also includes: determining a third evaluation result corresponding to a subsystem indicator based on the first evaluation result and / or the second evaluation result calculated based on the performance factor level indicator, and the weight value corresponding to each of the performance factor level indicators.

[0047] In an embodiment of the present application, establishing an evaluation grade includes: adopting a trapezoidal cloud model, and determining a comprehensive performance evaluation result of the system and an evaluation grade based on the third evaluation result and a weight value corresponding to the third evaluation result, wherein the evaluation grades include excellent, good, medium, poor and bad.

[0048] The establishment of evaluation rules for the evaluation results includes: adopting the decision principle of maximum attractiveness, determining the attractiveness of the evaluation level to the system performance cloud model based on the evaluation results and the evaluation level, and determining the work performance level corresponding to the evaluation object based on the attractiveness, wherein the attractiveness of the evaluation level to the system performance cloud model is determined by calculating the area of ​​cloud droplets of the system cloud model falling in the evaluation level.

[0049] In practical applications, on the basis of determining the evaluation index system and index weights, an efficiency aggregation algorithm based on matrix operations is adopted for performance factor-level indicators. If the two indicators are in a constraint relationship, the data matrix product is used to calculate the aggregate efficiency value. If the two indicators are in a cooperative relationship, the data matrix addition is used to calculate the aggregate efficiency value. The system efficiency is evaluated from bottom to top at all levels. Since the amount of data will increase rapidly during the evaluation process, a cloud model is used to visualize the data, and the randomness and fuzziness of the evaluation data are reflected through a qualitative and quantitative interchange model, and the evaluation level and evaluation rules for the evaluation results are established.

[0050] Specifically, regarding the determination of the evaluation level. Since the division of a certain evaluation level should have certain interval characteristics, the evaluation system adopts a trapezoidal cloud model to express five evaluation levels, namely excellent (95-100), good (75-85), medium (50-65), poor (25-40), and bad (0-15), and the evaluation value range is (0-100).

[0051] Determination of the judgment rules for the evaluation results. After obtaining the system comprehensive effectiveness, it is necessary to determine which evaluation level the visual cloud model of the system comprehensive effectiveness belongs to. Therefore, the decision principle of maximum attraction is adopted to calculate the area of ​​the cloud droplets of the system cloud model in each evaluation level. The larger the area, the higher the attraction of the level to the system effectiveness cloud model, thereby determining the work efficiency level of the evaluation object.

[0052] Step S110: Based on the multi-type early warning aircraft system effectiveness evaluation index system and the evaluation results, an evaluation result analysis report is generated.

[0053] In actual applications, an evaluation result analysis report is generated based on the saved performance indicators of each subsystem and the system's comprehensive performance results.

[0054] The embodiment of the present application provides a method for collecting and storing in-service mission data and support data of multiple types of early warning aircraft, and evaluating the performance and efficiency status of mission electronic systems. Based on a unified data specification, the interface definition, analysis, transformation, storage, and management of mission data and maintenance support data of multiple types of early warning aircraft are realized, and the original data, intermediate data, and result data are integrated into the warehouse and standardized management; on the basis of data management, the performance indicators of each subsystem are analyzed and evaluated, and the comprehensive performance and efficiency of the early warning aircraft are evaluated, providing support for the integrated support evaluation and maintenance auxiliary decision-making of the early warning aircraft.

[0055] When constructing the performance index system of early warning aircraft, according to the functional characteristics of various types of early warning aircraft in China, a universal index system is analyzed and constructed, which can be applied to the comprehensive effectiveness evaluation of early warning aircraft in multiple tasks and scenarios. In addition, when calculating the index weight and system effectiveness, the hierarchical analysis method and entropy weight method are used at different levels to calculate the index weight, and the index aggregation relationship and maximum attraction law are used to generate the system comprehensive effectiveness evaluation results.

[0056] See also Figure 2 , Figure 2 A flowchart of another method for evaluating the effectiveness of an early warning aircraft system provided in an embodiment of the present application is shown below. Figure 2 As shown, the specific steps include the following steps.

[0057] Step S201: Analyze the composition and structure of multiple types of early warning aircraft systems and subsystems.

[0058] Step S202: Establishing a multi-model early warning aircraft system effectiveness evaluation index system, i.e. index system design.

[0059] Step S203: Study on performance evaluation method.

[0060] Step S204: Analyze and calculate the effectiveness of the AWACS subsystem.

[0061] Step S2041: Analytical hierarchy process (qualitative).

[0062] Step S2042: Entropy weight method (quantitative).

[0063] Step S2041 and step S2042 are the indicator weight calculation method.

[0064] Step S205: Comprehensive effectiveness of the AWACS system.

[0065] Step S2051: Comprehensive cloud evaluation method of cloud model, i.e., performance evaluation method.

[0066] Step S204, step S2041, step S2042, step S205 and step S2051 are the evaluation algorithm.

[0067] Step S206: System comprehensive performance evaluation report.

[0068] An early warning aircraft system effectiveness evaluation method provided in the embodiment of the present application can be used to evaluate the comprehensive mission system effectiveness of single-mission and multi-mission of early warning aircraft of multiple models, which can support scientific researchers in grasping the performance efficiency status trend of early warning aircraft and provide data support for the comprehensive effectiveness prediction of early warning aircraft.

[0069] Corresponding to the above method embodiment, this specification also provides an embodiment of an early warning aircraft system effectiveness evaluation device, Figure 3 This is a schematic diagram of the structure of an early warning aircraft system effectiveness evaluation device provided in an embodiment of the present application. Figure 3 As shown, the device comprises:

[0070] The first generating module 302 is configured to analyze the composition and structure of the multi-type AWACS systems and corresponding subsystems, and generate AWACS effectiveness factors and AWACS effectiveness influencing factors;

[0071] The establishing module 304 is configured to establish a multi-type AWACS system effectiveness evaluation index system based on the AWACS effectiveness elements, wherein the multi-type AWACS system effectiveness evaluation index system carries a system level, a subsystem level, and a performance element level;

[0072] The allocation module 306 is configured to use a target method to allocate the indicator weights corresponding to the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level in the early warning aircraft effectiveness evaluation, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator;

[0073] The evaluation module 308 is configured to adopt a comprehensive cloud evaluation method of a cloud model, based on the multi-type early warning aircraft system effectiveness evaluation index system and the index weights, to conduct a comprehensive effectiveness evaluation on the multi-type early warning aircraft system, generate an evaluation result, and establish an evaluation level and an evaluation rule for the evaluation result;

[0074] The second generating module 310 is configured to generate an evaluation result analysis report based on the multi-type early warning aircraft system effectiveness evaluation index system and the evaluation results.

[0075] In an optional embodiment, the allocation module is further configured to:

[0076] Determine the subsystem level indicators corresponding to the subsystem level;

[0077] The qualitative analytic hierarchy process is used to determine the first indicator weight corresponding to the subsystem indicator in the early warning aircraft effectiveness evaluation.

[0078] In an optional embodiment, the allocation module 306 is further configured to:

[0079] Determine a performance factor level indicator corresponding to the performance factor level, and perform dimensionless normalization processing on the data corresponding to the performance factor level indicator to obtain an indicator entropy value, wherein the data corresponding to the performance factor level indicator includes original data, intermediate data and structure data;

[0080] A quantitative entropy weight method is adopted to determine the second indicator weight corresponding to the performance element level indicator corresponding to the performance element level in the early warning aircraft effectiveness evaluation based on the indicator entropy value.

[0081] In an optional embodiment, the evaluation module 308 is further configured to:

[0082] Based on the matrix operation, the performance aggregation algorithm can be used to evaluate the performance of any two indicators at the performance factor level.

[0083] In response to any two indicators in the performance factor level indicators being in a constraint relationship, a data matrix product calculation is performed to obtain a first evaluation result;

[0084] In response to any two indicators in the performance factor level indicators being in a cooperative relationship, a data matrix addition calculation is performed to obtain a second evaluation result.

[0085] In an optional embodiment, the evaluation module 308 is further configured to:

[0086] Based on the first evaluation result and / or the second evaluation result calculated based on the performance factor level indicators, and the weight values ​​corresponding to the performance factor level indicators, a third evaluation result corresponding to the subsystem indicator is determined.

[0087] In an optional embodiment, the evaluation module 308 is further configured to:

[0088] A trapezoidal cloud model is adopted to determine the comprehensive performance evaluation result of the system and the evaluation grade based on the third evaluation result and the weight value corresponding to the third evaluation result, wherein the evaluation grades include excellent, good, medium, poor and bad.

[0089] In an optional embodiment, the evaluation module 308 is further configured to:

[0090] Adopting the decision principle of maximum attractiveness, based on the evaluation result and the evaluation level, the attractiveness of the evaluation level to the system performance cloud model is determined, and based on the attractiveness, the work performance level corresponding to the evaluation object is determined, wherein the attractiveness of the evaluation level to the system performance cloud model is determined by calculating the area of ​​cloud droplets of the system cloud model falling in the evaluation level.

[0091] An early warning aircraft system effectiveness evaluation device provided by the embodiment of the present application can evaluate the comprehensive effectiveness of the single-task and multi-task mission systems of various types of early warning aircraft, support scientific researchers in grasping the performance efficiency status trend of early warning aircraft, and provide data support for the comprehensive effectiveness prediction of early warning aircraft.

[0092] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the early warning aircraft system effectiveness evaluation device, since it is basically similar to the early warning aircraft system effectiveness evaluation method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the early warning aircraft system effectiveness evaluation method embodiment.

[0093] Figure 4 This is a block diagram of a computing device provided in an embodiment of the present application. The components of the computing device 400 include but are not limited to a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and the database 450 is used to store data.

[0094] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 440 may include one or more of any type of network interface (e.g., a network interface card (NIC)) of wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0095] In one embodiment of the present specification, the above components of the computing device 400 and Figure 4 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 4 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0096] The computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 400 may also be a mobile or stationary server.

[0097] Among them, the processor 420 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned early warning aircraft system effectiveness evaluation method.

[0098] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computing device embodiment, since it is basically similar to the early warning aircraft system effectiveness evaluation method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the early warning aircraft system effectiveness evaluation method embodiment.

[0099] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned early warning aircraft system effectiveness evaluation method.

[0100] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer-readable storage medium embodiment, since it is basically similar to the early warning aircraft system effectiveness evaluation method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the early warning aircraft system effectiveness evaluation method embodiment.

[0101] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned early warning aircraft system effectiveness evaluation method.

[0102] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the computer program embodiment, since it is basically similar to the early warning aircraft system effectiveness evaluation method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the early warning aircraft system effectiveness evaluation method embodiment.

[0103] The above is a description of a specific embodiment of the specification. 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 embodiments and still achieve the desired results. In addition, the processes depicted in the 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.

[0104] The computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0105] It should be noted that the above is a description of a specific embodiment of the present specification. 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 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. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of the present specification.

[0106] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that technicians in the relevant technical field can well understand and use this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A method for evaluating the effectiveness of an early warning aircraft system, characterized in that: include: Analyze the composition and structure of various AWACS systems and corresponding subsystems, generate AWACS effectiveness factors and factors affecting AWACS effectiveness; Based on the effectiveness factors of the AWACS, a multi-model AWACS system effectiveness evaluation index system is established, wherein the multi-model AWACS system effectiveness evaluation index system carries the system level, subsystem level and performance factor level; Using a target method, assigning the corresponding indicator weights of the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level in the early warning aircraft effectiveness evaluation, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator; Using the comprehensive cloud evaluation method of the cloud model, based on the effectiveness evaluation index system of the multi-type early warning aircraft system and the index weights, a comprehensive effectiveness evaluation is performed on the multi-type early warning aircraft system, an evaluation result is generated, and an evaluation grade and an evaluation rule for the evaluation result are established; Based on the multi-model early warning aircraft system effectiveness evaluation index system and the evaluation results, an evaluation result analysis report is generated.

2. The method according to claim 1, characterized in that The target method is used to allocate the corresponding indicator weights of the subsystem-level indicators corresponding to the subsystem-level and the performance element-level indicators corresponding to the performance element-level in the early warning aircraft effectiveness evaluation, including: Determine the subsystem level indicators corresponding to the subsystem level; The qualitative analytic hierarchy process is used to determine the first indicator weight corresponding to the subsystem indicator in the early warning aircraft effectiveness evaluation.

3. The method according to claim 1, characterized in that The target method is used to allocate the corresponding indicator weights of the subsystem-level indicators corresponding to the subsystem-level and the performance element-level indicators corresponding to the performance element-level in the early warning aircraft effectiveness evaluation, including: Determine a performance factor level indicator corresponding to the performance factor level, and perform dimensionless normalization processing on the data corresponding to the performance factor level indicator to obtain an indicator entropy value, wherein the data corresponding to the performance factor level indicator includes original data, intermediate data and structure data; A quantitative entropy weight method is adopted to determine the second indicator weight corresponding to the performance element level indicator corresponding to the performance element level in the early warning aircraft effectiveness evaluation based on the indicator entropy value.

4. The method according to claim 1, characterized in that The comprehensive cloud evaluation method using the cloud model performs a comprehensive effectiveness evaluation on the multi-model early warning aircraft system based on the multi-model early warning aircraft system effectiveness evaluation index system and the index weights, and generates an evaluation result, including: Based on the matrix operation, the performance aggregation algorithm can be used to evaluate the performance of any two indicators at the performance factor level. In response to any two indicators in the performance factor level indicators being in a constraint relationship, a data matrix product calculation is performed to obtain a first evaluation result; In response to any two indicators in the performance factor level indicators being in a cooperative relationship, a data matrix addition calculation is performed to obtain a second evaluation result.

5. The method according to claim 1, characterized in that The comprehensive cloud evaluation method using the cloud model performs a comprehensive effectiveness evaluation on the multi-model early warning aircraft system based on the multi-model early warning aircraft system effectiveness evaluation index system and the index weights, and generates an evaluation result, including: Based on the first evaluation result and / or the second evaluation result calculated based on the performance factor level indicators, and the weight values ​​corresponding to the performance factor level indicators, a third evaluation result corresponding to the subsystem indicator is determined.

6. The method according to claim 1, characterized in that The establishment of the assessment level includes: A trapezoidal cloud model is adopted to determine the comprehensive performance evaluation result of the system and the evaluation grade based on the third evaluation result and the weight value corresponding to the third evaluation result, wherein the evaluation grades include excellent, good, medium, poor and bad.

7. The method according to claim 1, characterized in that The establishment of evaluation rules for the evaluation results includes: Adopting the decision principle of maximum attractiveness, based on the evaluation result and the evaluation level, the attractiveness of the evaluation level to the system performance cloud model is determined, and based on the attractiveness, the work performance level corresponding to the evaluation object is determined, wherein the attractiveness of the evaluation level to the system performance cloud model is determined by calculating the area of ​​cloud droplets of the system cloud model falling in the evaluation level.

8. An early warning aircraft system effectiveness evaluation device, characterized in that: include; The first generation module is configured to analyze the composition and structure of multiple types of AWACS systems and corresponding subsystems, and generate AWACS effectiveness factors and AWACS effectiveness influencing factors; An establishing module is configured to establish a multi-model AWACS system effectiveness evaluation index system based on the AWACS effectiveness elements, wherein the multi-model AWACS system effectiveness evaluation index system carries a system level, a subsystem level, and a performance element level; An allocation module is configured to use a target method to allocate the indicator weights corresponding to the subsystem-level indicators corresponding to the subsystem level and the performance element-level indicators corresponding to the performance element level when evaluating the effectiveness of the early warning aircraft, wherein the target method includes a hierarchical analysis method for determining the first indicator weight corresponding to the subsystem indicator and an entropy weight method for determining the second indicator weight corresponding to the performance element-level indicator; An evaluation module is configured to adopt a comprehensive cloud evaluation method of a cloud model, based on the multi-model early warning aircraft system effectiveness evaluation index system and the index weights, to conduct a comprehensive effectiveness evaluation on the multi-model early warning aircraft system, generate an evaluation result, and establish an evaluation grade and an evaluation rule for the evaluation result; The second generating module is configured to generate an evaluation result analysis report based on the multi-type early warning aircraft system effectiveness evaluation index system and the evaluation results.

9. A computer device, characterized in that: The computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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