Method and related device for intelligent evaluation and grading of underwater unmanned cluster

Abstract

The invention belongs to the technical field of underwater unmanned cluster evaluation and grading, and discloses a method for intelligent evaluation and grading of an underwater unmanned cluster and a related device. The method for intelligent evaluation and grading of the underwater unmanned cluster comprises the steps of determining an evaluation task type and obtaining environmental parameter statistics, task parameter statistics and technical feature statistics based on a function type of the underwater unmanned cluster to be subjected to intelligent evaluation and grading; based on the obtained standard score of the environment difficulty index and the standard score of the task effect index, calculating to obtain an external weight; multiplying the obtained external weight by the technical feature original score to obtain an improved technical feature score; and performing evaluation and grading based on the obtained improved technical feature score to obtain an underwater unmanned cluster intelligence evaluation and grading result. According to the technical scheme disclosed by the invention, the problems of subjective evaluation, generalization application, fuzzy definition and solidification classification in the prior art can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater unmanned swarm assessment and grading, and in particular relates to a method and related devices for underwater unmanned swarm intelligence assessment and grading. Background Art

[0002] At present, most of the existing unmanned system classification methods give a classification description based on the autonomy or automation level of various unmanned systems; among them, the subjective scoring of experts is the main classification basis, and most of them give a verbal description of the classification standards for drones or unmanned vehicles. There are few evaluation and classification methods that are directly applied to underwater unmanned clusters.

[0003] For example, the US Department of Defense (DoD) defines the autonomy levels based on the human-machine relationship, focusing on the role of humans in command and control during the operation process; the framework includes four levels: human operated, human delegated, human supervised, and fully autonomous. This framework focuses on the interaction between humans and systems and is suitable for evaluating the command and control relationship in military operations, especially in multi-system collaboration or tasks that require close human-machine collaboration. The PACT (Pilot Authorisation and Control of Tasks) framework was developed by the UK Ministry of Defense and defines the balance of automation levels between human and machine decision-making, including ten levels; the advantage of PACT is that it provides a detailed, progressive level from human-dominated to fully automated, which is suitable for analyzing the application of decision-making automation in actual tasks. NATO Standardization Agreement (STANAG) 4586 focuses on the interoperability of unmanned aerial vehicle systems and defines five levels of interoperability (LOI), each of which describes a different degree of control and data sharing. The ALFUS (Autonomy Levels for Unmanned Systems) framework developed by the National Institute of Standards and Technology (NIST) is more comprehensive and provides a quantitative method to evaluate the autonomy level of unmanned systems. It takes into account three dimensions, each of which is graded from 0 to 10, and the average is calculated to obtain a comprehensive autonomy level. This three-dimensional quantitative approach enables ALFUS to provide a detailed autonomy assessment, which helps to compare and evaluate the performance of unmanned systems in different tasks and environments.

[0004] In summary, the existing unmanned system classification scheme has the following shortcomings, including: 1) Subjective evaluation: all are subjective scoring evaluations and do not correspond to objective performance indicators; 2) Generalized application: The description is too broad, lacking detailed task description for a specific type of cluster, and lacking professionalism; 3) Fuzzy definition: no standardized testing method is provided, only vague language description; 4) Fixed classification: Except for ALFUS, there is no consideration of environmental change information and lack of adaptability to different environments; Therefore, a new scheme for evaluating and grading the intelligence of underwater unmanned swarms is urgently needed. Summary of the invention

[0005] The purpose of the present invention is to provide a method and related device for evaluating and grading the intelligence of underwater unmanned clusters to solve one or more of the above-mentioned technical problems. The technical solution disclosed by the present invention solves the technical problems of subjective evaluation, generalized application, fuzzy definition and solidified classification in the existing technical solutions; wherein, the technical solution of the present invention is specifically aimed at underwater unmanned clusters, covering environmental difficulty indicators and task performance indicators as external weights, so that the evaluation and grading method of the present invention can adapt to different task test scenarios; objective performance indicators are introduced to indicate different technical characteristics of environmental difficulty, task performance and intelligence.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for evaluating and grading the intelligence of underwater unmanned swarms, comprising the following steps: Determine the type of assessment task based on the functional type of the underwater unmanned swarm to be assessed and graded for intelligence; Based on the determined evaluation task type, respectively measuring the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned swarm to obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; Based on the obtained environmental parameter statistics and task parameter statistics, standard scores of environmental difficulty indicators and task effect indicators are obtained respectively by using probability score standardization transformation; based on the obtained standard scores of environmental difficulty indicators and task effect indicators, external weights are calculated; Based on the obtained technical feature statistics, the original technical feature score is obtained by using the probability score standardization transformation; The external weight obtained is multiplied by the original score of the technical feature to obtain an improved technical feature score; an evaluation and grading is performed based on the improved technical feature score obtained to obtain an evaluation and grading result of the intelligence of the underwater unmanned swarm.

[0007] A further improvement of the method of the present invention is that In the step of obtaining the standard scores of the environmental difficulty index and the task effect index respectively by using the probability score standardization transformation based on the obtained environmental parameter statistics and task parameter statistics, and in the step of obtaining the original score of the technical feature by using the probability score standardization transformation based on the obtained technical feature statistics, The expression for obtaining the standard score by using the probability score standardization transformation is: ; ; In the formula, is the standard score calculation value of the positive indicator, It is the calculated value of the standard score of negative indicators. A positive indicator is an indicator whose deserved score increases as the data increases, and a negative indicator is an indicator whose deserved score decreases as the data increases. is the mean value of the indicator; is the variance of the indicator; t is the integration variable of the standard score.

[0008] A further improvement of the method of the present invention is that In the step of calculating and obtaining the external weight based on the obtained standard score of the environmental difficulty index and the standard score of the task effect index, The calculation expression of external weight is: ; In the formula, Calculate values ​​for external weights; Environmental difficulty factors; is the task effect factor; ; ; In the formula, is the number of environmental difficulty indicators, Indicates Standard scores for each environmental difficulty indicator; is the number of task effect indicators, Indicates The standard score of each task performance indicator.

[0009] A further improvement of the method of the present invention is that The evaluation task type is a perception type, a cognitive type, a decision type or an action type; The environmental difficulty index includes one or more of water flow velocity, obstacle density, visibility, acoustic noise level and water temperature gradient; When the evaluation task type is a perception type, the task effect indicators include successful detection rate, maximum coverage area and detection efficiency; when the evaluation task type is a cognitive type, the task effect indicators include inference accuracy and response speed; when the evaluation task type is a decision-making type, the task effect indicators include qualified plan ratio and planning speed; when the evaluation task type is an action type, the task effect indicators include strike success rate and response speed; The technical characteristic indicators include human intervention frequency, information sharing frequency and effect improvement rate.

[0010] A further improvement of the method of the present invention is that In the step of obtaining the original score of the technical feature by using the probability score standardization transformation based on the obtained technical feature statistics, the original score of the technical feature includes the original score of autonomy, the original score of collaboration and the original score of learning.

[0011] A further improvement of the method of the present invention is that In the step of multiplying the external weight obtained by the original technical feature score to obtain an improved technical feature score; and performing evaluation and grading based on the improved technical feature score obtained to obtain an evaluation and grading result of the intelligence of the underwater unmanned swarm, Improved technical feature scores include autonomous improvement scores, collaborative improvement scores, and learning improvement scores; When the autonomy improvement score is less than 0.15, it is evaluated as autonomy level 1; when the autonomy improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as autonomy level 2; when the autonomy improvement score is greater than 0.85, it is evaluated as autonomy level 3; When the synergy improvement score is less than 0.15, it is evaluated as synergy level 1; when the synergy improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as synergy level 2; when the synergy improvement score is greater than 0.85, it is evaluated as synergy level 3; When the learning improvement score is less than 0.15, it is evaluated as learning level 1; when the learning improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as learning level 2; when the learning improvement score is greater than 0.85, it is evaluated as learning level 3.

[0012] A further improvement of the method of the present invention is that In the step of multiplying the external weight obtained by the original technical feature score to obtain an improved technical feature score; and performing evaluation and grading based on the improved technical feature score obtained to obtain an evaluation and grading result of the intelligence of the underwater unmanned swarm, The calculation expression of the intelligent evaluation and classification results of underwater unmanned swarm is: ; In the formula, Indicates the level of intelligence; They represent the three levels of autonomy, collaboration, and learning. .

[0013] In a second aspect, the present invention provides a system for evaluating and grading the intelligence of underwater unmanned swarms, comprising: An evaluation task type acquisition module is used to determine the evaluation task type based on the functional type of the underwater unmanned cluster to be evaluated and graded for intelligence; A statistics acquisition module is used to measure the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned cluster based on the determined evaluation task type, and obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; An external weight acquisition module is used to obtain the standard scores of the environmental difficulty index and the standard scores of the task effect index respectively based on the obtained environmental parameter statistics and task parameter statistics by using the probability score standardization transformation; based on the obtained standard scores of the environmental difficulty index and the standard scores of the task effect index, the external weight is calculated; A technical feature original score acquisition module is used to obtain the technical feature original score by using the probability score standardization transformation based on the obtained technical feature statistics; An evaluation and grading module is used to multiply the obtained external weight by the original score of the technical feature to obtain an improved technical feature score; perform evaluation and grading based on the obtained improved technical feature score to obtain an underwater unmanned swarm intelligence evaluation and grading result.

[0014] In a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating and grading the intelligence of underwater unmanned swarms as described in any one of the first aspect of the present invention is implemented.

[0015] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating and grading the intelligence of underwater unmanned swarms as described in any one of the first aspects of the present invention is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: In view of the problem that the existing unmanned system classification scheme mainly relies on subjective scoring evaluation and does not correspond to objective performance indicators, the present invention introduces objective performance indicators. These indicators are specific and quantifiable, and can directly reflect the different technical characteristics of environmental difficulty, task performance and intelligence (i.e., autonomy, collaboration, and learning), so that the evaluation results of the present invention no longer rely solely on subjective judgment, but have a more solid and objective basis. In view of the problem that the description of the existing scheme is too broad, lacks detailed task description of a certain type of specific cluster (such as underwater unmanned cluster), and lacks professionalism, the present invention is specifically designed for underwater unmanned clusters, covering environmental difficulty indicators and task performance indicators. These indicators are customized according to the characteristics and task requirements of underwater unmanned clusters, making the evaluation and classification more targeted and professional. In view of the problem that the existing scheme does not provide a standardized test method and only has vague language descriptions, the present invention not only provides clear evaluation indicators, but also designs a probability score standardization transformation method. This method can unify the different dimensions between indicators, making comparison and calculation between various indicators possible, making the evaluation process more scientific and rigorous, and avoiding ambiguity and uncertainty. In view of the problems in the prior art that the environmental change information is not taken into consideration and the adaptability to different environments is lacking, the present invention introduces an environmental difficulty index as an external weight in the evaluation and grading method. This index can reflect the environmental complexity under different task test scenarios, so that the evaluation results can be automatically adjusted according to different environments, making the evaluation and grading method more flexible and adaptable, and able to better adapt to changes in different environments and task requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of a method for evaluating and grading the intelligence of underwater unmanned swarms in an embodiment of the present invention; Figure 2 Schematic diagram of the principle of intelligent scoring and grading in an embodiment of the present invention; Figure 3 It is a three-dimensional illustration diagram of environmental task technical indicators in an embodiment of the present invention; Figure 4 is a schematic diagram of a technical feature scoring process in an embodiment of the present invention, taking a perception task as an example; Figure 5 1 is a schematic diagram of a probability score standardization transformation calculation process in an embodiment of the present invention; Figure 6 1 is a schematic diagram of a scoring-grading conversion process in an embodiment of the present invention; Figure 7 is a schematic diagram of a classification threshold value in an embodiment of the present invention; Figure 8 It is a schematic diagram of a system for evaluating and grading the intelligence of underwater unmanned swarms in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention; it is obvious that the described embodiments and technical solutions are only part of the embodiments of the present invention, not all of the embodiments.

[0020] All other embodiments obtained by those of ordinary skill in the art without creative work based on the technical solutions disclosed in the embodiments of the present invention belong to the scope of protection of the present invention. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] See also Figure 1 and Figure 2 , a method for evaluating and grading the intelligence of an underwater unmanned swarm provided by an embodiment of the present invention comprises the following steps: Step 1: Determine the type of evaluation task based on the functional type of the underwater unmanned cluster to be evaluated and graded for intelligence; wherein the evaluation task type is perception, cognition, decision-making or action; explanatory, the purpose of this step is to clarify the goal and scope of the evaluation, ensure the pertinence and effectiveness of the evaluation, and the determined evaluation task can fully reflect the intelligent performance of the underwater unmanned cluster in different scenarios; Step 2: Based on the determined evaluation task type, the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned cluster to be evaluated and graded for intelligence are measured respectively to obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; Explanatoryly, the environmental difficulty index is obtained by measuring the complexity of the environment in which the evaluation task is located, such as water flow velocity, visibility, obstacle density, etc. These indicators are used to reflect the difficulty of the underwater unmanned cluster working in a specific environment; the task effect index is obtained by measuring the effect of the cluster completing the task, such as task completion rate, task completion time, task execution accuracy, etc. These indicators directly reflect the intelligence level of the underwater unmanned cluster; the technical characteristic index is obtained by measuring the technical characteristics of the cluster in terms of autonomy, collaboration, learning, etc. These indicators are key elements for evaluating intelligence; Step 3, based on the environmental parameter statistics and task parameter statistics obtained in step 2, standard scores of the environmental difficulty index and the task effect index are obtained respectively by using the probability score standardization transformation; based on the standard scores of the environmental difficulty index and the task effect index, the external weight is calculated; Step 4, based on the technical feature statistics obtained in step 2, obtain the original technical feature score by using the probability score standardization transformation; Step 5, calculating based on the external weight obtained in step 3 and the original score of the technical feature obtained in step 4 to obtain an improved technical feature score; based on the improved technical feature score, obtaining the underwater unmanned swarm intelligence assessment grading result.

[0022] In the technical solution provided by the embodiment of the present invention, the evaluation task is first determined according to the function type; then the environmental difficulty index, task effect index and technical characteristic index are measured respectively, and then the improved technical characteristic scores of the technical characteristics (i.e., autonomy, collaboration and learning) are calculated through their respective statistics; finally, the intelligence evaluation grading result is determined according to the improved technical characteristic scores of autonomy, collaboration and learning. The present invention provides a comprehensive and systematic solution for the intelligence evaluation of underwater unmanned clusters by clarifying the evaluation tasks, measuring key indicators, calculating scores and grading, and comprehensively considering technical characteristics. Through this process, the intelligent performance of the cluster in different environments can be objectively evaluated, providing strong support for the optimization and improvement of the cluster.

[0023] See also Figure 3 In order to accurately characterize the strength of the technical characteristics (i.e., autonomy, collaboration, and learning) of underwater unmanned swarms in completing specific tasks in different environments, the technical solution of the embodiment of the present invention adds considerations for environmental complexity and task completion, that is, the external weights for technical characteristic indicators are calculated based on environmental difficulty indicators and task effect indicators. Figure 3As shown in the figure, the environmental difficulty index takes into account the influence of factors such as water flow velocity, obstacle density, acoustic noise level, and water temperature gradient; the task effect index considers indicators such as detection success rate, inference accuracy, planning speed, and strike success rate according to the different types of task assessment capabilities (such as perception, cognition, decision-making, and action); the technical characteristic index considers the three major characteristics of intelligence: autonomy, collaboration, and learning, which serve as the main basis for intelligence assessment and grading.

[0024] See also Figure 4 , the embodiment of the present invention takes the evaluation and grading process of the intelligence of perception capability as an example, and the specific evaluation and grading process is as follows: in the classic perception task "patrol reconnaissance", the commonly used environmental difficulty indicators are water flow velocity, obstacle density, acoustic noise level and temperature gradient, and the task effect indicators of the perception task are successful detection rate (detection accuracy rate within the specified time), coverage area (the maximum area of ​​effective detection of the cluster in the task), and detection efficiency (the area that can be covered by effective detection per unit time); the probability score standardization transformation is applied to the environmental difficulty index and the task effect index respectively, and the external weight of the underwater unmanned cluster in the test environment when performing the perception task is obtained; then, for the technical characteristic indicators of autonomy, collaboration, and learning: human intervention frequency (the number of interventions per unit time), information sharing frequency (the amount of information shared between the same type of unmanned boats per unit time, the amount of information shared between different types of unmanned boats per unit time), and effect improvement ratio (the ratio of the improvement of the main task completion index over time) are applied again to obtain the original score of the technical characteristics; the external weight is multiplied by the original score of the technical characteristics to obtain the improved technical characteristic score.

[0025] Explanatory, for different types of evaluation tasks, the environmental difficulty index and the technical characteristic indexes of autonomy, collaboration, and learning are consistent, and the task effect index is determined according to the evaluation task; in the specific technical solution, the task effect index is shown in Table 1.

[0026] Table 1. Task effect evaluation index table

[0027] See also Figure 5 , the probability score standardization transformation is a mapping of the original data to a score in the (0, 1) interval. The specific steps are as follows Figure 5 As shown, including: Most of the indicators in the environmental difficulty index and task effect index satisfy the normal distribution. After the probability score standardization transformation, the obtained standard score is expressed as: (positive indicator) or (negative indicator); In the formula, is the mean value of this indicator; is the variance of the indicator; t For time; A positive indicator means that the score that the indicator deserves needs to increase as the data increases (such as the frequency of collaborative information sharing); a negative indicator means that the score that the indicator deserves needs to decrease as the data increases (such as the frequency of autonomous manual intervention).

[0028] In the embodiment of the present invention, the probability score standardization transformation is recorded as , by this transformation, we can get a standard score of (0, 1), which represents the ratio value of the data exceeding the same type in the normal distribution.

[0029] When calculating the external weight, first calculate the environmental difficulty factor and the task effect factor respectively; the calculation expression of the environmental difficulty factor is: ; In the formula, is the number of environmental difficulty indicators; for example, there are four environmental difficulty indicators: flow velocity, obstacle density, acoustic noise level, and temperature gradient. ; Indicates Standard score for each indicator.

[0030] Similar to the environmental difficulty factor, the calculation expression of the task effect factor is: ; In the formula, is the number of task effect indicators ( Figure 4 For example, ), Indicates Standard score for each indicator.

[0031] In the embodiment of the present invention, the calculation expression for calculating the external weight using the environmental difficulty factor and the task effect factor is: ; See also Figure 6 In the embodiment of the present invention, when calculating the original scores of autonomy, collaboration, and learning, the probability score standardization transformation is still used. The original autonomous score (OAS), the original cooperative score (OCS), and the original learning score (OLS) are respectively: ; ; ; Multiply it by the external weight to get the corresponding improved score (Adjusted Score), and the calculation expression is:

[0032]

[0033]

[0034] This results in improved scores for the three technical features.

[0035] After calculating the improvement score, the levels of autonomy, collaboration, and learning can be defined based on the fixed threshold method.

[0036] See also Figure 7 , the specific division threshold is as follows Figure 7 As shown; in actual situations, the performance of underwater unmanned clusters mostly follows a normal distribution, so the grading results should also be small at both ends and large in the middle; the occupancy rates of the first, second, and third levels are set to 15%, 70%, and 15%, respectively, that is, when the improvement score of the autonomy, collaboration, or learning of the evaluated underwater unmanned cluster is less than 0.15, it is rated as the first level of autonomy / collaboration / learning; when the improvement score is between 0.15 and 0.85, it is rated as the second level of autonomy / collaboration / learning; when the improvement score is greater than 0.85, it is rated as the third level of autonomy / collaboration / learning.

[0037] Finally, the final intelligence level is determined based on the sum of the levels of the three types of technical features: autonomy, collaboration, and learning. , then the intelligence level , divided into 7 levels, the specific classification is shown in Table 2.

[0038] Table 2. Classification details

[0039] In Table 2, A1 represents autonomous level 1, C1 represents collaborative level 1, and L1 represents learning level 1; the same applies to A2, A3, etc.

[0040] In summary, the present invention takes into account the comprehensive influence of environmental difficulty and task completion in the evaluation test, so that the intelligent evaluation process of the underwater unmanned cluster has a certain adaptability to the environment and tasks; at the same time, a task-oriented evaluation test method is adopted to score with the objective index performance of the cluster to eliminate the error caused by subjective scoring; different task effect indicators are designed for the four main types of intelligence to increase certain specificity. In the specific data processing, the probability score standardization transformation adopted can transform the original data of various dimensions into dimensionless (0, 1) interval data, and it is interpretable. In the final classification process, the three major intelligent characteristics of autonomy, collaboration, and learning are taken into account. Each feature is graded using a fixed threshold method, and the threshold setting is in line with the actual normal data; the intelligent classification is calculated using the cumulative method, and the obtained classification combination also meets the actual needs. In summary, the technical solution of the embodiment of the present invention has made comprehensive and effective improvements to the shortcomings of the existing unmanned system classification scheme. By introducing objective performance indicators, designing evaluation schemes for specific clusters, providing standardized test methods, and considering environmental change information, it provides a more scientific, rigorous, flexible and practical solution for the intelligent evaluation and classification of underwater unmanned clusters.

[0041] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0042] See also Figure 8 In an embodiment of the present invention, a system for evaluating and grading the intelligence of underwater unmanned swarms is provided, comprising: An evaluation task type acquisition module is used to determine the evaluation task type based on the functional type of the underwater unmanned cluster to be evaluated and graded for intelligence; A statistics acquisition module is used to measure the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned cluster based on the determined evaluation task type, and obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; An external weight acquisition module is used to obtain the standard scores of the environmental difficulty index and the standard scores of the task effect index respectively based on the obtained environmental parameter statistics and task parameter statistics by using the probability score standardization transformation; based on the obtained standard scores of the environmental difficulty index and the standard scores of the task effect index, the external weight is calculated; A technical feature original score acquisition module is used to obtain the technical feature original score by using the probability score standardization transformation based on the obtained technical feature statistics; An evaluation and grading module is used to multiply the obtained external weight by the original score of the technical feature to obtain an improved technical feature score; perform evaluation and grading based on the obtained improved technical feature score to obtain an underwater unmanned swarm intelligence evaluation and grading result.

[0043] In one embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to perform the operation of the method for evaluating and grading the intelligence of underwater unmanned clusters.

[0044] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM (Random Access Memory) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for evaluating and grading the intelligence of underwater unmanned clusters in the above embodiment.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program codes.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0049] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for evaluating and grading the intelligence of underwater unmanned swarms, characterized in that: The following steps are involved: Determine the type of assessment task based on the functional type of the underwater unmanned swarm to be assessed and graded for intelligence; Based on the determined evaluation task type, respectively measuring the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned swarm to obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; Based on the obtained environmental parameter statistics and task parameter statistics, the standard scores of the environmental difficulty index and the standard scores of the task effect index are obtained respectively by using the probability score standardization transformation; Based on the obtained standard scores of the environmental difficulty index and the task effect index, the external weight is calculated; Based on the obtained technical feature statistics, the original technical feature score is obtained by using the probability score standardization transformation; Multiplying the obtained external weight by the original score of the technical feature to obtain an improved technical feature score; An evaluation and grading is performed based on the improved technical feature scores obtained to obtain an underwater unmanned swarm intelligence evaluation and grading result.

2. A method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 1, characterized in that: In the step of obtaining the standard scores of the environmental difficulty index and the task effect index respectively by using the probability score standardization transformation based on the obtained environmental parameter statistics and task parameter statistics, and in the step of obtaining the original score of the technical feature by using the probability score standardization transformation based on the obtained technical feature statistics, The expression for obtaining the standard score by using the probability score standardization transformation is: ； ； In the formula, is the standard score calculation value of the positive indicator, It is the calculated value of the standard score of negative indicators. A positive indicator is an indicator whose deserved score increases as the data increases, and a negative indicator is an indicator whose deserved score decreases as the data increases. is the mean value of the indicator; is the variance of the indicator; t is the integration variable of the standard score.

3. A method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 1, characterized in that: In the step of calculating and obtaining the external weight based on the obtained standard score of the environmental difficulty index and the standard score of the task effect index, The calculation expression of external weight is: ； In the formula, Calculate values ​​for external weights; Environmental difficulty factors; is the task effect factor; ； ； In the formula, is the number of environmental difficulty indicators, Indicates Standard scores for each environmental difficulty indicator; is the number of task effect indicators, Indicates The standard score of each task performance indicator.

4. A method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 1, characterized in that: The evaluation task type is a perception type, a cognitive type, a decision type or an action type; The environmental difficulty index includes one or more of water flow velocity, obstacle density, visibility, acoustic noise level and water temperature gradient; When the evaluation task type is a perception type, the task effect indicators include successful detection rate, maximum coverage area and detection efficiency; when the evaluation task type is a cognitive type, the task effect indicators include inference accuracy and response speed; when the evaluation task type is a decision-making type, the task effect indicators include qualified plan ratio and planning speed; when the evaluation task type is an action type, the task effect indicators include strike success rate and response speed; The technical characteristic indicators include human intervention frequency, information sharing frequency and effect improvement rate.

5. The method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 1, characterized in that: In the step of obtaining the original score of the technical feature by using the probability score standardization transformation based on the obtained technical feature statistics, the original score of the technical feature includes the original score of autonomy, the original score of collaboration and the original score of learning.

6. A method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 5, characterized in that: In the step of multiplying the external weight obtained by the original technical feature score to obtain an improved technical feature score; and performing evaluation and grading based on the improved technical feature score obtained to obtain an evaluation and grading result of the intelligence of the underwater unmanned swarm, Improved technical feature scores include autonomous improvement scores, collaborative improvement scores, and learning improvement scores; When the autonomy improvement score is less than 0.15, it is evaluated as autonomy level 1; when the autonomy improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as autonomy level 2; when the autonomy improvement score is greater than 0.85, it is evaluated as autonomy level 3; When the synergy improvement score is less than 0.15, it is evaluated as synergy level 1; when the synergy improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as synergy level 2; when the synergy improvement score is greater than 0.85, it is evaluated as synergy level 3; When the learning improvement score is less than 0.15, it is evaluated as level one of learning; When the learning improvement score is greater than or equal to 0.15 and less than or equal to 0.85, it is evaluated as learning level 2; when the learning improvement score is greater than 0.85, it is evaluated as learning level 3.

7. A method for evaluating and grading the intelligence of underwater unmanned swarms according to claim 6, characterized in that: In the step of multiplying the external weight obtained by the original technical feature score to obtain an improved technical feature score; and performing evaluation and grading based on the improved technical feature score obtained to obtain an evaluation and grading result of the intelligence of the underwater unmanned swarm, The calculation expression of the intelligent evaluation and classification results of underwater unmanned swarm is: ； In the formula, Indicates the level of intelligence; They represent the three levels of autonomy, collaboration, and learning. .

8. A system for evaluating and grading the intelligence of underwater unmanned swarms, characterized in that: include: An evaluation task type acquisition module is used to determine the evaluation task type based on the functional type of the underwater unmanned cluster to be evaluated and graded for intelligence; A statistics acquisition module is used to measure the environmental difficulty index, task effect index and technical characteristic index of the underwater unmanned cluster based on the determined evaluation task type, and obtain environmental parameter statistics, task parameter statistics and technical characteristic statistics; An external weight acquisition module is used to obtain the standard scores of the environmental difficulty index and the standard scores of the task effect index respectively based on the obtained environmental parameter statistics and task parameter statistics by using the probability score standardization transformation; based on the obtained standard scores of the environmental difficulty index and the standard scores of the task effect index, the external weight is calculated; A technical feature original score acquisition module is used to obtain the technical feature original score by using the probability score standardization transformation based on the obtained technical feature statistics; An evaluation and grading module, used for multiplying the obtained external weight by the original score of the technical feature to obtain an improved technical feature score; An evaluation and grading is performed based on the improved technical feature scores obtained to obtain an underwater unmanned swarm intelligence evaluation and grading result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the method for evaluating and grading the intelligence of underwater unmanned swarms as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating and grading the intelligence of underwater unmanned swarms as described in any one of claims 1 to 7 is implemented.

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