Progressive multi-stage collaborative password shooting range training system and method

Abstract

The invention discloses a progressive multi-level collaborative password target range training system and method, and the system comprises a theoretical target range module which is used for providing a cryptography basic knowledge assessment environment, and generating a knowledge graph according to a target shooting result of a user; the target range simulation module is used for analyzing the knowledge graph, obtaining user capability weak points and pushing a targeted enhanced training task according to an analysis result; the amusement target range module provides an interactive cryptographic algorithm interaction platform for the user, so that the user can modify parameters to know the cryptographic algorithm principle, reflect the mastery degree and proficiency degree of the user on the cryptographic knowledge through questions and answers, and feed back the mastery degree and proficiency degree to the knowledge graph; and the actual combat target range module is used for providing a security system building and multi-dimensional evaluation inspection platform, carrying out multi-dimensional comprehensive evaluation on the cryptographic knowledge mastered by the user, and synchronously updating an evaluation result to the knowledge graph. The problems of closed-loop breakage and no clear feedback mechanism in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of cryptography education technology, and more specifically to a progressive, multi-level collaborative cryptographic training system and method. Background Technology

[0002] Currently, cryptography, as a core technology of information security, is widely used in fields such as data encryption, identity authentication, and digital signatures. With the increasing complexity of cybersecurity threats, the demand for talent with a solid theoretical foundation and practical skills in cryptography is rising sharply. As a discipline highly dependent on practical verification, cryptography urgently needs to build a new type of training platform integrating learning, practice, testing, and research. With the development of virtualization technology, artificial intelligence, and cloud computing, constructing a highly realistic cryptographic training environment has become possible.

[0003] However, existing cryptography experimental platforms are limited to single functions, mostly confined to algorithm demonstrations or the use of simple encryption tools, and cannot simulate complex cryptographic application scenarios. Users find it difficult to form a systematic knowledge system during the learning process. At the same time, most existing experimental platforms have pre-approved fixed difficulty content, and there are few target ranges that adapt to the difficulty of users of different levels. In addition, most existing experimental platforms have relatively independent modules, each performing its own function, and lack a comprehensive feedback mechanism to enable the various modules of the platform to operate collaboratively.

[0004] Therefore, how to solve the problems of broken teaching loop, poor target adaptation, and lack of clear comprehensive feedback mechanism in the existing technology is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, in order to solve the problems of "broken teaching loop, poor target adaptation, and lack of clear comprehensive feedback mechanism" described in the background art, the present invention provides a progressive multi-level collaborative cryptographic target range training system and method.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a progressive multi-level collaborative cryptographic range training system, comprising: a theoretical range module, a simulated range module, an amusement range module, and a combat range module;

[0008] The theoretical target range module is used to provide an environment for assessing basic cryptographic knowledge, generate targets based on knowledge point weights, and generate a knowledge graph based on the user's target assessment results.

[0009] The simulated target range module is used to analyze the knowledge graph, identify the user's weak points, filter similar question types based on the analysis results, and push targeted reinforcement training tasks to the user.

[0010] The amusement range module provides users with an interactive cryptographic algorithm interaction platform, allowing users to modify parameters to understand the principles of cryptographic algorithms. Through question and answer, the user's skills acquired in the amusement range are fed back into the knowledge graph, thereby reflecting the user's mastery and proficiency in cryptographic knowledge.

[0011] The practical testing range module is used to provide a security system construction and multi-dimensional evaluation and verification platform, providing users with a practical training scenario, conducting multi-dimensional comprehensive evaluation of the cryptographic knowledge mastered by users, and synchronously updating the evaluation results to the knowledge graph.

[0012] Furthermore, the theoretical target range module includes: an intelligent survival target range engine, an anti-cheating monitoring unit, an automatic evaluation unit, and a review unit;

[0013] The intelligent survival range engine dynamically generates targets for the range based on a preset knowledge point weight matrix.

[0014] The anti-cheating monitoring unit is used to analyze the shooting time distribution, operation trajectory characteristics and answer similarity to identify abnormal behavior. When a suspicious operation pattern is detected, it automatically triggers an early warning and records a detailed operation log.

[0015] The automatic evaluation unit receives the shooting records, automatically scores the objective questions, and synchronizes the scoring results to the database in real time.

[0016] The review unit is used to provide a grading interface for reviewing subjective questions, including an answer comparison window, a scoring slider, and an annotation input box, and synchronizes the grading results to the database in real time.

[0017] Furthermore, the intelligent survival range engine specifically employs a difficulty matrix algorithm formula:

[0018] Q = ∑w i ×d i

[0019] Where Q represents difficulty, w i For the weight of knowledge points, d i The difficulty level is dynamic.

[0020] Furthermore, the simulated target range module includes: a capability analysis engine, an intelligent recommendation unit, and a wrong question re-practice unit;

[0021] The capability analysis engine analyzes the historical firing data of the theoretical target range module, uses the K-Means clustering algorithm and knowledge graph association rules to generate a multi-dimensional capability radar chart, and dynamically renders capability weaknesses.

[0022] The intelligent recommendation unit recommends corresponding weak knowledge point targets based on the weak points of ability according to a preset intelligent recommendation algorithm.

[0023] The incorrect question re-practice unit provides a review plan based on the Ebbinghaus forgetting curve, and adjusts the review plan when the accuracy of the target shooting meets the preset conditions.

[0024] Furthermore, the amusement range module includes: a cryptographic algorithm interaction unit, an intelligent question-answering unit, and a functional assistance unit;

[0025] The cryptographic algorithm interaction unit is used to provide a parameter modification window, which allows users to adjust the input in real time and observe the algorithm output and intermediate state changes through a visualized parameter space.

[0026] The intelligent question-answering unit provides users with a question-answering model based on pre-set knowledge in the field of cryptography. It understands the user's knowledge gaps through question-answering and feeds them back into the knowledge graph.

[0027] The functional auxiliary unit is used to provide users with functions such as cryptographic algorithm problem-solving competitions, code quality assessment, and learning trajectory analysis.

[0028] Furthermore, the practical range module includes: a scenario simulation unit, a topology verification unit, and a multi-dimensional scoring unit;

[0029] The scenario simulation unit is used to provide simulated scenarios for practical exercises, so that users can complete practical exercises in scenario building and test the feasibility of the cryptographic scheme in the simulated scenarios.

[0030] The topology verification unit is used to evaluate the topology structure built by the user, check the rationality of node connection logic and protocol configuration, and generate a verification report.

[0031] The multi-dimensional scoring unit is used to comprehensively evaluate a user's shooting ability based on the weights of protocol completeness, encryption strength, and topology efficiency, and feeds the evaluation results back to the knowledge graph.

[0032] Furthermore, it also includes: a dynamic monitoring module, a multi-role collaboration module, and a real-time leaderboard module;

[0033] The dynamic monitoring module is used to display the training status in real time, including a heat map of shooting progress, range completion rate, target difficulty distribution map and capability radar map.

[0034] The multi-role collaboration module is used to support collaborative operations between administrators and users. The administrator is configured to have global monitoring and system configuration permissions, as well as be responsible for target library maintenance and difficulty ratio settings. The user is configured to manage the training progress independently.

[0035] The real-time leaderboard module uses the WebSocket protocol to update scores in real time. The leaderboard is displayed by module category and supports sorting by total score and progress rate in multiple dimensions.

[0036] Secondly, embodiments of the present invention also provide a progressive multi-level collaborative cryptographic target range training method, using a progressive multi-level collaborative cryptographic target range training system as described in any embodiment of the first aspect, the method comprising the following steps:

[0037] S110. Provide basic knowledge assessment targets through the theoretical target range module, receive user shooting results, and generate corresponding knowledge graphs based on the shooting results.

[0038] S120. Analyze the user's weak points in the knowledge graph through the simulated target range module, and push targeted reinforcement training tasks.

[0039] S130. Provide a cryptographic algorithm interaction platform through the amusement range module, fill knowledge gaps in the amusement range module through hands-on practice and Q&A, and feed the results back into the knowledge graph;

[0040] S140. The practical test range module provides a security system construction and multi-dimensional evaluation and verification platform, provides practical training scenarios, conducts multi-dimensional comprehensive evaluation of the cryptographic knowledge mastered by users, and synchronously updates the evaluation results to the knowledge graph.

[0041] Furthermore, step S110 includes:

[0042] S111. Configure dynamic difficulty coefficient rules and question types according to teaching needs and the preset knowledge point weight matrix;

[0043] S112. Based on the weight matrix after configuring the dynamic difficulty coefficient and the question type, generate the corresponding target range.

[0044] S113. The shooting range is allocated through the system reservation function. During the reservation stage, the examination window is controlled by a timed task, and the shooting progress is cached to support recovery from abnormal interruption.

[0045] S114. Receive user shooting records, including: automatic scoring of objective questions and manual grading of subjective questions;

[0046] S115. Generate electronic certificates with anti-counterfeiting watermarks based on the scoring results, construct the corresponding knowledge graph, and update the corresponding scoring rankings in real time.

[0047] Furthermore, step S120 includes:

[0048] S121. Analyze the historical firing data of the theoretical target range module, use the K-Means clustering algorithm combined with the knowledge graph association rules to generate a multi-dimensional capability radar map and locate capability weaknesses;

[0049] S122. Based on the aforementioned weaknesses, a preset intelligent recommendation algorithm is used to filter and match targets in the question bank and push the corresponding reinforcement training queue.

[0050] S123. Arrange reinforcement training tasks for users through the error correction mechanism, and remove users from the reinforcement training queue when they meet the preset judgment conditions.

[0051] S124. The training behavior data of the simulated target range module is transmitted back in real time to dynamically update the mastery level of knowledge points in the knowledge graph.

[0052] Furthermore, step S130 includes:

[0053] S131. Through the algorithm interaction experimental platform, users can adjust the algorithm parameters and observe the changes in encryption results and algorithm behavior in real time.

[0054] S132. Interact with users through a cryptographic knowledge question and answer system to obtain structured knowledge answers;

[0055] S133. Based on the question-and-answer results and algorithm operation data, locate the user's knowledge gaps and recommend related learning resources and supplementary exercises;

[0056] S134. Feed back the algorithm mastery level and question-answering accuracy to the knowledge graph.

[0057] Furthermore, step S140 includes:

[0058] S141. Through the scenario simulation unit, simulated scenarios for practical exercises are provided so that users can complete practical exercises in scenario building and test the feasibility of the cryptographic scheme.

[0059] S142. Verify the topology structure built in the live-fire range module and generate a verification report;

[0060] S143. Automatic scoring is achieved through a multi-dimensional scoring model, and compliance verification is performed through a compliance verification engine to check whether it meets the GM / T standard. Annotations and secondary scoring are completed through a manual review interface.

[0061] S144. Generate a multi-dimensional comprehensive ability assessment report, feed the assessment results back to the knowledge graph, and complete the comprehensive ability assessment.

[0062] As can be seen from the above technical solution, compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. By constructing a range training system that includes a theoretical range module, a simulated range module, an amusement range module, and a combat range module, a new type of training platform integrating "learning, practice, testing, and research" has been built, which significantly improves the depth of learners' participation and knowledge retention rate.

[0064] 2. By designing a knowledge graph, various modules are linked together. The knowledge graph is generated through the theoretical target range module, parsed through the simulated target range module, and fed back through the amusement target range module and the practical target range module. Through the collaboration of various target range modules, the knowledge graph can comprehensively reflect the user's mastery of cryptographic knowledge and provide a basis for the user to fill in any gaps in their knowledge as much as possible. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0066] Figure 1 This is a schematic diagram of the structure of the cryptographic target training system of the present invention.

[0067] Figure 2 This is a schematic diagram of the cryptographic target range training system of the present invention.

[0068] Figure 3 This is a complete flowchart of the cryptographic target training method of the present invention.

[0069] Figure 4 A theoretical target range flowchart provided for the cryptographic target range training method of the present invention.

[0070] Figure 5 A flowchart of a simulated target range provided for the cryptographic target range training method of the present invention.

[0071] Figure 6 A flowchart of an amusement park provided for the cryptographic training method of the present invention.

[0072] Figure 7 A flowchart of a practical training range provided for the cryptographic training method of the present invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0074] This invention discloses a progressive, multi-level collaborative cryptographic target range training system, such as... Figure 1 As shown, it includes: a theoretical firing range module, a simulated firing range module, an amusement firing range module, and a combat firing range module;

[0075] The following is a detailed explanation of the four modules mentioned above:

[0076] 1. Theoretical Target Range Module: This module provides an environment for assessing basic cryptography knowledge, generates targets based on knowledge point weights, and generates a knowledge graph based on the user's target assessment results.

[0077] The theoretical range module further includes: an intelligent survival range engine, an anti-cheating monitoring unit, an automatic evaluation unit, and a review unit;

[0078] The intelligent survival range engine dynamically generates targets for the range based on a preset knowledge point weight matrix.

[0079] The anti-cheating monitoring unit is used to analyze the shooting time distribution, operation trajectory characteristics and answer similarity to identify abnormal behavior. When a suspicious operation pattern is detected, it automatically triggers an alert and records a detailed operation log.

[0080] The automatic evaluation unit receives shooting records, automatically scores objective questions, and synchronizes the scoring results to the database in real time.

[0081] The review unit provides a grading interface for subjective questions, including an answer comparison window, a scoring slider, and a comment input box, and synchronizes the grading results to the database in real time.

[0082] In this embodiment, the theoretical test range module is used for testing basic knowledge and generating a knowledge graph, which contains information about the user's mastery of cryptographic knowledge.

[0083] Specifically, the intelligent survival range engine employs a difficulty matrix algorithm, the formula of which is as follows:

[0084] Q = ∑w i ×d i

[0085] In the formula, Q represents the difficulty, and w i For the weight of knowledge points, d i The difficulty level is dynamic; wi Configured by experts according to the teaching syllabus, d i The system dynamically adjusts the difficulty level based on the user's historical performance to ensure that the target difficulty matches the learner's ability; dynamic difficulty coefficient d i The adjustment rule is as follows: if a user's historical accuracy rate on a knowledge point is lower than the threshold T, then d is reduced. i If a user's historical accuracy rate on this knowledge point is higher than the threshold T, then d is increased. i ;

[0086] During the user's first assessment, the system adopts a tiered initialization strategy: all knowledge points are initialized in a layered manner. i The initial value is 1.0. If the user completes the prerequisite ability test, the value will be adjusted according to the following rules: for knowledge points with an accuracy rate ≥ 80%, d i =1.2; for knowledge points with an accuracy rate ≤50%, d i =0.7; the rest remain d i =1.0.

[0087] The threshold T is set by combining teaching experience and data-driven approaches. The initial threshold is set at 75% for basic knowledge points and 65% for advanced content. It is dynamically calibrated quarterly: the new threshold = the average of the old threshold and the historical average accuracy rate minus 10%, maintaining a moderate level of challenge. At the same time, it is adapted to different assessment modes: theory +5%, practical training -5%, achieving scenario-based adaptation.

[0088] In dynamic difficulty coefficient d i In the adjustment mechanism, the system adopts a progressive ramping algorithm: when the user's accuracy exceeds the threshold T for 3 consecutive times, d i The value is increased by 0.1 to increase the challenge. If there is a single mistake but a good historical performance, it will be slightly adjusted by 0.05. If the performance is below T-20% for two consecutive times, it will be significantly reduced by 0.2 to adapt to the user's ability.

[0089] At the same time, the knowledge graph is used to connect the theoretical target range module, the simulated target range module, the amusement target range module, and the actual combat target range module to form a complete closed loop, enabling the various modules to operate in coordination.

[0090] 2. The simulated training range module provides an adaptive training environment; by analyzing the knowledge graph, it identifies the user's weaknesses, filters similar question types based on the analysis results, and pushes targeted reinforcement training tasks to the user.

[0091] Specifically, the simulated target range module further includes: a capability analysis engine, an intelligent recommendation unit, and a wrong question re-practice unit;

[0092] The capability analysis engine analyzes historical firing data from the theoretical range module, uses the K-Means clustering algorithm and knowledge graph association rules to generate a multi-dimensional capability radar chart, and dynamically renders capability weaknesses.

[0093] The intelligent recommendation unit recommends corresponding weak knowledge point targets based on the mentioned weak points in ability, according to a preset intelligent recommendation algorithm;

[0094] The "Revisit Incorrect Questions" unit provides a review plan based on the Ebbinghaus forgetting curve, and adjusts the review plan when the accuracy rate meets preset conditions.

[0095] In this embodiment, the simulated target range module is mainly used to analyze the knowledge gaps generated in the theoretical target range and arrange targeted reinforcement training for areas where the user's mastery is insufficient. The intelligent recommendation algorithm adopts a recommendation algorithm based on collaborative filtering. The simulated target range module analyzes the historical target data in the knowledge graph generated by the theoretical target range module. After analysis, it uses a recommendation algorithm based on collaborative filtering to select similar question types from the question bank for weak points and generates targeted reinforcement training tasks through the incorrect question re-practice unit, which are then pushed to the reinforcement training queue. The mechanism of the incorrect question re-practice unit is to generate interval reinforcement paths based on the Ebbinghaus forgetting curve. The user is automatically removed from the training queue if and only if the user's accuracy rate is ≥ threshold T for three consecutive times.

[0096] 3. The Amusement Park module provides users with an interactive cryptographic algorithm interaction platform, allowing users to modify parameters to understand the principles of cryptographic algorithms. Through question and answer, the user's abilities in the Amusement Park are fed back into the knowledge graph, thereby reflecting the user's mastery and proficiency in cryptographic knowledge.

[0097] The amusement range module includes: a cryptographic algorithm interaction unit, an intelligent question-and-answer unit, and a functional assistance unit;

[0098] The cryptographic algorithm interaction unit provides a parameter modification window, allowing users to adjust the input in real time and observe the algorithm output and intermediate state changes through a visual parameter space.

[0099] The intelligent question-answering unit provides users with a question-answering model based on pre-set knowledge in the field of cryptography. It understands the user's knowledge gaps through question-answering and feeds them back into the knowledge graph.

[0100] The functional support unit is used to provide users with functions such as cryptographic algorithm problem-solving competitions, code quality assessment, and learning trajectory analysis.

[0101] In this embodiment, as Figure 2As shown in the schematic diagram, the amusement range module is mainly used to expand cryptographic technology practice, including a cryptographic algorithm interaction unit and an intelligent question-and-answer unit. These units help users fill in gaps in their cryptographic knowledge through practice and question-and-answer sessions. The cryptographic algorithm interaction unit includes a parameter modification window, allowing users to adjust inputs via parameter controls and observe algorithm outputs and intermediate state changes. The intelligent question-and-answer unit provides users with a cryptographic knowledge question-and-answer system that integrates with an artificial intelligence large language model. It combines a cryptographic corpus for domain adaptation to achieve cryptographic principle question-and-answer. Through hands-on practice and question-and-answer sessions, the user's cryptographic knowledge is fed back to the knowledge graph.

[0102] Meanwhile, the functional support unit can regularly hold cryptographic algorithm problem-solving competitions, support users to upload and discuss cryptographic algorithm implementation code, provide code quality assessment and security detection functions, record users' training data in each module, and generate personalized ability development curves and improvement suggestions.

[0103] The practical range module provides a platform for building and evaluating security systems in multiple dimensions. It offers users a realistic training scenario to comprehensively evaluate their cryptographic knowledge and update the evaluation results to the knowledge graph.

[0104] The practical range module includes: a scenario simulation unit, a topology verification unit, and a multi-dimensional scoring unit;

[0105] The scenario simulation unit is used to provide simulated scenarios for practical exercises, so that users can complete practical exercises in scenario building and test the feasibility of cryptographic schemes.

[0106] The topology verification unit is used to evaluate the topology structure built by the user, check the rationality of node connection logic and protocol configuration, and generate a verification report.

[0107] The multi-dimensional scoring unit is used to comprehensively evaluate a user's shooting ability based on the weights of protocol completeness, encryption strength, and topology efficiency, and feeds the evaluation results back into the knowledge graph.

[0108] In this embodiment, as Figure 2As shown in the schematic diagram, the practical training range module is mainly used for realistic drills. It provides a simulation environment through scenario simulation units and allows users to build network topologies using an online topology editor. Users can freely build network topologies including professional equipment such as server cryptographic machines and security gateways, and complete scenario-based tasks by dragging and dropping pre-set network security elements and dynamically creating connections. The drawn data is saved to the server in real time, and the integrity of the topology is verified by the topology verification unit. Backend experts annotate and score the user-submitted topology diagrams, providing feedback on the review results. An evaluation report is generated through a multi-dimensional scoring unit. This multi-faceted evaluation system effectively guides users to build a secure and efficient cryptographic application system, and the mastery demonstrated in the practical training range is fed back into the knowledge graph.

[0109] 5. It also includes: a dynamic monitoring module, a multi-role collaboration module, and a real-time leaderboard module;

[0110] The dynamic monitoring module is used to display the training status in real time, including a heat map of shooting progress, range completion rate, target difficulty distribution map and capability radar map.

[0111] A multi-role collaboration module is used to support collaborative operations between administrators and users. The administrator is configured to have global monitoring and system configuration permissions, as well as be responsible for target library maintenance and difficulty ratio settings. The user is configured to manage the training progress independently.

[0112] The real-time leaderboard module uses the WebSocket protocol to update scores in real time. The leaderboard is categorized by module and supports sorting by multiple dimensions such as total score and progress rate.

[0113] In this embodiment, the heat map of the dynamic monitoring module uses a three-color gradient of red, yellow and green to represent the completion status. Red indicates that the target has not been met, yellow indicates that it is in progress, and green indicates that it has been completed. Experts can adjust the target difficulty ratio according to the heat map distribution.

[0114] The multi-role collaboration module uses JWT tokens to implement identity authentication and access control, ensuring secure isolation of operations for each role.

[0115] The real-time leaderboard module offers three display modes: daily, weekly, and overall, and is complemented by an achievement badge system to motivate users to continuously improve their training results.

[0116] This invention constructs a training system for shooting ranges that includes a theoretical shooting range module, a simulated shooting range module, an amusement shooting range module, and a combat shooting range module. This system creates a novel training platform that integrates learning, practice, testing, and research, significantly improving learners' engagement and knowledge retention. Furthermore, by introducing a knowledge graph feedback mechanism, the various shooting range modules are organically combined to form a complete teaching loop.

[0117] Based on the same inventive concept, embodiments of the present invention also provide a progressive multi-level collaborative cryptographic target range training method, based on the aforementioned progressive multi-level collaborative cryptographic target range training system; as... Figure 3 As shown, it includes the following steps:

[0118] S110. Provide basic knowledge assessment targets through the theoretical target range module, receive user shooting results, and generate corresponding knowledge graphs based on the shooting results.

[0119] S120. Analyze the user's weak points in the knowledge graph through the simulated target range module, and push targeted reinforcement training tasks.

[0120] S130. Provide a cryptographic algorithm interaction platform through the amusement range module, fill knowledge gaps in the amusement range module through hands-on practice and Q&A, and feed the results back into the knowledge graph;

[0121] S140. The practical test range module provides a security system construction and multi-dimensional evaluation and verification platform, provides practical training scenarios, conducts multi-dimensional comprehensive evaluation of the cryptographic knowledge mastered by users, and synchronously updates the evaluation results to the knowledge graph.

[0122] The cryptographic target range training method provided in this invention constructs a target range training system that includes a theoretical target range module, a simulated target range module, an amusement target range module, and a combat target range module. This system creates a novel training platform that integrates learning, practice, testing, and research, forming a complete teaching loop.

[0123] like Figure 4 As shown, step S110 specifically includes:

[0124] S111. Configure dynamic difficulty coefficient rules and question types according to teaching needs and the preset knowledge point weight matrix;

[0125] S112. Based on the weight matrix after configuring the dynamic difficulty coefficient and the question type, generate the corresponding target range.

[0126] S113. The shooting range is allocated through the system reservation function. During the reservation stage, the examination window is controlled by a timed task, and the shooting progress is cached to support recovery from abnormal interruption.

[0127] S114. Receive user shooting records, including: automatic scoring of objective questions and manual grading of subjective questions;

[0128] S115. Generate an electronic certificate with anti-counterfeiting watermark based on the scoring results, construct the corresponding knowledge graph, and update the corresponding scoring ranking in real time.

[0129] In this embodiment, the user's grasp of basic cryptography knowledge is assessed through the theoretical test range module, and a knowledge graph is generated. The knowledge graph serves as the core of the feedback mechanism and is used to connect the various test ranges together.

[0130] like Figure 5 As shown, step S120 above specifically includes:

[0131] S121. Analyze the historical firing data of the theoretical target range module, use the K-Means clustering algorithm combined with the knowledge graph association rules to generate a multi-dimensional capability radar map and locate capability weaknesses;

[0132] S122. Based on the aforementioned weaknesses, a preset intelligent recommendation algorithm is used to filter and match targets in the question bank and push the corresponding reinforcement training queue.

[0133] S123. Arrange reinforcement training tasks for users through the error correction mechanism, and remove users from the reinforcement training queue when they meet the preset judgment conditions.

[0134] S124. The training behavior data of the simulated target range module is transmitted back in real time to dynamically update the mastery level of knowledge points in the knowledge graph.

[0135] In this embodiment, the simulated firing range module is responsible for analyzing the historical firing data of the theoretical firing range module and locating the weak points in the capability, and arranging reinforcement training tasks for the user; the training status is fed back to the knowledge graph in real time; the simulated firing range module serves as a supplement to the theoretical firing range module, and performs reinforcement training to address the deficiencies shown in the theoretical firing range module, thereby filling in the gaps in theoretical knowledge through the simulated firing range module.

[0136] like Figure 6 As shown, step S130 above specifically includes:

[0137] S131. Through the algorithm interaction experimental platform, users can adjust the algorithm parameters and observe the changes in encryption results and algorithm behavior in real time.

[0138] S132. Interact with users through a cryptographic knowledge question and answer system to obtain structured knowledge answers;

[0139] S133. Based on the question-and-answer results and algorithm operation data, locate the user's knowledge gaps and recommend related learning resources and supplementary exercises;

[0140] S134. Feed back the algorithm mastery level and question-answering accuracy to the knowledge graph;

[0141] In this embodiment, the amusement range module is mainly used to feed back the user's cryptography knowledge to the knowledge graph through hands-on practice and question-and-answer sessions.

[0142] like Figure 7 As shown, step S140 above specifically includes:

[0143] S141. Through the scenario simulation unit, simulated scenarios for practical exercises are provided so that users can complete practical exercises in scenario building and test the feasibility of the cryptographic scheme.

[0144] S142. Verify the topology structure built in the live-fire range module and generate a verification report;

[0145] S143. Automatic scoring is achieved through a multi-dimensional scoring model, and compliance verification is performed through a compliance verification engine to check whether it meets the GM / T standard. Annotations and secondary scoring are completed through a manual review interface.

[0146] S144. Generate a multi-dimensional comprehensive ability assessment report, feed the assessment results back to the knowledge graph, and complete the comprehensive ability assessment.

[0147] In this embodiment, the live-fire range module is mainly used for realistic combat exercises. It generates an evaluation report through a multi-dimensional scoring unit and feeds back the mastery demonstrated in the live-fire range to the knowledge graph.

[0148] This method organically combines theoretical, simulated, recreational, and practical testing modules through the design of a knowledge graph. The theoretical module generates the knowledge graph, the simulated module parses and supplements it, and the recreational and practical modules provide feedback on the knowledge graph. Through theoretical assessments, practical exercises, Q&A sessions, and practical tests, it comprehensively reflects the user's mastery of cryptography knowledge and provides a basis for users to identify and fill knowledge gaps.

[0149] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0150] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A progressive, multi-level collaborative cryptographic training range system, characterized in that, include: Theoretical firing range module, simulated firing range module, recreational firing range module, and combat firing range module; The theoretical target range module is used to provide an environment for assessing basic cryptographic knowledge, generate targets based on knowledge point weights, and generate a knowledge graph based on the user's target assessment results. The simulated target range module is used to analyze the knowledge graph, identify the user's weak points, filter similar question types based on the analysis results, and push targeted reinforcement training tasks to the user. The amusement range module provides users with an interactive cryptographic algorithm interaction platform, allowing users to modify parameters to understand the principles of cryptographic algorithms. Through question and answer, the user's skills acquired in the amusement range are fed back into the knowledge graph, thereby reflecting the user's mastery and proficiency in cryptographic knowledge. The practical testing range module is used to provide a security system construction and multi-dimensional evaluation and verification platform, providing users with a practical training scenario, conducting multi-dimensional comprehensive evaluation of the cryptographic knowledge mastered by users, and synchronously updating the evaluation results to the knowledge graph.

2. The progressive multi-level collaborative cryptographic target range training system according to claim 1, characterized in that, The theoretical target range module includes: an intelligent survival target range engine, an anti-cheating monitoring unit, an automatic evaluation unit, and a review unit; The intelligent survival range engine dynamically generates targets for the range based on a preset knowledge point weight matrix. The anti-cheating monitoring unit is used to analyze behaviors such as target shooting time distribution and operation trajectory characteristics. When a suspicious operation mode is detected, it automatically triggers an early warning and records a detailed operation log. The automatic evaluation unit receives the shooting records, automatically scores the objective questions, and synchronizes the scoring results to the database in real time. The review unit is used to provide a grading interface for reviewing subjective questions, including an answer comparison window, a scoring slider, and an annotation input box, and synchronizes the grading results to the database in real time.

3. The progressive multi-level collaborative cryptographic target range training system according to claim 1, characterized in that, The simulated target range module includes: a capability analysis engine, an intelligent recommendation unit, and a wrong question re-practice unit; The capability analysis engine analyzes the historical firing data of the theoretical target range module, uses the K-Means clustering algorithm and knowledge graph association rules to generate a multi-dimensional capability radar chart, and dynamically renders capability weaknesses. The intelligent recommendation unit recommends corresponding weak knowledge point targets based on the weak points of ability according to a preset intelligent recommendation algorithm. The incorrect question re-practice unit provides a review plan based on the Ebbinghaus forgetting curve, and adjusts the review plan when the accuracy of the target shooting meets the preset conditions.

4. The progressive multi-level collaborative cryptographic target range training system according to claim 1, characterized in that, The amusement range module includes: a cryptographic algorithm interaction unit, an intelligent question-answering unit, and a functional assistance unit; The cryptographic algorithm interaction unit is used to provide a parameter modification window, which allows users to adjust the input in real time and observe the algorithm output and intermediate state changes through a visualized parameter space. The intelligent question-answering unit provides users with a question-answering model based on pre-set knowledge in the field of cryptography. It understands the user's knowledge gaps through question-answering and feeds them back into the knowledge graph. The functional auxiliary unit is used to provide users with functions such as cryptographic algorithm problem-solving competitions, code quality assessment, and learning trajectory analysis.

5. The progressive multi-level collaborative cryptographic target range training system according to claim 1, characterized in that, The combat range module includes: a scenario simulation unit, a topology verification unit, and a multi-dimensional scoring unit; The scenario simulation unit is used to provide simulated scenarios for practical exercises, so that users can complete practical exercises in scenario building and test the feasibility of the cryptographic scheme in the simulated scenarios. The topology verification unit is used to evaluate the topology structure built by the user, check the rationality of node connection logic and protocol configuration, and generate a verification report. The multi-dimensional scoring unit is used to comprehensively evaluate a user's shooting ability based on the weights of protocol completeness, encryption strength, and topology efficiency, and feeds the evaluation results back to the knowledge graph.

6. A progressive, multi-level collaborative cryptographic target range training method, characterized in that, The method of using the progressive multi-level collaborative cryptographic target range training system according to any one of claims 1-5 includes the following steps: S110. Provide basic knowledge assessment targets through the theoretical target range module, receive user shooting results, and generate corresponding knowledge graphs based on the shooting results. S120. Use the simulated target range module to analyze the user's weak points in the knowledge graph and push targeted reinforcement training tasks. S130, and provides a cryptographic algorithm interaction platform through the amusement range module, which fills the knowledge gaps in the amusement range module through hands-on practice and question-and-answer methods, and feeds the results back into the knowledge graph; S140. Based on the practical test range module, a security system construction and multi-dimensional evaluation and verification platform is provided, offering practical training scenarios, conducting multi-dimensional comprehensive evaluation of the cryptographic knowledge mastered by users, and synchronously updating the evaluation results to the knowledge graph.

7. The progressive multi-level collaborative cryptographic target range training method according to claim 6, characterized in that, Step S110 includes: S111. Configure dynamic difficulty coefficient rules and question types according to teaching needs and the preset knowledge point weight matrix; S112. Based on the weight matrix after configuring the dynamic difficulty coefficient and the question type, generate the corresponding target range. S113. The shooting range is allocated through the system reservation function. During the reservation stage, the examination window is controlled by a timed task, and the shooting progress is cached to support recovery from abnormal interruption. S114. Receive user shooting records, including: automatic scoring of objective questions and manual grading of subjective questions; S115. Generate electronic certificates with anti-counterfeiting watermarks based on the scoring results, construct the corresponding knowledge graph, and update the corresponding scoring rankings in real time.

8. The progressive multi-level collaborative cryptographic target range training method according to claim 6, characterized in that, Step S120 includes: S121. Analyze the historical firing data of the theoretical target range module, use the K-Means clustering algorithm combined with the knowledge graph association rules to generate a multi-dimensional capability radar map and locate capability weaknesses; S122. Based on the aforementioned weaknesses, a preset intelligent recommendation algorithm is used to select matching targets in the question bank and push the corresponding reinforcement training queue. S123. Arrange reinforcement training tasks for users through the error correction mechanism, and remove users from the reinforcement training queue when they meet the preset judgment conditions. S124. The training behavior data of the simulated target range module is transmitted back in real time to dynamically update the knowledge graph.

9. The progressive multi-level collaborative cryptographic target range training method according to claim 6, characterized in that, Step S130 includes: S131. Through the algorithm interaction experimental platform, users can adjust the algorithm parameters and observe the changes in encryption results and algorithm behavior in real time. S132. Interact with users through a cryptographic knowledge question and answer system to obtain structured knowledge answers; S133. Based on the question-and-answer results and algorithm operation data, locate the user's knowledge gaps and recommend related learning resources and supplementary exercises; S134. Feedback the algorithm mastery level and question-answering accuracy to the knowledge graph.

10. The progressive multi-level collaborative cryptographic target range training method according to claim 6, characterized in that, Step S140 includes: S141. Through the scenario simulation unit, simulated scenarios for practical exercises are provided so that users can complete practical exercises in scenario building and test the feasibility of the cryptographic scheme. S142. Verify the topology structure built in the live-fire range module and generate a verification report; S143. Automatic scoring through multi-dimensional scoring units and detection of compliance with GM / T standards through a compliance verification engine, completing annotation and secondary scoring; S144. Generate a multi-dimensional comprehensive ability assessment report, feed the assessment results back to the knowledge graph, and complete the comprehensive ability assessment.

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