Information synchronization method, system and equipment of embedded training simulator and medium

Abstract

The invention provides an information synchronization method, system and device for an embedded training simulator and a medium. The information synchronization method comprises the steps that task event information in the embedded training simulator is obtained; performing task classification on each piece of task event information to obtain each piece of task event information with attribute tags; according to the attribute tag of each task event information, performing task priority classification on each task event information by using an improved multi-factor priority scoring algorithm to obtain a task priority queue; according to the task priority queuing queue, generating a to-be-pushed data packet, and performing information synchronization on the to-be-pushed data packet; according to the invention, by introducing a task priority-based scheduling mechanism, the pushing sequence and the synchronization strategy of the task information are dynamically optimized, so that the scheduling rhythms and the transmission paths of the tasks with different priorities are intelligently distributed; and the dynamic adaptive capacity and the efficient task delivery capacity of the embedded training simulation in the training process can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of embedded systems, and particularly relates to a method, system, device, and medium for information synchronization of an embedded training simulator. Background Art

[0002] Currently, embedded training simulators are widely used in high-real-time scenarios such as military tactical drills, industrial process simulations, and medical operation trainings. The core requirement is to quickly, accurately, and synchronously transmit task event information among multiple modules within the system and make dynamic responses according to the importance of the tasks. However, there are still significant deficiencies in the existing information push and synchronization strategies. For example, most current embedded training simulators adopt a fixed-frequency synchronization mechanism (such as refreshing per second) or a simple event-trigger mechanism (such as immediately pushing when the state changes). Such mechanisms lack the intelligent perception ability for factors such as task urgency, interaction intensity, and data load, resulting in the following problems during training: Lack of task priority perception ability. The system cannot perform hierarchical scheduling according to the real-time importance of tasks, resulting in some key tasks (such as high-risk target recognition and user critical operations) not being pushed within the optimal time window, affecting the responsiveness and simulation consistency of training operations. Secondly, the existing mechanisms have a rigid scheduling method for task events, making it difficult to dynamically adjust the push strategy according to the training rhythm and unable to support the differential processing of different types of tasks (such as the lack of scheduling weight differences between high-frequency interaction tasks and low-frequency background tasks). In an embedded environment with limited resources, it is easy to cause system response jams or task losses. In addition, the task information generated during training is diverse, and the information transmission has timing sensitivity. If the tasks are not sorted by priority and structured and packaged, it will lead to problems such as data fragmentation, repeated pushing, or network congestion, further weakening the information synchronization efficiency and the consistency of terminal reception. Summary of the Invention

[0003] In order to solve the problem that in the existing embedded training simulator, due to the use of a fixed frequency or event-triggered method for information push, key information fails to be pushed in a timely manner at a critical moment, thus affecting the real-time performance and effectiveness of the training process, the present invention proposes a method for information synchronization of an embedded training simulator, including: Obtain each task event information in the embedded training simulator; Classify the task event information to obtain each task event information with an attribute label; According to the attribute labels of the task event information, use an improved multi-factor priority scoring algorithm to classify the task event information by task priority to obtain a task priority queuing queue; Generate the data packets to be pushed according to the task priority queuing queue, and synchronize the information of the data packets to be pushed; Among them, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boosting weight mapping.

[0004] Optionally, the task classification of the task event information is performed to obtain the task event information with attribute labels, including: Based on the task event information, extract the key feature dimensions of the task event information; According to the key feature dimensions of the task event information, generate the feature task vector of the task event information; Perform path judgment on the feature task vectors of the task event information through a decision tree, and output the task event information with attribute labels; Among them, the key feature dimensions include: event category, event urgency, and context training stage; The decision tree is generated based on context rules.

[0005] Optionally, according to the attribute labels of the task event information, use the improved multi-factor priority scoring algorithm to classify the task priority of the task event information to obtain a task priority queuing queue, including: Convert the attribute labels of the task event information into a vector to obtain a set of scoring vectors; According to the set of scoring vectors, use the improved multi-factor priority scoring algorithm to calculate the comprehensive priority score of the task event information; Sort the comprehensive priority scores of the task event information in descending order to form a task priority queuing queue.

[0006] Optionally, according to the set of scoring vectors, using the improved multi-factor priority scoring algorithm to calculate the comprehensive priority score of the task event information includes: Extract the time vector and discrete vector from the set of scoring vectors; Perform time sensitivity mapping on the time vector to obtain a set of time feature scores; Perform numerical boosting weight mapping on the discrete vector to obtain a set of discrete feature scores; Integrate the set of time feature scores and the set of discrete feature scores into a set of task scoring features; According to the set of task scoring features, use the comprehensive scoring function to calculate the comprehensive priority score of the task event information; Among them, the task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level, and task trigger time interval.

[0007] Optionally, the expression of the comprehensive scoring function is as follows: ; Where represents the comprehensive priority score of the th task event information; represents the task urgency of the th task event information; represents the task interaction intensity of the th task event information; represents the scenario priority factor of the th task event information; represents the data volume level of the th task event information; represents whether the th task event information is a critical task; represents the trigger time interval of the th task event information; represents the first adjustment parameter; is the second adjustment parameter; is the third adjustment parameter; is the fourth adjustment parameter; is the fifth adjustment parameter.

[0008] Optionally, generating the data packet to be pushed according to the task priority queue includes: Selecting the pushable tasks within the current scheduling period from the task priority queue to generate a push window task set; Generating a push candidate task set according to the push window task set by using a window scheduling mechanism; Generating a push scheduling plan according to the push candidate task set by using an adhesion weighted sorting strategy; Converting the push scheduling plan into a network transmission data packet, and using the network transmission data packet as the data packet to be pushed.

[0009] Optionally, the task event information includes one or more of the following: user input information, simulation status change information, and target behavior trigger information; The attribute tags include one or more of the following: task identifier, urgency level, task type, data volume, and timestamp; The information synchronization includes: sequence control, status feedback, and abnormal retransmission.

[0010] Based on the same inventive concept, the present invention also provides an information synchronization system for an embedded training simulator, including: An information acquisition module, configured to acquire various task event information in the embedded training simulator; A task classification module, configured to classify the various task event information to obtain the various task event information with attribute labels; A priority classification module, configured to, according to the attribute labels of the various task event information, use an improved multi-factor priority scoring algorithm to classify the various task event information by task priority to obtain a task priority queuing queue; An information synchronization module, configured to generate a data packet to be pushed according to the task priority queuing queue, and perform information synchronization on the data packet to be pushed; Wherein, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and promotion weight mapping.

[0011] Optionally, the task classification module includes: A dimension extraction sub-module, configured to extract key feature dimensions of the task event information based on the various task event information; A vector generation sub-module, configured to generate a feature task vector of the task event information according to the key feature dimensions of the task event information; A path judgment sub-module, configured to perform path judgment on the feature task vectors of the various task event information through a decision tree, and output the various task event information with attribute labels; Wherein, the key feature dimensions include: event category, event urgency, and context training stage; The decision tree is generated based on context rules.

[0012] Optionally, the priority classification module includes: A vector conversion sub-module, configured to convert the attribute labels of the various task event information into vectors to obtain a set of scoring vectors; A priority scoring sub-module, configured to calculate a comprehensive priority score of the various task event information according to the set of scoring vectors by using an improved multi-factor priority scoring algorithm; A task queue generation sub-module, configured to sort the comprehensive priority scores of the various task event information in descending order to form a task priority queuing queue.

[0013] Optionally, the priority scoring sub-module includes: A vector extraction unit, configured to extract a time vector and a discrete vector from the set of scoring vectors; A sensitive mapping unit for performing time-sensitive mapping on the time vector to obtain a set of time feature scores; A weight mapping unit for performing numerical boosting weight mapping on the discrete vector to obtain a set of discrete feature scores; A set integration unit for integrating the set of time feature scores and the set of discrete feature scores into a set of task scoring features; A scoring calculation unit for calculating the comprehensive priority score of each task event information according to the set of task scoring features by using a comprehensive scoring function; Wherein, the set of task scoring features includes: task urgency, task interaction intensity, scenario optimization factor, data volume level, and task trigger time interval.

[0014] Optionally, the expression of the comprehensive scoring function is as follows: ; Wherein, represents the comprehensive priority score of the th task event information; represents the task urgency of the th task event information; represents the task interaction intensity of the th task event information; represents the scenario priority factor of the th task event information; represents the data volume level of the th task event information; represents whether the th task event information is a critical task; represents the trigger time interval of the th task event information; represents the first adjustment parameter; is the second adjustment parameter; is the third adjustment parameter; is the fourth adjustment parameter; is the fifth adjustment parameter.

[0015] Optionally, the information synchronization module includes: A task generation sub-module for selecting pushable tasks within the current scheduling period from the task priority queuing queue to generate a set of push window tasks; A window scheduling sub-module for generating a set of push candidate tasks according to the set of push window tasks by using a window scheduling mechanism; A push scheduling sub-module for generating a push scheduling plan according to the set of push candidate tasks by using an adhesion weighted sorting strategy; A data push sub-module, configured to convert the push scheduling plan into a network transmission data packet, and use the network transmission data packet as the data packet to be pushed.

[0016] Optionally, the task event information includes one or more of the following: user input information, simulation status change information, and target behavior trigger information; The attribute label includes one or more of the following: task identifier, urgency level, task type, data volume, and timestamp; The information synchronization includes: sequence control, status feedback, and abnormal retransmission.

[0017] On the other hand, the present invention further provides an electronic device, including: at least one processor and a memory; the memory and the processor are connected through a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an information synchronization method of an embedded training simulator as described above is implemented.

[0018] On the other hand, the present invention further provides a computer-readable storage medium with an execution program stored thereon. When the execution program is executed, an information synchronization method of an embedded training simulator as described above is implemented.

[0019] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a method, system, device, and medium for information synchronization of an embedded training simulator, including: obtaining various task event information in the embedded training simulator; classifying the various task event information to obtain the various task event information with attribute tags; according to the attribute tags of the various task event information, using an improved multi-factor priority scoring algorithm to classify the various task event information by task priority to obtain a task priority queuing queue; generating a data packet to be pushed according to the task priority queuing queue, and synchronizing the information of the data packet to be pushed; wherein, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and promotion weight mapping; by classifying the task event information and extracting key attribute tags, the present invention can structurally express the original and messy training data, so as to achieve accurate recognition and classification management of task semantic features; by using a multi-factor priority scoring algorithm constructed based on time sensitivity mapping and promotion weight mapping to comprehensively score the tasks, dynamic sorting of tasks under multi-dimensional features is realized, which is beneficial to ensuring that key tasks obtain priority processing rights in resource-constrained or high-concurrency scenarios, effectively improving the rationality of information scheduling and the timeliness of system response; generating a data packet to be pushed based on the queuing result and orderly transmitting it through a synchronization mechanism can achieve structured pushing of task information and ensure sequential consistency, avoiding problems of out-of-sync training status caused by misaligned or delayed task transmission. At the same time, data distribution is combined with task priority to optimize system bandwidth utilization and improve overall synchronization efficiency and training real-time response performance; therefore, the method of the present invention can enhance the perception ability, dynamic scheduling ability, and task execution efficiency of the embedded training simulator by strengthening task discrimination. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flow chart of a method for information synchronization of an embedded training simulator provided by the present invention; Figure 2 It is a schematic framework diagram of priority scoring using an improved multi-factor priority scoring algorithm in a method for information synchronization of an embedded training simulator provided by the present invention; Figure 3 It is a schematic framework diagram of generating a data packet to be pushed in a method for information synchronization of an embedded training simulator provided by the present invention; Figure 4 It is a schematic structural composition diagram of a system for information synchronization of an embedded training simulator provided by the present invention; Figure 5 It is a schematic structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The present invention provides a method, system, device and medium for information synchronization of an embedded training simulator. The following further elaborates on the specific embodiments of the present invention with reference to the accompanying drawings.

[0022] Embodiment 1: The present invention provides a method for information synchronization of an embedded training simulator. The schematic flow diagram is as Figure 1 shown, including: Step 1: Obtain each task event information in the embedded training simulator; Step 2: Classify the task event information to obtain each task event information with attribute tags; Step 3: According to the attribute tags of the task event information, use an improved multi-factor priority scoring algorithm to classify the task priority of the task event information to obtain a task priority queuing queue; Step 4: Generate a data packet to be pushed according to the task priority queuing queue, and synchronize the information of the data packet to be pushed; Among them, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and enhanced weight mapping.

[0023] Generally, an embedded training simulator depends on a preset operation instruction stream or state change sequence during training, mainly focusing on data-driven training around device state parameters, simulation scenario processes or image rendering data. Although this type of training method can simulate a specific operation environment, it lacks in-depth recognition and dynamic response to real-time task events during training, and often fails to reflect real-time task characteristics such as key behaviors triggered by operators, system state changes or virtual target interactions in complex training scenarios, resulting in lagged event response and inaccurate task synchronization during training, thereby affecting the training rhythm and operation authenticity. For this reason, the present invention proposes a training mechanism oriented to task event information, obtains and processes the core task event information during training, and realizes priority recognition and efficient synchronization of information through classification, scoring and scheduling of task events, thereby improving the training adaptability and simulation quality of the embedded training simulator in a complex dynamic task environment; For example, the task event information in the above step 1 may include one or more of the following: user input information, simulation state change information and target behavior trigger information; the embedded training simulation can obtain operation instructions and behavior feedback in real time by collecting user input information before training, identify the operator's intentions and interaction paths, thereby improving the timeliness and accuracy of the embedded training simulator's task perception; introduce simulation state change information so that the simulator can perceive state switching or module responses in the simulation environment, build a full-process perception of the operating status, and ensure the continuity of information synchronization and the consistency of system feedback; integrate target behavior trigger information to capture the dynamic changes of virtual enemy and friendly targets in training, such as movement, attack, identification and other behaviors, thereby supporting task urgency assessment and high-priority scheduling, and enhancing the system's real-time response capabilities to tactical situations.

[0024] In order to achieve structured processing and effective perception of the above multi-source task event information, we can consider further classifying the task events to mine their key attributes and assign quantifiable task labels, so as to achieve in-depth analysis of event content, occurrence context and training stage characteristics. Specifically: In one implementation, the process of classifying the task event information in step 2 to obtain the task event information with attribute tags may include: Based on each task event information, extract key feature dimensions of the task event information; Generating a feature task vector of the task event information according to a key feature dimension of the task event information; Perform path judgment on the characteristic task vector of each task event information through a decision tree, and output each task event information with an attribute label; The key feature dimensions may include: event category, event urgency, and context training stage; The decision tree is generated based on context rules; The attribute tag may include one or more of the following: task identifier, urgency, task type, data volume, and timestamp; In this implementation, by extracting the key feature dimensions of task event information, constructing a feature task vector, and using a decision tree generated based on context rules for path judgment, task event information with attribute labels is finally output, realizing the transformation from raw event data to structured and semantic task information. By selecting the event category, event urgency, and context training stage as feature dimensions, the behavior type, response timeliness, and training scenario in which the task occurs can be accurately described, ensuring that the model not only captures the essence of the task but also takes into account its importance in the current environment; through the classification judgment of the decision tree model, compared with traditional linear rule matching, it has higher expression ability and scalability, can automatically select the optimal classification path according to different context conditions, and effectively avoid the problems of information loss or inaccurate classification in task classification; the finally output attribute labels not only provide a quantitative basis for subsequent priority scoring but also construct the association structure between tasks, making the entire information scheduling process controllable and transparent. In this implementation, the decision tree (generated based on context rules) is applied to the classification of task event information in an embedded training simulator, and combined with the task key feature dimensions and context training stage rules to generate a path judgment model, which takes the behavior characteristics (such as event category), time sensitivity (event urgency), and system operating environment (training stage) of event occurrence as combined dimensions, constructs a task vector that can express training intent and operation rhythm through feature engineering and inputs it into the decision path, so that its judgment logic is not only data-driven but also closely coupled with the phased process of training tasks, thereby enhancing the pertinence and adaptability of the decision path. Different from directly using static threshold classification or preset condition triggering in the prior art, this method has the ability to dynamically build a tree, enabling the simulator to output different classification results and label semantics under different training stages, thus ensuring the timeliness and structural coherence of task information classification. In addition, in order to further adjust the splitting priority of each feature dimension in the decision tree, this implementation can consider introducing an adaptive feature weighting mechanism based on training history records. By statistically analyzing the impact of different event types on the system response efficiency in different training stages in historical training tasks, feature weight coefficients are automatically generated, making the decision path more sensitive to high-impact features and more refined in judgment. After introducing this technical feature, it no longer relies on manual specification of feature importance or static configuration, but optimizes the decision model through historical feedback, making the task classification process more in line with the actual training effect and scenario evolution trend. This structure not only improves the adaptability of the classification model to complex task behaviors but also enhances the self-optimization ability of the embedded simulator to changes in operation habits and task distribution during long-term training.

[0025] Through the above implementation method, the classification of task event information can be completed and structured attribute tags can be obtained. Based on these tags, the importance assessment and scheduling priority determination of tasks can be further considered to achieve the orderly management and differential processing of different task events, thus leading to the following priority classification process. Specifically: In one implementation, in step 3 above, according to the attribute tags of the task event information, the process of classifying the task priorities of the task event information by using an improved multi-factor priority scoring algorithm to obtain a task priority queue may include: Convert the attribute tags of the task event information into vectors to obtain a set of scoring vectors; According to the set of scoring vectors, use an improved multi-factor priority scoring algorithm to calculate the comprehensive priority scores of the task event information; Sort the comprehensive priority scores of the task event information in descending order to form a task priority queue; In this implementation, by converting the attribute tags of task event information into a set of scoring vectors and combining an improved multi-factor priority scoring algorithm to calculate the comprehensive priority score of tasks, the transition of task information from a structured description vector to a quantifiable decision-making basis is achieved. Moreover, the scoring algorithm integrates multiple dimensions such as task urgency, interaction intensity, data volume, and time interval, and comprehensively models through mechanisms such as non-linear weighting, time sensitivity mapping, and task criticality adjustment, making the scoring results more accurately reflect the processing priority of tasks in the current training scenario. Different from traditional static priority mechanisms, this scoring method has the ability of dynamic adjustment, and can adjust the scoring results according to the real-time changes in the training process, so as to generate a more reasonable task ranking. Although scoring algorithms and priority rankings have been applied in some computing scenarios as general scheduling strategies, in this implementation, a set of scoring and classification mechanisms driven by attribute tags are constructed for multi-source heterogeneous task information in an embedded training simulator. The generation of scoring vectors is not a general field concatenation, but is based on task tags generated by context classification in the previous stage, with strong training context relevance. The scoring function itself integrates time sensitivity processing (such as exponential correction for tasks that have not been processed for a long time), key task gain terms (such as multiplicative factors for key tasks and low data volume), and task behavior feature coupling terms (such as logarithmic combinations of interactivity and scenario weights), and significantly exceeds the linear weighting model in terms of expressive ability. Therefore, by linking this scoring algorithm with the task tag system and using the results for task queue construction, the simulator can not only identify task attributes, but also dynamically adapt to the training situation, optimize resource allocation, and form a closed-loop control mechanism for task scheduling. Therefore, the implementation path of this scoring and queuing mechanism has technical pertinence in the field of embedded real-time information processing. In addition, to improve the accuracy of scoring, a scoring factor adjustment mechanism based on the system resource status can be further introduced in this implementation, that is, system operation parameters such as the current CPU load and communication bandwidth occupancy are dynamically introduced as correction terms for the scoring function during the scoring process. This new feature can make the priority scoring not only reflect the importance of the task itself, but also reflect the current bearing capacity of the system, thus avoiding high-priority tasks from continuing to occupy key resources when the system is under high load, resulting in performance bottlenecks or system instability; through this mechanism, the task scheduling strategy can achieve adaptive coordinated adjustment with the system resource status, which is conducive to improving the stability of the overall scheduling strategy and resource utilization efficiency.

[0026] Specifically, as Figure 2 shown, in the above implementation, according to the set of scoring vectors, the process of calculating the comprehensive priority score of each task event information by using the improved multi-factor priority scoring algorithm may include: Extracting a time vector and a discrete vector from the set of scoring vectors; Perform a time-sensitivity mapping on the time vector to obtain a set of time feature scores; Perform a numerical boosting weight mapping on the discrete vector to obtain a set of discrete feature scores; Integrate the set of time feature scores and the set of discrete feature scores into a set of task score features; According to the set of task score features, use a comprehensive scoring function to calculate the comprehensive priority scores of the task event information; Among them, the task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level, and task trigger time interval; in this specific implementation, by structuring different types of data in the scoring vector set, the time vector and the discrete vector are extracted respectively, and combined with the time sensitivity mapping and the numerical weight enhancement mechanism, the behavior characteristics and scheduling requirements of the task are transformed into a unified scoring feature set, so as to provide more expressive and discriminative inputs for the comprehensive scoring function. The time sensitivity mapping enables the simulator to identify and quantify the duration of tasks that have not been processed during the training process, dynamically enhance the processing priority of stale tasks, and avoid key tasks being ignored for a long time in scheduling; the discrete features enhance the influence of task criticality and type differences in the scoring model through numerical enhancement mapping, enabling the model to have a stronger perception of task semantics and policy value; after integrating the time and discrete features, a unified task scoring feature set is formed, which can significantly improve the comprehensive judgment ability of the scoring model for task urgency, scenario adaptability, and resource consumption impact, making the final priority score more in line with the actual operation logic of multi-task scheduling in the embedded training environment, thus effectively supporting the priority push and scheduling optimization of key tasks. This implementation reconstructs the scoring process structurally by combining the task rhythm and data type characteristics of the embedded training simulator. In this scenario, tasks have clear real-time windows, while the information sources are complex and task behaviors are diverse. If only a unified scoring method is used, it is difficult to adapt to the time and semantic sensitivity of different tasks. This implementation explicitly distinguishes the original scoring vector into a time vector and a discrete vector, and performs non-linear processing on them respectively, constructs a mapping mechanism for enhancing time sensitivity and improving semantic importance, then fuses the two types of features to form a task scoring feature set, and then conducts unified scoring modeling. This processing method not only maintains the interpretability of the model input, but also provides a personalized scoring adjustment path for different task characteristics, especially adapting to the actual situation where sudden tasks are frequent and information lag has a significant impact in the training system, which belongs to the combined application of the scoring process structure and the task feature modeling method. In addition, in this implementation, a structure enhancement mechanism based on task dependencies can be further considered, introducing the predecessor and successor dependencies between task events as additional structural features into the scoring process. Specifically, a "dependency weight factor" field can be added to the task scoring feature set to describe whether the task is a prerequisite task for subsequent key processes or is affected by the completion status of other tasks. The introduction of this feature will enable the scoring function to not only reflect the attributes of the task itself, but also consider its logical position in the overall task chain, so as to preferentially schedule key node tasks with a greater influence range, further improving the overall scheduling efficiency and task delivery integrity of the system, especially suitable for highly concurrent or process-sensitive embedded simulation training environments.

[0027] Exemplarily, the expression of the above comprehensive scoring function can be as follows: ; wherein, represents the comprehensive priority score of the th task event information; represents the task urgency of the th task event information; represents the task interaction intensity of the th task event information; represents the scenario priority factor of the th task event information; represents the data volume level of the th task event information; represents whether the th task event information is a critical task; represents the trigger time interval of the th task event information; represents the first adjustment parameter; is the second adjustment parameter; is the third adjustment parameter; is the fourth adjustment parameter; is the fifth adjustment parameter; In this example, by introducing the method of multi-factor coupling modeling, the task event information is scored and calculated from multiple dimensions such as urgency, interactivity, scenario relevance, data load, and time sensitivity, and a task priority evaluation mechanism with adjustability and scenario adaptability is constructed, which can effectively support the intelligent scheduling and information synchronization of the embedded training simulator in a high-concurrency task environment.

[0028] Through the above implementation method, a task priority queuing queue sorted by the comprehensive priority score can be obtained. In order to improve the orderly synchronization when the embedded training simulator executes task data, an efficient task push mechanism can be further constructed based on this task priority queuing queue to realize the generation of the data packet to be pushed. Specifically: In one implementation method, as Figure 3 shown, the process of generating the data packet to be pushed according to the task priority queuing queue and performing information synchronization in step 4 above may include: Select the pushable tasks within the current scheduling period from the task priority queuing queue to generate a push window task set; According to the push window task set, use the window scheduling mechanism to generate a push candidate task set; According to the push candidate task set, use the sticky weighted sorting strategy to generate a push scheduling plan; Convert the push scheduling plan into network transmission data packets, and use the network transmission data packets as the data packets to be pushed; Synchronize information according to the data packets to be pushed; Among them, the information synchronization may include one or more of the following: sequence control, status feedback, and abnormal retransmission; Exemplarily, the above window scheduling mechanism is used to select a subset of tasks that can be executed or pushed at the current moment in the task priority queuing queue. By setting a "scheduling window", the window size can be dynamically adjusted according to the current processing capacity of the simulator (such as CPU load, bandwidth occupancy). Tasks with a priority score higher than the threshold or tasks whose trigger conditions are met within the window range are selected as the "push window task set". Applying this mechanism is beneficial to restricting the number of single push tasks, preventing data congestion, and at the same time improving scheduling flexibility and system load stability; Exemplarily, the above adhesion weighted sorting strategy is a multi-factor packing and sorting method that combines task similarity and scheduling efficiency. The purpose is to aggregate tasks with strong correlation into a scheduling unit when generating data packets to improve data packet utilization and system response efficiency. This strategy comprehensively calculates the "adhesion degree" score between tasks based on the following multiple factors: target terminal consistency factor (weighted if the sending targets are the same); task type similarity factor (such as both being interactive tasks or status tasks); trigger time proximity factor (such as the task generation time interval is less than the threshold); data volume coupling factor (to avoid the combined data volume exceeding the transmission limit); the "adhesion score" of each task to the combined target can be given by the weighted linear combination of the above factors. For example, the expression is as follows: ; Among them, represents task event information and task event information The adhesion score between them, the larger the value, the more suitable the two tasks are to be aggregated in the same data packet; are all weighted factors, respectively used to control the influence degree of different adhesion factors on the total score, and the value ranges are all [0, 1]; represents the task type similarity function. If and belong to the same task type (such as both being interactive tasks), the value is 1, otherwise it is 0 or a similarity score less than 1; represents the target consistency function. If and have the same target device number, the value is 1, otherwise it is 0; is an exponential function; represents task event information and task event information The triggering time interval between; The push technology in the embedded training simulator needs to achieve the real-time and accuracy of data. This requires its deep integration with the hardware and software systems of the simulator, having efficient data carrying and scheduling capabilities, being able to handle the transmission of a large amount of training task data, and ensuring the integrity and order of data packets during transmission. To meet this requirement, the push technology should also support the data adhesion function, by combining multiple pieces of information with logical relevance or temporal proximity between tasks into a single data packet for unified transmission, improving the data transmission efficiency and the terminal processing speed. In addition, the presentation method of the push technology needs to be coordinated with the interface design of the simulator to enhance the clarity of information transmission and the interaction experience. Based on this requirement, in the implementation of the present invention, a push window task set is constructed based on the task priority queuing queue, and combined with the window scheduling mechanism and the adhesion weighted sorting strategy to generate a push scheduling plan, and finally form the data packet to be pushed and complete the information synchronization. This structural task push method not only significantly improves the transmission efficiency and response speed, but also can dynamically adjust the push task scope according to the current resource status and task load, reducing the system pressure; the adhesion weighted sorting strategy realizes the intelligent packaging of highly relevant tasks by evaluating the context relevance, target device consistency and data volume coordination between tasks, further improving the bandwidth utilization rate and task processing consistency. After integrating the two strategies, the simulator forms a collaborative optimization in global scheduling and local packaging, and has the ability to construct an adaptive task push path according to the task score and resource status, so as to effectively avoid transmission redundancy and task delay while ensuring the timely response of high-priority tasks, improving the synchronization stability and scheduling efficiency of the overall training process. In addition, this implementation method can support converting the scheduling plan into a network transmission data packet and supplementing it with sequence control, status feedback and abnormal retransmission mechanisms, enhancing the reliability and fault tolerance of task transmission, and adapting to the state consistency maintenance in complex network environments. Further, the real-time control function of the embedded training simulator is precisely realized relying on this task push mechanism, and can dynamically adjust the push content and frequency according to the changes in the training scenario, ensuring that key data is efficiently delivered to the target device at key nodes, supporting the high-intensity simulation interaction requirements. At the same time, to improve the overall user experience, the display method of the push information has also been deeply optimized. According to the principles of human-computer interaction and visual design, it is finely designed in terms of information layout, color hierarchy and dynamic effect response, provides personalized display solutions in combination with different scenarios and device platforms, and is linked with the user interaction logic to realize the actionable feedback on the information, improving the overall performance of the training system in terms of functionality and interactivity.

[0029] Therefore, for the special application scenario of the embedded training simulator, which is real-time, highly interactive, and task-concurrency intensive, a window scheduling strategy and adhesion aggregation mechanism based on priority driving are constructed in this implementation method. It has a clear structural combination and processing path, forms a linkage logic between the task priority score output and the window scheduling mechanism, and realizes the coordinated adjustment of the task scheduling granularity and the system load status. By packing similar task information through the adhesion weighting strategy, it avoids information fragmentation and multiple repeated transmissions, which is significantly different from the conventional independent task queuing and batch pushing methods. At the same time, the sequence control, status feedback, and abnormal retransmission mechanisms introduced by information synchronization do not exist in isolation, but are bound to the scheduling process, forming a full-process closed-loop control from task recognition, scheduling, pushing to confirmation. This structural cooperation enables the task scheduling and information synchronization to achieve a controllable, adjustable, and traceable linkage execution process from the policy layer to the data layer. In addition, in order to further reduce the information synchronization failure rate and improve the real-time performance of critical data transmission and the overall system task completion rate, a dynamic selection mechanism for the task pushing path can be considered on the basis of this implementation method. Specifically, after generating the pushing scheduling plan, according to the network status, terminal load, or the processing capacity of the task module, the optimal transmission channel or timing is selected to form a coupling scheduling strategy for the task transmission path and the system resource status. After introducing this feature, the simulator can, while ensuring the task priority principle, avoid network congestion nodes or high-load terminals, and preferentially arrange critical tasks to follow low-latency and high-stability paths, thereby reducing the information synchronization failure rate and improving the real-time performance of critical data transmission and the overall system task completion rate, which is particularly suitable for embedded training systems with multiple terminal collaborations or heterogeneous node participations.

[0030] In summary, the present invention addresses the following problems existing in the synchronization of interactive information, task assignment information, and virtual target information in existing embedded training simulators: Information transmission delay: Limited computing power and network bandwidth of embedded devices lead to information transmission delay, affecting the real-time nature and immersion of training; Difficulty in ensuring information consistency: Different data update frequencies and transmission delays of different information sources easily result in information inconsistency, affecting the training effect; Lack of a flexible synchronization mechanism: Existing synchronization mechanisms usually adopt fixed frequencies or event-triggered methods, making it difficult to adapt to different training scenarios and requirements. To solve the above problems, the present invention proposes an information synchronization method for an embedded training simulator. By classifying task event information and extracting key attribute tags, it can structurally express the original and chaotic training data, thereby achieving accurate identification and classification management of task semantic features; By using a multi-factor priority scoring algorithm constructed based on time sensitivity mapping and enhanced weight mapping to comprehensively score tasks, dynamic sorting of tasks under multi-dimensional features is realized, which is conducive to ensuring that key tasks obtain priority processing rights in resource-constrained or high-concurrency scenarios, effectively improving the rationality of information scheduling and the timeliness of system response; Generating packets to be pushed based on the queuing results and orderly transmitting them through a synchronization mechanism can achieve structured pushing of task information and ensure sequential consistency, avoiding problems of out-of-sync training status caused by misaligned or delayed task transmissions. At the same time, data distribution is combined with task priorities to optimize system bandwidth utilization and improve overall synchronization efficiency and training real-time response performance; Therefore, the method of the present invention can enhance the information transmission ability, transmission information consistency, and information transmission synchronization of the embedded training simulator by strengthening task discrimination.

[0031] Embodiment 2: Based on the same inventive concept, the present invention also provides an information synchronization system for an embedded training simulator. The schematic structural composition is as Figure 4 shown, including: An information acquisition module for acquiring various task event information in the embedded training simulator; A task classification module for classifying the various task event information to obtain the various task event information with attribute tags; A priority classification module for classifying the task priorities of the various task event information according to the attribute tags of the various task event information by using an improved multi-factor priority scoring algorithm to obtain a task priority queuing queue; An information synchronization module for generating packets to be pushed according to the task priority queuing queue and synchronizing the packets to be pushed; Among them, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and enhanced weight mapping; Exemplarily, the above task event information may include one or more of the following: user input information, simulation status change information, and target behavior trigger information.

[0032] In one implementation, the above task classification module may include: A dimension extraction sub-module, configured to extract key feature dimensions of the task event information based on each task event information; A vector generation sub-module, configured to generate a feature task vector of the task event information according to the key feature dimensions of the task event information; A path judgment sub-module, configured to perform path judgment on the feature task vectors of the task event information through a decision tree, and output each task event information with an attribute label; Wherein, the key feature dimensions may include: event category, event urgency, and context training stage; The attribute label may include one or more of the following: task identifier, urgency level, task type, data volume, and timestamp; The decision tree is generated based on context rules.

[0033] In one implementation, the above priority classification module may include: A vector conversion sub-module, configured to perform vector conversion on the attribute labels of the task event information to obtain a set of scoring vectors; A priority scoring sub-module, configured to calculate a comprehensive priority score of each task event information by using an improved multi-factor priority scoring algorithm according to the set of scoring vectors; A task queue generation sub-module, configured to sort the comprehensive priority scores of the task event information in descending order to form a task priority queue.

[0034] In this implementation, the above priority scoring sub-module may include: A vector extraction unit, configured to extract a time vector and a discrete vector from the set of scoring vectors; A sensitivity mapping unit, configured to perform time sensitivity mapping on the time vector to obtain a set of time feature scores; A weight mapping unit, configured to perform numerical enhancement weight mapping on the discrete vector to obtain a set of discrete feature scores; A set integration unit, configured to integrate the set of time feature scores and the set of discrete feature scores into a set of task scoring features; A scoring calculation unit, configured to calculate a comprehensive priority score of each task event information by using a comprehensive scoring function according to the set of task scoring features; Among them, the task scoring feature set may include: task urgency, task interaction intensity, scenario optimization factor, data volume level, and task trigger time interval.

[0035] Exemplarily, the expression of the above comprehensive scoring function may be as follows: ; Among them, represents the comprehensive priority score of the th task event information; represents the task urgency of the th task event information; represents the task interaction intensity of the th task event information; represents the scenario priority factor of the th task event information; represents the data volume level of the th task event information; represents whether the th task event information is a critical task; represents the trigger time interval of the th task event information; represents the first adjustment parameter; is the second adjustment parameter; is the third adjustment parameter; is the fourth adjustment parameter; is the fifth adjustment parameter.

[0036] In one implementation, the above information synchronization module may include: A task generation sub-module, configured to select pushable tasks within the current scheduling period from the task priority queuing queue, and generate a push window task set; A window scheduling sub-module, configured to generate a push candidate task set according to the push window task set by using a window scheduling mechanism; A push scheduling sub-module, configured to generate a push scheduling plan according to the push candidate task set by using an adhesion weighted sorting strategy; A data push sub-module, configured to convert the push scheduling plan into a network transmission data packet, and use the network transmission data packet as the data packet to be pushed.

[0037] The information synchronization may include one or more of the following: sequence control, status feedback, and abnormal retransmission.

[0038] Embodiment 3: As Figure 5As shown in the figure, the present invention also provides an electronic device, which may be a computer device, a single-chip microcomputer device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected through a bus; the memory can be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, and this data can be called and / or modified when the instructions are executed.

[0039] The processor may be a Central Processing Unit (CPU), or it may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of an information synchronization method for an embedded training simulator in the above embodiment.

[0040] Embodiment 4: Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device-readable storage medium (Memory). The electronic device-readable storage medium is a memory device in the electronic device, and is used to store programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device, and of course, it can also include the extended storage medium supported by the electronic device. The storage medium provides a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory, or it can also be a non-volatile memory, such as at least one disk memory. By the processor loading and executing one or more instructions stored in the storage medium, the steps of an information synchronization method for an embedded training simulator in the above embodiment can be implemented.

[0041] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0042] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0043] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0045] 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 the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications, or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications, or equivalent replacements are all within the scope of the protection of the pending claims of the application.

Claims

1. An information synchronization method for an embedded training simulator, characterized in that, Including: Obtain various task event information in the embedded training simulator; Classify the various task event information to obtain the various task event information with attribute tags; According to the attribute tags of the various task event information, use the improved multi-factor priority scoring algorithm to classify the priority of the various task event information to obtain a task priority queuing queue; Generate a data packet to be pushed according to the task priority queuing queue, and synchronize the information of the data packet to be pushed; Among them, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and promotion weight mapping.

2. The method according to claim 1, wherein The classifying the various task event information to obtain the various task event information with attribute tags includes: Based on the various task event information, extract the key feature dimensions of the task event information; Generate a feature task vector of the task event information according to the key feature dimensions of the task event information; Perform path judgment on the feature task vectors of the various task event information through a decision tree, and output the various task event information with attribute tags; Among them, the key feature dimensions include: event category, event urgency, and context training stage; The decision tree is generated based on context rules.

3. The method according to claim 1, wherein The classifying the priority of the various task event information according to the attribute tags of the various task event information by using the improved multi-factor priority scoring algorithm to obtain a task priority queuing queue includes: Convert the attribute tags of the various task event information into vectors to obtain a set of scoring vectors; According to the set of scoring vectors, use the improved multi-factor priority scoring algorithm to calculate the comprehensive priority score of the various task event information; Sort the comprehensive priority scores of the various task event information in descending order to form a task priority queuing queue.

4. The method according to claim 3, wherein The calculating the comprehensive priority score of the various task event information by using the improved multi-factor priority scoring algorithm according to the set of scoring vectors includes: Extract the time vector and discrete vector from the set of scoring vectors; Perform time sensitivity mapping on the time vector to obtain a set of time feature scores; Perform numerical promotion weight mapping on the discrete vector to obtain a set of discrete feature scores; Integrate the set of time feature scores and the set of discrete feature scores into a task scoring feature set; According to the task scoring feature set, use a comprehensive scoring function to calculate the comprehensive priority score of the various task event information; Among them, the task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level, and task trigger time interval.

5. The method according to claim 4, wherein The expression of the comprehensive scoring function is as follows: ； Among them, represents the comprehensive priority score of the th task event information; represents the task urgency of the th task event information; represents the task interaction intensity of the th task event information; represents the scenario priority factor of the th task event information; represents the data volume level of the th task event information; represents whether the th task event information is a critical task; represents the trigger time interval of the th task event information; represents the first adjustment parameter; is the second adjustment parameter; is the third adjustment parameter; is the fourth adjustment parameter; is the fifth adjustment parameter.

6. The method according to claim 1, wherein The generating a data packet to be pushed according to the task priority queuing queue includes: Select the pushable tasks within the current scheduling period from the task priority queuing queue to generate a push window task set; According to the push window task set, use a window scheduling mechanism to generate a set of push candidate tasks; According to the set of push candidate tasks, use an adhesion weighted sorting strategy to generate a push scheduling plan; Convert the push scheduling plan into a network transmission data packet, and use the network transmission data packet as the data packet to be pushed.

7. The method according to claim 1, wherein The task event information includes one or more of the following: user input information, simulation status change information, and target behavior trigger information; The attribute tags include one or more of the following: task identifier, urgency level, task type, data volume, and timestamp; The information synchronization includes: sequence control, status feedback, and abnormal retransmission.

8. An information synchronization system for an embedded training simulator, characterized in that, It includes: An information acquisition module, configured to acquire each task event information in the embedded training simulator; A task classification module, configured to classify the task event information to obtain each task event information with attribute tags; A priority classification module, configured to classify the task priorities of the task event information according to the attribute tags of the task event information by using an improved multi-factor priority scoring algorithm, to obtain a task priority queuing queue; An information synchronization module, configured to generate a data packet to be pushed according to the task priority queuing queue, and perform information synchronization on the data packet to be pushed; Wherein, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and promotion weight mapping.

9. An electronic device, characterized in that, It includes: At least one processor and a memory; The memory and the processor are connected through a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an information synchronization method of an embedded training simulator as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, There is an execution program stored thereon, and when the execution program is executed, an information synchronization method of an embedded training simulator as described in any one of claims 1 to 7 is implemented.

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