Information synchronization method, system, device and medium for embedded training simulator

Abstract

The present invention provides an information synchronization method, system, device and medium for an embedded training simulator, comprising: obtaining each task event information in the embedded training simulator; classifying each task event information into tasks to obtain each task event information with attribute tags; classifying each task event information into task priorities according to the attribute tags of each task event information using an improved multi-factor priority scoring algorithm to obtain a task priority queuing queue; generating a data packet to be pushed according to the task priority queuing queue, and performing information synchronization on the data packet to be pushed; the present invention introduces a scheduling mechanism based on task priority to dynamically optimize the push order and synchronization strategy of task information, thereby realizing intelligent allocation of scheduling rhythms and transmission paths for tasks of different priorities, which is beneficial to improving the dynamic adaptability and efficient task delivery capability of the embedded training simulation during the training process.

Description

Technical Field

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

[0002] Currently, embedded training simulators are widely used in high-real-time scenarios such as military tactical exercises, industrial process simulation, and medical operation training. Their core requirements are: to quickly, accurately, and synchronously transmit mission event information between multiple modules within the system, and to dynamically respond based on the importance of the mission. However, existing systems still have significant deficiencies in information push and synchronization strategies. For example, most current embedded training simulators use fixed-frequency synchronization mechanisms (such as refresh every second) or simple event triggering mechanisms (such as immediate push notifications on status changes). These mechanisms lack the ability to intelligently perceive factors such as mission urgency, interaction intensity, and data load, leading to the following problems during training:

[0003] The system lacks task priority awareness, preventing it from scheduling tasks hierarchically based on their real-time importance. This results in some critical tasks (such as high-risk target identification and critical user operations) not being pushed within the optimal time window, impacting the responsiveness of training operations and simulation consistency. Furthermore, the existing mechanism's rigid scheduling of task events makes it difficult to dynamically adjust push strategies based on the training cadence. It also fails to support differentiated processing of different task types (for example, there's a lack of scheduling weighting between high-frequency interactive tasks and low-frequency background tasks). This can easily lead to system response blockage or task loss in resource-constrained embedded environments. Furthermore, the task information generated during training is diverse, and information transmission is sensitive to timing. Failure to prioritize and structure tasks will lead to data fragmentation, duplicate pushes, or network congestion, further impairing information synchronization efficiency and the consistency of terminal reception. Summary of the Invention

[0004] In order to solve the problem that existing embedded training simulators fail to push key information in a timely manner at critical moments due to the use of fixed frequency or event triggering when pushing information, thereby affecting the real-time and effectiveness of the training process, the present invention proposes an information synchronization method for embedded training simulators, comprising:

[0005] Obtain information about each task event in the embedded training simulator;

[0006] Classifying each task event information to obtain each task event information with an attribute label;

[0007] According to the attribute labels of the task event information, the task event information is classified by using an improved multi-factor priority scoring algorithm to obtain a task priority queue;

[0008] Generate a data packet to be pushed according to the task priority queue, and synchronize information of the data packet to be pushed;

[0009] The improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boost weight mapping.

[0010] Optionally, the task event information is classified into task categories to obtain task event information with attribute tags, including:

[0011] Extracting key feature dimensions of the task event information based on each task event information;

[0012] generating a feature task vector of the task event information according to key feature dimensions of the task event information;

[0013] Perform path judgment on the feature task vectors of each task event information through a decision tree, and output each task event information with an attribute label;

[0014] The key feature dimensions include: event category, event urgency, and context training stage;

[0015] The decision tree is generated based on contextual rules.

[0016] Optionally, the step of performing task priority classification on each task event information using an improved multi-factor priority scoring algorithm based on the attribute tags of each task event information to obtain a task priority queue includes:

[0017] Convert the attribute labels of each task event information into vectors to obtain a score vector set;

[0018] Calculating the comprehensive priority score of each task event information using an improved multi-factor priority scoring algorithm based on the scoring vector set;

[0019] The comprehensive priority scores of the task event information are sorted in descending order to form a task priority queue.

[0020] Optionally, the step of calculating the comprehensive priority score of each task event information based on the score vector set using an improved multi-factor priority scoring algorithm includes:

[0021] Extracting a time vector and a discrete vector from the set of scoring vectors;

[0022] Performing time sensitivity mapping on the time vector to obtain a time feature score set;

[0023] Performing numerical boosting weight mapping on the discrete vector to obtain a discrete feature score set;

[0024] Integrating the temporal feature score set and the discrete feature score set into a task score feature set;

[0025] Calculating a comprehensive priority score for each task event information using a comprehensive scoring function based on the task scoring feature set;

[0026] The task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level and task triggering time interval.

[0027] Optionally, the comprehensive scoring function is expressed as follows:

[0028] ;

[0029] in, Indicates the Comprehensive priority score of task event information; Indicates the The urgency of each task event information; Indicates the The task interaction intensity of each task event information; Indicates the The scenario priority factor of each task event information; Indicates the The data volume level of each task event information; Indicates the Whether the task event information is a critical task; Indicates the The triggering time interval of each 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.

[0030] Optionally, generating a data packet to be pushed according to the task priority queue includes:

[0031] Selecting pushable tasks within the current scheduling period from the task priority queue to generate a push window task set;

[0032] Generate a push candidate task set based on the push window task set using a window scheduling mechanism;

[0033] Generate a push scheduling plan based on the push candidate task set using a sticky weighted sorting strategy;

[0034] The push scheduling plan is converted into a network transmission data packet, and the network transmission data packet is used as the data packet to be pushed.

[0035] Optionally, the task event information includes one or more of the following: user input information, simulation state change information, and target behavior triggering information;

[0036] The attribute tag includes one or more of the following: task identifier, urgency, task type, data volume and timestamp;

[0037] The information synchronization includes: sequence control, status feedback and abnormal retransmission.

[0038] Based on the same inventive concept, the present invention also provides an information synchronization system for an embedded training simulator, comprising:

[0039] An information acquisition module is used to obtain information about each task event in the embedded training simulator;

[0040] A task classification module is used to classify each task event information to obtain each task event information with an attribute label;

[0041] A priority classification module is used to classify the task priority of each task event information according to the attribute label of each task event information using an improved multi-factor priority scoring algorithm to obtain a task priority queue;

[0042] An information synchronization module is used to generate a data packet to be pushed according to the task priority queue and synchronize information of the data packet to be pushed;

[0043] The improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boost weight mapping.

[0044] Optionally, the task classification module includes:

[0045] A dimension extraction submodule, configured to extract key feature dimensions of task event information based on each task event information;

[0046] A vector generation submodule, configured to generate a characteristic task vector of the task event information according to key characteristic dimensions of the task event information;

[0047] A path judgment submodule is used to perform path judgment on the characteristic task vectors of each task event information through a decision tree, and output each task event information with an attribute label;

[0048] The key feature dimensions include: event category, event urgency, and context training stage;

[0049] The decision tree is generated based on contextual rules.

[0050] Optionally, the priority classification module includes:

[0051] A vector conversion submodule is used to convert the attribute labels of each task event information into vectors to obtain a score vector set;

[0052] A priority scoring submodule, configured to calculate a comprehensive priority score of each task event information based on the scoring vector set using an improved multi-factor priority scoring algorithm;

[0053] The task queue generation submodule is used to sort the comprehensive priority scores of the task event information in descending order to form a task priority queuing queue.

[0054] Optionally, the priority scoring submodule includes:

[0055] A vector extraction unit, configured to extract a time vector and a discrete vector from the score vector set;

[0056] a sensitivity mapping unit, configured to perform time sensitivity mapping on the time vector to obtain a time feature score set;

[0057] A weight mapping unit, configured to perform numerical boosting weight mapping on the discrete vector to obtain a discrete feature score set;

[0058] a set integration unit, configured to integrate the temporal feature score set and the discrete feature score set into a task score feature set;

[0059] A scoring calculation unit, configured to calculate a comprehensive priority score of each task event information using a comprehensive scoring function based on the task scoring feature set;

[0060] The task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level and task triggering time interval.

[0061] Optionally, the comprehensive scoring function is expressed as follows:

[0062] ;

[0063] in, Indicates the Comprehensive priority score of task event information; Indicates the The urgency of each task event information; Indicates the The task interaction intensity of each task event information; Indicates the The scenario priority factor of each task event information; Indicates the The data volume level of each task event information; Indicates the Whether the task event information is a critical task; Indicates the The triggering time interval of each 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.

[0064] Optionally, the information synchronization module includes:

[0065] A task generation submodule is used to select pushable tasks within the current scheduling period from the task priority queue and generate a push window task set;

[0066] A window scheduling submodule, configured to generate a push candidate task set based on the push window task set and using a window scheduling mechanism;

[0067] A push scheduling submodule is used to generate a push scheduling plan based on the push candidate task set using a sticky weighted sorting strategy;

[0068] The data push submodule is used 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.

[0069] Optionally, the task event information includes one or more of the following: user input information, simulation state change information, and target behavior triggering information;

[0070] The attribute tag includes one or more of the following: task identifier, urgency, task type, data volume and timestamp;

[0071] The information synchronization includes: sequence control, status feedback and abnormal retransmission.

[0072] In another aspect, the present invention further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0073] The memory is used to store one or more programs;

[0074] When the one or more programs are executed by the at least one processor, the information synchronization method of the embedded training simulator as described above is implemented.

[0075] On the other hand, the present invention also provides a computer device readable storage medium, on which an execution program is stored. When the execution program is executed, the information synchronization method of the embedded training simulator as described above is implemented.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] The present invention provides an information synchronization method, system, device and medium for an embedded training simulator, comprising: obtaining task event information of each task in the embedded training simulator; classifying the task event information into tasks to obtain task event information with attribute labels; classifying the task event information into task priorities based on the attribute labels of the task event information using an improved multi-factor priority scoring algorithm to obtain a task priority queue; According to the task priority queuing queue, a data packet to be pushed is generated, and information synchronization is performed on the data packet to be pushed; wherein, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boosted weight mapping; the present invention can structure the original and messy training data by classifying task event information and extracting key attribute labels, thereby realizing accurate identification and classification management of task semantic features; by using the multi-factor priority scoring algorithm constructed based on time sensitivity mapping and boosted 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; based on the queuing results, a data packet to be pushed is generated and transmitted in an orderly manner through a synchronization mechanism, which can realize structured push of task information and sequential consistency guarantee, avoid the problem of training state synchronization caused by task transmission misalignment or delay, and at the same time, data distribution combined with task priority can optimize system bandwidth utilization, improve overall synchronization efficiency and training real-time response performance; therefore, the method of the present invention can improve the perception ability, dynamic scheduling ability and task execution efficiency of the embedded training simulator by enhancing task differentiation. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1A schematic flow chart of an information synchronization method for an embedded training simulator provided by the present invention;

[0079] Figure 2 A schematic diagram of a framework for priority scoring using an improved multi-factor priority scoring algorithm in an information synchronization method for an embedded training simulator provided by the present invention;

[0080] Figure 3 A schematic diagram of a framework for generating a data packet to be pushed in an information synchronization method for an embedded training simulator provided by the present invention;

[0081] Figure 4 A schematic diagram of the structural composition of an information synchronization system for an embedded training simulator provided by the present invention;

[0082] Figure 5 This is a structural diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0083] The present invention provides an information synchronization method, system, device and medium for an embedded training simulator. The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0084] Example 1:

[0085] The present invention provides an information synchronization method for an embedded training simulator, the flow chart of which is as follows: Figure 1 Shown, including:

[0086] Step 1: Obtain the event information of each task in the embedded training simulator;

[0087] Step 2: Classify each task event information to obtain each task event information with attribute labels;

[0088] Step 3: Based on the attribute labels of each task event information, the task event information is classified into task priority categories using an improved multi-factor priority scoring algorithm to obtain a task priority queue;

[0089] Step 4: Generate a data packet to be pushed according to the task priority queue, and synchronize information of the data packet to be pushed;

[0090] The improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boost weight mapping.

[0091] Generally, embedded training simulators rely on preset operation instruction flows or state change sequences during training, and mainly conduct data-driven training around device state parameters, simulation scene processes or image rendering data. Although this type of training method can simulate a specific operating environment, it lacks in-depth identification and dynamic response to real-time task events during training, and often cannot reflect real-time task characteristics such as key behaviors, system state changes or virtual target interactions triggered by operators in complex training scenarios, resulting in delayed event responses and inaccurate task synchronization during training, which in turn affects the training rhythm and operational authenticity. To this end, the present invention proposes a training mechanism oriented to task event information, which acquires and processes the core task event information during the training process, and realizes information priority identification and efficient synchronization through the classification, scoring and scheduling of task events, thereby improving the training adaptability and simulation quality of embedded training simulators in complex dynamic task environments;

[0092] 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 intention and interaction path, and thus improve the timeliness and accuracy of the embedded training simulator's task perception; introduce simulation state change information so that the simulator can perceive the state switching or module response 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 capability to tactical situations.

[0093] To achieve structured processing and effective perception of the multi-source task event information, we can consider further classifying task events to explore their key attributes and assign quantifiable task labels, thereby achieving in-depth analysis of event content, occurrence context, and training phase characteristics. Specifically:

[0094] 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:

[0095] Extracting key feature dimensions of the task event information based on each task event information;

[0096] generating a feature task vector of the task event information according to key feature dimensions of the task event information;

[0097] Perform path judgment on the feature task vectors of each task event information through a decision tree, and output each task event information with an attribute label;

[0098] The key feature dimensions may include: event category, event urgency, and context training stage;

[0099] The decision tree is generated based on context rules;

[0100] The attribute tag may include one or more of the following: task identifier, urgency, task type, data volume, and timestamp;

[0101] 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 contextual rules to make path judgments, the system ultimately outputs task event information with attribute labels, achieving the transformation from raw event data to structured and semantic task information. By selecting event category, event urgency, and contextual training stage as feature dimensions, the system can accurately describe the behavior type, response timeliness, and training context of the task, ensuring that the model not only captures the essence of the task but also takes into account its importance in the current environment. Classification judgments are made using a decision tree model, which has higher expressiveness and scalability than traditional linear rule matching. It can automatically select the optimal classification path based on different contextual conditions, effectively avoiding information loss or inaccurate classification in task classification. The attribute labels finally output not only provide a quantitative basis for subsequent priority scoring, but also build an association structure between tasks, making the entire information scheduling process controllable and transparent. In this implementation, decision trees (generated using contextual rules) are applied to the classification of task event information in an embedded training simulator. A path judgment model is generated by combining the key feature dimensions of the task and the contextual training phase rules. The behavioral characteristics of the event (such as event category), time sensitivity (event urgency), and the system operating environment (training phase) are combined as dimensions. Through feature engineering, a task vector input decision path that can express training intent and operation rhythm is constructed. This allows the judgment logic to be not only data-driven but also tightly coupled with the phased process of the training task, thereby enhancing the pertinence and adaptability of the decision path. Unlike the prior art that directly uses static threshold classification or preset condition triggering, this method has the ability to dynamically build trees, enabling the simulator to output different classification results and label semantics at different training phases, thereby ensuring the timeliness and structural consistency of task information classification. In addition, in order to further adjust the splitting priority of each feature dimension in the decision tree, this implementation method can consider introducing an adaptive feature weighting mechanism based on training history records. By statistically analyzing the impact of different event types in historical training tasks on the system response efficiency at different training stages, the feature weight coefficient is automatically generated, so that the decision path is more sensitive to high-impact features and the judgment is more refined. After the introduction of this technical feature, it no longer relies on manually specified feature importance or static configuration, but optimizes the decision model through historical feedback, so that the task classification process is 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 for changes in operating habits and task distribution during long-term training.

[0102] The above implementation method can complete the classification of task event information and obtain structured attribute labels. Based on these labels, we can further consider task importance assessment and scheduling priority determination to achieve orderly management and differentiated processing of different task events. This leads to the following priority classification process, specifically:

[0103] In one implementation, in step 3, the process of classifying the task priority of each task event information using an improved multi-factor priority scoring algorithm based on the attribute tags of each task event information to obtain the task priority queue may include:

[0104] Convert the attribute labels of each task event information into vectors to obtain a score vector set;

[0105] Calculating the comprehensive priority score of each task event information using an improved multi-factor priority scoring algorithm based on the scoring vector set;

[0106] Sort the comprehensive priority scores of the task event information in descending order to form a task priority queue;

[0107] This implementation transforms task information from a structured description vector into a quantifiable decision-making basis by converting the attribute labels of task event information into a set of scoring vectors and combining them with an improved multi-factor priority scoring algorithm to calculate a comprehensive priority score. The scoring algorithm also incorporates multiple dimensions, such as task urgency, interaction intensity, data volume, and time interval. Through comprehensive modeling using nonlinear weighting, time sensitivity mapping, and task criticality adjustment, the scoring results more accurately reflect the processing priority of tasks in the current training scenario. Unlike traditional static priority mechanisms, this scoring method possesses dynamic adjustment capabilities, adapting scoring results to real-time changes in the training process, thereby generating more appropriate task sequencing. While scoring algorithms and priority sorting have been applied as general scheduling strategies in some computing scenarios, this implementation constructs an attribute-label-driven scoring and classification mechanism for multi-source heterogeneous task information in embedded training simulators. The scoring vectors are generated not by concatenating common fields but by basing them on task labels generated through contextual classification in a previous stage, thus retaining strong relevance to the training context. The scoring function itself incorporates time-sensitive processing (such as exponential corrections for long-unprocessed tasks), key task gain terms (such as the multiplicative factor between key tasks and low data volume), and task behavior feature coupling terms (such as the logarithmic combination of interactivity and scenario weights). Its expressive power significantly exceeds that of linear weighted models. Therefore, this scoring algorithm is linked to the task labeling system, and the results are used to construct task queues. This enables the simulator to not only identify task attributes but also dynamically adapt to training situations, optimize resource allocation, and form a closed-loop control mechanism for task scheduling. Therefore, this scoring and queuing mechanism has technical relevance in the field of embedded real-time information processing. In addition, to increase the accuracy of the scoring, this implementation can further introduce a scoring factor adjustment mechanism based on the system resource status, that is, dynamically introduce system operating parameters such as the current CPU load and communication bandwidth occupancy as correction terms of the scoring function during the scoring process. This new feature enables the priority score to reflect not only the importance of the task itself, but also the current carrying capacity of the system, thereby 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.

[0108] Specifically, such as Figure 2 As shown, in the above implementation, the process of calculating the comprehensive priority score of each task event information using the improved multi-factor priority scoring algorithm according to the scoring vector set may include:

[0109] Extracting a time vector and a discrete vector from the set of scoring vectors;

[0110] Performing time sensitivity mapping on the time vector to obtain a time feature score set;

[0111] Performing numerical boosting weight mapping on the discrete vector to obtain a discrete feature score set;

[0112] Integrating the temporal feature score set and the discrete feature score set into a task score feature set;

[0113] Calculating a comprehensive priority score for each task event information using a comprehensive scoring function based on the task scoring feature set;

[0114] The task scoring feature set includes task urgency, task interaction intensity, scenario optimization factor, data volume level, and task triggering interval. In this specific implementation, by structured processing the different types of data in the scoring vector set, extracting time vectors and discrete vectors, and combining time sensitivity mapping with a numerical weighting mechanism, the task's behavioral characteristics and scheduling requirements are transformed into a unified scoring feature set, providing a more expressive and discriminative input for the comprehensive scoring function. Time sensitivity mapping enables the simulator to identify and quantify the duration of unprocessed tasks during training, dynamically increasing the processing priority of stale tasks and preventing critical tasks from being neglected in scheduling. The numerical weighting mapping of discrete features enhances the influence of task criticality and type differences in the scoring model, giving the model a stronger sense of task semantics and strategic value. The integration of time and discrete features into a unified task scoring feature set significantly improves the scoring model's ability to comprehensively assess task urgency, scenario adaptability, and resource consumption, making the final priority score more aligned with the actual operational logic of multi-task scheduling in embedded training environments, effectively supporting the prioritized push and scheduling optimization of critical tasks. This implementation restructures the scoring process by incorporating the task cadence and data type characteristics of an embedded training simulator. In this scenario, tasks have clear real-time windows, complex information sources, and diverse task behaviors. Using a single, unified scoring approach makes it difficult to adapt to the temporal and semantic sensitivities of different tasks. This implementation explicitly separates the original scoring vector into a time vector and a discrete vector, applying nonlinear processing to each, and constructs a mapping mechanism that enhances temporal sensitivity and semantic importance. These two types of features are then fused to form a task scoring feature set, which is then used for unified scoring modeling. This processing approach not only maintains the interpretability of the model input but also provides personalized scoring adjustment paths for different task characteristics. This approach is particularly well-suited for the frequent bursty tasks and significant information lags in training systems. This represents a combination of scoring process structure and task feature modeling. Furthermore, this implementation could consider introducing a structural enhancement mechanism based on task dependencies, incorporating the predecessor-follower dependencies between task events as additional structural features into the scoring process. Specifically, a "dependency weight factor" field could be added to the task scoring feature set to describe whether the task is a prerequisite for subsequent key processes or whether it 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, thereby prioritizing the scheduling of critical node tasks with a wider impact range, further improving the overall scheduling efficiency and task delivery integrity of the system, and is especially suitable for highly concurrent or process-sensitive embedded simulation training environments.

[0115] For example, the expression of the above comprehensive scoring function can be as follows:

[0116] ;

[0117] in, Indicates the Comprehensive priority score of task event information; Indicates the The urgency of each task event information; Indicates the The task interaction intensity of each task event information; Indicates the The scenario priority factor of each task event information; Indicates the The data volume level of each task event information; Indicates the Whether the task event information is a critical task; Indicates the The triggering time interval of each 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 a multi-factor coupling modeling approach, 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 embedded training simulators in a high-concurrency task environment.

[0118] Through the above implementation, we can obtain a task priority queue sorted by comprehensive priority score. In order to improve the orderly synchronization of task data when the embedded training simulator executes task data, we can consider further building an efficient task push mechanism based on the task priority queue to realize the generation of data packets to be pushed. Specifically:

[0119] In one implementation, Figure 3 As shown, the process of queuing the task priority queue in step 4, generating a data packet to be pushed, and performing information synchronization based on the data packet to be pushed may include:

[0120] Selecting pushable tasks within the current scheduling period from the task priority queue to generate a push window task set;

[0121] Generate a push candidate task set based on the push window task set using a window scheduling mechanism;

[0122] Generate a push scheduling plan based on the push candidate task set using a sticky weighted sorting strategy;

[0123] 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;

[0124] Performing information synchronization according to the data packet to be pushed;

[0125] Wherein, the information synchronization may include one or more of the following: sequence control, status feedback and abnormal retransmission;

[0126] For example, the above-mentioned 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 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 and bandwidth usage). Tasks with priority scores higher than the threshold or tasks that meet the trigger conditions are selected within the window range as the "push window task set". By applying this mechanism, it is helpful to limit the number of tasks pushed at a single time, prevent data congestion, and improve scheduling flexibility and system load stability.

[0127] For example, the above-mentioned stickiness weighted sorting strategy is a multi-factor packaging and sorting method that combines task similarity and scheduling efficiency. The purpose is to aggregate highly related tasks into a scheduling unit when generating data packets to improve data packet utilization and system response efficiency. This strategy calculates the "stickiness" score between tasks by combining the following factors: target terminal consistency factor (weighted if the sending targets are the same); task type similarity factor (such as both are 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 total amount of combined data exceeding the transmission limit); the "stickiness score" of each task to the combined target can be given by a weighted linear combination of the above factors, for example, the expression is as follows:

[0128] ;

[0129] in, Indicates task event information Task event information The adhesion score between the two tasks, the larger the value, the more suitable it is for the two tasks to be aggregated in the same data package; Both are weighted factors, used to control the influence of different adhesion factors on the total score, and their value ranges are [0, 1]; Represents the task type similarity function, if and If they belong to the same task type (e.g., they are all interactive tasks), the value is 1; otherwise, the similarity score is 0 or less than 1; represents the target consistency function, if and If the target device number is the same as the target device number, the value is 1, otherwise it is 0; is an exponential function; Indicates task event information Task event information The trigger time interval between

[0130] Push technology in embedded training simulators needs to achieve real-time and accurate data. This requires deep integration with the simulator's hardware and software systems, efficient data carrying and scheduling capabilities, the ability to handle the transmission of large amounts of training task data, and ensuring the integrity and sequence of data packets during transmission. To meet this requirement, push technology should also support data adhesion, combining multiple pieces of information with logical dependencies or temporal proximity between tasks into a single data packet for unified transmission, improving data transmission efficiency and terminal processing speed. Furthermore, the presentation of push technology must be consistent with the simulator's interface design to enhance the clarity of information transmission and the interactive experience. Based on this demand, in the implementation of the present invention, a push window task set is constructed based on the task priority queue, and a push scheduling plan is generated by combining the window scheduling mechanism with the adhesion weighted sorting strategy, which ultimately forms the data packet to be pushed and completes information synchronization. This structured task push method not only significantly improves transmission efficiency and response speed, but also dynamically adjusts the push task scope according to the current resource status and task load, reducing system pressure; the adhesion weighted sorting strategy achieves intelligent packaging of highly related tasks by evaluating the context relevance, target device consistency and data volume coordination between tasks, further improving bandwidth utilization 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 based on task scoring and resource status, thereby ensuring timely response of high-priority tasks while effectively avoiding transmission redundancy and task delays, improving the synchronization stability and scheduling efficiency of the overall training process. In addition, the implementation method can support the conversion of the scheduling plan into network transmission data packets and supplemented by sequence control, state feedback and abnormal retransmission mechanisms, enhancing the reliability and fault tolerance of task transmission, and adapting to state consistency maintenance in complex network environments. Furthermore, the embedded training simulator's real-time control function is enabled by this task push mechanism, dynamically adjusting push content and frequency based on changes in training scenarios, ensuring that key data is efficiently delivered to target devices at key nodes, supporting the demands of high-intensity simulation interaction. At the same time, to enhance the overall user experience, the presentation of pushed information has been deeply optimized. Following the principles of human-computer interaction and visual design, detailed design has been implemented in terms of information layout, color hierarchy, and dynamic response. Personalized presentation solutions are provided for different scenarios and device platforms, and integrated with user interaction logic to achieve actionable feedback on information, enhancing the overall performance of the training system in both functionality and interactivity.

[0131] Therefore, this implementation method, targeting the special application scenario of embedded training simulators, which are real-time, highly interactive, and task-intensive, constructs a priority-driven window scheduling strategy and adhesion aggregation mechanism. This method has a clear structural combination and processing path, and forms a linkage logic between the task priority score output and the window scheduling mechanism, achieving coordinated adjustment of task scheduling granularity and system load status. It also uses an adhesion weighting strategy to package similar task information to avoid information fragmentation and repeated transmission, which is significantly different from conventional independent task queuing and batch push methods. At the same time, the sequence control, state feedback, and exception retransmission mechanisms introduced by information synchronization do not exist in isolation, but are instead bound to the scheduling process, forming a closed-loop control of the entire process from task identification and scheduling to push and confirmation. This structural coordination enables 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 key data transmission and the overall task completion of the system, it is possible to consider adding a dynamic selection mechanism for the task push path on the basis of this implementation method. Specifically, after generating the push scheduling plan, the optimal transmission channel or timing is selected according to the network status, terminal load or the processing capacity of the module to which the task belongs, so as to form a coupling scheduling strategy for the task transmission path and the system resource status. After introducing this feature, the simulator can avoid network congested nodes or high-load terminals while ensuring the principle of task priority, and give priority to arranging key tasks to take low-latency and high-stability paths, thereby reducing the information synchronization failure rate and improving the real-time performance of key data transmission and the overall task completion of the system. It is particularly suitable for embedded training systems with multiple terminals collaborating or heterogeneous nodes participating.

[0132] In summary, the present invention addresses the following problems in the synchronization of interactive information, task allocation information and virtual target information in existing embedded training simulators: Information transmission delay: The computing power and network bandwidth of embedded devices are limited, resulting in information transmission delay, affecting the real-time and immersive nature of training; Information consistency is difficult to ensure: Different information sources have different data update frequencies and transmission delays, which can easily lead to information inconsistency and affect training effects; Lack of flexible synchronization mechanism: Existing synchronization mechanisms usually adopt fixed frequencies or event triggering methods, which are difficult to adapt to different training scenarios and needs. In order to solve the above problems, the present invention proposes an information synchronization method for embedded training simulators, which can classify task event information and extract key attribute labels to express original and messy training data in a structured manner, thereby realizing accurate identification and classification management of task semantic features; Tasks are comprehensively scored by utilizing a multi-factor priority scoring algorithm constructed based on time sensitivity mapping and boosted weight mapping, Realizing dynamic sorting of tasks under multi-dimensional characteristics 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 data packets to be pushed based on queuing results and transmitting them in order through a synchronization mechanism can realize structured push of task information and sequential consistency guarantee, avoiding the problem of training status asynchrony caused by task transmission misalignment or delay, and at the same time, combining task priority for data distribution can optimize system bandwidth utilization, improve overall synchronization efficiency and training real-time response performance; therefore, the method of the present invention can improve the information transmission capability, transmission information consistency and information transmission synchronization of the embedded training simulator by strengthening task differentiation.

[0133] Example 2:

[0134] The present invention based on the same inventive concept also provides an information synchronization system for an embedded training simulator, the structural composition diagram of which is shown in FIG. Figure 4 Shown, including:

[0135] An information acquisition module is used to obtain information about each task event in the embedded training simulator;

[0136] A task classification module is used to classify each task event information to obtain each task event information with an attribute label;

[0137] A priority classification module is used to classify the task priority of each task event information according to the attribute label of each task event information using an improved multi-factor priority scoring algorithm to obtain a task priority queue;

[0138] An information synchronization module is used to generate a data packet to be pushed according to the task priority queue and synchronize information of the data packet to be pushed;

[0139] Wherein, the improved multi-factor priority scoring algorithm is constructed based on time sensitivity mapping and boost weight mapping;

[0140] For example, the above-mentioned task event information may include one or more of the following: user input information, simulation state change information, and target behavior triggering information.

[0141] In one implementation, the task classification module may include:

[0142] A dimension extraction submodule, configured to extract key feature dimensions of task event information based on each task event information;

[0143] A vector generation submodule, configured to generate a characteristic task vector of the task event information according to key characteristic dimensions of the task event information;

[0144] A path judgment submodule is used to perform path judgment on the characteristic task vectors of each task event information through a decision tree, and output each task event information with an attribute label;

[0145] The key feature dimensions may include: event category, event urgency, and context training stage;

[0146] The attribute tag may include one or more of the following: task identifier, urgency, task type, data volume, and timestamp;

[0147] The decision tree is generated based on contextual rules.

[0148] In one implementation, the priority classification module may include:

[0149] A vector conversion submodule is used to convert the attribute labels of each task event information into vectors to obtain a score vector set;

[0150] A priority scoring submodule, configured to calculate a comprehensive priority score of each task event information based on the scoring vector set using an improved multi-factor priority scoring algorithm;

[0151] The task queue generation submodule is used to sort the comprehensive priority scores of the task event information in descending order to form a task priority queuing queue.

[0152] In this implementation, the priority scoring submodule may include:

[0153] A vector extraction unit, configured to extract a time vector and a discrete vector from the score vector set;

[0154] a sensitivity mapping unit, configured to perform time sensitivity mapping on the time vector to obtain a time feature score set;

[0155] A weight mapping unit, configured to perform numerical boosting weight mapping on the discrete vector to obtain a discrete feature score set;

[0156] a set integration unit, configured to integrate the temporal feature score set and the discrete feature score set into a task score feature set;

[0157] A scoring calculation unit, configured to calculate a comprehensive priority score of each task event information using a comprehensive scoring function based on the task scoring feature set;

[0158] The task scoring feature set may include: task urgency, task interaction intensity, scenario optimization factor, data volume level and task triggering time interval.

[0159] For example, the expression of the above comprehensive scoring function can be as follows:

[0160] ;

[0161] in, Indicates the Comprehensive priority score of task event information; Indicates the The urgency of each task event information; Indicates the The task interaction intensity of each task event information; Indicates the The scenario priority factor of each task event information; Indicates the The data volume level of each task event information; Indicates the Whether the task event information is a critical task; Indicates the The triggering time interval of each 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.

[0162] In one implementation, the information synchronization module may include:

[0163] A task generation submodule is used to select pushable tasks within the current scheduling period from the task priority queue and generate a push window task set;

[0164] A window scheduling submodule, configured to generate a push candidate task set based on the push window task set and using a window scheduling mechanism;

[0165] A push scheduling submodule is used to generate a push scheduling plan based on the push candidate task set using a sticky weighted sorting strategy;

[0166] The data push submodule is used 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.

[0167] The information synchronization may include one or more of the following: sequence control, status feedback, and abnormal retransmission.

[0168] Example 3:

[0169] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer, a single-chip microcomputer, a smart mobile device, or the like. The electronic device in this embodiment may include a processor, a memory, a transceiver component, and the like. The memory, processor, and transceiver component are connected via a bus; the memory may be used to store an execution program, which may include instructions; and the processor may be used to execute the instructions stored in the memory. The memory may also be used to store data, which may be accessed and / or modified during the execution of the instructions.

[0170] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) 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, which is suitable for implementing one or more instructions, specifically 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 realize the steps of the information synchronization method of an embedded training simulator in the above embodiment.

[0171] Example 4:

[0172] 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, used to store programs and data. It is understood that the storage medium herein may include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more executable programs (including program code). It should be noted that the storage medium herein may be high-speed RAM memory or non-volatile memory, such as at least one disk storage device. The processor loads and executes the one or more instructions stored in the storage medium, thereby implementing the steps of the information synchronization method for an embedded training simulator described in the above-mentioned embodiment.

[0173] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0174] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art may still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims.

Claims

1. An information synchronization method for an embedded training simulator, characterized in that: include: Obtain information about each task event in the embedded training simulator; Classifying each task event information to obtain each task event information with an attribute label; Convert the attribute labels of each task event information into vectors to obtain a scoring vector set, and perform structured processing on the scoring vector set; Extracting a time vector and a discrete vector from the score vector set after structured processing; Performing a time sensitivity mapping on the time vector to obtain a time feature score set; the time sensitivity mapping is used to enable the embedded training simulator to identify and quantify the duration of tasks that have not been processed during training, and dynamically increase the processing priority of stale tasks; Performing numerical boosting weight mapping on the discrete vector to obtain a discrete feature score set; The numerical enhancement weight mapping is used to enhance the influence of task criticality and type differences, and improve the perception of task semantics; Integrating the temporal feature score set and the discrete feature score set into a task score feature set; Calculating a comprehensive priority score for each task event information using a comprehensive scoring function based on the task scoring feature set; Sort the comprehensive priority scores of the task event information in descending order to form a task priority queue; Generate a data packet to be pushed according to the task priority queue, and synchronize information of the data packet to be pushed; Among them, the task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level and task triggering time interval; the attribute tag includes one or more of the following: task identifier, urgency, task type, data volume and timestamp.

2. The method according to claim 1, wherein The task event information is classified into task categories to obtain each task event information with an attribute tag, including: Extracting key feature dimensions of the task event information based on each task event information; generating a feature task vector of the task event information according to key feature dimensions of the task event information; Perform path judgment on the characteristic task vectors of each task event information through a decision tree, and output each task event information with an attribute label; The key feature dimensions include: event category, event urgency, and context training stage; The decision tree is generated based on contextual rules.

3. The method according to claim 1, wherein The expression of the comprehensive scoring function is as follows: ； in, Indicates the Comprehensive priority score of task event information; Indicates the The urgency of each task event information; Indicates the The task interaction intensity of each task event information; Indicates the The scenario priority factor of each task event information; Indicates the The data volume level of each task event information; Indicates the Whether the task event information is a critical task; Indicates the The triggering time interval of each 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.

4. The method according to claim 1, wherein The step of queuing the task priorities and generating the data packet to be pushed comprises: Selecting pushable tasks within the current scheduling period from the task priority queue to generate a push window task set; Generate a push candidate task set based on the push window task set using a window scheduling mechanism; Generate a push scheduling plan based on the push candidate task set using a sticky weighted sorting strategy; The push scheduling plan is converted into a network transmission data packet, and the network transmission data packet is used as the data packet to be pushed.

5. The method according to claim 1, wherein The task event information includes one or more of the following: user input information, simulation state change information and target behavior triggering information; The information synchronization includes: sequence control, status feedback and abnormal retransmission.

6. An information synchronization system for an embedded training simulator, characterized in that: include: An information acquisition module is used to obtain information about each task event in the embedded training simulator; A task classification module is used to classify each task event information to obtain each task event information with an attribute label; A priority classification module is used to classify the task priority of each task event information according to the attribute label of each task event information using an improved multi-factor priority scoring algorithm to obtain a task priority queue; An information synchronization module is used to generate a data packet to be pushed according to the task priority queue and synchronize information of the data packet to be pushed; The priority classification module includes: A vector conversion submodule is used to convert the attribute labels of each task event information into vectors to obtain a scoring vector set, and perform structured processing on the scoring vector set; A priority scoring submodule, configured to calculate a comprehensive priority score of each task event information based on the scoring vector set using an improved multi-factor priority scoring algorithm; The task queue generation submodule is used to sort the comprehensive priority scores of the task event information in descending order to form a task priority queue; The priority scoring submodule includes: A vector extraction unit, configured to extract a time vector and a discrete vector from the score vector set after structured processing; a sensitivity mapping unit, configured to perform time sensitivity mapping on the time vector to obtain a time feature score set; the time sensitivity mapping is configured to enable the embedded training simulator to identify and quantify the duration of unprocessed tasks during training, and to dynamically increase the processing priority of stale tasks; A weight mapping unit is used to perform numerical weight mapping on the discrete vector to obtain a discrete feature score set; the numerical weight mapping is used to enhance the influence of task criticality and type differences and improve the perception of task semantics; a set integration unit, configured to integrate the temporal feature score set and the discrete feature score set into a task score feature set; A scoring calculation unit, configured to calculate a comprehensive priority score of each task event information using a comprehensive scoring function based on the task scoring feature set; Among them, the task scoring feature set includes: task urgency, task interaction intensity, scenario optimization factor, data volume level and task triggering time interval; the attribute tag includes one or more of the following: task identifier, urgency, task type, data volume and timestamp.

7. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via 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 for an embedded training simulator as claimed in any one of claims 1 to 5 is implemented.

8. A computing device readable storage medium, characterized in that: An execution program is 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 5 is implemented.

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