A method for generating virtual training scenarios for all combat positions of shipborne weapons

Abstract

This application discloses a method for generating virtual training scenarios for all combat positions of shipborne weapons, belonging to the field of scenario generation technology. The method includes generating virtual emergency commands and writing them into the current communication message queue; calculating the logical processing weight of repeated commands based on information resonance intensity; calculating the penetration probability of virtual emergency commands based on the logical processing weight; monitoring the processing status of virtual emergency commands within a preset time window based on the penetration probability; updating the blocking index based on the timeout duration and information resonance intensity when the processing status is timed out; outputting link paralysis scenario data and marking subsequent regular commands as lost when the blocking index exceeds a preset overflow threshold; and clearing the communication message queue data corresponding to the repeated commands, resetting the blocking index, and exiting the dynamic blocking judgment loop when a protocol downgrade confirmation message is received. This application improves the efficiency of virtual training scenario generation.

Description

Technical Field

[0001] This application relates to the field of scene generation technology, and in particular to a method for generating virtual training scenes for all combat positions of shipborne weapons. Background Technology

[0002] In the field of virtual training for all combat positions of shipborne weapons, constructing highly realistic communication and command and control confrontation scenarios is the core of improving the combat capabilities of trainees.

[0003] In related technologies, existing command and control simulation systems typically employ linear queuing and forwarding logic when processing network packets, treating the interaction data between combat positions as independent events with equal weight. This lacks in-depth analysis of the timing characteristics of command flows and the coupling relationship with feedback. In intense adversarial environments, command positions often issue the same operational commands frequently due to a lack of timely execution feedback. The confirmation receipts generated by the executing positions after receiving the commands intertwine with the high-frequency retransmission streams from the source end, easily forming information resonance loops within the narrowband command and control network. This allows existing training systems to remain uninterrupted or only exhibit normal queuing delays when faced with high-frequency repetitive command streams, completely masking the risk of system resource exhaustion faced by the underlying mechanisms of real equipment when dealing with massive redundant interruptions. Due to the lack of nonlinear modeling of the logical level blocking evolution mechanism, high-priority emergency commands in traditional systems can still be successfully parsed. This fails to realistically reproduce the command penetration failure and implicit paralysis of the entire network link caused by excessive compression of processing weights due to redundant information in actual combat. As a result, trainees missed the opportunity to practice key emergency response actions such as manual intervention interception and protocol downgrade takeover. There are areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method for generating virtual training scenarios for all combat positions of shipborne weapons.

[0005] This application provides a method for generating virtual training scenarios for all combat positions of shipborne weapons, including the following steps: Capture command and control network communication messages during the virtual training process of all combat positions, and parse the command and control network communication messages to obtain the command identification sequence, command sending timestamp sequence, and associated feedback timestamp sequence; Duplicate instructions are filtered according to the instruction identifier sequence, and the unit retransmission frequency of the same duplicate instruction is calculated using the instruction sending timestamp sequence; Pair the instruction sending timestamp sequence with the associated feedback timestamp sequence to calculate the delay jitter value corresponding to the same repeated instruction; The unit retransmission frequency and delay jitter value are normalized and mapped to generate information resonance intensity; When the information resonance intensity is greater than the preset resonance threshold, the dynamic blocking determination loop is entered. The dynamic blocking determination loop includes: Generate a virtual emergency command and write it into the current communication message queue; The logic processing weights occupied by repeated instructions are calculated based on the information resonance intensity. The penetration probability of the virtual emergency command is calculated based on the logical processing weights; Monitor the processing status of virtual emergency commands within a preset time window based on penetration probability; When the processing status is timeout and not processed, the blocking index is updated based on the timeout duration and the information resonance intensity. When the blocking index exceeds the preset overflow threshold, output the link paralysis scenario data and mark subsequent regular commands as lost. When a protocol downgrade confirmation message is received, the communication message queue data corresponding to the duplicate instruction is cleared, the blocking index is reset, and the dynamic blocking determination loop is exited.

[0006] In summary, this application includes at least one of the following beneficial technical effects: 1. This application provides a method for generating virtual training scenarios for all combat positions of shipborne weapons. By capturing command and control network communication messages during virtual training at all combat positions, parsing and matching command transmission timestamp sequences with associated feedback timestamp sequences, and extracting the unit retransmission frequency and delay jitter value of repeated commands for normalization mapping to generate information resonance intensity, it is possible to accurately quantify the underlying logic congestion state caused by high-frequency interactive feedback within the command and control network. This effectively avoids the rigidity problem caused by relying solely on physical line disconnection or random packet loss to simulate network failures in traditional virtual training scenario generation methods. It makes the feature extraction and abnormal state judgment of the self-excited oscillation effect of the command and control link more closely aligned with the actual equipment operation mechanism and the actual operating rhythm of trainees under high-pressure combat conditions. 2. This application utilizes dynamically generated information resonance intensity as a foundation, introduces a dynamic blocking judgment loop, and implants virtual emergency commands into the current communication message queue. Based on the logical processing weight derived from the resonance intensity, the penetration probability and timeout status of the command are calculated. Then, the blocking index is dynamically accumulated to trigger the generation of link paralysis scenario data and the protocol degradation recovery mechanism. Based on nonlinear evolution and evaluation logic, this not only helps trainees to realistically perceive and experience the crisis of implicit paralysis of the entire command and control network caused by excessive compression of redundant information in virtual training, but also provides them with a realistic feedback environment for implementing key emergency actions such as manual intervention, interception, and protocol degradation takeover. This significantly improves the accuracy of combat-oriented training of communication control skills for all positions and the level of emergency decision-making training in complex battlefield situations. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart illustrating the method for generating virtual training scenarios for all combat positions of shipborne weapons according to an embodiment of this application.

[0009] Figure 2 This is a schematic diagram illustrating the implementation logic connection of the unit retransmission frequency in an embodiment of this application.

[0010] Figure 3 This is a schematic diagram of the logic connection for implementing the delay jitter value in an embodiment of this application.

[0011] Figure 4 This is a schematic diagram of the logic connection for implementing the information resonance intensity in an embodiment of this application. Detailed Implementation

[0012] The following description, in conjunction with the implementation of the present invention, is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention, and all such modifications and additions should fall within the protection scope of the present invention.

[0013] This implementation method addresses the generation of abnormal scenarios in command and control network communication during virtual training of shipborne weapons across all combat positions. The core idea is not to simply create external faults such as link breaks or packet loss, but rather to start from the command and control network communication messages themselves, continuously extracting instruction identifier sequences, instruction sending timestamp sequences, and associated feedback timestamp sequences. It identifies the high-frequency closed-loop relationship formed between repeated instructions and feedback messages, and then converts this closed-loop relationship into information resonance intensity, logical processing weight, penetration probability, and blocking index. Finally, it generates link paralysis scenario data, and the resulting training scenario can reflect the instruction blocking process caused by communication messages crowding each other in the processing queue. This means that trainees are no longer facing a single communication interruption, but a dynamic blocking state caused by repeated instruction sending and feedback coupling.

[0014] Example

[0015] This application discloses a method for generating virtual training scenarios for all combat positions of shipborne weapons.

[0016] Reference Figure 1-4 A method for generating virtual training scenarios for all combat positions of shipborne weapons includes the following steps: Capture command and control network communication messages during the virtual training process of all combat positions, and parse the command and control network communication messages to obtain the command identification sequence, command sending timestamp sequence, and associated feedback timestamp sequence; Furthermore, the command and control network communication messages during the virtual training process at all combat positions are captured, and the command and control network communication messages are parsed to obtain the instruction identifier sequence, instruction sending timestamp sequence, and associated feedback timestamp sequence, including: Read the message type field, instruction identifier field, source address field, destination address field, and message timestamp field from the network communication message; Data records whose message type field represents instruction messages are arranged according to the order of the message timestamp fields to generate an instruction identifier sequence and an instruction sending timestamp sequence. The data record that represents the message type field as the feedback message is matched according to the reverse correspondence between the instruction identifier field and the source address field and the destination address field to obtain the feedback message corresponding to each instruction message; Extract the message timestamp field from the feedback message, and write it according to the corresponding instruction message's position in the instruction sending timestamp sequence to generate an associated feedback timestamp sequence.

[0017] In one specific embodiment, the method may be executed by a scene generation program deployed within a virtual training system.

[0018] The scenario generation program reads command and control network communication messages from the communication record interface during the virtual training process at all combat positions. These messages are stored using a unified data record format, with each record including at least a message type field, an instruction identifier field, a source address field, a destination address field, and a message timestamp field. The message type field distinguishes between instruction messages, feedback messages, regular instructions, and protocol downgrade confirmation messages; the instruction identifier field marks the service number of the same instruction; the source address and destination address fields characterize the transmission direction of the message between combat positions; and the message timestamp field records the time the message entered the command and control network communication message acquisition buffer.

[0019] After receiving a batch of command and control network communication messages, the scenario generation program first organizes the messages according to their timestamp fields, and removes data records with missing fields, unparseable timestamp fields, and empty instruction identifier fields, so that the input data for subsequent processing has a consistent field structure.

[0020] After completing the basic organization, the scenario generation program parses the command and control network communication messages to obtain the instruction identifier sequence, instruction sending timestamp sequence, and associated feedback timestamp sequence.

[0021] For data records whose message type field represents an instruction message, the scenario generation program arranges them from earliest to latest according to the message timestamp field, writes the instruction identifier field in each data record into the instruction identifier sequence in sequence, and writes the corresponding message timestamp field into the instruction sending timestamp sequence in sequence.

[0022] The instruction identifier sequence and the instruction sending timestamp sequence adopt the same arrangement relationship. For example, the first instruction identifier in the instruction identifier sequence corresponds to the first sending timestamp in the instruction sending timestamp sequence, and the two together represent the same instruction message.

[0023] For data records whose message type field indicates a feedback message, the scenario generation program uses the matching conditions of identical instruction identifier fields and reversed source and destination address fields to find the corresponding instruction message. If the source address field of an instruction message is the command position address and the destination address field is the execution position address, then the source address field of the corresponding feedback message is the execution position address and the destination address field is the command position address, and their instruction identifier fields are the same.

[0024] After a successful match, the scene generation program extracts the message timestamp field from the feedback message and writes it into the associated feedback timestamp sequence according to the corresponding instruction message's position in the instruction sending timestamp sequence. Thus, the instruction identifier sequence, instruction sending timestamp sequence, and associated feedback timestamp sequence form a one-to-one correspondence in their positions, eliminating the need to re-retrieve the original message when calculating the frequency of repeated instructions and feedback delays.

[0025] Based on the above scheme, the process of parsing the command and control network communication messages can be further refined into field-level processing.

[0026] When the scenario generation program reads the message type field, instruction identifier field, source address field, destination address field, and message timestamp field in the command and control network communication message, it first performs a validity check on the message type field, marking unrecognizable types as invalid data records and excluding them.

[0027] For data records whose message type field represents instruction messages, the scenario generation program arranges them according to the order of the message timestamp fields, generating an instruction identifier sequence and an instruction sending timestamp sequence. If two instruction messages have the same message timestamp field, their sequential relationship can be maintained according to the collection order, avoiding order jumps in the queue.

[0028] For data records whose message type field indicates a feedback message, the scenario generation program matches them based on the inverse correspondence between the instruction identifier field and the source address and destination address fields. For example, if an instruction message has an instruction identifier field of A102, a source address field of C01, and a destination address field of E03, then a feedback message can only be considered a feedback message corresponding to that instruction message if its instruction identifier field is also A102, its source address field is E03, and its destination address field is C01.

[0029] After matching is complete, the scene generation program extracts the message timestamp field from the feedback message and writes it into the associated feedback timestamp sequence according to the corresponding instruction message's position in the instruction sending timestamp sequence. Using this position-based writing method, the associated feedback timestamp sequence does not exist independently of the instruction sending timestamp sequence, and subsequent pairing calculations can directly read the two timestamps at the same position.

[0030] Duplicate instructions are filtered according to the instruction identifier sequence, and the unit retransmission frequency of the same duplicate instruction is calculated using the instruction sending timestamp sequence; Furthermore, duplicate instructions are filtered according to the instruction identifier sequence, and the unit retransmission frequency of the same duplicate instruction is calculated using the instruction sending timestamp sequence, including: The occurrence count of each instruction identifier in the instruction identifier sequence is counted, and the instruction identifiers that occur a predetermined number of times are identified as duplicate instruction identifiers; Extract the sending timestamps corresponding to the duplicate instruction identifiers from the instruction sending timestamp sequence, and form a duplicate instruction timestamp subsequence according to the chronological order; The difference between adjacent transmission timestamps in the repetitive instruction timestamp subsequence is calculated to obtain the set of retransmission time intervals; The number of time intervals in the retransmission time interval set is taken as the retransmission number, and the difference between the last timestamp and the first timestamp of the repeated instruction timestamp subsequence is taken as the retransmission time span. The ratio of the number of retransmissions to the retransmission time span is calculated to obtain the unit retransmission frequency of the same repeated instruction.

[0031] In one specific embodiment, the scene generation program filters duplicate instructions according to the instruction identifier sequence. During actual runtime, the scene generation program traverses the instruction identifier sequence, counts the occurrences of each instruction identifier, and identifies instruction identifiers with a predetermined number of occurrences as duplicate instruction identifiers. The predetermined number of occurrences can be configured to three based on the training intensity, or it can be configured to other integer values ​​by the training scheme. After identifying duplicate instruction identifiers, the scene generation program returns to the instruction sending timestamp sequence, extracts the corresponding sending timestamp based on the position of the duplicate instruction identifier in the instruction identifier sequence, and forms a duplicate instruction timestamp subsequence in chronological order. This duplicate instruction timestamp subsequence only contains the sending timestamps of the same duplicate instruction and does not mix in the time data of other instructions.

[0032] After the repetitive instruction timestamp subsequence is formed, the scene generation program uses the instruction sending timestamp sequence to calculate the unit retransmission frequency of the same repetitive instruction. Specifically, the scene generation program calculates the difference between adjacent sending timestamps in the repetitive instruction timestamp subsequence to obtain a set of retransmission time intervals. Each time interval in the retransmission time interval set is obtained by subtracting the previous sending timestamp from the subsequent sending timestamp, and the unit is time, expressed in seconds or milliseconds.

[0033] The scenario generation program uses the number of time intervals in the retransmission time interval set as the retransmission count and the difference between the last and first timestamps of the repeated instruction timestamp subsequence as the retransmission time span. Since the first transmission timestamp indicates the first time the instruction enters the statistical window, and subsequent adjacent intervals correspond to retransmission behavior, using the number of time intervals as the retransmission count avoids miscounting the first transmission as a retransmission. Finally, the scenario generation program calculates the ratio of the retransmission count to the retransmission time span to obtain the unit retransmission frequency for the same repeated instruction. The unit retransmission frequency is measured in times per unit time and is subsequently used to determine the occupancy intensity of the current communication message queue for this repeated instruction.

[0034] Specifically, the formula for calculating the unit retransmission frequency is: , in, This represents the number of retransmissions, which is the number of time intervals. This represents the time span of the resend. The retransmission frequency per unit time represents the number of times the same repeated instruction is retransmitted per unit time. It is used to measure the intensity of bandwidth and processing resources consumed by the instruction in the command and control network. The higher the retransmission frequency per unit time, the more frequent the retransmissions are within a short period of time.

[0035] Pair the instruction sending timestamp sequence with the associated feedback timestamp sequence to calculate the delay jitter value corresponding to the same repeated instruction; Furthermore, the instruction sending timestamp sequence is paired with the associated feedback timestamp sequence to calculate the latency jitter value corresponding to the same repeated instruction, including: Based on the position of the repeated instructions in the instruction identifier sequence, extract the corresponding instruction sending timestamp and associated feedback timestamp; Subtract the corresponding instruction sending timestamp from each associated feedback timestamp to obtain the feedback delay sequence; Calculate the mean delay of the feedback delay sequence, and calculate the deviation between each feedback delay in the feedback delay sequence and the mean delay; After converting each deviation to its absolute value, the mean value is calculated to obtain the delay jitter value corresponding to the same repeated instruction.

[0036] In one specific embodiment, after obtaining the unit retransmission frequency, the scene generation program pairs the instruction sending timestamp sequence with the associated feedback timestamp sequence to calculate the latency jitter value corresponding to the same repeated instruction. During pairing, the scene generation program still uses the position of the repeated instruction in the instruction identifier sequence as an index to extract the corresponding instruction sending timestamp and associated feedback timestamp. Subtracting the corresponding instruction sending timestamp from each associated feedback timestamp yields a feedback latency, and multiple feedback latency sequences are arranged in chronological order to form a feedback latency sequence. The feedback latency sequence reflects the change in the time interval between sending and receiving feedback for the same repeated instruction.

[0037] The scenario generation program first calculates the mean delay of the feedback delay sequence, and then calculates the deviation of each feedback delay in the sequence from the mean delay. To avoid positive and negative deviations canceling each other out, each deviation is averaged after absolute value processing to obtain the delay jitter value corresponding to the same repeated instruction. The smaller the delay jitter value, the more stable the time relationship between the repeated instruction and the feedback message; when the repetition frequency is high, this stable feedback relationship is more likely to form a closed loop of instruction feedback.

[0038] Specifically, the formula for calculating the latency jitter value is as follows: , Among them, single feedback delay With the mean delay The calculation formulas are as follows: , , in, This is expressed as a latency jitter value, reflecting the fluctuation and instability of network latency during multiple transmissions of the repeated instruction. A larger latency jitter value indicates more disordered feedback timing in the control network; a smaller latency jitter value indicates more stable feedback timing. Furthermore, in information resonance determination, a high-frequency, low-jitter state is highly likely to form a logical closed loop. The number of samples representing feedback latency is the total number of data pairs that successfully matched the instruction sending timestamp with the associated feedback timestamp. This is represented by the timestamp of the corresponding instruction sent, that is, the timestamp of the repeated instruction. The exact network time of the next transmission. This is represented as the associated feedback timestamp, that is, the timestamp of the first feedback. The specific network time at which the issued command was received by the executing station and a confirmation message was returned, and .

[0039] The unit retransmission frequency and delay jitter value are normalized and mapped to generate information resonance intensity; Furthermore, the unit retransmission frequency and delay jitter value are normalized and mapped to generate information resonance intensity, including: Obtain the sorting position of the unit retransmission frequency in the current communication message queue among the retransmission frequencies of each repeated instruction unit, and convert the sorting position into a frequency mapping value; Obtain the reverse sorting position of the delay jitter value among the delay jitter values ​​of each repeated instruction in the current communication message queue, and convert the reverse sorting position into a jitter mapping value; Generate a feedback consistency mapping value based on the ratio between the number of times the feedback message content matches the repeated instruction and the number of times the repeated instruction is sent; The information resonance intensity is obtained by weighted synthesis of the frequency mapping value, jitter mapping value, and feedback consistency mapping value.

[0040] In one specific embodiment, the scene generation program normalizes and maps the unit retransmission frequency and latency jitter value to generate information resonance intensity. The normalization mapping does not directly use the original values ​​with fixed dimensions; instead, it first performs relative sorting within the current communication message queue. For the unit retransmission frequency, the scene generation program obtains its ranking position among the repetitive instruction unit retransmission frequencies in the current communication message queue and converts this ranking position into a frequency mapping value. During the conversion, unit retransmission frequencies ranked higher are assigned a frequency mapping value close to one, while those ranked lower are assigned a frequency mapping value close to zero, allowing frequency data from different training batches to be compared on the same scale.

[0041] For the latency jitter value, the scenario generation program obtains the reverse sorting position of the latency jitter value among the latency jitter values ​​of each repeated instruction in the current communication message queue, and converts the reverse sorting position into a jitter mapping value. Since a smaller latency jitter value better reflects the stable coupling relationship between the instruction and the feedback, after reverse sorting, a smaller latency jitter value corresponds to a larger jitter mapping value.

[0042] To avoid misjudgments based solely on retransmission frequency and latency stability, the scenario generation program also generates a feedback consistency mapping value based on the ratio of the number of times the feedback message content matches the repeated instruction to the number of times the repeated instruction was sent. The number of times the feedback message content matches can be obtained by comparing the feedback status field, feedback payload field, and fixed fields related to the instruction processing result in the feedback message. The message timestamp field is not included in the statistics of the number of times the feedback message content matches. If the feedback message content received for the same repeated instruction is identical each time, the feedback consistency mapping value is close to one; if there are many differences in the feedback message content, the feedback consistency mapping value will decrease accordingly.

[0043] Subsequently, the scene generation program weights and synthesizes the frequency mapping value, jitter mapping value, and feedback consistency mapping value to obtain the information resonance intensity. A feasible configuration is that the frequency mapping value has a weight of 40%, the jitter mapping value has a weight of 30%, and the feedback consistency mapping value has a weight of 30%, with the sum of all weights being one. The information resonance intensity serves as the direct basis for determining whether to enter the dynamic blocking decision loop, and also as an important input for calculating the weights and blocking index in the logic.

[0044] Specifically, the formula for calculating the information resonance intensity is as follows: , To ensure the normalization properties of the synthesis results, the weighting coefficients must satisfy the following: , in, This is expressed as information resonance intensity, which comprehensively characterizes the severity of the self-excited closed-loop phenomenon formed by a specific instruction in the command and control network queue. A higher information resonance intensity indicates more frequent instruction retransmissions, smaller latency jitter, and highly consistent feedback content; the closer the system is to the critical point of logical blocking. Represented as a frequency mapping value, it is a normalized representation of the relative magnitude of the unit retransmission frequency of this repeat instruction among all repeating messages in the current communication message queue. The earlier the message is in the queue (i.e., the higher the transmission frequency), the closer the mapping value is to 1. This is represented as a jitter mapping value, which characterizes the relative stability of the feedback time system. Since a smaller jitter value not only indicates lower network latency but also represents a more stable instruction loop, a reverse sorting is used: the smaller the original jitter value, the higher the reverse sorting rank, and the closer it is to 1 when converted to this mapping value. To provide feedback on the number of times the message content matches, The number of times the repeat command is sent. It is represented as a weighting coefficient, and its setting is based on system preset constants or parameters dynamically loaded with the training mode.

[0045] When the information resonance intensity is greater than the preset resonance threshold, the dynamic blocking determination loop is entered. The dynamic blocking determination loop includes: Furthermore, when the information resonance intensity exceeds a preset resonance threshold, a dynamic blocking determination loop is entered, including: The communication message queue is segmented according to a fixed sampling duration to obtain multiple sampling segments; Calculate the information resonance intensity of the same repeated instruction within each sampling segment; When the information resonance intensity in a consecutive sampling segment of a predetermined number of bits is greater than the preset resonance threshold, the instruction identifier corresponding to the repeated instruction is written into the blocking candidate list. The repeating instruction with the highest information resonance intensity value is selected from the blocking candidate list as the target repeating instruction, and the communication message queue data corresponding to the target repeating instruction is used as the input data for the dynamic blocking determination loop.

[0046] In one specific embodiment, before entering the dynamic blocking determination loop, the scene generation program can also perform sampling segment-level confirmation of the information resonance intensity to reduce false triggering caused by occasional duplicate messages. The scene generation program segments the communication message queue according to a fixed sampling duration, obtaining multiple sampling segments. The fixed sampling duration can be configured to one second, two seconds, or other durations suitable for the training rhythm.

[0047] Within each sampling segment, the scene generation program calculates the information resonance intensity of the same repeated instruction and records the start and end times of that sampling segment. When the information resonance intensity in consecutive sampling segments of a predetermined number of positions is greater than a preset resonance threshold, the scene generation program writes the instruction identifier corresponding to the repeated instruction into the blocking candidate list. The blocking candidate list is used to store the identifiers of repeated instructions that already have a blocking tendency and their corresponding information resonance intensities.

[0048] If multiple duplicate instructions exist in the blocking candidate list, the scenario generation program selects the duplicate instruction with the highest information resonance intensity value as the target duplicate instruction, and uses the communication message queue data corresponding to the target duplicate instruction as the input data for the dynamic blocking determination loop. After confirmation using continuous sampling segments, the input data of the dynamic blocking determination loop has temporal continuity, which can more closely resemble the real process of duplicate instructions gradually occupying the processing queue.

[0049] Generate a virtual emergency command and write it into the current communication message queue; Furthermore, a virtual emergency command is generated and written to the current communication message queue, including: Read the instruction identifier field, source address field, destination address field, and sending timestamp field of the target duplicate instruction in the current communication message queue; A virtual emergency instruction is generated based on the same source address field and destination address field, and an instruction identifier field is configured for the virtual emergency instruction to distinguish it from the target duplicate instruction; Set the timestamp field of the virtual emergency command to the entry time of the dynamic blocking determination loop; Insert the virtual emergency command into the current communication message queue and generate the queue position data of the virtual emergency command based on the insertion position.

[0050] In one specific embodiment, after the target repeat instruction is determined, the generation of the virtual emergency instruction proceeds in the communication direction of the target repeat instruction.

[0051] The scenario generation program reads the instruction identifier field, source address field, destination address field, and sending timestamp field of the target duplicate instruction from the current communication message queue. To ensure that the virtual emergency instruction can accurately test the congestion status on the same communication path, the scenario generation program generates a virtual emergency instruction using the same source address field and destination address field, and configures an instruction identifier field for the virtual emergency instruction to distinguish it from the target duplicate instruction. For example, if the instruction identifier field of the target duplicate instruction is A102, the virtual emergency instruction can be configured as E9001 to avoid confusion with the target duplicate instruction when retrieving and processing feedback messages later.

[0052] The timestamp field of the virtual emergency command is set to the entry time of the dynamic blocking determination loop. This entry time is provided by the unified clock of the scenario generation program and is based on the same time base as the timestamp fields of other messages in the current communication message queue. Subsequently, the scenario generation program inserts the virtual emergency command into the current communication message queue and generates queue position data for the virtual emergency command based on the insertion position. The queue position data can represent the sequence number of the virtual emergency command in the current communication message queue, or it can represent the number of unprocessed data records preceding it. This queue position data is directly used when calculating the penetration probability later.

[0053] Calculate the logical processing weight of repeated instructions based on the information resonance intensity; calculate the penetration probability of virtual emergency instructions based on the logical processing weight; Furthermore, the logical processing weights of repeated instructions are calculated based on the information resonance intensity; the penetration probability of virtual emergency instructions is calculated based on the logical processing weights, including: Count the number of target duplicate instructions occupied in the queue before the virtual emergency instruction is inserted, and count the total number of messages in the current communication message queue; The proportion of duplicate instructions is obtained by comparing the ratio between the number of queues occupied and the total number of messages. The information resonance intensity is squared and amplified, and the result of the squared amplification is multiplied by the proportion of repeated instructions to obtain the logic processing weight. Calculate the remaining processing share corresponding to the virtual emergency instruction based on the logical processing weight and the queue position data of the virtual emergency instruction; The remaining processing share is mapped to the penetration probability of virtual emergency orders.

[0054] In one specific embodiment, after the virtual emergency command is written into the current communication message queue, the scenario generation program begins to calculate the logical processing weight and penetration probability.

[0055] The scenario generation program first calculates the queue occupancy count of the target duplicate instruction before the insertion of the virtual emergency instruction, and then calculates the total number of messages in the current communication message queue. The queue occupancy count refers to the number of data records in the current communication message queue whose instruction identifier field matches the target duplicate instruction. The total number of messages refers to the total number of all unprocessed data records in the current communication message queue. The scenario generation program calculates the duplicate instruction occupancy ratio based on the ratio between the queue occupancy count and the total number of messages.

[0056] Subsequently, the scene generation program squares the information resonance intensity and multiplies the result by the proportion of repeated instructions to obtain the logic processing weight. This square amplification process amplifies the impact of high information resonance intensity and compresses the impact of low information resonance intensity, thus forming a non-linear blocking description.

[0057] Next, the scene generation program calculates the remaining processing share corresponding to the virtual emergency command based on the logical processing weight and the queue position data of the virtual emergency command. One executable approach is to take the total processing share of the current processing cycle as one, first deduct the share occupied by the logical processing weight, and then reduce the remaining processing share according to the queue position data of the virtual emergency command; the further back the virtual emergency command is in the queue, the lower its remaining processing share.

[0058] Finally, the scenario generation program maps the remaining processing share to the penetration probability of the virtual emergency command, with the mapping result limited to between zero and one. The closer the penetration probability is to zero, the more difficult it is for the virtual emergency command to obtain a processing opportunity in the current communication message queue.

[0059] Specifically, the calculation formulas for logical processing weights and penetration probabilities are as follows: The formula for calculating logical processing weights is as follows: , The formula for calculating the penetration probability is: First, calculate the remaining processing share. : , The penetration probability is then obtained through a mapping function: , in, Represented as logical processing weights, This is represented as the information resonance intensity. When the resonance intensity is low, the squared value is even smaller, preventing normal communication from being mistaken for congestion. When the resonance intensity approaches a high level, the squaring process can realistically simulate the physical phenomenon of rapidly deteriorating resource consumption before the queue is about to collapse. This represents the number of queues in use. This represents the total number of messages, and , This represents the percentage of repetitive instructions, indicating the absolute congestion caused by the target instruction in terms of queue length. Represented as the remaining processing share. Represented as queue position data, The penetration probability represents the probability that the virtual emergency command can be successfully parsed and executed by the system without timeout in a command and control network congested by massive amounts of duplicate messages. Its value ranges from [value missing]. The closer the penetration probability is to 0, the deeper the network logic failure.

[0060] The processing status of virtual emergency commands within a preset time window is monitored based on the penetration probability; when the processing status is timeout and unprocessed, the blocking index is updated based on the timeout duration and information resonance intensity. Furthermore, based on the penetration probability, the processing status of virtual emergency commands within a preset time window is monitored. When the processing status is "timeout without processing," the blocking index is updated according to the timeout duration and information resonance intensity, including: Retrieve the processing feedback message corresponding to the instruction identifier field of the virtual emergency instruction within the preset time window; When a processing feedback message is retrieved, the difference between the message timestamp field of the processing feedback message and the sending timestamp field of the virtual emergency command is calculated to obtain the response time. When no processing feedback message is found, the window duration corresponding to the preset time window is determined as the response duration; The processing status is obtained by comparing the response time with the window duration corresponding to the preset time window. When the processing status is timed out and not processed, calculate the timeout duration when the response time exceeds the window duration, and map the timeout duration, penetration probability, and information resonance intensity together as the current round of blocking increment. The current round of blocking increments are added to the previous round of blocking index to obtain the updated blocking index.

[0061] In one specific embodiment, monitoring of the processing status relies on the instruction identifier field of the virtual emergency instruction. The scenario generation program retrieves processing feedback messages corresponding to the instruction identifier field of the virtual emergency instruction within a preset time window. The search range begins with the sending timestamp field of the virtual emergency instruction and ends with the sending timestamp field plus the window duration corresponding to the preset time window. If a processing feedback message is found within this time range, the scenario generation program calculates the difference between the message timestamp field of the processing feedback message and the sending timestamp field of the virtual emergency instruction to obtain the response duration. If no processing feedback message is found, the scenario generation program determines the window duration corresponding to the preset time window as the response duration and records a feedback missing marker in the internal processing status.

[0062] Subsequently, the scene generation program compares the response time with the window duration corresponding to the preset time window to obtain the processing status. If the response time is less than or equal to the window duration and there is no missing feedback flag, the processing status is "processed"; if the response time exceeds the window duration, or the response time reaches the window duration but there is a missing feedback flag, the processing status is "timeout unprocessed". For the case of timeout unprocessed, the scene generation program calculates the timeout duration for the response time exceeding the window duration; for the case of missing feedback flags, one message refresh cycle can be used as the compensation amount for the timeout duration in the calculation, so that even in the case of no feedback, the blocking index update process can be entered.

[0063] Subsequently, the scene generation program maps the timeout duration, penetration probability, and information resonance intensity together as the current round's blocking increment. During mapping, the longer the timeout duration, the larger the current round's blocking increment; the lower the penetration probability, the larger the current round's blocking increment; and the higher the information resonance intensity, the greater the amplification of the current round's blocking increment. The current round's blocking increment is accumulated to the previous round's blocking index to obtain the updated blocking index, which is then used as input data for the next round's dynamic blocking determination loop.

[0064] When the blocking index exceeds the preset overflow threshold, output link paralysis scenario data and mark subsequent regular instructions as lost; when a protocol downgrade confirmation message is received, clear the communication message queue data corresponding to the duplicate instruction, reset the blocking index and exit the dynamic blocking judgment loop.

[0065] Furthermore, when the blocking index exceeds a preset overflow threshold, the system outputs link failure scenario data and marks subsequent regular commands as lost; when a protocol downgrade confirmation message is received, the system clears the communication message queue data corresponding to duplicate commands, resets the blocking index, and exits the dynamic blocking determination loop, including: When the blocking index exceeds the preset overflow threshold, link paralysis scenario data is generated, including the instruction identifier of the target repeated instruction, the blocking index, the virtual emergency instruction processing status, and the number of queues occupied. Write the link failure scenario data into the virtual training scenario status table, and mark the regular instructions that enter after the target repeated instructions as lost in the virtual training scenario status table. Parse the message type field and instruction identifier field in subsequent communication messages. When the parsing result indicates a protocol downgrade confirmation message, read the instruction identifier of the target duplicate instruction carried in the protocol downgrade confirmation message. Delete the corresponding duplicate message record in the current communication message queue according to the instruction identifier of the target duplicate instruction, and generate the queue clearing result; Based on the queue clearing results, the blocking index is reset to its initial value, and the link recovery scenario data is written into the virtual training scenario status table, thus ending the dynamic blocking determination loop.

[0066] In one specific embodiment, when the updated blocking index exceeds a preset overflow threshold, the scenario generation program generates link paralysis scenario data. The link paralysis scenario data includes the instruction identifier of the target repeating instruction, the blocking index, the processing status of the virtual emergency instruction, and the number of queues occupied. It may also include data items for retrospective analysis, such as the current time, information resonance intensity, and penetration probability.

[0067] The scenario generation program writes the link failure scenario data into the virtual training scenario state table, and marks the regular instructions that follow the target duplicate instruction as lost in the virtual training scenario state table. These regular instructions can be identified through the message type field. After being marked as lost, the training system will no longer treat them as successfully processed instructions in subsequent scenario presentation and evaluation, thus forming the training result after the link logic failure. The dynamic blocking judgment loop continues to listen for subsequent communication messages. The scenario generation program parses the message type field and instruction identifier field in the subsequent communication messages. When the parsing result indicates a protocol degradation confirmation message, it reads the instruction identifier of the target duplicate instruction carried in the protocol degradation confirmation message.

[0068] Subsequently, the scenario generation program deletes the corresponding duplicate message record from the current communication message queue based on the instruction identifier of the target duplicate instruction, generating a queue clearing result. The queue clearing result may include the number of deleted data records, the total number of messages before and after deletion, and the number of remaining target duplicate instructions. If the queue clearing result indicates that the corresponding duplicate message record has been cleared, the scenario generation program resets the blocking index to its initial value based on the queue clearing result and writes link recovery scenario data into the virtual training scenario status table, ending the dynamic blocking judgment loop. Through this process, link paralysis scenario data and link recovery scenario data establish a before-after correspondence in the same virtual training scenario status table, facilitating the training system's recording and evaluation of the communication control and handling process.

[0069] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0070] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0071] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for generating virtual training scenarios for all combat positions of shipborne weapons, characterized in that, Includes the following steps: Capture command and control network communication messages during the virtual training process of all combat positions, and parse the command and control network communication messages to obtain the command identification sequence, command sending timestamp sequence, and associated feedback timestamp sequence; Duplicate instructions are filtered according to the instruction identifier sequence, and the unit retransmission frequency of the same duplicate instruction is calculated using the instruction sending timestamp sequence; Pair the instruction sending timestamp sequence with the associated feedback timestamp sequence to calculate the delay jitter value corresponding to the same repeated instruction; The unit retransmission frequency and delay jitter value are normalized and mapped to generate information resonance intensity; When the information resonance intensity is greater than the preset resonance threshold, the dynamic blocking determination loop is entered. The dynamic blocking determination loop includes: Generate a virtual emergency command and write it into the current communication message queue; Calculate the logical processing weight of repeated instructions based on the information resonance intensity; calculate the penetration probability of virtual emergency instructions based on the logical processing weight; The processing status of virtual emergency commands within a preset time window is monitored based on the penetration probability; when the processing status is timeout and unprocessed, the blocking index is updated based on the timeout duration and information resonance intensity. When the blocking index exceeds the preset overflow threshold, output link paralysis scenario data and mark subsequent regular instructions as lost; when a protocol downgrade confirmation message is received, clear the communication message queue data corresponding to the duplicate instruction, reset the blocking index and exit the dynamic blocking judgment loop.

2. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 1, characterized in that, Capture command and control network communication messages during the virtual training process of all combat positions, and parse the command and control network communication messages to obtain the command identification sequence, command sending timestamp sequence, and associated feedback timestamp sequence, including: Read the message type field, instruction identifier field, source address field, destination address field, and message timestamp field from the network communication message; Data records whose message type field represents instruction messages are arranged according to the order of the message timestamp fields to generate an instruction identifier sequence and an instruction sending timestamp sequence. The data record that represents the message type field as the feedback message is matched according to the reverse correspondence between the instruction identifier field and the source address field and the destination address field to obtain the feedback message corresponding to each instruction message; Extract the message timestamp field from the feedback message, and write it according to the corresponding instruction message's position in the instruction sending timestamp sequence to generate an associated feedback timestamp sequence.

3. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 1, characterized in that, Duplicate instructions are filtered according to the instruction identifier sequence, and the unit retransmission frequency of the same duplicate instruction is calculated using the instruction sending timestamp sequence, including: The occurrence count of each instruction identifier in the instruction identifier sequence is counted, and the instruction identifiers that occur a predetermined number of times are identified as duplicate instruction identifiers; Extract the sending timestamps corresponding to the duplicate instruction identifiers from the instruction sending timestamp sequence, and form a duplicate instruction timestamp subsequence according to the chronological order; The difference between adjacent transmission timestamps in the repetitive instruction timestamp subsequence is calculated to obtain the set of retransmission time intervals; The number of time intervals in the retransmission time interval set is taken as the retransmission number, and the difference between the last timestamp and the first timestamp of the repeated instruction timestamp subsequence is taken as the retransmission time span. The ratio of the number of retransmissions to the retransmission time span is calculated to obtain the unit retransmission frequency of the same repeated instruction.

4. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 1, characterized in that, Pair the instruction sending timestamp sequence with the associated feedback timestamp sequence to calculate the latency jitter value corresponding to the same repeated instruction, including: Based on the position of the repeated instructions in the instruction identifier sequence, extract the corresponding instruction sending timestamp and associated feedback timestamp; Subtract the corresponding instruction sending timestamp from each associated feedback timestamp to obtain the feedback delay sequence; Calculate the mean delay of the feedback delay sequence, and calculate the deviation between each feedback delay in the feedback delay sequence and the mean delay; After converting each deviation to its absolute value, the mean value is calculated to obtain the delay jitter value corresponding to the same repeated instruction.

5. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 1, characterized in that, The unit retransmission frequency and delay jitter value are normalized and mapped to generate the information resonance intensity, including: Obtain the sorting position of the unit retransmission frequency in the current communication message queue among the retransmission frequencies of each repeated instruction unit, and convert the sorting position into a frequency mapping value; Obtain the reverse sorting position of the delay jitter value among the delay jitter values ​​of each repeated instruction in the current communication message queue, and convert the reverse sorting position into a jitter mapping value; Generate a feedback consistency mapping value based on the ratio between the number of times the feedback message content matches the repeated instruction and the number of times the repeated instruction is sent; The information resonance intensity is obtained by weighted synthesis of the frequency mapping value, jitter mapping value, and feedback consistency mapping value.

6. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 1, characterized in that, When the information resonance intensity is greater than the preset resonance threshold, the dynamic blocking determination loop is entered, including: The communication message queue is segmented according to a fixed sampling duration to obtain multiple sampling segments; Calculate the information resonance intensity of the same repeated instruction within each sampling segment; When the information resonance intensity in a consecutive sampling segment of a predetermined number of bits is greater than the preset resonance threshold, the instruction identifier corresponding to the repeated instruction is written into the blocking candidate list. The repeating instruction with the highest information resonance intensity value is selected from the blocking candidate list as the target repeating instruction, and the communication message queue data corresponding to the target repeating instruction is used as the input data for the dynamic blocking determination loop.

7. The method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 6, characterized in that, Generate a virtual emergency command and write it to the current communication message queue, including: Read the instruction identifier field, source address field, destination address field, and sending timestamp field of the target duplicate instruction in the current communication message queue; A virtual emergency instruction is generated based on the same source address field and destination address field, and an instruction identifier field is configured for the virtual emergency instruction to distinguish it from the target duplicate instruction; Set the timestamp field of the virtual emergency command to the entry time of the dynamic blocking determination loop; Insert the virtual emergency command into the current communication message queue and generate the queue position data of the virtual emergency command based on the insertion position.

8. A method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 7, characterized in that, The logic processing weights for repeated instructions are calculated based on the information resonance intensity. The penetration probability of virtual emergency instructions is then calculated based on these logic processing weights, including: Count the number of target duplicate instructions occupied in the queue before the virtual emergency instruction is inserted, and count the total number of messages in the current communication message queue; The proportion of duplicate instructions is obtained by comparing the ratio between the number of queues occupied and the total number of messages. The information resonance intensity is squared and amplified, and the result of the squared amplification is multiplied by the proportion of repeated instructions to obtain the logic processing weight. Calculate the remaining processing share corresponding to the virtual emergency instruction based on the logical processing weight and the queue position data of the virtual emergency instruction; The remaining processing share is mapped to the penetration probability of virtual emergency orders.

9. A method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 8, characterized in that, Based on the penetration probability, the processing status of virtual emergency commands within a preset time window is monitored. When the processing status is "timeout unprocessed," the blocking index is updated according to the timeout duration and information resonance intensity, including: Retrieve the processing feedback message corresponding to the instruction identifier field of the virtual emergency instruction within the preset time window; When a processing feedback message is retrieved, the difference between the message timestamp field of the processing feedback message and the sending timestamp field of the virtual emergency command is calculated to obtain the response time. When no processing feedback message is found, the window duration corresponding to the preset time window is determined as the response duration; The processing status is obtained by comparing the response time with the window duration corresponding to the preset time window. When the processing status is timed out and not processed, calculate the timeout duration when the response time exceeds the window duration, and map the timeout duration, penetration probability, and information resonance intensity together as the current round of blocking increment. The current round of blocking increments are added to the previous round of blocking index to obtain the updated blocking index.

10. A method for generating virtual training scenarios for all combat positions of shipborne weapons according to claim 9, characterized in that, When the blocking index exceeds a preset overflow threshold, output link failure scenario data and mark subsequent regular commands as lost; when a protocol downgrade confirmation message is received, clear the communication message queue data corresponding to duplicate commands, reset the blocking index, and exit the dynamic blocking determination loop, including: When the blocking index exceeds the preset overflow threshold, link paralysis scenario data is generated, including the instruction identifier of the target repeated instruction, the blocking index, the virtual emergency instruction processing status, and the number of queues occupied. Write the link failure scenario data into the virtual training scenario status table, and mark the regular instructions that enter after the target repeated instructions as lost in the virtual training scenario status table. Parse the message type field and instruction identifier field in subsequent communication messages. When the parsing result indicates a protocol downgrade confirmation message, read the instruction identifier of the target duplicate instruction carried in the protocol downgrade confirmation message. Delete the corresponding duplicate message record in the current communication message queue according to the instruction identifier of the target duplicate instruction, and generate the queue clearing result; Based on the queue clearing results, the blocking index is reset to its initial value, and the link recovery scenario data is written into the virtual training scenario status table, thus ending the dynamic blocking determination loop.

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