Efficient and intelligent battlefield scenario editing method

Abstract

The invention belongs to the technical field of simulation technology and military informatization, and provides an efficient and intelligent battlefield scenario editing method, which comprises the following steps of: obtaining characteristic data of historical unqualified scenario schemes, constructing a scenario scheme analysis library, performing risk degree division on the scenario schemes in the scenario scheme analysis library, and obtaining a scenario scheme database; identifying a high-risk-degree scenario scheme; according to the high-risk-degree scenario scheme, clustering analysis is carried out on the high-risk-degree scenario scheme according to failure links, frequency analysis is carried out on each type of failure links, and high-frequency failure links are determined; in combination with a response measure of a high-frequency failure link, a response measure of the high-frequency failure link in a subsequent battlefield scenario scheme editing process is updated, critical value updating is automatically triggered when the proportion of an unqualified scheme exceeds the limit, a detection standard is promoted to evolve along with a battlefield environment, the failure rate of the scenario scheme in deduction is remarkably reduced, and the accuracy of battlefield scenario scheme deduction is improved. And reliable scheme support is provided for military training.

Description

Technical Field

[0001] The invention belongs to the field of simulation technology and military information technology, and specifically is an efficient and intelligent battlefield scenario editing method. Background Art

[0002] In modern military training and combat preparation, the scientific nature and reliability of battlefield scenario plans directly affect the quality of military exercises and the improvement of actual combat capabilities.

[0003] There are many shortcomings in the existing technology. On the one hand, there is a lack of systematic data management of historical unqualified scenarios, and a multidimensional analysis library containing basic information, defect information and scenario information has not been built. Risk assessment relies too much on the subjective experience of experts, resulting in low accuracy in identifying high-risk scenarios and easy omissions or misjudgments. On the other hand, the analysis of failure links remains at the qualitative description level, and has not been combined with the weights of combat elements for quantitative clustering, making it impossible to accurately locate high-frequency failure links. In addition, the detection standards are static and have not been dynamically adjusted according to actual combat conditions such as combat type and environment. There is a hidden risk of "qualified in theory but failed in actual combat", which leads to a high failure rate of scenario plans in simulations, seriously restricting the effectiveness of actual combat training.

[0004] To this end, the present invention provides an efficient and intelligent battlefield scenario editing method. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is: Obtain the characteristic data of historical unqualified scenarios, build a scenario analysis library, and classify the risk levels of the scenarios in the scenario analysis library to identify scenarios with high risk levels; According to the high-risk scenarios, cluster the high-risk scenarios according to the failure links, and conduct frequency analysis on each type of failure link to determine the high-frequency failure links; Combined with the countermeasures for high-frequency failure links, update the critical value of failure response intensity for high-frequency failure links in the subsequent battlefield scenario editing process, implement the updated critical value of failure response intensity and make result judgments; Obtain the scenarios that have passed the effectiveness test, identify whether there are scenarios with high-frequency failure links, and if so, count and compare the percentages to determine whether the failure response strength critical value of the high-frequency failure link needs to be updated again. If necessary, update it again.

[0007] As a further solution of the present invention: the specific process of identifying the high-risk scenario is: The analytic hierarchy process is used to determine the weights of feature data, and a safety impact coefficient is introduced to comprehensively calculate the campaign risk assessment index; If the campaign risk assessment index is greater than the campaign risk assessment index threshold, the corresponding scenario plan is classified as a high-risk level.

[0008] As a further solution of the present invention: The specific process of clustering and analyzing the scenario plans at the high-risk level according to the failure links is as follows: The scenario plans classified into the high-risk level are recorded as high-risk scenario plans; the high-risk scenario plans are decomposed into several key elements according to the corresponding combat processes and integrated into a combat element set: For each high-risk scenario plan, count the number of occurrences of each failure link in different combat elements; preset the original frequency of a certain failure link on the combat element; According to the weighted frequency formula, multiply the original frequency by the element weight to obtain the weighted frequency value of the failure link, and integrate it into the weighted frequency vector of the failure link; Use the K-Means algorithm for clustering and output the failure link clusters.

[0009] As a further solution of the present invention: The determination process of the high-frequency failure links is as follows: Count the number of high-risk scenario plans in each failure link cluster after iteration, calculate the occurrence frequency of each failure link cluster in all high-risk scenario plans, screen out the clusters with a frequency greater than the frequency threshold, and the failure link corresponding to the cluster is the high-frequency failure link.

[0010] As a further solution of the present invention: The acquisition process of the updated critical value of the failure response intensity is as follows: For high-frequency failure links, combine the clustering frequency of high-frequency failure links to calculate the updated critical value of the failure response intensity.

[0011] As a further solution of the present invention: The acquisition process of the scenario plans that pass the effectiveness detection is as follows: For each high-frequency failure link, perform effectiveness detection according to the updated critical value of the failure response intensity, conduct task qualification tests on the high-frequency failure links of each scenario plan, obtain the actual troop loss rate, actual troop loss rate, and task completion degree in real time, determine the task completion degree coefficient and the failure avoidance performance degree. If the failure avoidance performance degree is less than 1, then combine the task completion degree coefficient to calculate the qualified judgment coefficient; If the qualified judgment coefficient is greater than the qualified judgment threshold, it is determined that the corresponding scenario plan is qualified for high-frequency failure links.

[0012] As a further solution of the present invention: The acquisition process of the task completion degree coefficient is as follows: Take the ratio of the task completion degree in the actual deduction to the preset target completion degree as the task completion degree coefficient.

[0013] As a further solution of the present invention: the process of obtaining the failure avoidance performance degree is as follows: Analyze the actual troop loss rate and the actual troop loss rate to determine the deviation degree of the troop loss rate and the deviation degree of the intelligence misjudgment rate; The failure avoidance performance degree is obtained by weighted summation of the deviation degree of the troop loss rate and the deviation degree of the intelligence misjudgment rate.

[0014] As a further solution of the present invention: the process of determining the deviation degree of the troop loss rate and the deviation degree of the intelligence misjudgment rate is as follows: Take the ratio of the actual troop loss rate to the preset troop loss rate to obtain the deviation degree of the troop loss rate; Take the ratio of the intelligence misjudgment rate to the preset intelligence misjudgment rate to obtain the deviation degree of the intelligence misjudgment rate.

[0015] As a further solution of the present invention: the specific process of determining whether it is necessary to update the critical value of the failure response intensity for high-frequency failure links again is as follows: Extract the scenario plans that pass the effectiveness detection and conduct simulation operations. For high-frequency failure links, detect whether the scenario plans are qualified; Record the unqualified scenario plans as unqualified scenario plans; calculate the proportion of the number of unqualified scenario plans in the scenario plans; If the proportion of the number of unqualified scenario plans is less than or equal to the limit value of the proportion of the number of unqualified scenario plans, there is no need to update again; If the proportion of the number of unqualified scenario plans is greater than the limit value of the proportion of the number of unqualified scenario plans, it is necessary to enter the update of the critical value of the failure response intensity again.

[0016] The beneficial effects of the present invention are as follows: Obtain the characteristic data of historical unqualified scenario plans, construct a scenario plan analysis library, classify the risk levels of the scenario plans in the scenario plan analysis library, and identify the scenario plans with high risk levels; According to the scenario plans with high risk levels, cluster and analyze the scenario plans with high risk levels according to the failure links, and conduct frequency analysis on each type of failure link to determine the high-frequency failure links; Combine the countermeasures for high-frequency failure links, update the critical value of the failure response intensity for high-frequency failure links during the subsequent battlefield scenario plan editing process, implement the updated critical value of the failure response intensity and conduct result determination; Obtain the scenario plans that pass the effectiveness detection, identify whether there are scenario plans with high-frequency failure links. If so, count the proportion and compare to determine whether it is necessary to update the critical value of the failure response intensity for high-frequency failure links again. If necessary, conduct the update again; By constructing a multi-dimensional analysis library and quantifying the risk index, combining the analytic hierarchy process and the safety impact coefficient, the present invention accurately identifies high-risk plans, uses the K-Means algorithm to cluster the failure links, determines the high-frequency failure points based on the weights of combat elements, focuses the detection resources on key weak links to improve the identification efficiency, dynamically updates the critical value of the response intensity according to the failure frequency and risk coefficient, forms a closed-loop iteration through simulation operation, automatically triggers the update of the critical value when the proportion of unqualified plans exceeds the limit, promotes the evolution of the detection standard with the combat environment, significantly reduces the failure rate of scenario plans during the deduction, and provides a reliable plan support for military training. Description of the Drawings

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is the step flowchart of an efficient and intelligent battlefield scenario editing method according to an embodiment of the present invention; Figure 2 is the system block diagram of an efficient and intelligent battlefield scenario editing system according to an embodiment of the present invention. Detailed Embodiments

[0019] In order to make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0020] Embodiment 1 Please refer to Figure 1 As shown, an efficient and intelligent battlefield scenario editing method according to an embodiment of the present invention includes the following steps: Step 1: Obtain the characteristic data of historical unqualified scenario plans, construct a scenario plan analysis library, classify the risk levels of the scenario plans in the scenario plan analysis library, and identify the scenario plans with high risk levels; Unsatisfactory scenarios in the past year can be collected from historical simulation logs in wargames; Enter the unqualified scenario information through the unified scenario defect collection form, the fields include: Basic information: plan number, exercise name, combat type (offense / defense / special operations), director rating.

[0021] Defect information: type of non-conformity (such as strategic misjudgment, tactical loopholes), expert review opinions, and key failure nodes in simulation.

[0022] Scenario information: combat environment (mountain / city / desert), enemy force size, and our equipment models; Based on historical scenarios, obtain characteristic data of each scenario; Specifically, the characteristic data include but are not limited to: enemy situation misjudgment rate, main attack direction deviation, and number of delays in arms coordination; Data features are preprocessed, including: missing value and outlier processing and data standardization, elimination of confidential information (such as real unit numbers, precise coordinates), replacement with numbers (such as "A Division" and "Region X"), and unified military measurement standards: force density: refer to the standard value of the "Combined Forces Campaign Tactics Calculation Manual"; fire damage probability: calculated based on the weapon equipment performance parameter model (such as the hit rate of a certain type of artillery); Use a relational database (such as PostgreSQL) to design the table structure: Basic plan information table: stores plan ID, operation type, rating, and timestamp; Feature details table: associated with the solution ID, storing quantitative features such as "enemy misjudgment rate" and "cooperative conflict number"; Review opinion table: stores expert text opinions and automatically extracted keyword tags (such as "air defense loopholes" and "logistics disconnection") to build an analysis library for unqualified scenarios; Combined with the characteristic data of the hypothetical scenario, the safety impact coefficient is introduced to comprehensively calculate the campaign risk assessment index; Specifically, the calculation process of the campaign risk assessment index is: The analytic hierarchy process is used to determine the weights and construct a linear weighted formula: ,in, is the safety impact factor, is the weight of the jth characteristic parameter. The weight distribution is set by those skilled in the art based on industry experience. is the standardized value of the jth feature parameter; The analytic hierarchy process is a method well known to those skilled in the art; For the setting of the safety impact coefficient, if the scenario does not cause the failure of the campaign, the safety impact coefficient takes 1; if the scenario causes the failure of the campaign, the safety impact coefficient can be set to a value greater than 1, generally set to 1.2 or 1.5, which is set by those skilled in the art according to the characteristics of the actual campaign failure; In some embodiments, the campaign risk assessment index is compared with the grading threshold, and the specific comparison process is as follows; If the campaign risk assessment index is less than or equal to the campaign risk assessment index threshold, the corresponding scenario is classified as a low risk level; If the campaign risk assessment index is greater than the campaign risk assessment index threshold, the corresponding scenario is classified as a high risk level; Step 2: According to the scenarios with a high risk level, cluster the scenarios with a high risk level according to the failure links, and perform frequency analysis on each type of failure link to determine the high-frequency failure links; The scenarios classified as a high risk level are recorded as high-risk scenarios; Decompose the high-risk scenarios into several key elements (such as reconnaissance, command and control, communication, strike, support, etc.) according to the corresponding operation process, and integrate them into an operation element set: For each high-risk scenario, count the number of occurrences of each failure link in different operation elements; preset the original frequency of a certain failure link on the operation element; According to the weighted frequency formula, multiply the original frequency by the element weight to obtain the weighted frequency value of the failure link, and integrate it into the weighted frequency vector of the failure link; It can be understood that if the original frequency dimensions of each operation element are different, the Z-score needs to be used to standardize the vector to eliminate the dimension difference, and the element weight is set by those skilled in the art according to historical experience; Use the K-Means algorithm for clustering to determine the number of clusters K; The method for determining the number of clusters K is: by the elbow method, calculate the sum of squared errors SSE under different K values; where, is the kth cluster, and is the cluster center coordinate; Select the K value where the downward trend of SSE slows down as the optimal number of clusters, Exemplarily, if the initially set K = 6, traverse K = 1 to K = 6, calculate the sum of squared errors corresponding to each K, and the calculation results of SEE are 120000, 60000, 25000, 20000, 23000, 17000 corresponding to K values 1 - 6; draw the K-SSE curve, and observe that the curve slope significantly slows down when K = 4, then select K = 4 as the optimal number of clusters; According to the determined K value, use the K-Means++ algorithm to initialize the cluster center; The initial cluster centers include: randomly selecting the weighted frequency vector of any failed link as the center; Calculate the distance from the weighted frequency vector of each failed link to the nearest selected cluster center through the Euclidean distance formula; Recalculate the cluster centers of each cluster, and iterate until the centers no longer change significantly or reach the maximum number of iterations. Set the maximum number of iterations to 50, output the failed link clusters, and each cluster corresponds to a type of failed link; Exemplarily, for link A: "reconnaissance misjudgment", the weighted frequency vector is [1.2, 0.2, 0.1, 0.1] (the weight of the reconnaissance element is high, and the influence of other elements is low); For link B: "command delay", the weighted frequency vector is [0.3, 1.6, 0.2, 0.1] (the weight of the command element is high, and the influence of other elements is low); The Euclidean distance between the two is:

[0023] Since the weighted frequencies of the two in the core elements (reconnaissance / command) are significantly different and the distance is far, they will be assigned to different categories during clustering; Count the number of high-risk scenario plans in each cluster of failed links after iteration, calculate the occurrence frequency of each cluster of failed links in all high-risk scenario plans, and the formula for frequency is: the frequency of a cluster = the number of high-risk scenario plans in the cluster / the total number of high-risk scenario plans; Set a frequency threshold, filter out the clusters with a frequency greater than the frequency threshold, and the failed links corresponding to the clusters are high-frequency failed links; Step 3: Combine the countermeasures for high-frequency failed links, update the failure response intensity critical value for high-frequency failed links during the subsequent battlefield scenario plan editing process, implement the updated failure response intensity critical value and conduct result judgment; Extract all scenario plans belonging to the high-frequency failed link clusters and associate the following data: Operational element parameters: the historical weights of the operational elements corresponding to the failed links (such as reconnaissance, command and control), and the scenario adaptability (such as the weight of the communication element is higher in urban warfare); Failure impact parameters: casualty rate, intelligence misjudgment rate; Historical data of countermeasures: preventive measures designed for this link in previous scenario plans (such as backup communication links) and their effectiveness in the deduction (number of successful avoidance / total number of applications); For high-frequency failed links, combine the clustering frequency of high-frequency failed links to calculate the updated failure response intensity critical value, and the formula is: ; where Updated failure response intensity critical value; is the original critical value; f is the clustering frequency corresponding to the high-frequency failure bit link, and this value is a normalized value; is the risk coefficient, which is set by those skilled in the art according to experience; Implement the effectiveness detection and determination of the updated failure response intensity critical value; For each high-frequency failure link, perform effectiveness detection according to the updated failure response intensity critical value, conduct task qualification tests on the high-frequency failure links of each scenario plan, and obtain the actual troop loss rate, actual troop loss rate, and task completion rate in real time; Based on the task completion coefficient and the failure avoidance performance degree, determine whether the corresponding scenario plan is qualified in the high-frequency failure link; The failure avoidance performance degree is obtained by weighted summation of the troop loss rate deviation degree and the intelligence misjudgment rate deviation degree, and the weights are set by those skilled in the art according to the importance of combat elements; Perform a ratio process on the actual troop loss rate and the preset troop loss rate to obtain the troop loss rate deviation degree; Perform a ratio process on the intelligence misjudgment rate and the preset intelligence misjudgment rate to obtain the intelligence misjudgment rate deviation degree; If the failure avoidance performance degree is greater than 1, it is determined that the corresponding scenario plan is unqualified in the high-frequency failure link; If the failure avoidance performance degree is less than 1, then make a determination in combination with the task completion coefficient, calculate the qualified judgment coefficient, and the calculation formula is: ; where HP is the qualified judgment coefficient, RW is the task completion coefficient, and SG is the failure avoidance performance degree; Take the ratio of the task completion degree in the actual deduction to the preset target completion degree as the task completion coefficient; If the qualified judgment coefficient is less than or equal to the qualified judgment threshold, it is determined that the corresponding scenario plan is unqualified in the high-frequency failure link, indicating that the countermeasure is ineffective; If the qualified judgment coefficient is greater than the qualified judgment threshold, it is determined that the corresponding scenario plan is qualified in the high-frequency failure link, indicating that the countermeasure is effective; Step 4: Obtain the scenario plans that pass the effectiveness detection, identify whether there are still scenario plans with high-frequency failure links. If so, judge whether it is necessary to update the failure response intensity critical value of the high-frequency failure link again according to the proportion of the number of unqualified scenario plans. If necessary, perform a second update; Extract the scenario plans that pass the effectiveness detection and perform simulation runs. For high-frequency failure links, detect whether the scenario plans are qualified; Record the unqualified scenario plans as unqualified scenario plans; count the number of unqualified scenario plans in the scenario plans, and calculate the proportion of the number of unqualified scenario plans in the scenario plans; Compare the proportion of the number of unqualified scenario plans with the limit value of the proportion of the number of unqualified scenario plans. If the proportion of the number of unqualified scenario plans is less than or equal to the limit value of the proportion of the number of unqualified scenario plans, it indicates that the current critical value of the failure response intensity is effective and does not need to be updated again; If the proportion of the number of unqualified scenario plans is greater than the limit value of the proportion of the number of unqualified scenario plans, it indicates that there is still a risk of insufficient response in the current critical value of the failure response intensity, and it is necessary to enter the update of the critical value of the failure response intensity again; The technical solution of this embodiment is as follows: Obtain the characteristic data of historical unqualified scenario plans, construct a scenario plan analysis library, divide the risk levels of the scenario plans in the scenario plan analysis library, and identify the scenario plans with high risk levels; According to the scenario plans with high risk levels, cluster and analyze the scenario plans with high risk levels according to the failure links, and perform frequency analysis on each type of failure link to determine the high-frequency failure links; Combine the countermeasures for high-frequency failure links to update the critical value of the failure response intensity for high-frequency failure links in the subsequent battlefield scenario plan editing process, implement the updated critical value of the failure response intensity and perform result judgment; Obtain the scenario plans that pass the effectiveness detection, identify whether there are scenario plans with high-frequency failure links. If so, count the proportion and compare to determine whether it is necessary to update the critical value of the failure response intensity for high-frequency failure links again. If necessary, perform the update again; The present invention constructs a multi-dimensional analysis library and quantifies the risk index, combines the analytic hierarchy process and the safety impact coefficient, accurately identifies high-risk plans, uses the K-Means algorithm to cluster the failure links, determines the high-frequency failure points based on the weights of combat elements, focuses the detection resources on key weak links, improves the identification efficiency, dynamically updates the critical value of the response intensity according to the failure frequency and risk coefficient, forms a closed-loop iteration through simulation operation, automatically triggers the update of the critical value when the proportion of unqualified plans exceeds the limit, promotes the evolution of the detection standard with the combat environment, significantly reduces the failure rate of scenario plans in the deduction, and provides reliable plan support for military training.

[0024] Embodiment 2 Please refer to Figure 2 As shown, an efficient and intelligent battlefield scenario editing system described in an embodiment of the present invention includes the following modules: Data acquisition module: Obtain the characteristic data of historical unqualified scenario plans, construct a scenario plan analysis library, divide the risk levels of the scenario plans in the scenario plan analysis library, and identify the scenario plans with high risk levels; Failure link analysis module: According to the scenario plans with high risk levels, cluster and analyze the scenario plans with high risk levels according to the failure links, and perform frequency analysis on each type of failure link to determine the high-frequency failure links; Validity judgment module: Combining the countermeasures for high-frequency failure links, update the failure response intensity critical value for high-frequency failure links during the subsequent battlefield scenario planning scheme editing process, implement the updated failure response intensity critical value, and conduct result judgment; Detection and update module: Obtain the scenario planning scheme that passes the validity detection, identify whether there is a scenario planning scheme with high-frequency failure links. If so, count the quantity proportion and make a comparison to determine whether it is necessary to update the failure response intensity critical value for high-frequency failure links again. If necessary, perform the update again.

[0025] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and intelligent battlefield scenario editing method, characterized in that: Including: Obtain the characteristic data of historical unqualified scenario plans, construct a scenario plan analysis library, classify the risk levels of the scenario plans in the scenario plan analysis library, and identify the scenario plans with high risk levels; According to the scenario plans with high risk levels, cluster and analyze the scenario plans with high risk levels according to the failure links, and conduct frequency analysis on each type of failure link to determine the high-frequency failure links; Combined with the countermeasures for high-frequency failure links, update the critical value of the failure response intensity for high-frequency failure links during the subsequent battlefield scenario plan editing process, implement the updated critical value of the failure response intensity, and conduct result judgment; Obtain the scenario plans that pass the effectiveness detection, identify whether there are scenario plans with high-frequency failure links. If so, count the quantity ratio and compare to determine whether it is necessary to update the critical value of the failure response intensity for high-frequency failure links again. If necessary, conduct the update again.

2. The efficient and intelligent battlefield scenario editing method according to claim 1, characterized in that: The specific process of identifying the scenario plans with high risk levels is as follows: Use the analytic hierarchy process to determine the weights of the characteristic data, introduce the safety impact coefficient, and comprehensively calculate the campaign risk assessment index; If the campaign risk assessment index is greater than the campaign risk assessment index threshold, classify the corresponding scenario plan as a high-risk level.

3. An efficient and intelligent battlefield scenario editing method according to claim 1, characterized in that: The specific process of clustering and analyzing the scenario plans with high risk levels according to the failure links is as follows: Record the scenario plans classified as high risk levels as high-risk scenario plans; decompose the high-risk scenario plans into several key elements according to the corresponding combat processes, and integrate them into a combat element set: For each high-risk scenario plan, count the occurrence times of each failure link in different combat elements; Preset the original frequency of a certain failure link on the combat element; According to the weighted frequency formula, multiply the original frequency by the element weight to obtain the weighted frequency value of the failure link, and integrate it into the weighted frequency vector of the failure link; Use the K-Means algorithm for clustering and output the failure link clusters.

4. An efficient and intelligent battlefield scenario editing method according to claim 3, characterized in that: The determination process of the high-frequency failure links is as follows: Statistically count the number of high-risk scenario plans in each type of failure link cluster after iteration, calculate the occurrence frequency of each type of failure link cluster in all high-risk scenario plans, screen out the clusters with frequencies greater than the frequency threshold, and the failure links corresponding to the clusters are the high-frequency failure links.

5. An efficient and intelligent battlefield scenario editing method according to claim 4, characterized in that: The acquisition process of the updated critical value of the failure response intensity is as follows: For high-frequency failure links, combine the clustering frequencies of high-frequency failure links to calculate the updated critical value of the failure response intensity.

6. An efficient and intelligent battlefield scenario editing method according to claim 5, characterized in that: The acquisition process of the scenario plans that pass the effectiveness detection is as follows: For each high-frequency failure link, conduct effectiveness detection according to the updated critical value of the failure response intensity, conduct task qualification tests on the high-frequency failure links of each scenario plan, obtain the actual force loss rate, actual force loss rate, and task completion degree in real time, determine the task completion coefficient and failure avoidance performance degree. If the failure avoidance performance degree is less than 1, combine the task completion coefficient to calculate the qualified judgment coefficient; If the qualified judgment coefficient is greater than the qualified judgment threshold, determine that the corresponding scenario plan is qualified for high-frequency failure links.

7. An efficient and intelligent battlefield scenario editing method according to claim 6, characterized in that: The acquisition process of the task completion coefficient is as follows: Take the ratio of the task completion degree in the actual deduction to the preset target completion degree as the task completion degree coefficient.

8. An efficient and intelligent battlefield scenario editing method according to claim 6, characterized in that: The process of obtaining the failure avoidance performance degree is as follows: Analyze the actual force loss rate and the actual force loss rate to determine the deviation degree of the force loss rate and the deviation degree of the intelligence misjudgment rate; The failure avoidance performance degree is obtained by weighted summation of the deviation degree of the force loss rate and the deviation degree of the intelligence misjudgment rate.

9. An efficient and intelligent battlefield scenario editing method according to claim 8, characterized in that: The process of determining the deviation degree of the force loss rate and the deviation degree of the intelligence misjudgment rate is as follows: Perform a ratio process on the actual force loss rate and the preset force loss rate to obtain the deviation degree of the force loss rate; Perform a ratio process on the intelligence misjudgment rate and the preset intelligence misjudgment rate to obtain the deviation degree of the intelligence misjudgment rate.

10. An efficient and intelligent battlefield scenario editing method according to claim 6, characterized in that: The specific process of determining whether it is necessary to update the critical value of the failure response intensity for high-frequency failure links again is as follows: Extract the scenario plans that pass the effectiveness test and conduct simulation operations. For high-frequency failure links, detect whether the scenario plans are qualified; Record the unqualified scenario plans as unqualified scenario plans; calculate the proportion of the number of unqualified scenario plans in the scenario plans; If the proportion of the number of unqualified scenario plans is less than or equal to the limit value of the proportion of the number of unqualified scenario plans, there is no need to update again; If the proportion of the number of unqualified scenario plans is greater than the limit value of the proportion of the number of unqualified scenario plans, it is necessary to re-enter the update of the critical value of the failure response intensity.

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