Action debriefing method and apparatus based on the correlation between events and situations

Abstract

This invention provides a method and apparatus for action debriefing based on the correlation between events and situations, belonging to the field of debriefing analysis technology. The invention first parses the debriefing conditions to obtain fuzzy query condition information and performs a fuzzy search on relevant event data and target trajectory data; then, it calculates the correlation between event data and target trajectory data under the fuzzy query condition information to determine whether the event data and target trajectory data are related; next, it analyzes the related target trajectory data and event data to obtain the correlation between target execution tasks and the correlation between targets; finally, it returns debriefing data to the user for action debriefing. This action debriefing scheme based on the correlation between events and situations can automatically collect data and automatically mine the correlation between data, reducing the workload of operators, increasing efficiency, and improving the reliability of analysis results.

Description

Technical Field

[0001] This invention relates to the field of debriefing analysis technology, specifically to an action debriefing method and apparatus based on the correlation between events and situations. Background Technology

[0002] As a crucial component of the operational command information system, action debriefing serves two main purposes: firstly, as a concise reporting tool during routine shift changes; and secondly, as a vital element for rapid analysis and problem identification during exercises. Therefore, conducting rapid and accurate action debriefings is of paramount importance in operational command. However, because event data and target situation data (situation refers to the state and condition of events, including target trajectories and battlefield events) are often stored independently, and the target names used in event data often differ from those in the target situation data, establishing a correlation between event data and target situation data and rapidly generating action debriefing data is one of the significant challenges in action debriefing within current operational command information systems.

[0003] Currently, the data for action review is generated entirely by manual collection from different functions of the system. Then, the correlation between events and target situations is established manually. In this process, the system only plays the role of storing and querying relevant data.

[0004] However, manual data collection is labor-intensive, time-consuming, and tedious. Furthermore, the process of constructing relationships between events and target situations, target execution tasks, or targets is cumbersome. Relying entirely on manual methods is not only inefficient but also leads to problems such as low accuracy and unreliability of analysis results. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an action debriefing method and apparatus based on the correlation between events and situations, which solves the problems of low efficiency and low accuracy in existing manual action debriefing techniques.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] Firstly, this invention proposes an action debriefing method based on the correlation between events and situations, the method comprising:

[0010] Analyze the review conditions to obtain fuzzy query condition information;

[0011] Based on the aforementioned fuzzy query conditions, perform a fuzzy search on the event data and the target trajectory data;

[0012] Calculate the correlation between the event data and the target trajectory data on the fuzzy query condition information. If the correlation is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related.

[0013] Analyze the associated target trajectory data and event data to obtain the correlation between the target's execution tasks and the correlation between targets;

[0014] The review data is returned based on the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between events and targets, the correlation between target execution tasks, or the correlation between targets.

[0015] Preferably, the fuzzy query condition information includes: time range information, occurrence area / location information, and target information.

[0016] Preferably, calculating the correlation between the event data and the target trajectory data on the fuzzy query condition information includes:

[0017] The event data and target trajectory data are comprehensively compared across three dimensions: time, location / occurrence, and target, in the following manner:

[0018] Res=W_1*SJRes+W_2*DDRes+W_3*MBRes

[0019] Wherein, Res represents the correlation between event data and target trajectory data on the fuzzy query condition information; SJRes, DDRes, and MBRes are the comparison results of event data and target trajectory data in terms of time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system.

[0020] Preferably, the comparison method between event data and target trajectory data in terms of time, occurrence area / location, and target is as follows:

[0021] Time comparison formula:

[0022] SJRes=(sj-mb) / sumime

[0023] Wherein, SJRes represents the comparison result of event data and target trajectory data in time; sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span (in seconds) of the entire operation review.

[0024] Formula for comparing occurrence areas / locations (results: DDRes):

[0025] DDRes = distance / sumArea

[0026] Where DDRes represents the comparison results of event data and target trajectory data in the area / location where the event occurred; distance is the nearest distance between the event area / location and the target's trajectory point information A; sumArea is the total area of ​​the region where the action debriefing took place;

[0027] Target comparison method: After cleaning the targets involved in the event data according to the cleaning rules, determine whether the target trajectory data contains the target. If it contains the target, return 0; if it does not contain the target, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

[0028] Secondly, the present invention also proposes an action debriefing device based on the correlation between events and situations, the device comprising:

[0029] The review condition parsing module is used to parse review conditions to obtain fuzzy query condition information;

[0030] The fuzzy search module is used to perform fuzzy search on event data and target trajectory data based on the fuzzy query conditions.

[0031] The correlation determination module is used to calculate the correlation degree between the event data and the target trajectory data on the fuzzy query condition information. If the correlation degree is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related.

[0032] The correlation acquisition module is used to analyze the associated target trajectory data and event data and acquire the correlation between target execution tasks and the correlation between targets;

[0033] The review data recommendation module is used to return review data according to the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between events and targets, the correlation between target execution tasks, or the correlation between targets.

[0034] Preferably, the fuzzy query condition information includes: time range information, occurrence area / location information, and target information.

[0035] Preferably, the correlation determination module calculates the correlation between the event data and the target trajectory data on the fuzzy query condition information, including:

[0036] The event data and target trajectory data are comprehensively compared across three dimensions: time, location / occurrence, and target, in the following manner:

[0037] Res=W_1*SJRes+W_2*DDRes+W_3*MBRes

[0038] Wherein, Res represents the correlation between event data and target trajectory data on the fuzzy query condition information; SJRes, DDRes, and MBRes are the comparison results of event data and target trajectory data in terms of time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system.

[0039] Preferably, the comparison method between event data and target trajectory data in terms of time, occurrence area / location, and target is as follows:

[0040] Time comparison formula:

[0041] SJRes=(sj-mb) / sumime

[0042] Wherein, SJRes represents the comparison result of event data and target trajectory data in time; sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span (in seconds) of the entire operation review.

[0043] Formula for comparing occurrence areas / locations (results: DDRes):

[0044] DDRes = distance / sumArea

[0045] Where DDRes represents the comparison results of event data and target trajectory data in the area / location where the event occurred; distance is the nearest distance between the event area / location and the target's trajectory point information A; sumArea is the total area of ​​the region where the action debriefing took place;

[0046] Target comparison method: After cleaning the targets involved in the event data according to the cleaning rules, determine whether the target trajectory data contains the target. If it contains the target, return 0; if it does not contain the target, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

[0047] Thirdly, the present invention proposes a computer-readable storage medium storing a computer program for action debriefing based on event and situation correlation, wherein the computer program causes a computer to execute the action debriefing method based on event and situation correlation as described above.

[0048] Fourthly, the present invention further proposes an electronic device, comprising:

[0049] One or more processors;

[0050] Memory; and

[0051] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including an action debriefing method based on event and situation correlation as described above.

[0052] (III) Beneficial Effects

[0053] This invention provides a method and apparatus for action debriefing based on the correlation between events and situations. Compared with the prior art, it has the following advantages:

[0054] 1. This invention first uses fuzzy query conditions obtained after analyzing the review conditions to perform a fuzzy search on relevant event data and target trajectory data. Then, it calculates the correlation between the event data and target trajectory data under the fuzzy query conditions to determine whether the event data and target trajectory data are related. Next, it analyzes the related target trajectory data and event data to obtain the correlation between target execution tasks and the correlation between targets. Finally, it returns review data to the user for action review. This invention's action review scheme based on the correlation between events and situations can automatically collect data and automatically mine the correlation between data, reducing the workload of operators and improving the reliability of analysis results.

[0055] 2. On the one hand, this invention uses conditional information such as time range, location, and target to quickly and automatically perform fuzzy search to retrieve relevant data for action review; on the other hand, it compares event data and target status in dimensions such as time, location, and target to determine whether there is a correlation between event data and target trajectory data. This not only reduces the workload of manually collecting relevant data and establishing the correlation between events and target status, but also improves the efficiency of related work.

[0056] 3. This invention analyzes the event data associated with the target and the target's historical trajectory to uncover the target's task execution or the relationship between targets. Compared with the traditional method of relying solely on the target's historical trajectory data and the order of entering and leaving the area to construct the relationship between targets, the analysis results are more reliable. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is an embodiment diagram of an action debriefing method based on the correlation between events and situations according to the present invention;

[0059] Figure 2 This is a flowchart illustrating the process of determining whether event data and target trajectory data are correlated in an embodiment of the present invention;

[0060] Figure 3 This is a flowchart illustrating the process of mining target execution tasks or the relationships between targets in an embodiment of the present invention;

[0061] Figure 4 This is a system block diagram of the action review device based on the correlation between events and situations in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] This application provides an action review method and apparatus based on the correlation between events and situations, which solves the problems of low efficiency and low accuracy in existing manual action review techniques, and achieves the goal of fast and accurate action review.

[0064] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0065] Existing technologies rely solely on manual data collection from different system functions, followed by manual establishment of correlations between events and target states to generate action review data. However, this approach is labor-intensive and time-consuming in manual data collection, and the process of constructing correlations between events and target states, target tasks, or between targets is cumbersome, resulting in low efficiency and low accuracy of analysis results. To address these issues, this invention first parses the review conditions input by the operator to obtain fuzzy query conditions, and then performs a fuzzy search for event data and target trajectory data in relevant databases based on these conditions. Next, it calculates the correlation between event data and target trajectory data based on the fuzzy query conditions to determine whether the event data and target trajectory data are related. Then, it analyzes the associated target trajectory data and event data to obtain correlations between target tasks and between targets. Finally, it uses this relevant data as review data and returns it to the user for action review according to the degree of correlation between targets and events.

[0066] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0067] Example 1:

[0068] Firstly, this invention proposes an action debriefing method based on the correlation between events and situations, see [link to relevant documentation]. Figure 1 The method includes:

[0069] S1. Analyze the review conditions to obtain fuzzy query condition information;

[0070] S2. Based on the fuzzy query conditions, perform a fuzzy search on the event data and the target trajectory data;

[0071] S3. Calculate the correlation between the event data and the target trajectory data on the fuzzy query condition information. If the correlation is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related.

[0072] S4. Analyze the associated target trajectory data and event data to obtain the correlation between the target's execution tasks and the correlation between targets;

[0073] S5. Return review data according to the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between the event and the target, the correlation between the target's execution tasks, or the correlation between targets.

[0074] As can be seen, this embodiment first performs a fuzzy search on relevant event data and target trajectory data based on the fuzzy query conditions obtained after parsing the review conditions. Then, it calculates the correlation between the event data and the target trajectory data under the fuzzy query conditions to determine whether the event data and the target trajectory data are related. Next, it analyzes the related target trajectory data and the event data to obtain the correlation between target execution tasks and the correlation between targets. Finally, it returns review data to the user for action review. This action review scheme based on the correlation between events and situations can automatically collect data and automatically mine the correlation between data, reducing the workload of operators and improving the reliability of the analysis results.

[0075] The following is in conjunction with the appendix Figure 1-3 The following details the implementation process of an embodiment of the present invention, including explanations of the specific steps S1-S5.

[0076] S1. Analyze the review conditions to obtain fuzzy query condition information.

[0077] The operators input the debriefing conditions into the combat command information system according to the instructions of their superiors. The system semantically analyzes the debriefing conditions and analyzes the time range, location, and targets involved, which are included in the debriefing conditions and are used for subsequent fuzzy query conditions.

[0078] For example, on the afternoon of April 3, 2022, the operators received a task instruction from their superiors to review the "transit of M / XX through the T Strait". After receiving the instruction, the operators created a new review case on the client and entered the review conditions: "Review the transit of M / XX through the T Strait from April 1 to April 3, 2022".

[0079] Then, the combat command information system analyzes the review conditions and outputs fuzzy query condition information, such as: time range: April 1, 2022 - April 3, 2022, location: T Strait, associated target: M / XX.

[0080] S2. Based on the fuzzy query conditions, perform a fuzzy search on the event data and the target trajectory data.

[0081] After the analysis of the review conditions in step S1 above, fuzzy query condition information is obtained. Based on this fuzzy query condition information, the combat command information system performs a fuzzy search for event data and target trajectory data in the tagged event database and target trajectory database, respectively. The target trajectory data is a description of the target's process from appearance to disappearance.

[0082] For example, a fuzzy search of the tagged event database based on fuzzy search conditions yielded 257 relevant event data; a fuzzy search of the target trajectory database based on fuzzy search conditions yielded 650 relevant target trajectory data, of which the 650 relevant target trajectory data included trajectory data for M / XX.

[0083] S3. Calculate the correlation between the event data and the target trajectory data on the fuzzy query condition information. If the correlation is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related.

[0084] The event data and target trajectory data are compared separately under each fuzzy query condition. In this embodiment, the event data and target trajectory data are compared in dimensions such as time, occurrence area / location, and target, and multiple sets of thresholds are used to determine whether the event data and target trajectory data are correlated.

[0085] Before comparing event data and target trajectory data, first identify the trajectory point information A in the target trajectory point list that is closest in time to the moment the event occurred. Then, use A as a parameter for subsequent calculations. Here, a target trajectory point is the attribute information of a single point on the target, and the target trajectory point list is a combination of a series of target trajectory points.

[0086] The process of comparing event data and target trajectory data in dimensions such as time, location, and target is as follows: Figure 2 As shown, the details are as follows:

[0087] 1) Time comparison formula (result: SJRes):

[0088] (sj-mb) / sumime

[0089] Where sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span (in seconds) of the entire operation review.

[0090] 2) Comparison formula for occurrence area / location (results: DDRes):

[0091] distance / sumArea

[0092] Where distance is the closest distance (in meters) from the event location to the target's trajectory point information A. sumArea is the total area (in square meters) of the area where the action debriefing took place.

[0093] 3) Target Comparison Formula (Result: MBRes): The targets involved in the event data are cleaned according to the cleaning rules, and it is determined whether they contain the targets corresponding to the target trajectory data. If they contain them, return 0; if they do not contain them, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

[0094] Then, the three calculation results above are applied using the formula: Res=W_1*SJRes+W_2*DDRes+W_3*MBRes. Where SJRes, DDRes, and MBRes are the comparison results for time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system. ， The system default values ​​are 2, 5, and 3, but users can also manually set these threshold parameters according to their actual needs.

[0095] Finally, the similarity threshold (Res) is compared to see if it is greater than the preset similarity threshold. If it is greater than the threshold, the event data and the target trajectory data are considered to be related; otherwise, they are not related. The similarity threshold is 6 by default, but it can be manually modified and adjusted as needed.

[0096] For example, the event data "At time m, an unidentified vessel was spotted in sea area Q" is labeled as: "Time: time m, Location: sea area Q, Target involved: unidentified vessel". By comparing this with the location coordinates of M / XX at that time, the calculation model shows that this event data and M / XX are related.

[0097] S4. Analyze the associated target trajectory data and event data to obtain the correlation between target execution tasks and the correlation between targets.

[0098] Event data may describe the relationships between targets in textual form, such as: "An aircraft YY took off from XX airport and headed to ZZ airspace to intercept and drive away an M / P-8A." 。 By analyzing event data associated with the target and the target's historical trajectory data, we can uncover the relationships between the target's tasks or between targets themselves. See also... Figure 3 First, we extract the named entities contained in the event (in this embodiment, we take weapons and equipment as entities), then clean them using weapon and equipment cleaning rules and transform them to obtain the standard names of weapons and equipment (named entities), and finally analyze the relationships between weapons and equipment based on the dependency syntax tree.

[0099] For example, semantic parsing of event data extracts named entities and the relationships between entities: A target, expulsion, B target. Then target A and target B have a relationship of expulsion and being expelled.

[0100] S5. Return review data according to the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between the event and the target, the correlation between the target's execution tasks, or the correlation between targets.

[0101] The combat command information system returns review data, such as event data, target trajectory data, the correlation between events and targets, the correlation between targets performing tasks, or the correlation between targets, in descending order of the correlation between targets and events, to support users in reviewing their actions.

[0102] This completes the entire process of the action review method based on the correlation between events and situations according to the present invention.

[0103] Example 2:

[0104] Secondly, the present invention also provides an action review device based on the correlation between events and situations, see [link to relevant documentation]. Figure 4 The device includes:

[0105] The review condition parsing module is used to parse review conditions to obtain fuzzy query condition information;

[0106] The fuzzy search module is used to perform fuzzy search on event data and target trajectory data based on the fuzzy query conditions.

[0107] The correlation determination module is used to calculate the correlation degree between the event data and the target trajectory data on the fuzzy query condition information. If the correlation degree is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related.

[0108] The correlation acquisition module is used to analyze the associated target trajectory data and event data and acquire the correlation between target execution tasks and the correlation between targets;

[0109] The review data recommendation module is used to return review data according to the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between events and targets, the correlation between target execution tasks, or the correlation between targets.

[0110] Optionally, the fuzzy query conditions include: time range information, occurrence area / location information, and target information.

[0111] Optionally, the correlation determination module calculates the correlation between the event data and the target trajectory data on the fuzzy query condition information, including:

[0112] The event data and target trajectory data are comprehensively compared across three dimensions: time, location / occurrence, and target, in the following manner:

[0113] Res=W_1*SJRes+W_2*DDRes+W_3*MBRes

[0114] Wherein, Res represents the correlation between event data and target trajectory data on the fuzzy query condition information; SJRes, DDRes, and MBRes are the comparison results of event data and target trajectory data in terms of time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system.

[0115] Optionally, the comparison methods between event data and target trajectory data in terms of time, occurrence area / location, and target are as follows:

[0116] Time comparison formula:

[0117] SJRes=(sj-mb) / sumime

[0118] Wherein, SJRes represents the comparison result of event data and target trajectory data in time; sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span (in seconds) of the entire operation review.

[0119] Formula for comparing occurrence areas / locations (results: DDRes):

[0120] DDRes = distance / sumArea

[0121] Where DDRes represents the comparison results of event data and target trajectory data in the area / location where the event occurred; distance is the nearest distance between the event area / location and the target's trajectory point information A; sumArea is the total area of ​​the region where the action debriefing took place;

[0122] Target comparison method: After cleaning the targets involved in the event data according to the cleaning rules, determine whether the target trajectory data contains the target. If it contains the target, return 0; if it does not contain the target, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

[0123] It is understood that the action review system based on the correlation between events and situations provided in this embodiment of the invention corresponds to the action review method based on the correlation between events and situations described above. The explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding content in the action review method based on the correlation between events and situations, and will not be repeated here.

[0124] Example 3:

[0125] A computer-readable storage medium storing a computer program for action debriefing based on event and situation correlations, wherein the computer program causes a computer to execute the action debriefing method based on event and situation correlations as described above.

[0126] Example 4:

[0127] An electronic device, comprising:

[0128] One or more processors;

[0129] Memory; and

[0130] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including an action debriefing method based on event and situation correlation as described above.

[0131] In summary, compared with existing technologies, it has the following beneficial effects:

[0132] 1. This invention first uses fuzzy query conditions obtained after analyzing the review conditions to perform a fuzzy search on relevant event data and target trajectory data. Then, it calculates the correlation between the event data and target trajectory data under the fuzzy query conditions to determine whether the event data and target trajectory data are related. Next, it analyzes the related target trajectory data and event data to obtain the correlation between target execution tasks and the correlation between targets. Finally, it returns review data to the user for action review. This invention's action review scheme based on the correlation between events and situations can automatically collect data and automatically mine the correlation between data, reducing the workload of operators and improving the reliability of analysis results.

[0133] 2. On the one hand, this invention uses conditional information such as time range, location, and target to quickly and automatically perform fuzzy search to retrieve relevant data for action review; on the other hand, it compares event data and target status in dimensions such as time, location, and target to determine whether there is a correlation between event data and target trajectory data. This not only reduces the workload of manually collecting relevant data and establishing the correlation between events and target status, but also improves the efficiency of related work.

[0134] 3. This invention analyzes the event data associated with the target and the target's historical trajectory to uncover the target's task execution or the relationship between targets. Compared with the traditional method of relying solely on the target's historical trajectory data and the order of entering and leaving the area to construct the relationship between targets, the analysis results are more reliable.

[0135] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for action debriefing based on the correlation between events and situations, characterized in that, The method includes: Analyze the review conditions to obtain fuzzy query condition information; Based on the aforementioned fuzzy query conditions, perform a fuzzy search on the event data and the target trajectory data; Calculate the correlation between the event data and the target trajectory data on the fuzzy query condition information. If the correlation is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related. Analyze the associated target trajectory data and event data to obtain the correlation between the target's execution tasks and the correlation between targets; The review data is returned based on the degree of correlation between the target and the event for the user's action review; the review data includes event data, target trajectory data, the correlation between events and targets, the correlation between tasks performed by targets, or the correlation between targets; The comparison methods between event data and target trajectory data in terms of time, location, and target are as follows: Time comparison formula: SJRes=(sj-mb) / sumime Wherein, SJRes represents the comparison result of event data and target trajectory data in time; sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span of the entire operation review. Formula for comparing the areas / locations where the event occurs: DDRes = distance / sumArea Where DDRes represents the comparison results of event data and target trajectory data in the area / location where the event occurred; distance is the nearest distance between the event area / location and the target's trajectory point information A; sumArea is the total area of ​​the region where the action debriefing took place; Target comparison method: After cleaning the targets involved in the event data according to the cleaning rules, determine whether the target trajectory data contains the target. If it contains the target, return 0; if it does not contain the target, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

2. The method as described in claim 1, characterized in that, The fuzzy query conditions include: time range information, occurrence area / location information, and target information.

3. The method as described in claim 2, characterized in that, The calculation of the correlation between the event data and the target trajectory data on the fuzzy query condition information includes: The event data and target trajectory data are comprehensively compared across three dimensions: time, location / occurrence, and target, in the following manner: Res=W_1*SJRes+W_2*DDRes+W_3*MBRes Wherein, Res represents the correlation between event data and target trajectory data on the fuzzy query condition information; SJRes, DDRes, and MBRes are the comparison results of event data and target trajectory data in terms of time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system.

4. An action debriefing device based on the correlation between events and situations, characterized in that, The device includes: The review condition parsing module is used to parse review conditions to obtain fuzzy query condition information; The fuzzy search module is used to perform fuzzy search on event data and target trajectory data based on the fuzzy query conditions. The correlation determination module is used to calculate the correlation degree between the event data and the target trajectory data on the fuzzy query condition information. If the correlation degree is not greater than a preset similarity threshold, it is determined that the event data and the target trajectory data are not related; otherwise, it is determined that the event data and the target trajectory data are related. The correlation acquisition module is used to analyze the associated target trajectory data and event data and acquire the correlation between target execution tasks and the correlation between targets; The debriefing data recommendation module is used to return debriefing data according to the degree of correlation between the target and the event for the user's action debriefing; the debriefing data includes event data, target trajectory data, the correlation between events and targets, the correlation between tasks performed by targets, or the correlation between targets; The comparison methods between event data and target trajectory data in terms of time, location, and target are as follows: Time comparison formula: SJRes=(sj-mb) / sumime Wherein, SJRes represents the comparison result of event data and target trajectory data in time; sj is the number of seconds since the event occurred in 1970; mb is the number of seconds since the target's trajectory point information A occurred in 1970; and sumime is the time span of the entire operation review. Formula for comparing the areas / locations where the event occurs: DDRes = distance / sumArea Where DDRes represents the comparison results of event data and target trajectory data in the area / location where the event occurred; distance is the nearest distance between the event area / location and the target's trajectory point information A; sumArea is the total area of ​​the region where the action debriefing took place; Target comparison method: After cleaning the targets involved in the event data according to the cleaning rules, determine whether the target trajectory data contains the target. If it contains the target, return 0; if it does not contain the target, but there is a corresponding aircraft or ship type, return 0.25; if the event contains unidentified equipment, return 0.5; otherwise, return 1.

5. The apparatus as described in claim 4, characterized in that, The fuzzy query conditions include: time range information, occurrence area / location information, and target information.

6. The apparatus as claimed in claim 5, characterized in that, The correlation determination module calculates the correlation between the event data and the target trajectory data on the fuzzy query condition information, including: The event data and target trajectory data are comprehensively compared across three dimensions: time, location / occurrence, and target, in the following manner: Res=W_1*SJRes+W_2*DDRes+W_3*MBRes Wherein, Res represents the correlation between event data and target trajectory data on the fuzzy query condition information; SJRes, DDRes, and MBRes are the comparison results of event data and target trajectory data in terms of time, occurrence area / location, and target, respectively; W_1, W_2, and W_3 are the threshold parameters configured by the system.

7. A computer-readable storage medium, characterized in that, It stores a computer program for action debriefing based on event and situation correlation, wherein the computer program causes a computer to execute the action debriefing method based on event and situation correlation as described in any one of claims 1-3.

8. An electronic device, characterized in that, include: One or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including an action debriefing method based on event and situation correlation as described in any one of claims 1-3.

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