A threat situation assessment display method and system based on comprehensive weights

Abstract

The application provides a threat situation assessment display method and system based on comprehensive weights, comprising: obtaining multi-dimensional threat feature data of threat entities and state data and spatial position data of threatened entities; using a threat level membership function to standardize the multi-dimensional threat feature data corresponding to each threat entity to obtain a standardized feature matrix; using an entropy weight method and an analytic hierarchy process to generate first and second weight vectors, determining linear combination coefficients of the two vectors according to an optimization objective function, and obtaining a comprehensive weight vector; constructing a weighted normalization matrix according to the comprehensive weight vector, determining positive and negative ideal solutions, and calculating the relative closeness of each threat entity; screening threat entities in a situation display area according to the closeness from high to low to obtain a display set and a hidden set; and grading and rendering entities in the display set according to the closeness, reserving data links of entities in the hidden set and not rendering the entities, to realize accurate threat level assessment and target adaptive display.

Description

Technical Field

[0001] This application relates to the field of security protection technology, and in particular to a threat situation assessment and display method and system based on comprehensive weights. Background Technology

[0002] Threat situation assessment refers to the process of comprehensively analyzing and determining the threat level, priority, and evolution trend of objects in the current scenario based on collected information such as targets, events, environment, and business status. The results of threat situation assessment are usually visualized through maps, topology diagrams, situation maps, or monitoring interfaces to help security operations personnel, command personnel, or decision-makers quickly identify high-risk objects and take response measures.

[0003] In application scenarios such as cybersecurity, urban low-altitude security, border and coastal defense monitoring, and protection of critical areas, threats are characterized by diverse sources, high density, rapid changes in status, dynamic spatial location, and heterogeneous threat attributes. For example, attack sources, abnormal traffic, and attacked assets in cyberspace, drone targets in low-altitude scenarios, and suspicious moving targets in border and coastal defense scenarios all require comprehensive judgment based on factors such as distance, speed, direction of movement, target capabilities, environmental conditions, and the importance of the protected object.

[0004] Existing threat posture assessment solutions typically employ pre-set fixed weights and static thresholds to linearly weight and sum multiple threat features. The weights are often determined solely based on historical data or expert experience, making it difficult to adjust them in response to changes in threat behavior, target status, and environmental scenarios. This can easily lead to inaccurate identification of high-risk targets and distorted threat priority ranking. Furthermore, existing solutions lack unified and differentiated quantification processing for static attributes and dynamic behavioral features of different natures, resulting in insufficient assessment accuracy in complex scenarios.

[0005] Furthermore, current situational awareness displays typically employ fixed hierarchies and uniform rendering methods. When there are a large number of threat targets or they are densely distributed, issues such as overlapping icons, text obscuring, and information overload can easily occur. Low-risk targets may obscure high-risk targets, which does not conform to the priority logic of situational awareness.

[0006] Therefore, it is necessary to provide a threat situation assessment and display method and system that can dynamically determine comprehensive weights based on multi-dimensional threat characteristics, realize priority screening of threat objects, and adaptive visualization. Summary of the Invention

[0007] This application provides a threat situation assessment and display method and system based on comprehensive weights, which can achieve accurate threat assessment, adaptive display, and rapid response in threat situation assessment and display.

[0008] In a first aspect, embodiments of this application provide a threat posture assessment and display method based on comprehensive weights, the method comprising: Acquire multidimensional threat characteristic data of each threat entity in the threat entity set, as well as the state data and spatial location data of the threatened entity; the multidimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics and environmental characteristics, and the spatial distance and the entry angle are determined based on the spatial location data; The multidimensional threat feature data corresponding to each threat entity are standardized using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data. Based on the standardized feature matrix, the first weight vector is determined by the entropy weight method, the second weight vector is determined by the analytic hierarchy process, and the linear combination coefficients of the first weight vector and the second weight vector are determined according to the preset optimization objective function to obtain the comprehensive weight vector. A weighted normalized matrix is ​​constructed based on the comprehensive weight vector, and positive and negative ideal solutions are determined based on the weighted normalized matrix. The distances between each threat entity and the positive and negative ideal solutions are calculated to determine the relative proximity of each threat entity. According to the order of relative proximity from high to low, the threat entities in the situation display area are filtered to obtain the threat entity display set and the threat entity hidden set; Based on the relative proximity of each threat entity in the threat entity display set, hierarchical visualization rendering is performed, while for threat entities in the threat entity hidden set, data links are preserved but no graphics rendering is performed.

[0009] Secondly, embodiments of this application provide a threat situation assessment and display system based on comprehensive weights, the system comprising: The data acquisition module is used to acquire multi-dimensional threat characteristic data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entity; the multi-dimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics and environmental characteristics, and the spatial distance and the entry angle are determined based on the spatial location data; The standardization processing module is used to standardize the multidimensional threat feature data corresponding to each threat entity using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data. The comprehensive weight determination module is used to determine the first weight vector using the entropy weight method and the second weight vector using the analytic hierarchy process based on the standardized feature matrix, and to determine the linear combination coefficient of the first weight vector and the second weight vector based on the preset optimization objective function, so as to obtain the comprehensive weight vector. The proximity calculation module is used to construct a weighted normalized matrix based on the comprehensive weight vector, determine the positive ideal solution and the negative ideal solution based on the weighted normalized matrix, calculate the distance between each threat entity and the positive ideal solution and the negative ideal solution respectively, and determine the relative proximity of each threat entity; The dynamic filtering module is used to filter threat entities in the situation display area according to the order of relative proximity from high to low, so as to obtain a threat entity display set and a threat entity hidden set. The hierarchical rendering module is used to perform hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and to retain the data link for threat entities in the threat entity hidden set without performing graphics rendering.

[0010] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a communication interface; wherein, the memory stores a computer program, and when the computer program is executed by the processor, the processor can at least implement the threat situation assessment and display method based on comprehensive weights as described in the first aspect.

[0011] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor of an electronic device, enables the processor to at least implement the threat posture assessment and presentation method based on comprehensive weights as described in the first aspect.

[0012] Fifthly, embodiments of this application provide a computer program product, including: a computer program or instructions, which, when executed by a processor of an electronic device, enable the processor to at least implement the threat posture assessment and display method based on comprehensive weights as described in the first aspect.

[0013] The solution provided in this application first constructs a multi-dimensional threat feature quantification system covering spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics, and establishes corresponding threat level membership functions for standardization processing, so that multi-source heterogeneous threat data are uniformly mapped to a comparable metric space. Next, the entropy weighting method and the analytic hierarchy process (AHP) are used to generate a first weight vector reflecting the inherent discriminative power of the data and a second weight vector reflecting prior experience and cognition, respectively. The linear combination coefficient is determined by minimizing the sum of squared deviations between the comprehensive weight and the two types of weight vectors, ensuring that the final comprehensive weight retains both objective data statistical regularities and subjective experience and cognition. This solves the drawback of traditional fixed weights being unable to adapt to dynamic attack and defense scenarios, and improves the accuracy and reliability of multi-dimensional threat level assessment. Subsequently, based on a comprehensive weighting of the proximity of each threat entity, a refined sorting is performed. Then, a greedy selection process is applied, combining proximity from high to low with pixel display distance and scale constraints, prioritizing the display of high-level threats and hiding low-level threats that conflict with already displayed entities in pixel space. This solves the problems of icon overlap and information overload in massive target scenarios, ensuring that critical threats are not covered by low-risk data. Finally, the displayed sets are assigned differentiated brightness, size, and blinking effects according to proximity levels, while the hidden sets retain data links but are not rendered, resulting in a clear hierarchy and highlighting of key points in the situational awareness display. Therefore, this application's solution achieves accurate threat assessment, adaptive display, highlighting of key points, and rapid response in situational awareness presentation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a threat posture assessment and presentation method based on comprehensive weights, provided for embodiments of this application; Figure 2 A schematic diagram illustrating a threat posture assessment and presentation method provided in this application embodiment; Figure 3 A schematic diagram illustrating the process of determining a threat entity display and hiding set as provided in an embodiment of this application; Figure 4 A schematic diagram of the structure of a threat situation assessment and display system based on comprehensive weights, provided for an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be noted that, in the cases involving user information in the embodiments of this application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) comply with relevant laws and standards.

[0018] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0019] The threat posture assessment and display method based on comprehensive weights provided in this application can be executed by an electronic device, such as a PC, laptop, or smartphone, or a server. The server can be a physical server containing an independent host, a virtual server, a cloud server, or a server cluster.

[0020] Figure 1 A flowchart illustrating a threat posture assessment and presentation method based on comprehensive weights, as provided in this application embodiment, is shown below. Figure 1 As shown, it may include the following steps 101-106: 101. Obtain multidimensional threat characteristic data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entities; the multidimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics and environmental characteristics, and the spatial distance and entry angle are determined based on the spatial location data.

[0021] 102. Standardize the multidimensional threat feature data corresponding to each threat entity using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data.

[0022] 103. Based on the standardized feature matrix, the first weight vector is determined by the entropy weight method, the second weight vector is determined by the analytic hierarchy process, and the linear combination coefficients of the first and second weight vectors are determined according to the preset optimization objective function to obtain the comprehensive weight vector.

[0023] 104. Construct a weighted normalized matrix based on the comprehensive weight vector, and determine the positive ideal solution and the negative ideal solution based on the weighted normalized matrix. Calculate the distance between each threat entity and the positive ideal solution and the negative ideal solution respectively, and determine the relative proximity of each threat entity.

[0024] 105. According to the order of relative proximity from high to low, the threat entities in the situation display area are filtered to obtain the threat entity display set and the threat entity hidden set.

[0025] 106. Perform hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and retain the data link for threat entities in the threat entity hidden set without performing graphics rendering.

[0026] In practical applications, existing threat posture assessment and display solutions suffer from numerous technical problems. For example, weight parameters rely on historical data or expert experience for pre-setting, failing to adapt to dynamic changes in network attack behavior, threat target status, and environmental scenarios, leading to distorted risk assessments and inaccurate identification of high-risk threats. Furthermore, posture display employs a fixed hierarchy and uniform rendering strategy; when the number of threat targets in the scene surges, icon overlap, text obscuring, and information overload occur, with critical high-risk threats being covered and hidden by a large amount of low-risk data, resulting in a lack of focus in the posture display. These issues impact the efficiency and accuracy of security operations and maintenance analysis and command decision-making.

[0027] To address at least one of the aforementioned technical problems, embodiments of this application provide a threat situation assessment and display method based on comprehensive weighting. In this method, for threat entities, a multi-dimensional threat feature quantification system is constructed, encompassing spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics. A combined weighting mechanism combining entropy weighting and analytic hierarchy process (AHP) is employed to achieve dynamic adaptive updates of the comprehensive weights, improving the accuracy and reliability of multi-dimensional threat level assessment. Furthermore, based on the relative proximity of threat entities from high to low, adaptive filtering is performed according to pixel space constraints within the situation display area, prioritizing the display of high-level threats and automatically hiding low-level threats, effectively solving the problems of visual congestion and information overload in scenarios with massive numbers of targets. Further, the displayed entities are rendered in a hierarchical manner according to threat level, while hidden entities retain data links but are not rendered graphically, achieving a clear and focused situation display effect.

[0028] The threat situation assessment and display scheme based on comprehensive weights provided in this application can be applied to scenarios such as cyberspace security defense, airspace surveillance and monitoring, maritime situation awareness, urban low-altitude security and border control. It can accurately assess and visualize the situation in complex environments with a large number of threat targets, diverse types and dynamic states.

[0029] Figure 2 This is a schematic diagram illustrating a threat situation assessment and presentation method provided in an embodiment of this application. Next, in conjunction with... Figure 2 Taking steps 101 to 106 as examples, the threat situation assessment and display method based on comprehensive weights provided in this application will be explained in detail.

[0030] 101. Obtain multidimensional threat characteristic data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entities; the multidimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics and environmental characteristics, and the spatial distance and entry angle are determined based on the spatial location data.

[0031] In this embodiment, a threat entity refers to a target object that may pose a security threat to a threatened entity. A threatened entity refers to the reference object and protection target of the threat assessment, that is, the object threatened by the threat entity.

[0032] The specific types of threat entities and threatened entities differ across application scenarios. For example, in cyberspace security scenarios, threat entities can be source IP addresses, malicious domains, network data streams, or attack source hosts that launch attacks against network nodes, while threatened entities can be attacked servers, network nodes, or data centers. In airspace surveillance scenarios, threat entities can be aircraft, missiles, or drones, while threatened entities can be key locations or facilities within the surveillance area. In maritime surveillance scenarios, threat entities can be ships, submarines, or torpedoes, while threatened entities can be ports or islands. In urban low-altitude security scenarios, threat entities can be low-altitude, slow-moving small aircraft, while threatened entities can be core protected areas, important buildings, or activity areas.

[0033] This application does not limit the specific types of threatening entities and threatened entities. Any entity that can pose a potential security threat to the threatened entity can be treated as a threatening entity.

[0034] The threat entity set refers to a set consisting of at least one threat entity, denoted as S={S1,S2,...,Sn}, where n is the total number of threat entities. The value of n is determined based on the actual number of threat targets detected. When there are no threat targets in the monitoring area, the value of n is 0, and the subsequent evaluation steps can be skipped to directly output the security status.

[0035] For any threat entity Si in the threat entity set, during the execution of the threat situation assessment and display method provided in this application embodiment, the multi-dimensional threat feature data corresponding to the threat entity Si is first obtained, and a multi-dimensional threat feature set corresponding to the threat entity Si is constructed. ,in, Let Si be the dimension of the threat feature. .

[0036] Optionally, the set of multidimensional threat features corresponding to the threat entity Si includes threat features of at least one dimension among spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics.

[0037] Spatial distance refers to the straight-line distance between the threatening entity and the threatened entity, which can be calculated based on the spatial coordinates of the threatening entity and the threatened entity.

[0038] Movement speed represents the rate at which a threat entity moves in space. It can be calculated by dividing the change in the position of the threat entity at adjacent time points by the time interval, or it can be obtained directly through radar speed measurement, ADS-B (Automatic Dependent Surveillance-Broadcast) message parsing, or other speed measurement methods.

[0039] The approach angle represents the azimuth angle of a threatening entity relative to the direction of approach of a threatened entity. It characterizes the direction in which the threatening entity approaches the threatened entity, and different approach angles imply different threat situations. For example, when a threatening entity is approaching the threatened entity at high speed, its threat level is generally higher than that of a threatening entity flying tangentially or away from it. Optionally, the motion direction vector of the threatening entity can be determined based on its current position coordinates and its position coordinates at the previous moment. Then, the angle between this motion direction vector and the direction vector of the threatened entity pointing towards the threatening entity's position can be calculated, and this angle can be used as the approach angle.

[0040] Target type characteristics represent the target category to which a threat entity belongs and its corresponding inherent threat attributes. Different target types have different threat level baselines. For example, in airspace scenarios, fighter jets have a higher inherent threat level than civilian airliners; in maritime scenarios, warships have a higher inherent threat level than merchant ships; and in network scenarios, known malicious IPs have a higher inherent threat level than unknown IPs. Optionally, a mapping table between target types and characteristic values ​​can be preset according to the actual application scenario. This mapping table pre-sets the characteristic values ​​corresponding to each type of target. The value range of target type characteristic values ​​is [0, 10], with larger values ​​indicating a higher inherent threat level for that type of target. When the threat entity type cannot be identified or belongs to a new unknown target, the target type characteristic value can be set to an intermediate value or compensated and corrected through subsequent other dimensional features.

[0041] Target capability characteristics represent the level of a threat entity's ability to carry out attacks, reconnaissance, jamming, breaching, or other threatening actions. Target capability characteristics reflect the potential harm a threat entity can pose to a target at the physical or logical level, and are typically related to parameters such as the threat entity's payload capacity, strike radius, and penetration capability. In practice, target capability characteristic values ​​can be comprehensively assessed based on objective parameters such as the threat entity's maximum effective range, maximum flight range, and payload, or determined based on intelligence data or pre-defined capability assessment rules.

[0042] Environmental characteristics are used to represent the impact of the environment in which a threatening entity is located on its threat level. The introduction of environmental characteristics allows this application's solution to perceive the influence of external environmental factors on threat assessment results. For example, under adverse weather conditions, the maneuverability of an aircraft is limited, and its actual threat level may be lower than the assessment results under ideal conditions; similarly, in areas with strong electromagnetic interference, the navigation accuracy of UAVs decreases or communication is interrupted, and their threat level may decrease accordingly; in maritime scenarios, when sea states are high, the attack accuracy and stability of small vessels decrease, and their threat level also decreases accordingly. Optionally, the main environmental factors affecting the threat level can be selected according to the actual scenario, and a mapping relationship between environmental factors and characteristic values ​​can be preset to obtain environmental characteristic values ​​by comprehensively considering various environmental factors.

[0043] Spatial location data of the threatened entity is used to determine the spatial distance and approach angle of the threatening entity relative to the threatened entity. Spatial location data may include the coordinate information of the threatened entity in three-dimensional space or two-dimensional plane, which can be obtained through satellite positioning, ground-based radar, inertial navigation or other positioning methods.

[0044] The status data of the threatened entity is used to determine at least one coefficient parameter in the threat level membership function. The status data may include, but is not limited to, the threatened entity's protection level, mission importance level, current mobility status, equipment availability, and personnel configuration status. For example, when the threatened entity's protection level is low, the distance coefficient in the distance membership function can be adjusted accordingly to increase the threat assessment result at the same distance, reflecting the objective fact that the threatened entity is more vulnerable. The specific type and value range of the status data can be flexibly set by those skilled in the art according to the actual application scenario; this application embodiment does not limit this.

[0045] Optionally, the spatial distance, movement speed, approach angle, target type characteristics, target capability characteristics, and environmental characteristics in the multidimensional threat characteristic data can be acquired in real time or within a certain time window. In specific implementations, the aforementioned multidimensional threat characteristic data can be obtained by fusing multiple data sources, such as acquiring the spatial location and movement speed data of threat entities through radar equipment, acquiring the target type and capability information of threat entities through signals intelligence, and acquiring environmental data through meteorological observations. This application does not limit the specific source and acquisition method of the multidimensional threat characteristic data in its embodiments.

[0046] Taking an airspace threat situational awareness scenario as an example, assuming there are n=50 threat entities (such as aircraft) in a certain airspace at a certain moment, the spatial coordinates and speed of each aircraft can be obtained through radar detection data, the target type and capability characteristics of each aircraft can be obtained through an intelligence database, the environmental characteristics of the current airspace can be obtained through a meteorological observation system, the spatial coordinates of the threatened entities can be obtained through their positioning systems, and their current protection level can be obtained through a mission planning system. Then, the above data is organized according to a preset data structure to form multi-dimensional threat characteristic data corresponding to each of the 50 threat entities, as well as the status and spatial location data of the threatened entities, which serve as inputs for subsequent evaluation steps.

[0047] 102. Standardize the multidimensional threat feature data corresponding to each threat entity using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data.

[0048] In practical applications, the threat features in the multi-dimensional threat feature set corresponding to the threat entity Si, such as spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics, have different dimensions and orders of magnitude. For example, spatial distance may range from hundreds to thousands of meters, movement speed may range from tens to hundreds of meters per second, while target type characteristics are dimensionless scores. If the above multi-dimensional threat feature data is directly weighted and fused, the difference in dimensions will cause large numerical features to dominate the evaluation results, while the information of small numerical features will be overwhelmed, failing to objectively reflect the actual contribution of each dimension to the threat level. Therefore, it is necessary to uniformly map the feature data of each dimension to the same metric scale for comparison and fusion.

[0049] As an optional method for standardizing multidimensional threat features, corresponding threat level membership functions can be established for spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics. Then, the multidimensional threat feature data of each threat entity is substituted into the corresponding threat level membership function to obtain the feature membership degree, which represents the contribution of each threat feature to the threat level. The feature membership degree ranges from 0 to 1, and the larger the feature membership degree, the greater the contribution of the corresponding threat feature to the threat level. Finally, based on the feature membership degrees corresponding to the multidimensional threat feature data of each threat entity, a standardized feature matrix corresponding to each threat entity is constructed.

[0050] This scheme uses spatial distance membership function, motion speed membership function, entry angle membership function, target type characteristic membership function, target capability characteristic membership function and environmental characteristic membership function as examples for illustrative purposes, but it is not limited to these.

[0051] The spatial distance membership function maps the spatial distance between a threatening entity and a threatened entity to a threat membership degree in the distance dimension. Generally, the closer a threatening entity is to a threatened entity, the greater the threat it can pose, and the higher its distance threat membership degree should be; conversely, the farther a threatening entity is from a threatened entity, the lower its threat level.

[0052] In practice, the spatial distance membership function takes the following form: in, As a threatening entity Distance to the threatened entity This is the distance coefficient, for example, with a value of 100. This function has the property of monotonically decreasing as distance increases, and its range is (0,1). Distance coefficient The value of determines how quickly the membership degree decays with distance. The larger the value, the slower the decay, indicating that a certain degree of threat feature membership can still be maintained at a greater distance; The smaller the value, the faster the decay, indicating that the threat feature membership degree is only significant within a relatively short distance.

[0053] The motion speed membership function maps the motion speed of a threat entity to a threat membership degree in the speed dimension. Generally, the faster a threat entity moves, the more obvious its penetration capability and reaction time advantage, and the greater the threat it poses to the target; conversely, the slower the speed, the lower the threat level.

[0054] In practice, the membership function for motion velocity takes the following form: in, As a threatening entity Real-time motion speed, in m / s. This is the velocity coefficient, for example, a value of 340. This function monotonically increases with increasing velocity, and its range is [0,1). Velocity coefficient The value of determines how quickly the membership degree increases with speed. The larger the value, the slower the membership level increases, indicating that a higher rate is needed to reach the same threat membership level. The smaller the value, the faster the membership degree increases, meaning that a higher threat membership degree can be generated at a lower speed.

[0055] The entry angle membership function maps the entry angle of a threatening entity to a threat membership degree in the angular dimension. The entry angle reflects the direction in which the threatening entity approaches the threatened entity. Generally, the threat level is highest when the threatening entity approaches the threatened entity head-on; the threat level is lower when it flies tangentially or away from the threatened entity.

[0056] In practice, the entry angle membership function takes the following form: in, As a threatening entity The entry angle is defined as [0, π], where 0° indicates the threatening entity is flying towards the threatened entity, 180° indicates the threatening entity is flying away from the threatened entity, and 90° indicates the threatening entity is flying tangentially. The function takes a maximum value of 1 at 0° and a minimum value of 0 at 180°.

[0057] The target type attribute membership function maps the target type attribute value of a threat entity to the threat membership degree of the type dimension. The target type attribute value reflects the category attribute of the threat entity and its inherent threat level; different target types correspond to different threat baselines.

[0058] In practice, the membership function of the target type characteristic takes the following form: in, As a threatening entity The target type characteristic value has a numerical range of [0, 10]. For type coefficients.

[0059] Optionally, The possible value is 5. In practical applications, The specific value can be adjusted based on the state data of the threatened entity. For example, when the threatened entity is performing a high-value task, the value can be reduced accordingly. The value of increases the threat membership assessment value under the same target type, reflecting the increased sensitivity of high-value targets to various threats.

[0060] The target capability characteristic membership function maps the target capability characteristic value of a threat entity to the threat membership degree of the capability dimension. The target capability characteristic value reflects the potential capability level of the threat entity to carry out threat behavior; the stronger the capability, the higher the threat level.

[0061] In practice, the membership function of the target capability characteristic takes the following form: in, As a threatening entity Maximum target capability characteristic value, This is the target capability characteristic coefficient, which can be set to 100. The higher the capability characteristic value, the greater the potential harm that the threatening entity can cause, and the higher its threat membership degree.

[0062] The environmental characteristic membership function maps the environmental characteristic value of a threat entity to a threat membership degree in the environmental dimension. Environmental characteristics reflect the influence of external environmental conditions on the threat level of a threat entity. The more favorable the environmental conditions are for a threat entity to exert its threat capabilities, the higher the environmental threat membership degree; conversely, the less favorable the environmental conditions are for a threat entity to exert its threat capabilities, the lower the environmental threat membership degree.

[0063] In practice, the environmental characteristic membership function takes the following form: in, As a threatening entity Environmental characteristic values, with a range of [0, 10]. This is the type coefficient, which can take a value of 5. The higher the environmental characteristic value, the more favorable the current environmental conditions are for the threatening entity to exert its threatening capabilities, and the higher the threat contribution of the environmental dimension.

[0064] In practice, the original multidimensional feature data of each threat entity Si can be obtained through the above six threat level membership functions. Each threat feature Substitute the corresponding membership function Calculate single threat characteristics Corresponding threat level That is, the feature membership degree; based on the six feature membership degrees corresponding to the threat entity Si. This yields a standardized data feature membership vector where all six dimensions are mapped to [0,1]. ; The feature membership vectors corresponding to each threat entity Arrange them by row to construct a standardized feature matrix corresponding to each threat entity. Wherein, the i-th row corresponds to the standardized feature vector of threat entity Si, and the j-th column corresponds to the membership distribution of the j-th dimension threat feature across all threat entities.

[0065] 103. Based on the standardized feature matrix, the first weight vector is determined by the entropy weight method, the second weight vector is determined by the analytic hierarchy process, and the linear combination coefficients of the first and second weight vectors are determined according to the preset optimization objective function to obtain the comprehensive weight vector.

[0066] As mentioned earlier, the standardized feature matrix Each column corresponds to the membership degree distribution of the same threat feature across all threat entities, and each row corresponds to the membership degree vector of the same threat entity across the six feature dimensions. Since different threat features have varying importance for threat level assessment, each feature dimension needs to be assigned a corresponding weight coefficient.

[0067] In practical applications, single weighting methods have inherent limitations. For example, relying solely on data-driven objective weighting may ignore domain experience and security awareness, while relying solely on experience-driven subjective weighting may be influenced by human bias and is difficult to adapt to dynamically changing data distributions. Therefore, this application's embodiments adopt a combined weighting strategy, obtaining data-driven objective weights through the entropy weighting method and experience-driven subjective weights through the analytic hierarchy process (AHP), and then optimally combining the two linearly to balance objective data patterns and subjective experience awareness.

[0068] In the specific implementation process, firstly, based on the data dispersion of each dimension of threat features in the standardized feature matrix, the entropy weight method is used to calculate the first weight vector that reflects the inherent distinguishability of each dimension of threat features.

[0069] Specifically, for the standardized feature matrix Calculate the i-th threat entity under the j-th dimension feature. The feature weights pij are calculated, and then the information entropy ej of the j-th dimension feature is calculated based on pij. Finally, the entropy weight of the j-th dimension feature is calculated. threatening entities First weight vector The j-th component in the vector, where all components are greater than 0 and their sum is 1.

[0070] In this scheme, the entropy weight method can automatically determine the weights based on the dispersion of feature data in each dimension of the standardized feature matrix. The greater the dispersion of the data, the stronger its ability to distinguish threatening entities, and should be given a higher weight in the comprehensive evaluation; conversely, the smaller the dispersion of the data, the weaker its ability to distinguish, and should be given a lower weight in the comprehensive evaluation. For the specific calculation process of the entropy weight method, please refer to the relevant prior art; the embodiments in this application will not be repeated here.

[0071] Subsequently, based on the relative importance of the threat features in each dimension, the analytic hierarchy process (AHP) is used to calculate the second weight vector that reflects prior knowledge.

[0072] Specifically, a hierarchical model is first established, with threat level assessment set as the target layer and m-dimensional threat features set as the criterion layer. Then, domain experts use a nine-level scaling method to compare the relative importance of each feature pairwise, constructing a judgment matrix. , where ajk represents the importance scale of the j-th feature relative to the k-th feature, and satisfies ajk>0, ajk=1 / akj, ajj=1.

[0073] Optionally, the specific scale values ​​in the judgment matrix can be preset according to the actual application scenario. For example, in the airspace threat situation awareness scenario, it is generally believed that movement speed and approach angle have a more direct indicative role in judging the urgency of the threat, and their importance is higher than that of environmental characteristics. Therefore, movement speed can be assigned a higher scale value relative to environmental characteristics in the judgment matrix. In the cyberspace security scenario, target type characteristics and target capability characteristics may be given more importance.

[0074] After constructing the judgment matrix, calculate the maximum eigenvalue λmax of the judgment matrix and its corresponding eigenvector. Then, normalize the eigenvector to obtain the threat entity. Second weight vector All components in this vector are greater than 0 and their sum is 1. For the specific calculation process of the analytic hierarchy process, please refer to the relevant prior art; the embodiments in this application will not be repeated here.

[0075] The first weight vector determined by the entropy weight method reflects the objective laws at the data level, while the second weight vector determined by the analytic hierarchy process (AHP) reflects the subjective cognition at the empirical level. Each has its own emphasis and they are complementary. Therefore, the first weight vector... Second weight vector A linear combination is performed to construct a comprehensive weight vector. Specifically, the first and second weight vectors each correspond to a linear combination coefficient, and the sum of the two linear combination coefficients is 1 and both are greater than 0, which constitutes the comprehensive weight vector. ,in, , The coefficients are linear combination coefficients. + =1, and >0, >0.

[0076] To determine , The optimal value is determined by minimizing the sum of squared deviations between the comprehensive weight vector and the first and second weight vectors, which means minimizing the overall deviation of the comprehensive weight vector from the first and second weight vectors.

[0077] Alternatively, the optimization objective function can be expressed as: .

[0078] Will Substituting the values ​​into the objective function and solving it under constraints yields the optimal number of linear combination coefficients. and ,in, + =1, and >0, >0. After obtaining the optimal linear combination coefficients, their values ​​are used to perform a weighted linear combination of the first and second weight vectors to obtain the comprehensive weight vector. This allows for the construction of a comprehensive weight matrix. = .

[0079] 104. Construct a weighted normalized matrix based on the comprehensive weight vector, and determine the positive ideal solution and the negative ideal solution based on the weighted normalized matrix. Calculate the distance between each threat entity and the positive ideal solution and the negative ideal solution respectively, and determine the relative proximity of each threat entity.

[0080] In the specific implementation process, the standardized feature matrix obtained in step 102 is used as a reference. Multiply the membership degree of each threat entity in column j by the comprehensive weight ωj obtained in step 103 to construct a weighted normalized matrix. = = .

[0081] Based on the weighted normalization matrix, the maximum value is selected from each column of the weighted normalization matrix to form the positive ideal solution. = ,in, = The minimum value is selected from each column to form the negative ideal solution. = ,in, = Among them, the positive ideal solution represents the hypothetical threat entity that is at the highest threat level in all threat characteristics, and the negative ideal solution represents the hypothetical threat entity that is at the lowest threat level in all threat characteristics.

[0082] Then, for each threat entity Si, calculate the Euclidean distance Di+ to the positive ideal solution and the Euclidean distance Di- to the negative ideal solution from its weighted normalized vector. = , = .

[0083] Finally, for each threat entity Si, the relative proximity is calculated. The value of Ci ranges from [0,1]. The larger the value, the higher the overall threat level of the threat entity.

[0084] 105. According to the order of relative proximity from high to low, the threat entities in the situation display area are filtered to obtain the threat entity display set and the threat entity hidden set.

[0085] Understandably, after calculating the relative proximity Ci of each threat entity in step 104, each threat entity possesses a unified and comparable quantitative index of comprehensive threat level. However, when there are a large number of threat entities within the situation display area, rendering and displaying all of them would inevitably lead to issues such as icon overlap, text obscuring, and information overload due to limitations in the screen size and resolution of the situation display area. This would result in critical high-risk threats being covered and hidden by a large amount of low-risk data. To address this, this application's embodiment introduces a greedy filtering mechanism based on pixel space conflict constraints to prioritize the visibility of high-level threats under limited screen display resources.

[0086] Figure 3 This is a schematic diagram illustrating the process of determining a set of threat entities for display and hiding, as provided in an embodiment of this application. Figure 3 As shown, firstly, a copy of the threat entity set is made to generate a temporary candidate set. Then, a threat entity is selected from the temporary candidate set in descending order of relative proximity. and add it to the threat entity display set. and in the temporary candidate set The threat entity is marked as handled, for example, in the set Middle Mark The similarity is -1.

[0087] Next, the temporary candidate set is traversed and calculated. The remaining unprocessed threat entities, i.e., entities with a proximity score other than -1. With the entity that has already been shown as a threat The pixel display distance on the situation map If the pixel display distance between an unprocessed threat entity and a displayed threat entity... Smaller than the preset minimum pixel display distance If the two entities overlap or cause visual congestion on the screen, the threat entity is added to the hidden threat entity set C and marked as processed in the temporary candidate set A. This process is repeated until all threat entities in the temporary candidate set A are marked as processed, thus completing the division of the display and hidden sets.

[0088] The core logic of the above-mentioned greedy filtering mechanism is that the threat entities with higher proximity are removed earlier and have the right to occupy screen display resources first; the threat entities with low proximity that are removed later and conflict with the already displayed entities in pixel space are included in the hidden set, thereby ensuring that high-level threats are displayed first and low-level threats are appropriately hidden.

[0089] In practice, since the pixel position of the threat entity on the situation map changes with the user's view operation, a preset minimum pixel display distance is required. It is not fixed, but rather adjusts according to changes in the display density setting of the situation map. The higher the display density setting, the lower the preset minimum pixel display distance. The smaller the value, the lower the display density setting, and the lower the preset minimum pixel display distance. The larger.

[0090] 106. Perform hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and retain the data link for threat entities in the threat entity hidden set without performing graphics rendering.

[0091] After dividing the threat set into the display set and the hidden set, hierarchical visualization rendering is performed based on the relative proximity of each threat entity in the display set, while the data links of the threat entities in the hidden set are preserved and no graphics rendering is performed.

[0092] Optionally, threat entities in the display set can be classified into high-level, medium-level, and low-level threat entities based on a preset proximity threshold. The preset proximity threshold refers to one or more pre-defined proximity limits, such as a first threshold and a second threshold. Entities with a proximity greater than the first threshold are classified as high-level, those between the first and second thresholds are classified as medium-level, and those less than the second threshold are classified as low-level. The first threshold is greater than the second threshold. In specific implementations, the aforementioned proximity thresholds can be flexibly set according to the actual application scenario and display strategy; this embodiment does not impose specific limitations on this.

[0093] For high-level threat entities, a first display style is assigned. This first display style features a target brightness exceeding a preset brightness threshold, a target size exceeding a preset size threshold, and an overlay of periodic flashing effects. The preset brightness threshold and preset size threshold are reference values ​​relative to a baseline display style. Optionally, high-level threat entities may use highly saturated colors, large icons, and periodic flashing at a frequency of 2Hz to 4Hz to highlight their high-threat status, facilitating quick user location and attention.

[0094] For medium-level threat entities, a second display style is assigned. The second display style is less bright and smaller than the first display style, and does not include any flickering effects. Medium-level threat entities can be rendered using standard saturation colors and medium-sized icons to distinguish them from the prominent display effect of high-level entities, while still ensuring they are clearly visible.

[0095] For low-level threat entities, a third display style is assigned. The brightness and size of the third display style are lower than the corresponding display parameters of the second display style. Optionally, low-level threat entities can be rendered using low-saturation light colors and small-sized icons, which preserves their presence on the situation map without affecting the visual presentation of high-level threats.

[0096] For threat entities that are included in the threat entity hiding set, maintain the data link with the data source to resume graphics rendering when the display trigger condition is met. During the period when graphics rendering is not performed, the underlying data state is continuously updated.

[0097] The display trigger conditions include, but are not limited to: user zooming of the situation map causing the scale to enter a scale range that allows the display of low-level entities; changes in the pixel spatial conflict relationship of threat entities after the situation display area is shifted; user actively adjusting the display density threshold through interactive operations; and recalculation of threat assessment results after the threatened entity is switched. When any of the above display trigger conditions are met, threat entities in the hidden set will re-participate in the screening calculation in step 105, and may be moved into the display set and have their graphics rendering restored.

[0098] In practical applications, if a situation assessment and display method uses a timed full refresh to update situational data, then regardless of the magnitude of the data change, all threat entities within the entire situation display area will be recalculated and re-rendered, resulting in high computational redundancy. In scenarios with a surge in the number of threat targets or frequent situational interaction operations, the computational load from a full refresh will lead to significant delays in screen updates, making it unsuitable for high-frequency, sudden, and dynamically changing threat confrontation scenarios. This severely impacts the real-time requirements of security operations and command decision-making for situational awareness.

[0099] To address the aforementioned technical issues, the threat situation assessment and display method based on comprehensive weights provided in this application also introduces an intelligent update mechanism based on data types.

[0100] Specifically, it receives external input data in real time. This external input data includes: situational awareness data, real-time update data of threat entities, and status update data of threatened entities.

[0101] Among them, situation interaction data refers to the interactive command data generated by the user's operation on the situation map, including but not limited to panning, zooming, adjusting the display ratio, switching threatened entities, and adjusting the display density.

[0102] Real-time update data for threat entities refers to the updated data pushed when the state of a threat entity changes, such as the addition of a threat entity, the disappearance of a threat entity, or changes in the spatial location or movement speed of a threat entity.

[0103] Threatened entity status update data refers to the updated data pushed when the status of the threatened entity itself changes, such as the protection level of the threatened entity being upgraded or downgraded, or its spatial location changing.

[0104] Then, the received external input data is intelligently judged in terms of data type and validity, and the corresponding update mechanism is triggered based on the judgment result.

[0105] Specifically, if the received data is situational interaction data, it is further determined whether the interaction operation is a valid operation. Among them, a valid operation refers to an operation that can have a substantial impact on the situational display content, including but not limited to: panning the situational display area (e.g., the user drags the map to change the display area), zooming (e.g., the user scrolls the scroll wheel or clicks the zoom button to change the scale), adjusting the display scale, switching threatened entities, and customizing the display density.

[0106] If the operation is determined to be valid, a full recalculation is triggered, meaning the calculation process from steps 101 to 106 is re-executed to update the view with new view states and display parameters. If the operation is determined to be invalid, such as intermediate operations like mouse hover, incomplete selection, or double-click selection without execution, no calculation is triggered to avoid unnecessary waste of computing resources.

[0107] If the received data is real-time update data of threat entities or status update data of threatened entities, further determine whether the update data affects the relative proximity, display set, or hidden set of threat entities within the situation display area.

[0108] If the relative proximity, display set, or hidden set of threat entities within the situation display area is affected, then the affected threat entities are identified, and incremental recalculation is performed only for the affected threat entities. That is, only for threat entities whose status has changed or whose relative proximity ranking or display / hidden affiliation has changed due to the change, the relative proximity calculation, set filtering, and visualization rendering update are re-performed, while the original assessment results and display status of unaffected threat entities are retained.

[0109] If the updated data does not affect the display status of threat entities within the situation display area, such as if the update occurs outside the current display area or the updated content does not affect the threat level assessment result, no calculation will be triggered, and the current situation display will remain unchanged.

[0110] In this solution, two types of trigger sources—view operations and data updates—are treated differently. View operations alter the user's perspective and scale of situational awareness, affecting the pixel positions and display density of all threat entities on the screen. Therefore, a full recalculation is employed to ensure the integrity and consistency of the display logic. Entity data updates, on the other hand, typically involve only localized changes in the state of threat entities. Incremental recalculation updates only the affected portions, avoiding full recalculation of the entire situational awareness display area. This significantly reduces computational redundancy, improves the real-time performance and smoothness of situational data updates, and effectively adapts to the real-time requirements of high-frequency, sudden, and dynamically changing threat adversarial scenarios.

[0111] In summary, the threat situation assessment and display method based on comprehensive weights provided in this application constructs a multi-dimensional threat feature quantification system covering spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics. It then uses a threat level membership function for standardization, achieving a unified measurement of multi-source heterogeneous threat data. Furthermore, it optimally linearly combines the objective weights determined by the entropy weight method with the subjective weights determined by the analytic hierarchy process. This allows the comprehensive weights to capture the inherent statistical patterns of real-time data while incorporating the experience and knowledge of domain experts, overcoming the shortcomings of traditional fixed weights in adapting to dynamic attack and defense scenarios. This significantly improves the accuracy and reliability of multi-dimensional threat level assessment. Furthermore, the relative proximity of each threat entity is calculated to achieve a refined ranking of the overall threat level. Based on this, an adaptive greedy filtering is performed in conjunction with pixel display distance and scale constraints, prioritizing the display of high-level threats and automatically hiding low-level threats that conflict with already displayed entities in pixel space. This effectively solves the problems of icon overlap, information overload, and key threats being covered and hidden by low-risk data in massive target scenarios. Then, hierarchical differentiated rendering makes the situation display hierarchy clear and highlights key points. At the same time, by distinguishing between situation interaction operations and entity data updates and triggering full recalculation or incremental recalculation respectively, the system's computational redundancy is greatly reduced, and the real-time performance and smoothness of situation data updates are improved.

[0112] Therefore, the proposed solution achieves accurate threat assessment, adaptive display, highlighting key points, and rapid response in overall situational assessment, effectively meeting the practical application needs of security operation and maintenance analysis and command decision-making for high-precision, real-time, and visualized threat situational awareness.

[0113] The following describes in detail one or more embodiments of the threat posture assessment and display apparatus based on comprehensive weights of this application. Those skilled in the art will understand that these apparatuses can be configured using commercially available hardware components through the steps taught in this solution.

[0114] Figure 4 A schematic diagram of a threat situation assessment and display device based on comprehensive weights provided in this application embodiment is shown below. Figure 4 As shown, the device includes: a data acquisition module 11, a standardization processing module 12, a comprehensive weight determination module 13, a proximity calculation module 14, a dynamic filtering module 15, and a hierarchical rendering module 16.

[0115] The data acquisition module 11 is used to acquire multi-dimensional threat feature data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entity; the multi-dimensional threat feature data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics and environmental characteristics, and the spatial distance and the entry angle are determined based on the spatial location data; Standardization processing module 12 is used to standardize the multi-dimensional threat feature data corresponding to each threat entity using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data. The comprehensive weight determination module 13 is used to determine the first weight vector by using the entropy weight method and the second weight vector by using the analytic hierarchy process according to the standardized feature matrix, and to determine the linear combination coefficient of the first weight vector and the second weight vector according to the preset optimization objective function, so as to obtain the comprehensive weight vector. The proximity calculation module 14 is used to construct a weighted normalized matrix based on the comprehensive weight vector, determine the positive ideal solution and the negative ideal solution based on the weighted normalized matrix, calculate the distance between each threat entity and the positive ideal solution and the negative ideal solution respectively, and determine the relative proximity of each threat entity; The dynamic filtering module 15 is used to filter the threat entities in the situation display area according to the order of relative proximity from high to low, so as to obtain a threat entity display set and a threat entity hidden set. The hierarchical rendering module 16 is used to perform hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and to retain the data link for threat entities in the threat entity hidden set without performing graphics rendering.

[0116] In an optional embodiment, the system further includes an interactive update module for receiving situation interaction data, real-time update data of threat entities, and status update data of threatened entities; when the situation interaction data is valid operational data, it triggers a full recalculation of threat entities within the situation display area, wherein the valid operational data includes panning, scaling, adjusting the display ratio, switching of threatened entities, and adjusting the display density of the situation display area; when the real-time update data of threat entities or the status update data of threatened entities affect the relative proximity, display set, or hidden set of threat entities within the situation display area, it identifies the affected threat entities and performs relative proximity calculation, set filtering, and visualization rendering update only for the affected threat entities.

[0117] Figure 4 The device shown can perform the steps described in the foregoing embodiments. For detailed execution process and technical effects, please refer to the description in the foregoing embodiments, which will not be repeated here.

[0118] In one possible design, the above Figure 4 The structure of the threat situation assessment and display device based on comprehensive weights shown can be implemented as an electronic device, such as... Figure 5 As shown, the electronic device may include: a memory 21, a processor 22, and a communication interface 23. The memory 21 stores a computer program, which, when executed by the processor 22, enables the processor 22 to at least implement the threat situation assessment and display method based on comprehensive weights as provided in the foregoing embodiments.

[0119] The aforementioned memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0120] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the steps in the above-described method embodiments. The computer-readable storage medium includes volatile or non-volatile components, or a combination thereof, and can be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, CD-ROM, digital video disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium.

[0121] Accordingly, this application also provides a computer program product, which includes a computer program or instructions that, when executed by a processor, enable the processor to implement the steps in the above-described method embodiments. It should be understood that each step or combination of steps in the above-described method flow can be implemented by the computer program or instructions. Furthermore, these computer programs or instructions can be applied to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable threat situation assessment and display device based on comprehensive weights, enabling the processor of the general-purpose computer, special-purpose computer, embedded processor, or other programmable threat situation assessment and display device based on comprehensive weights to function as an apparatus for implementing the corresponding functions in the above-described method embodiments.

[0122] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. This application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] Finally, it should be noted that 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 process, method, article, or apparatus. Without further limitation, 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 that element.

[0125] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A threat posture assessment and presentation method based on comprehensive weights, characterized in that, include: Acquire multidimensional threat characteristic data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entities; The multidimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics. The spatial distance and the entry angle are determined based on the spatial location data. The multidimensional threat feature data corresponding to each threat entity are standardized using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data. Based on the standardized feature matrix, the first weight vector is determined by the entropy weight method, the second weight vector is determined by the analytic hierarchy process, and the linear combination coefficients of the first weight vector and the second weight vector are determined according to the preset optimization objective function to obtain the comprehensive weight vector. A weighted normalized matrix is ​​constructed based on the comprehensive weight vector, and positive and negative ideal solutions are determined based on the weighted normalized matrix. The distances between each threat entity and the positive and negative ideal solutions are calculated to determine the relative proximity of each threat entity. According to the order of relative proximity from high to low, the threat entities in the situation display area are filtered to obtain the threat entity display set and the threat entity hidden set; Based on the relative proximity of each threat entity in the threat entity display set, hierarchical visualization rendering is performed, while for threat entities in the threat entity hidden set, data links are preserved but no graphics rendering is performed.

2. The threat situation assessment and display method based on comprehensive weights according to claim 1, characterized in that, The target type characteristic is used to represent the target category to which the threat entity belongs and its corresponding inherent threat attributes. The target capability characteristic is used to represent the threat entity's ability to carry out attacks, reconnaissance, interference, breakthroughs, or other threatening behaviors. The environmental characteristic is used to represent the impact of the environment in which the threat entity is located on its own threat level. The target type characteristic, target capability characteristic, and environmental characteristic are determined according to preset type evaluation rules, capability evaluation rules, and environmental evaluation rules, respectively. The standardization process, which utilizes threat level membership functions to normalize the multidimensional threat feature data corresponding to each threat entity, yields a standardized feature matrix for each threat entity, including: For spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics, corresponding threat level membership functions are established respectively; Substitute the multidimensional threat feature data of each threat entity into the corresponding threat level membership function to obtain the feature membership degree, which represents the contribution of each threat feature to the threat level. The feature membership degree ranges from 0 to 1. The larger the feature membership degree, the greater the contribution of the corresponding threat feature to the threat level. Based on the feature membership degrees corresponding to the multidimensional threat feature data of each threat entity, a standardized feature matrix is ​​constructed for each threat entity.

3. The threat situation assessment and display method based on comprehensive weights according to claim 1, characterized in that, The process of determining a first weight vector using the entropy weighting method based on the standardized feature matrix, determining a second weight vector using the analytic hierarchy process (AHP), and determining the linear combination coefficients of the first and second weight vectors according to a preset optimization objective function to obtain a comprehensive weight vector includes: Based on the data dispersion of each dimension of threat features in the standardized feature matrix, the first weight vector reflecting the inherent discriminative power of each dimension of threat features is calculated using the entropy weight method. Based on the relative importance of each threat feature, the analytic hierarchy process is used to calculate the second weight vector that reflects prior experience and cognition. The first weight vector and the second weight vector are linearly combined to construct a comprehensive weight vector. The first weight vector and the second weight vector each correspond to a linear combination coefficient, and the sum of the two linear combination coefficients is 1 and both are greater than 0. With the objective of minimizing the sum of squared deviations between the comprehensive weight vector and the first weight vector and the second weight vector respectively, the values ​​of the two linear combination coefficients are determined so that the overall deviation of the comprehensive weight vector from the first weight vector and the second weight vector is minimized. By using the determined values ​​of the two linear combination coefficients, a weighted linear combination is performed on the first weight vector and the second weight vector to obtain a comprehensive weight vector.

4. The threat situation assessment and display method based on comprehensive weights according to claim 1, characterized in that, The threat entities within the situation display area are filtered according to their relative proximity from high to low to obtain a threat entity display set and a threat entity hiding set, including: Select the threat entity with the highest relative proximity from the threat entities that have not been screened in the situation display area and add them to the threat entity display set; Calculate the pixel display distance on the situation map between the remaining unfiltered threat entities and the threat entities in the threat entity display set; When the pixel display distance is less than the preset minimum pixel display distance, the corresponding threat entity is added to the threat entity hiding set.

5. The threat situation assessment and display method based on comprehensive weights according to claim 1, characterized in that, The step of performing hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and preserving the data link for threat entities in the threat entity hidden set without performing graphics rendering, includes: Based on a preset proximity threshold, the threat entities in the threat entity display set are divided into high-level threat entities, medium-level threat entities, and low-level threat entities; The high-level threat entity is given a first display style, which has a target brightness higher than a preset brightness threshold, a target size greater than a preset size threshold, and a periodic flashing effect. The medium-level threat entity is assigned a second display style, wherein the brightness of the second display style is lower than that of the first display style, the size is smaller than that of the first display style, and it does not contain a flickering effect; The low-level threat entity is assigned a third display style, the brightness and size of which are lower than the corresponding display parameters of the second display style; The threat entities in the hidden threat entity set maintain a data link with the data source to resume graphics rendering when the display trigger condition is met, and their underlying data state is continuously updated during periods when graphics rendering is not performed.

6. The threat situation assessment and display method based on comprehensive weights according to claim 1, characterized in that, Also includes: Receive situational interaction data, real-time update data of threat entities, and status update data of threatened entities; When the situation interaction data is valid operational data, a full recalculation of the threat entities within the situation display area is triggered. The valid operational data includes panning, scaling, adjusting the display ratio, switching of threatened entities, and adjusting the display density of the situation display area. When the real-time update data of the threat entity or the status update data of the threatened entity affects the relative proximity, display set, or hidden set of the threat entities within the situation display area, the affected threat entities are identified, and relative proximity calculation, set filtering, and visualization rendering update are performed only for the affected threat entities.

7. A threat situation assessment and display system based on comprehensive weights, characterized in that, include: The data acquisition module is used to acquire multidimensional threat characteristic data of each threat entity in the threat entity set, as well as the status data and spatial location data of the threatened entities; The multidimensional threat characteristic data includes spatial distance, movement speed, entry angle, target type characteristics, target capability characteristics, and environmental characteristics. The spatial distance and the entry angle are determined based on the spatial location data. The standardization processing module is used to standardize the multidimensional threat feature data corresponding to each threat entity using the threat level membership function to obtain the standardized feature matrix corresponding to each threat entity. At least one coefficient parameter in the threat level membership function is determined based on the state data. The comprehensive weight determination module is used to determine the first weight vector using the entropy weight method and the second weight vector using the analytic hierarchy process based on the standardized feature matrix, and to determine the linear combination coefficient of the first weight vector and the second weight vector based on the preset optimization objective function, so as to obtain the comprehensive weight vector. The proximity calculation module is used to construct a weighted normalized matrix based on the comprehensive weight vector, determine the positive ideal solution and the negative ideal solution based on the weighted normalized matrix, calculate the distance between each threat entity and the positive ideal solution and the negative ideal solution respectively, and determine the relative proximity of each threat entity; The dynamic filtering module is used to filter threat entities in the situation display area according to the order of relative proximity from high to low, so as to obtain a threat entity display set and a threat entity hidden set. The hierarchical rendering module is used to perform hierarchical visualization rendering based on the relative proximity of each threat entity in the threat entity display set, and to retain the data link for threat entities in the threat entity hidden set without performing graphics rendering.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a communication interface; wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the threat situation assessment and presentation method based on comprehensive weights as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor of an electronic device, causes the processor to perform the threat posture assessment and presentation method based on comprehensive weights as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, include: A computer program or instruction, when executed by a processor of an electronic device, causes the processor to perform the threat posture assessment and presentation method based on comprehensive weights as described in any one of claims 1 to 6.

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