Fighting drill simulation integrated system based on local area network

Abstract

The invention relates to the technical field of simulation, in particular to a fight drill simulation integrated system based on a local area network, which comprises a node state synchronization module, a confrontation parameter correction module, a tactical event arbitration module and a battlefield situation updating module. According to the method, multi-node state synchronization is achieved through UDP multicast, transmission delay is reduced, battlefield parameters are guaranteed to be collected in real time and rapidly packaged, space superposition is combined with a Voronoi graph to establish a dynamic density subdivision mechanism, firepower coverage density nonlinear changes are recognized, conflict area detection precision is improved, the TOPSIS model comprehensively evaluates the battle distance, unit density and terrain blindage, and the method has the advantages of being high in practicability and high in practicability. According to the method, path planning adaptability is enhanced, tactical avoidance instruction generation is accelerated, Bezier curve trajectory smoothness is optimized, a thermal power suppression boundary is dynamically adjusted through convex hull reconstruction, a closed-loop feedback link is constructed through real-time data distribution, the time delay of the whole process from collection to situation updating is shortened, and the complex event handling accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a battle drill simulation integration system based on a local area network. Background Art

[0002] The field of simulation technology involves the use of computers and other information technology to model and simulate actual systems or processes, enabling the virtual reproduction and process deduction of complex systems. The core of this technology involves building mathematical models of physical systems, operational processes, or behavioral scenarios, and conducting digital simulations and interactive testing of key elements and dynamic processes in virtual space to support applications such as system operation research, decision-making evaluation, and training exercises. Simulation technology encompasses physical modeling, system integration, data exchange, and interactive control, and is a crucial foundation for the simulation and verification of complex systems across multiple domains.

[0003] The battle drill simulation integration system is an integrated solution for virtual confrontation drills that uses local area network (LAN)-based data interconnection to achieve real-time information exchange, unified scheduling, and virtual confrontation process control among multiple participating units. This patent focuses on key issues such as task instruction transmission, role behavior interaction, and scene state synchronization in a battle drill environment. It typically uses simulation modeling, LAN communication protocol design, distributed task scheduling, and real-time information processing to achieve data synchronization and dynamic collaboration among participating nodes during the drill process.

[0004] Existing technologies use a centralized task scheduling architecture, which is prone to command transmission delays in multi-node concurrent scenarios, resulting in a decrease in the synchronization between tactical actions and battlefield situation. The fixed threshold detection mechanism is difficult to adapt to the dynamic changes in fire coverage density, and the misjudgment rate increases significantly in high-density confrontation scenarios. The path planning model lacks a multi-dimensional assessment of terrain cover parameters, and the evasive paths generated in complex terrain environments are exposed. The periodic polling mechanism does not update frequently enough to capture the rapid deformation of the fire suppression range in real time, resulting in geometric deviations in the reconstruction of the battlefield situation. In a strong interference environment, the data synchronization stability of existing technologies is insufficient, affecting the tactical coherence and deduction credibility of confrontation exercises. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a battle drill simulation integrated system based on a local area network.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a battle drill simulation integrated system based on a local area network comprises:

[0007] The node status synchronization module is used to obtain the position coordinates, ammunition reserves, and armor integrity parameter sets of each combat unit through the UDP multicast protocol, perform weighted fusion of the elevation mutation rate matrix and the terrain barrier frequency factor, input the parameter set into the real-time data distribution service for packaging, and generate battlefield snapshot data to pass to the confrontation parameter correction module;

[0008] A confrontation parameter correction module is used to perform spatial superposition operations on the fire coverage parameters using the battlefield snapshot data, convert the spatial coordinates into metric units, and then calculate the geometric intersection area. When the area of ​​the overlapping area exceeds a threshold, a Voronoi diagram algorithm is used to perform deployment density segmentation, generate the coordinates of the fire conflict area, and transmit them to the tactical event arbitration module;

[0009] The tactical event arbitration module is used to sort the engagement distance, unit density, and terrain cover parameters in the coordinates of the fire conflict area through the TOPSIS decision model, optimize the sorting weights through the gradient descent method, establish a threat gradient distribution model, and generate tactical avoidance path instructions to pass to the battlefield situation update module.

[0010] As a further solution of the present invention, the battlefield snapshot data includes the position coordinates of the combat unit, ammunition reserves, and armor integrity parameters; the fire conflict area coordinates are specifically coverage overlap, density gradient, and boundary vertex coordinates; and the tactical avoidance path instructions include path priority queue, turning angle threshold, and bunker utilization coefficient.

[0011] As a further solution of the present invention, the node status synchronization module includes:

[0012] The data acquisition submodule receives combat unit status data packets through the UDP multicast protocol, extracts position coordinates, ammunition reserves, and armor integrity parameters, filters out abnormal data units that exceed the 200ms delay threshold, and generates a combat unit status parameter set;

[0013] The data integration submodule calls the combat unit state parameter set, converts the coordinates to a standard spatial reference system through Gauss-Krüger projection, standardizes the ammunition reserve to a percentage format, calibrates the armor integrity value range, integrates the spatial position, resource status, and protection status data, and generates a standardized state matrix;

[0014] The snapshot generation submodule constructs a spatial topological relationship based on the standardized state matrix, calculates the weighted fusion value of ammunition reserves and armor integrity, performs binary serialization operations and appends MD5 checksums through real-time data distribution services, and generates battlefield snapshot data.

[0015] As a further solution of the present invention, the adversarial parameter correction module includes:

[0016] The spatial superposition operation submodule obtains fire coverage parameters from the battlefield snapshot data, performs spatial superposition operations on multiple sets of parameters, calculates the geometric intersection area of ​​the coverage areas of multiple fire units, sets a tactical conflict threshold of 50 square meters based on armored vehicle mobility performance experimental data, compares the intersection area with a preset tactical conflict threshold, and generates an overlapping area value;

[0017] The density profiling submodule is based on the overlapping area value. When the tactical conflict threshold is exceeded, the formula is used:

[0018]

[0019] Calculate and obtain the deployment density correction coefficient, call the Voronoi diagram algorithm to spatially divide the firepower deployment points, and generate the deployment density distribution value;

[0020] Among them, A t represents the tactical conflict area threshold, which is 50 square meters. i Represents the Euclidean distance between the ith deployment point and the conflict boundary, in meters, N represents the total number of deployment points on the current battlefield, S c is the density correction coefficient, which is used to adjust the Voronoi diagram subdivision weight;

[0021] The conflict coordinate generation submodule extracts the boundary coordinates of the area where the density gradient change rate exceeds the critical value according to the deployment density distribution value, uses a second-order continuously differentiable cubic spline interpolation function to perform interpolation operation on adjacent boundaries, and generates the coordinates of the firepower conflict area.

[0022] As a further solution of the present invention, the tactical event arbitration module includes:

[0023] The battlefield situation acquisition submodule detects the engagement distance, unit density, and terrain cover parameters in the coordinates of the fire conflict area, uses a 100m×100m grid division method to count the number of combat units within a unit area, extracts the meter-level value of the engagement distance through spatial coordinate analysis technology, and uses a terrain elevation model to calculate the cover coverage rate to generate a dynamic parameter set;

[0024] The conflict situation quantification submodule performs inverse proportional normalization on the engagement distance based on the dynamic parameter set, converts the unit density into the number of combat units per square kilometer, calculates the weight coefficients of terrain shelter parameters using the entropy weight method, uses the standard deviation method to determine the indicator dispersion, and uses the range normalization method to eliminate dimensional differences to establish a situation coefficient matrix;

[0025] The path decision generation submodule calls the situation coefficient matrix and uses the TOPSIS model to calculate the Euclidean distance between multiple coordinate points and the ideal solution. Based on the armored forces' actual combat exercise data, a 20% threshold is set as the safe coordinate point screening ratio. The sorting priority is determined by the ratio of positive and negative ideal solution distances, and tactical avoidance path instructions are generated.

[0026] As a further embodiment of the present invention, the system further comprises:

[0027] A battlefield situation update module is used to perform Bezier curve interpolation on movement trajectory parameters according to the tactical avoidance path instruction, perform convex hull reconstruction operation on fire suppression range parameters, and input the updated parameters into the real-time data distribution service.

[0028] As a further solution of the present invention, the update parameters specifically refer to the movement trajectory control point set, the fire suppression boundary vertex sequence, and the path curvature radius parameter.

[0029] As a further solution of the present invention, the Bezier curve control point selection rule is to set an intermediate control point at 1 / 3 of the coordinate difference between adjacent trajectory points.

[0030] As a further solution of the present invention, the battlefield situation update module includes:

[0031] The trajectory interpolation submodule obtains the movement trajectory parameters in the tactical evasion path instruction, establishes a cubic Bezier curve control point selection rule, sets the curvature radius threshold to 1.2 times the minimum turning radius of the armored vehicle, calculates the coordinates of the intermediate control point by the difference between the coordinates of adjacent trajectory points, and uses the piecewise interpolation method to perform continuity processing on the discrete trajectory points to generate a trajectory smoothness coefficient;

[0032] The suppression range reconstruction submodule calls the trajectory smoothness coefficient to extract the polar coordinate parameters of the discrete point set of the fire suppression range. When the difference between the convex hull perimeter and the original point set perimeter exceeds 15%, the convex hull reconstruction is triggered, and the Graham scan algorithm is used to construct the convex hull vertex sequence to generate the suppression range coverage.

[0033] The parameter integration submodule performs a difference operation on the trajectory smoothness coefficient and the curvature threshold, and performs a ratio operation on the suppression range coverage and the area threshold, and adopts a dynamic weighted summation formula

[0034] W=0.6ΔS+0.4R

[0035] Calculations are performed, where ΔS represents the difference in trajectory smoothness coefficients and R represents the suppression range coverage ratio, to generate updated parameters.

[0036] Compared with the prior art, the advantages and positive effects of the present invention are:

[0037] In the present invention, multi-node state synchronization is achieved through the UDP multicast protocol, which significantly reduces network transmission delays and ensures real-time collection and rapid packaging of battlefield parameters. Spatial superposition operations are combined with the Voronoi diagram algorithm to establish a dynamic density subdivision mechanism, accurately identify nonlinearly changing fire coverage density, and improve the accuracy of conflict area detection. The TOPSIS multi-criteria decision model comprehensively evaluates engagement distance, unit density, and terrain cover parameters, enhances the environmental adaptability of path planning, and accelerates the efficiency of tactical avoidance instruction generation. Bezier curve interpolation optimizes the smoothness of the moving trajectory, and the convex hull reconstruction operation dynamically adjusts the fire suppression boundary to achieve high-frequency updates of the battlefield situation. The real-time data distribution service builds a closed-loop feedback link, significantly shortening the entire process delay from parameter collection to situation update, and improving the accuracy of complex event handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a system flow chart of the present invention;

[0039] Figure 2 This is a flow chart of the node status synchronization module of the present invention;

[0040] Figure 3 This is a flow chart of the resistance parameter correction module of the present invention;

[0041] Figure 4 This is a flow chart of the tactical event arbitration module of the present invention;

[0042] Figure 5 This is a flow chart of the battlefield situation update module of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0045] Example 1

[0046] See also Figure 1, a battle drill simulation integrated system based on a local area network includes:

[0047] The node status synchronization module is used to obtain the position coordinates, ammunition reserves, and armor integrity parameter sets of each combat unit through the UDP multicast protocol, perform weighted fusion of the elevation mutation rate matrix and the terrain barrier frequency factor, input the parameter set into the real-time data distribution service for packaging, and generate battlefield snapshot data to pass to the confrontation parameter correction module;

[0048] The adversarial parameter correction module is used to perform spatial superposition operations on fire coverage parameters using battlefield snapshot data, convert spatial coordinates into metric units, and calculate the geometric intersection area. When the overlapping area exceeds a threshold, the Voronoi diagram algorithm is used to perform deployment density segmentation, generating the coordinates of the fire conflict area and transmitting them to the tactical event arbitration module.

[0049] The tactical event arbitration module is used to sort the engagement distance, unit density, and terrain cover parameters in the fire conflict area coordinates using the TOPSIS decision model, optimize the sorting weights using the gradient descent method, establish a threat gradient distribution model, and generate tactical avoidance path instructions to pass to the battlefield situation update module;

[0050] The battlefield situation update module is used to perform Bezier curve interpolation on movement trajectory parameters based on tactical avoidance path instructions, perform convex hull reconstruction operations on fire suppression range parameters, and input the updated parameters into the real-time data distribution service.

[0051] Battlefield snapshot data includes combat unit position coordinates, ammunition reserves, and armor integrity parameters. The coordinates of the fire conflict area are specifically coverage overlap, density gradient, and boundary vertex coordinates. Tactical avoidance path instructions include path priority queue, turning angle threshold, and bunker utilization coefficient. The update parameters specifically refer to the movement trajectory control point set, fire suppression boundary vertex sequence, and path curvature radius parameters.

[0052] The rule for selecting control points of the Bezier curve is to set the middle control point at 1 / 3 of the difference in coordinates between adjacent trajectory points.

[0053] See also Figure 2 , the node status synchronization module includes:

[0054] The data acquisition submodule receives combat unit status data packets through the UDP multicast protocol, extracts position coordinates, ammunition reserves, and armor integrity parameters, filters out abnormal data units that exceed the 200ms delay threshold, and generates a combat unit status parameter set;

[0055] The data acquisition submodule receives combat unit status data packets via the UDP multicast protocol. These data packets are data units encapsulated in a predetermined format. Specifically, the fixed length of a combat unit status data packet is set to 64 bytes, and its internal structure is as follows: the unit's unique identifier occupies 8 bytes (such as "Tank001A"), the precise timestamp when the data packet was generated occupies 8 bytes (indicating the number of nanoseconds since a specific epoch, such as midnight UTC on January 1, 1970, such as 1678886400123456789 nanoseconds), the longitude of the current position occupies 8 bytes (double-precision floating-point number) (such as 116.397128 degrees), the latitude of the current position occupies 8 bytes (double-precision floating-point number) (such as 39.916527 degrees), and the altitude occupies 4 bytes (single-precision floating-point number) (such as 50. 5 meters), the main weapon system ammunition reserve occupies a 4-byte integer (such as 30 rounds), the auxiliary weapon system ammunition reserve occupies a 4-byte integer (such as 1500 rounds), the current equivalent protection value of the vehicle body armor occupies a 4-byte integer (such as 480, this value represents the equivalent homogeneous steel armor thickness, unit is mm RHA), the vehicle body armor design benchmark protection value occupies a 4-byte integer (such as 500 mm RHA), the turret armor current equivalent protection value occupies a 4-byte integer (such as 390 mm RHA), the turret armor design benchmark protection value occupies a 4-byte integer (such as 400 mm RHA), and the data checksum (such as a 4-byte checksum generated by the CRC32 checksum algorithm).

[0056] After receiving the original byte stream from the network interface layer, the module determines that it is combat unit status data based on the data packet header information or the preset UDP port number (for example, port 30500 is dedicated to receiving combat unit status data). Subsequently, the various parameters are parsed and extracted according to the predefined byte offset and data type: 8 bytes are read from byte offset 0 as the unit ID "Tank001A"; 8 bytes are read from byte offset 8 as the timestamp 1678886400123456789 nanoseconds; the longitude 116.397128 degrees and the latitude 39.916527 degrees are read from byte offsets 16 and 24 respectively; the altitude 50.5 meters is read from byte offset 32; the main weapon ammunition count 30 rounds are read from byte offset 36; the auxiliary weapon ammunition count 1500 rounds are read from byte offset 40; the vehicle body current armor equivalent value 480 mm RHA and the design benchmark armor value 500 mm RHA are read from byte offsets 44 and 48 respectively; the turret current armor equivalent value 390 mm RHA and the design benchmark armor value 400 mm RHA are read from byte offsets 52 and 56 respectively.

[0057] After completing the basic parameter extraction, the module obtains the current system time, accurate to nanoseconds, recorded as T current (such as 1678886400323456789 nanoseconds), and the timestamp T in the data packet packet(1678886400123456789 nanoseconds) is subtracted to obtain the data transmission delay time ΔT = T current -T packet In this case,

[0058] ΔT = 1678886400323456789 - 1678886400123456789 = 200,000,000 nanoseconds. For ease of comparison, nanoseconds must be converted to milliseconds using the following conversion rule: 1 millisecond = 1,000,000 nanoseconds. Therefore, the delay is 200,000,000 / 1,000,000 = 200 milliseconds. This calculated delay value is compared with a preset 200 millisecond delay threshold. This 200 millisecond delay threshold is based on a statistical analysis of UDP packet transmission performance in battlefield communication networks under varying load conditions (simulating low to high intensity information exchange). The specific experimental process involved constructing a network environment containing 50 simulated combat unit nodes. Each node transmitted status packets at a randomly selected frequency ranging from 1 Hz to 10 Hz, continuously monitoring the network for 24 hours. The experiment collected end-to-end delay data for a total of approximately 1 million valid data packets. Statistical results show that 95% of the data packets had a delay of less than 190 milliseconds. Taking into account the sudden network fluctuations and electromagnetic interference that may exist in a battlefield environment, and to ensure the timeliness of received data, a delay threshold of 200 milliseconds, slightly higher than the 95th percentile, was selected. The rationality of this threshold was verified through subsequent system stress tests conducted in a simulated complex electromagnetic interference environment (for example, with a 20dB increase in signal interference strength). The test results showed that the use of a 200-millisecond threshold can effectively filter out more than 98% of abnormal and outdated data caused by significant network congestion or transmission errors.

[0059] If the delay value of 200 milliseconds calculated above is less than or equal to the threshold of 200 milliseconds, the data packet is judged to be valid, and its extracted position coordinates (longitude 116.397128, latitude 39.916527, altitude 50.5 meters), ammunition reserves (30 rounds for main weapons, 1500 rounds for auxiliary weapons), and armor integrity parameters (current 480mm RHA for the hull / designed 500mm RHA, current 390mm RHA for the turret / designed 400mm RHA) will be retained. If the delay calculated for another data packet is 250 milliseconds, because its 250 milliseconds is greater than 200 milliseconds, the data packet and all its extracted parameters will be judged as abnormal data units and discarded, and will not be sent to the subsequent processing flow. All valid data parameters that pass the delay check are organized into a structured record. For example, for unit "Tank001A", the record is

[0060] {ID: "Tank001A", Longitude: 116.397128, Latitude: 39.916527, Altitude: 50.5, AmmoMain: 30, AmmoAux: 1500, ArmorHullCurrent: 480, ArmorHullMax: 500, ArmorTurretCurrent: 390, ArmorTurretMax: 400}. These records are combined to generate the combat unit status parameter set.

[0061] The data integration submodule calls the combat unit status parameter set, converts the coordinates to the standard space reference system through Gauss-Krüger projection, standardizes the ammunition reserve to a percentage system, calibrates the armor integrity value range, integrates the spatial position, resource status, and protection status data, and generates a standardized state matrix;

[0062] The data integration submodule calls the combat unit status parameter set generated in the previous steps. This parameter set is a list that contains the original status information records of each combat unit. It is assumed that the parameter set contains the following two records:

[0063] 1:{ID:"Tank001A",Longitude:116.397128,Latitude:39.916527,Altitude:50.5,Ammo Main:30,AmmoAux:1500,ArmorHullCurrent:480,ArmorHullMax:500,ArmorTurretCurr ent:390,ArmorTurretMax:400} record

[0064] 2:{ID: "APC003B",Longitude:116.407500,Latitude:39.906800,Altitude:52.1,Ammo Main:1000,AmmoAux:0,ArmorHullCurrent:150,ArmorHullMax:150,ArmorTurretCurrent:0,ArmorTurretMax:0}

[0065] For each record's geographic coordinates (longitude, latitude), using the coordinates of "Tank001A" (longitude 116.397128 degrees, latitude 39.916527 degrees) as an example, the module performs a Gauss-Krüger projection conversion from the WGS-84 geodetic coordinate system (a universally used geographic coordinate system) to a target standard spatial reference system. This target reference system is set to zone 20 of the Gauss-Krüger 6-zone projection, with the central meridian at 117 degrees east. The conversion process involves a series of precise mathematical operations: First, the longitude difference between the longitude of the point to be converted, 116.397128 degrees, and the central meridian, 117 degrees, is calculated: l = 116.397128 - 117 = -0.602872 degrees. Substitute the latitude B = 39.916527 degrees (39.916527 × Π / 180 ≈ 0.69676 radians in radian) and the longitude difference l (-0.602872 × Π / 180 ≈ -0.01052 radians in radian) into the Gauss-Krüger projection forward calculation formula. During the calculation, the Earth's semi-major axis a is taken as 6378137 meters, and the Earth's first eccentricity squared e is taken as 2 The value is 0.00669437999013. The radius of curvature of the yoke is calculated using these parameters.

[0066] We get N≈6388539.7 meters. We further calculate the horizontal coordinate X after projection. GK and the vertical coordinate Y GK (Here Y GK Usually the tape number is multiplied by one million and the horizontal coordinate offset is 500,000 meters, and X GK is the true north distance from the equator), and the plane coordinate X is calculated for "Tank001A" GK =4418350.25 meters, Y GK = 20499730.10 meters (this Y coordinate is an example value including band number 20 and an offset of 500000 meters).

[0067] The ammunition reserve is standardized and converted into a percentage. Taking "Tank001A" as an example, its main weapon ammunition is 30 rounds. From the equipment performance database, by unit ID or type (such as "Tank001A" corresponds to "ZT99A main battle tank"), it is found that its main weapon maximum design capacity is 40 rounds. The standardized main weapon ammunition percentage is (30 / 40)×100%=75.0%. Its auxiliary weapon ammunition is 1500 rounds, and its maximum capacity is 2000 rounds. The standardized percentage is

[0068] (1500 / 2000)×100%=75.0%. For the APC003B, its main weapon ammunition capacity is 1000 rounds. If the corresponding model (such as the ZBL08 infantry fighting vehicle) has a maximum capacity of 1000 rounds, then its main weapon ammunition percentage is 100%.

[0069] The armor integrity value is calibrated and converted into a ratio value between 0 and 1, which represents the current degree of protection relative to the design benchmark. Taking "Tank001A" as an example, its current armor equivalent value is 480 mm RHA, and the design benchmark value is 500 mm RHA, so the armor integrity of the vehicle body is

[0070] 480 / 500 = 0.960. The turret's current armor equivalent value is 390 mm RHA, and the design baseline value is 400 mm RHA. Therefore, the turret's armor integrity is 390 / 400 = 0.975. These ratios intuitively reflect the damage level of various parts.

[0071] The integrated spatial position data is the transformed plane coordinate X GK ,Y GK and the original altitude (e.g., 50.5 meters). Resource status data is the standardized percentage of each type of ammunition (e.g., 75.0% for main weapons and 75.0% for auxiliary weapons). Protection status data is the calibrated armor integrity ratio of each major part (e.g., 0.960 for the hull and 0.975 for the turret). These standardized data for each combat unit are organized into a vector of fixed length and order. For example, the data vector for "Tank001A" can be represented as

[0072] [4418350.25, 20499730.10, 50.5, 75.0, 75.0, 0.960, 0.975] (X coordinate, Y coordinate, altitude, primary ammunition percentage, secondary ammunition percentage, hull armor ratio, turret armor ratio, respectively). These data vectors for all combat units are combined and arranged in rows (each row represents a combat unit, and the order and meaning of the columns are fixed) to generate a standardized state matrix.

[0073] The snapshot generation submodule constructs spatial topological relationships based on the standardized state matrix, calculates the weighted fusion value of ammunition reserves and armor integrity, performs binary serialization operations through the real-time data distribution service and appends MD5 checksums to generate battlefield snapshot data.

[0074] The snapshot generation submodule constructs a snapshot of the global battlefield situation based on the standardized state matrix generated in the previous steps. This matrix contains the standardized spatial position, resource status, and protection status of each combat unit. Imagine the matrix contains the data row for "Tank001A":

[0075] [4418350.25,20499730.10,50.5,75.0,75.0,0.960,0.975], and the data row for "APC003B": [X2,Y2,Z2,AmmoP main2 ,AmmoP aux2 ,ArmorP hull2 ,ArmorP turret2 ].

[0076] First, the spatial topological relationship between units is constructed based on the standardized state matrix. This step involves identifying the proximity relationship between units by calculating the three-dimensional Euclidean distance between any two combat units (set as unit i and unit j). Set an effective perception or tactical coordination range threshold R between battlefield units link , its value is 2000 meters. link The threshold is set based on multiple field measurements of effective point-to-point communication distance using standardized tactical communication equipment issued to such combat units in a typical hilly terrain with medium vegetation cover. The experimental results show that within this 2,000-meter distance, a reliable data link (bit error rate less than 10%) can be maintained. -4 ) has a communication success rate higher than 90%. If the calculated distance D between unit i and unit j ij Less than R link (2000 meters), then the two units are considered to have a direct tactical topological connection in the current snapshot. This connection information can be used for subsequent network-centric warfare analysis.

[0077] Next, calculate the weighted fusion value of each combat unit's ammunition reserve and armor integrity. This fusion value V u Used to comprehensively evaluate the unit's sustained combat potential and battlefield survivability. The weight coefficient was set based on the evaluation conducted by a military operations research expert group through multiple rounds of Delphi method, combined with the regression analysis results of nearly 20 simulated confrontation exercises. The analysis shows that under typical high-intensity confrontation scenarios, the impact of armor integrity on the unit's survivability and sustained mission execution capability is slightly greater than the immediate ammunition reserve. Therefore, the weight w of the comprehensive ammunition reserve percentage (the main and auxiliary ammunition can be further weighted averaged or the key ammunition can be taken) is set. ammo =0.4, weight w of comprehensive armor integrity (the armor integrity of each part can be weighted average or take the minimum value) armor =0.6, ensuring w ammo +w armor = 1.0. For "Tank001A", its main weapon ammunition is 75.0%, auxiliary weapon ammunition is 75.0%, assuming the comprehensive ammunition reserve percentage

[0078] AmmoP1=(75.0%+75.0%) / 2=0.75. Its hull armor integrity is 0.960, and turret armor integrity is 0.975. Assuming the overall armor integrity is

[0079] ArmorP1=(0.960+0.975) / 2=0.9675. Then its weighted fusion value is

[0080] V1=w ammo ×AmmoP1+w armor ×ArmorP1=0.4×0.75+0.6×0.9675=0.3000+0.5805=0.8805. Perform this calculation for all combat units in the standardized state matrix to obtain the comprehensive state evaluation value of each unit.

[0081] The module then performs a binary serialization operation via a real-time data distribution service (using an efficient publish / subscribe messaging queue system such as Apache Kafka or a binary transmission protocol designed specifically for tactical data links). This operation converts the entire standardized state matrix (which may include raw standardized data, calculated spatial topological connectivity information, and the weighted fusion state values ​​of each unit) into a compact binary byte stream. During this serialization process, floating-point numbers are encoded according to the IEEE 754 standard, integers are directly converted to multi-byte representations in network byte order (e.g., 32-bit integers), and string unit IDs are encoded using UTF-8, with their byte sequence and length stored. The logical structure of the serialized data packet is: [snapshot generation timestamp (8 bytes, nanoseconds)] [total number of combat units N (4-byte integer)] [unit 1 data block (length x 1 byte)] [unit 2 data block (length x 2 bytes)] … [unit N data block (length x N bytes)]. Each unit data block contains the unit ID, projection coordinates, altitude, standardized ammunition information, standardized armor information, and the calculated weighted fusion value.

[0082] After serialization is completed, perform MD5 on the entire generated binary byte stream

[0083] (MessageDigestAlgorithm5) checksum calculation. The MD5 algorithm maps binary data of arbitrary length into a 128-bit hash value through a series of hash operations. The value is usually expressed as 32 hexadecimal characters. For example, after calculating a specific battlefield situation binary serialized data block, the resulting MD5 value is "e4d909c290d0fb1ca068ffaddf22cbd0". This MD5 checksum is appended to the end of the binary serialized data or as a dedicated field in the packet header. The final data packet containing the serialized battlefield situation data and the MD5 checksum is a battlefield snapshot data with integrity and timeliness.

[0084] See also Figure 3 , the adversarial parameter correction module includes:

[0085] The spatial overlay operation submodule obtains fire coverage parameters from battlefield snapshot data, performs spatial overlay operations on multiple sets of parameters, calculates the geometric intersection area of ​​the coverage areas of multiple fire units, sets a tactical conflict threshold of 50 square meters based on armored vehicle mobility performance experimental data, compares the intersection area with the preset tactical conflict threshold, and generates the area value of the overlapping area;

[0086] The spatial superposition operation submodule obtains the battlefield snapshot data generated by the above steps. From the snapshot data, the current fire coverage parameters of each fire unit (such as tanks, self-propelled artillery, armed helicopters, etc.) are extracted. These parameters are defined according to the type of each fire unit, the selected ammunition, the current direction, the weapon pitch angle, the effective range and the fire sector angle (or sweeping range), and are ultimately expressed as a geometric area on a two-dimensional geographic plane. In this embodiment, the fire coverage of each fire unit is accurately described as a polygon, and the vertex sequence of the polygon is calculated by the real-time status (position, direction) of the fire unit and its weapon performance parameters (effective range, strike sector width) through a preset geometric model. For example, the coverage of the fire unit F1 is defined by the vertex coordinate sequence

[0087] P F1 ={(x 1,1 ,y 1,1 ),(x 1,2 ,y 1,2 ),...,(x 1,k ,y 1,k )}, the coverage of the firepower unit F2 is defined by the vertex coordinate sequence P F2 ={(x 2,1 ,y 2,1 ),(x 2,2 ,y 2,2 ),...,(x 2,m ,y 2,m)}definition.

[0088] The module performs spatial superposition operations on multiple sets of such fire coverage parameters on the battlefield. Specifically, it calculates the geometric intersection area between two or more polygons representing the fire coverage. For example, if you want to analyze the fire coordination effect of fire units F1 and F2, you need to calculate

[0089] P F1 and P F2 The geometric intersection of the two polygons will produce a new polygon.

[0090] P intersect =P F1 ∩P F2 , represents the area that can be covered by two fire units at the same time. If the coordinated suppression of three fire units F1, F2, and F3 is considered, the calculation

[0091] P F1 ∩P F2 ∩P F3 This intersection operation is performed using mature computational geometry algorithms, such as the Weiler-Atherton algorithm or the Sutherland-Hodgman polygon clipping algorithm, which can accurately handle the intersection and clipping operations of complex polygons and output the vertex coordinates of the intersection area.

[0092] Then, calculate the area of ​​the intersection region or regions. intersect is a polygon consisting of p vertices, whose vertices are arranged in counterclockwise or clockwise order.

[0093] xv1,yv1),(xv2,yv2),...,(xv p ,yv p ), then its area A intersect It can be calculated using the shoelace formula:

[0094] Among them is agreed

[0095] (xv p+1 ,yv p+1 )=(xv1,yv1) to close the polygon. After calculation, the intersection area P of the firepower coverage of the two firepower units F1 and F2 is obtained. intersect The area is 75.8 square meters.

[0096] Next, the calculated intersection area is compared with a preset tactical conflict threshold, set at 50 square meters. This threshold was determined based on a series of simulation experiments examining the mobility and survivability of various armored vehicle types (including heavy main battle tanks and medium tracked infantry fighting vehicles) in different tactical contexts. The experiments set up simulated crossfire zones of varying sizes (increasing from 10 to 100 square meters in steps of 10 square meters). Armored vehicles attempted to quickly navigate these crossfire zones at their tactical maneuvering speeds (e.g., a search and advance speed of 15 km / h, or a short-range assault speed of 30 km / h). The probability of the vehicle being "effectively hit" by virtual fire (determined by the hit location and equivalent probability of damage) during the passage, or the time required to safely navigate, was recorded.

[0097] Table 1 Experimental data on the success rate of armored vehicles avoiding crossfire areas of different sizes

[0098]

[0099] Table 1 lists the effects of different overlap areas on the evasive maneuver success rates of two typical armored vehicles (tank model A and infantry fighting vehicle model B) in simulation experiments. The data shows that when the overlap area increases from 40 to 50 square meters, the average evasive maneuver success rate drops from 89.0% to 67.5%. When the overlap area increases from 50 to 60 square meters, the success rate drops further from 67.5% to 47.5%. This indicates that 50 square meters is a critical transition point. Below this area, vehicles can still utilize their mobility (such as quick stops and turns, accelerating past edges) to achieve a high chance of survival. Above this area, the probability of survival decreases significantly due to prolonged exposure time or limited evasive space. Furthermore, referring to the guiding principles of fire coordination and effective suppression in relevant military regulations, it is believed that an overlap area of ​​less than 50 square meters is unlikely to provide a sustained and stable suppression effect against a typical armored cluster with a certain degree of mobility. Therefore, based on a combination of experimental data and tactical principles, 50 square meters was selected as the area threshold for determining whether a tactical conflict has occurred.

[0100] The module calculates the actual intersection area A intersect (75.8 square meters) and this preset tactical conflict threshold A t = 50 square meters. In this specific case, 75.8 square meters exceeds 50 square meters, indicating that the area of ​​the overlapping fire zone has reached the tactical conflict threshold. Finally, the module outputs the calculated overlap area value of 75.8 square meters, which is used by the subsequent density segmentation and conflict analysis modules to generate the overlap area value.

[0101] The density profiling submodule is based on the overlapping area value. When the tactical conflict threshold is exceeded, the formula is used:

[0102]

[0103] Calculate and obtain the deployment density correction coefficient, call the Voronoi diagram algorithm to spatially divide the firepower deployment points, and generate the deployment density distribution value;

[0104] Among them, A t represents the tactical conflict area threshold, which is 50 square meters. i Represents the Euclidean distance between the ith deployment point and the conflict boundary, in meters, N represents the total number of deployment points on the current battlefield, S c is the density correction coefficient, which is used to adjust the Voronoi diagram subdivision weight;

[0105] The density profiling submodule receives the overlapping area value generated in the previous step (this value is 75.8 square meters). First, it determines whether this area value exceeds the set tactical conflict threshold (50 square meters). Since 75.8 square meters > 50 square meters, the condition is met, so the subsequent deployment density correction coefficient calculation is started. At this time, the formula is used. To calculate the deployment density correction coefficient S c .

[0106] The meaning of each parameter in the formula and the way to obtain the value are as follows: A t Represents the tactical conflict area threshold, with a value of 50, in square meters. The basis for setting this value has been detailed in the "Spatial Superposition Operation Submodule" and is determined based on armored vehicle mobility performance experimental data and military experience. i Represents the shortest Euclidean distance between the i-th fire deployment point (the actual position coordinates of the fire unit) and the boundary of the 75.8 square meter overlapping fire area (i.e., the conflict area) currently being analyzed, in meters. These fire deployment points refer to the fire units that caused the formation of this conflict area. If a fire deployment point is located inside the conflict area, its d i is defined as 0. In this embodiment, in order to analyze the boundary effect, the fire deployment points considered are all located outside the conflict area, but their fire sectors participate in constituting the conflict area. Represents all n fire deployment points that participate in the current specific conflict area The sum of the values ​​is calculated. n is the number of firepower units that directly or indirectly contribute to the formation of the current 75.8 square meter overlap area. N represents the total number of active firepower deployment points in the entire battlefield snapshot data. This value is directly obtained from the battlefield snapshot data by counting all units marked as "firepower unit" and in the "combat-ready" status.

[0107] Now let's proceed with the parameter assignment and calculation process: Assume that the conflict area currently being analyzed, with an area of ​​75.8 square meters, is formed by the superposition of the fire ranges of n = 3 fire units (numbered F1, F2, and F3). The coordinates of the deployment points of these fire units are known. Using precise computational geometry methods (for example, the point-to-polygon boundary shortest distance algorithm), the closest distances from each of these deployment points to the polygonal boundary of the 75.8 square meter conflict area are calculated, resulting in the following values: d1 = 2.5 meters (the distance from the deployment point of fire unit F1 to the conflict area boundary), d2 = 3.0 meters (the distance from the deployment point of fire unit F2 to the conflict area boundary), and d3 = 1.8 meters (the distance from the deployment point of fire unit F3 to the conflict area boundary). These distances are obtained by taking the minimum of the perpendicular distances from the deployment point to each edge of the conflict polygon and the straight-line distances to each vertex. For example, if the coordinates of the F1 deployment point are (100,100), and a line segment on the conflict area boundary connects the points (90,110) and (110,110), then the distance from F1 to this line segment is calculated and compared with the distances from F1 to all other boundary segments and vertices, and the minimum value is taken.

[0108] From the battlefield snapshot data, the total number of active firepower deployment points in the current battlefield is N = 25. Tactical conflict area threshold A t =50 (square meters).

[0109] Substitute these values ​​into the formulas for the calculations: First calculate

[0110]

[0111] 6.25 square meters + 9.00 square meters + 3.24 square meters = 18.49 square meters. At this time, the first part of the formula is

[0112] The second part of the formula is Here, N is a unitless count (25 deployment points). t is the area threshold, which has a value of 50. For calculations in this particular empirical formula, the rule is to use A directly. t The value of 50. Therefore, the denominator is 50 + 1 = 51. This rule is set because the formula is designed to combine the value representing the area (A t ) and the number representing the quantity (N) to generate a correction coefficient, the final effect of which is reflected in the impact of the numerical value on the subsequent weight adjustment, rather than its physical unit. Therefore, the calculation is

[0113]

[0114] Therefore, the deployment density correction factor S c=31.51 square meters×0.70014004≈22.061. c The value is a numerical coefficient that combines the characteristics of the local conflict area (the difference from the threshold area, the sum of the squares of the distances from the relevant firing points to the border) and the influence of the number of firing points on the battlefield. Its value will be used to adjust the weight in the subsequent spatial subdivision process, and the size of the value directly affects the magnitude of the adjustment. The formula is beneficial in that it makes the density correction coefficient S c Can dynamically reflect the concentration of local firepower (reflected in d i The value is small, resulting in The correlation between the firepower points (larger) and the overall deployment scale of the battlefield (reflected in the N value). i Generally small) and the number of units deployed on the battlefield is large, S c The value will be relatively high, and vice versa.

[0115] Get the deployment density correction factor S c (its value is 22.061), the module calls the spatial subdivision algorithm, specifically the subdivision method based on the Voronoi diagram. This method divides the entire battlefield space according to the positions of all N = 25 firepower deployment points. Each firepower deployment point will generate a unique Voronoi unit (a convex polygonal area). The distance from any point in the unit to its corresponding firepower deployment point is less than the distance to any other firepower deployment point, thereby clearly defining the "sphere of influence" of each firepower point. When generating these Voronoi units, the deployment density correction coefficient S calculated above is used. c (22.061) is used as an important adjustment parameter. In particular, for those fire deployment points that participate in or are adjacent to the 75.8 square meter conflict area currently analyzed, the properties of the corresponding Voronoi cells will be affected by S c One way to achieve this is to change S c Or its derived value (for example, normalized or hierarchically quantized) is assigned as an additional "density weight" attribute to the Voronoi cells associated with the conflict area. In the weighted Voronoi diagram, the weight of a point can affect the area and shape of its corresponding cell. Ultimately, a set of Voronoi cells covering the entire battlefield space is formed, each with density information. The geometric properties of these cells (such as cell area, perimeter, shape factor) and the additional density weight attribute together constitute a quantitative description of the density distribution state of battlefield firepower deployment, that is, the generated deployment density distribution value.

[0116] The conflict coordinate generation submodule extracts the boundary coordinates of the area where the density gradient change rate exceeds the critical value based on the deployment density distribution value, and uses the second-order continuously differentiable cubic spline interpolation function to perform interpolation operations on adjacent boundaries to generate the coordinates of the firepower conflict area.

[0117] The conflict coordinate generation submodule operates according to the deployment density distribution value generated in the previous step. This deployment density distribution value is expressed as a series of density attributes associated with each Voronoi cell. Each Voronoi cell V j Has a quantized density value Density j , this value is based on S c and the unit’s own characteristics (such as area) are calculated comprehensively.

[0118] The first task of the module is to identify the boundaries of areas with drastic density changes in the battlefield space. This is achieved by calculating the density gradient between adjacent Voronoi cells.

[0119] E jk The adjacent Voronoi cell V j and V k , the density gradient between them Grad jk Calculated as Density j -Density k | is the absolute difference between the two cell density values, dist(Centroid j ,Centroid k ) is the Euclidean distance between the centroids of the two Voronoi cells. Set a critical value T for the rate of change of the density gradient grad , whose value is 10.0 (the unit of this gradient value depends on Density j The unit of the distance is assumed to be dimensionless density value / meter). This critical value T grad The setting of is based on a statistical analysis of Voronoi density distribution maps generated from 100 simulated battlefield situations of varying complexity. In this analysis, the density gradient values ​​between all adjacent cells were calculated and their distributions statistically ranked. The results showed that the 85th percentile of these gradient values ​​was 12.5. To ensure that the boundaries of areas with truly significant changes in battlefield firepower density are captured while avoiding excessive fluctuations in trivial details, a critical value of approximately 80% of this 85th percentile was selected: 12.5 × 0.8 = 10.0.

[0120] When a shared boundary E jk The density gradient Grad calculated above jk Greater than the critical value Tgrad (10.0), determine the boundary E jk Belongs to the area of ​​high density gradient change. The module collects the endpoint coordinates of all boundary edges that meet this condition. These endpoint coordinates initially constitute a discrete boundary point set of the potential area of ​​fire conflict, such as {(xb1,yb1),(xb2,yb2),(xb3,yb3),...,(xb q ,yb q )}. These points may be disordered and discontinuous in their initial state and require further processing to form a structured boundary. The module topologically sorts and connects these extracted discrete boundary points (for example, by finding points that are spatially adjacent and belong to a region of high gradient change), forming a sequence of irregular but continuous boundary segments.

[0121] Then, a second-order continuously differentiable cubic spline interpolation function is used to interpolate the sequences of adjacent discrete boundary points (for example, one of the sequences is S1=

[0122] {(xb1,yb1),(xb2,yb2),(xb3,yb3),(xb4,yb4)}) performs smooth interpolation operation. For each pair of adjacent points (xb k ,yb k ) and (xb k+1 ,yb k+1 ), construct a parameterized cubic polynomial curve segment P k (t) = (X k (t),Y k (t)), where the parameter t ranges from [0,1]. The curve segment must satisfy P k (0) = (xb k ,yb k ) and P k (1) = (xb k+1 ,yb k+1 The key is that at each internal connection point (such as (xb2, yb2), (xb3, yb3)), two adjacent cubic polynomial segments P k-1 (t) and P k (t) must ensure continuity of its first-order derivative (representing the direction of the tangent) and second-order derivative (representing the change in curvature). This is achieved by applying the cubic spline interpolation algorithm to the X-coordinate sequence and the Y-coordinate sequence independently. This involves solving two independent tridiagonal linear equations to determine the coefficients of each polynomial segment, ensuring continuity of the derivatives at the connection points. For example, for the point sequence (0,0), (2,3), (5,1), (6,4), interpolation produces a smooth curve passing through these four points, with continuous tangents and curvature at the interior points (2,3) and (5,1).

[0123] This cubic spline interpolation process is performed on all identified high-density gradient boundary segment sequences. This interpolation results in a series of smooth curve segments. The module then uses a geometric algorithm to attempt to connect the endpoints of these smooth curve segments, ultimately forming one or more closed polygonal contours. The vertex coordinate sequences of these closed contours (these vertices are sampling points on the interpolation curve, and their number is determined by the required accuracy) constitute the final coordinates of the fire conflict zone.

[0124] See also Figure 4 , the tactical event arbitration module includes:

[0125] The battlefield situation acquisition submodule detects engagement distance, unit density, and terrain cover parameters in the coordinates of the fire conflict area. It uses a 100m×100m grid division method to count the number of combat units per unit area, extracts meter-level values ​​of engagement distance through spatial coordinate analysis technology, and calculates cover coverage using a terrain elevation model to generate a dynamic parameter set.

[0126] The battlefield situation acquisition submodule detects the fire conflict area coordinates generated in the previous step. This coordinate data describes one or more closed polygonal areas defined by a series of vertex sequences. Assume that one of the identified fire conflict areas is CR1, whose boundary is defined by the vertex coordinate sequence

[0127] {(cx 1,1 ,cy 1,1 ),(cx 1,2 ,cy 1,2 ),...,(cx 1,p ,cy 1,p )}Precisely defined.

[0128] The module first uses a 100m x 100m square grid to spatially divide the entire combat area of ​​interest (which must include all identified fire conflict areas). This means superimposing a standard Cartesian grid system on the battlefield map, with each grid cell (Cell) having a side length of 100 meters and an area of ​​10,000 square meters. For each grid cell ij (where i, j are the row and column indices of the grid), the module counts the number of combat units (the precise location information of these combat units comes from the standardized state matrix) that fall within the geographical range of the unit, recorded as N ij This statistical process is done by converting the normalized plane coordinates (X k ,Y k ) (unit: meter) is accurately mapped to the grid unit where it is located. The specific mapping method is: if the coordinates of the origin (lower left corner) of the grid system are (Origin X ,OriginY ), then the combat unit U1, its coordinates are (X U1 ,Y U1 ), the column index of the grid to which it belongs and row index For example, if the coordinates of combat unit U1 are (4418370,20499760) and the grid origin is (4418000,20499000), then U1 is located at Grid cell 3,7 (Index starts at 0). After performing this attribution calculation on all combat units and accumulating the counts, the number of combat units in each grid unit N is obtained. ij .

[0129] Next, the engagement distance within the identified fire conflict area CR1 is extracted through spatial coordinate analysis technology. The engagement distance here specifically refers to the actual distance between the combat units of the enemy and our side (or potential adversaries) within the conflict area. The module will traverse all pairs of combat units identified as hostile in the conflict area CR1 (the enemy and our side identification information comes from the camp affiliation labels of each unit in the battlefield snapshot data, or is dynamically determined according to the preset engagement rules). For example, in the conflict area CR1, the position coordinates of an enemy unit E1 are (ex1, ey1), and the position coordinates of a friendly unit F1 are (fx1, fy1), then the straight-line engagement distance between them is The unit is meters. The module calculates the distances between all such enemy and friendly unit pairs within the conflict region and extracts statistical characteristic values, such as the average engagement distance, minimum engagement distance, or median engagement distance within the conflict region. In this embodiment, the arithmetic mean of the engagement distances between all enemy unit pairs within conflict region CR1 is extracted, resulting in an average engagement distance of 350 meters.

[0130] Furthermore, the module uses a digital terrain elevation model (DEM) to calculate the terrain cover coverage rate within the fire conflict area. This DEM is a fine rasterized surface elevation dataset, for example, a DEM with a spatial resolution of 5 meters × 5 meters, where each grid point stores the precise altitude of the point. For each friendly combat unit within the fire conflict area CR1, its terrain visibility relative to the potential threat direction (usually refers to the direction toward the nearest enemy unit within the effective range of its weapons) is analyzed. The specific approach is: from our unit U s The position (antenna or main sighting equipment height) along a specific threat direction (towards the enemy unit U t ) Project a virtual line of sight. Based on the DEM data, check whether there is a terrain point P on the line of sight path terrain The elevation H terrain, the elevation value is higher than the elevation H that the sight line should have at the horizontal position of the terrain point los (In tactical small-scale calculations, the effects of the earth's curvature and standard atmospheric refraction are usually simplified or ignored, and the terrain undulations are mainly considered.) If there is such a terrain point, it is considered that there is terrain shielding in the threat direction. The shelter coverage rate is further quantified as follows: within the conflict area CR1, 100 pairs of enemy and friendly units with potential combat relationships are randomly selected. For each pair of units, DEM data is used to determine whether there is an effective terrain line-of-sight path between them (that is, the line of sight is not blocked by the terrain). The number of pairs of units that cannot directly see each other due to terrain shielding is counted. If 45 pairs of these 100 pairs of units are determined to be non-line-of-sight, the terrain shelter coverage rate of the conflict area CR1 is calculated to be 45 / 100=0.45 (or expressed as 45%).

[0131] Finally, the number of combat units per unit area (for example, all grid cells N in the conflict area CR1) is counted. ij The average value of the conflict zone, or the density value obtained by directly dividing the total number of units in the conflict zone by the total area of ​​the conflict zone), the extracted engagement distance (such as the average engagement distance of 350 meters), and the calculated terrain cover coverage rate (such as 0.45) are integrated into a structured data set to generate a dynamic parameter set for subsequent situation quantification.

[0132] The conflict situation quantification submodule uses a dynamic parameter set to perform inverse proportional normalization on the engagement distance, converting unit density into the number of combat units per square kilometer. The entropy weight method is used to calculate the weight coefficients of terrain shelter parameters, using the standard deviation method to determine the indicator dispersion. The range normalization method is used to eliminate dimensional differences and establish a situation coefficient matrix.

[0133] The conflict situation quantification submodule receives the dynamic parameter set generated in the previous step. This parameter set contains the key situation elements that describe a specific conflict area or battlefield grid unit. For example, for a certain conflict area, its average engagement distance D avg = 350 meters, unit density (after conversion, for example, there are an average of N per 100 meters × 100 meters, or 0.01 square kilometers). unit = 2.5 combat units), and terrain cover coverage C cover =0.45.

[0134] The module first engages at distance D avg Perform inverse proportional normalization. The purpose of this process is to make the smaller the distance (usually meaning the greater the threat or the closer the engagement) correspond to a larger normalized value, so as to facilitate subsequent unified comparison and weighting with other indicators (especially those with larger values ​​that are more unfavorable). The normalization range is limited to the interval [0,1]. The normalization formula used is Among them, D ref_max The maximum effective engagement distance commonly observed on the battlefield is set at 2000 meters. This value is determined based on the maximum effective range of typical vehicle-mounted weapon systems and the range of general battlefield observation and command. ref_min The minimum effective engagement distance is set at 50 meters because a distance below this usually means entering into extremely close combat or contact, where the threat model and response strategy change significantly and are not suitable for the macro-situation standardization method at this stage. avg =350 meters Substitute to obtain the standardized engagement distance The larger this value is, the closer the engagement distance is to the minimum reference distance and the more tense the situation is.

[0135] Next, the unit density is converted from the original statistical value based on a specific grid size to the internationally accepted number of combat units per square kilometer. The original unit density is 2.5 combat units per 0.01 square kilometer (100 meters x 100 meters). The conversion rule is: 1 square kilometer = 100 x (0.01 square kilometers). Therefore, the number of combat units per square kilometer is Density km 2 =2.5 units / 0.01 km 2 =

[0136] 250 units / km 2 .

[0137] Terrain shelter parameters (i.e., terrain shelter coverage C cover =0.45) In the subsequent comprehensive evaluation, its relative importance will be combined with other parameters such as engagement distance and unit density. After the situation coefficient matrix is ​​established, the weight w in the TOPSIS model will be used. j In this step, the terrain cover coverage C cover (Its value range is 0 to 1) will be normalized along with other indicators to eliminate the impact of differences in parameter dimensions and value ranges.

[0138] The Min-Max Normalization method linearly maps the original data to the [0,1] interval. Its standard formula is: For the calculated reverse normalized engagement distance D′ std =0.846, since its calculation process has ensured that its value is between 0 and 1 (if D avg In D ref_min and D ref_max (between), we will not repeat the standardization here, that is, D″ norm=0.846. For the converted unit density Density km 2 =250 units / km 2 , it is necessary to set its minimum and maximum reference values ​​in a typical battlefield environment. Based on the statistical analysis of historical combat data and exercise data in similar battlefield environments, it is determined that under the intensity of the confrontation concerned, the combat unit density is usually between Density min =10 units / km 2 To Density max =500 units / km 2 The normalized density value For the terrain cover coverage C cover =0.45, its natural range is [0,1] (0% to 100% coverage), so its minimum reference value C min =0 and the maximum reference value C max =1. Then the standardized cover coverage rate is

[0139] These standardized parameters (D norm ,Density′ norm ,C′ cover_norm ), specifically the values ​​(0.846, 0.489, 0.45), serve as a feature vector describing the comprehensive situation of the conflict zone or grid cell. The aforementioned quantization and standardization process is performed on all identified conflict zones on the battlefield, or all grid cells requiring situation assessment. These processed feature vectors are aggregated, with each row representing the situational characteristics of an assessment object. This creates a multidimensional situation coefficient matrix, which is then used by the subsequent path decision module.

[0140] The path decision generation submodule calls the situation coefficient matrix and uses the TOPSIS model to calculate the Euclidean distance between multiple coordinate points and the ideal solution. Based on the armored forces' actual combat exercise data, a 20% threshold is set as the safe coordinate point screening ratio. The sorting priority is determined by the ratio of positive and negative ideal solution distances to generate tactical avoidance path instructions.

[0141] The path decision generation submodule calls the situation coefficient matrix established in the previous steps. Each row of this matrix represents the quantized situation feature vector of an evaluation point on the battlefield (these evaluation points can be the geometric center of a certain fire conflict area, the center of the battlefield basic grid unit, or a series of potential path nodes preset for maneuver path planning). Assume that the feature vector of one of the evaluation points P1 is [v 11 ,v 12 ,v 13]=0.846,0.489,0.45]. These three values ​​correspond to the normalized reverse engagement distance, unit density, and terrain cover coverage, respectively. In this setting, the first two metrics (reverse engagement distance and unit density) are cost-based metrics; that is, the larger their values, the more dangerous the environment at that point is, or the less favorable it is for friendly operations. The third metric (terrain cover coverage) is set as a benefit-based metric in this scenario; that is, the larger the value (the better the cover), the more advantageous it is for friendly tactical evasion.

[0142] The module uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model to calculate the Euclidean distance between multiple candidate coordinate points (i.e., each evaluation point in the situation coefficient matrix) and the ideal solution, and then sorts and selects the best solution based on this distance. The specific implementation process of the TOPSIS model is as follows:

[0143] First, according to the situation coefficient values ​​of all candidate evaluation points (assuming there are M evaluation points in total), determine the positive ideal solution A on the entire evaluation set + and negative ideal solution A - . Positive ideal solution A + =

[0144] It is composed of the optimal value of each indicator (a total of m indicators, in this example m = 3) among all M alternative solutions. It is composed of the worst value of each indicator in all M alternatives. For cost-type indicator j, its optimal value is the minimum value and its worst value is the maximum value, that is, and (i ranges from 1 to M). For benefit-type indicator j, its optimal value is the maximum value, and its worst value is the minimum value, that is, and

[0145] By statistically analyzing the situation coefficients of all M=100 alternative path nodes in the current battlefield, the actual value ranges of each indicator are determined as follows: Indicator 1 (reverse engagement distance, cost type): the actual value range is [0.200, 0.900]. Indicator 2 (unit density, cost type): The actual value range is [0.100, 0.700]. Indicator 3 (terrain shelter coverage, efficiency type): The actual value range is [0.100, 0.800]. Therefore, the positive ideal solution A + =[0.200,0.100,0.800], negative ideal solution A - =[0.900,0.700,0.100].

[0146] Next, calculate each evaluation point P i (Its eigenvector is [v i1 ,v i2 ,v i3 ]) to the positive ideal solution A + The weighted Euclidean distance and to the negative ideal solution A - The weighted Euclidean distance The distance calculation formula is in represents the value of the jth index in the ideal solution, w j is the weight of the jth indicator. In this embodiment, for simplicity of explanation, it is assumed that the three situation indicators are equally important in the current tactical decision, so the weight of each indicator w j are all set to 1 / 3. In more complex practical applications, these weights can be dynamically determined based on specific tactical objectives and battlefield environment characteristics through more sophisticated methods such as the Analytic Hierarchy Process (AHP), entropy weight method, or expert experience scoring method to accurately reflect the relative importance of different situational factors. For the eigenvector [0.846, 0.489, 0.450] of the evaluation point P1: its ideal solution A + distance

[0147] Its negative ideal solution A - distance

[0148] The module then sets a 20% threshold for screening safe coordinate points. This threshold is based on an in-depth analysis of data from actual combat exercises conducted by armored forces in various complex battlefield environments. The results show that when planning tactical maneuver paths, if the selected path nodes rank outside the top 20% in the comprehensive safety assessment (i.e., selecting relatively riskier nodes), the average combat loss rate of the troops during the maneuver will show a nonlinear and significant increase.

[0149] Table 2 Relationship between the screening ratio and average combat loss rate of path nodes sorted by safety assessment (exercise data statistics)

[0150] Path node screening ratio (%) Average combat loss rate (%) Top 10 5.2 Top 20 8.1 Top 30 15.7 Top 40 24.9

[0151] Table 2 summarizes the impact of different safety assessment ratings on the average troop casualty rate (CTR) during 10 large-scale armored force simulated confrontation exercises. When only the top 10% of nodes were selected to form the CTR, the average CTR was 5.2%; when the top 20% of nodes were selected, the average CTR was 8.1%. However, when the CTR was expanded to include the top 30% and 40% of nodes, the average CTR rose sharply to 15.7% and 24.9%, respectively. To strike a balance between ensuring troop mobility and maintaining necessary tactical flexibility, a 20% threshold for selecting CTR points was set based on this data analysis.

[0152] For each evaluation point P i , calculate its closeness to the ideal solution C i The value range is [0,1]. The larger the value, the better the evaluation point P. i The closer it is to the positive ideal solution (i.e., the more superior and safer), the further away it is from the negative ideal solution. For the evaluation point P1, its closeness The module's closeness to all M = 100 evaluation points C i Calculate the value and press C i The values ​​are sorted in descending order from high to low. According to the 20% safety coordinate point screening ratio threshold, select C i The coordinate points with the top 100×20%=20 values ​​are prioritized as safe coordinate points.

[0153] The 20 selected safe coordinate points are ranked according to their geographical proximity in the battlefield space and their TOPSIS ranking priority (C i The higher the value, the better. A path planning algorithm (such as the A* algorithm or the rapidly expanding random tree (RRT) algorithm, combined with vehicle dynamics constraints) is then used to plan one or more complete, actionable tactical evasive paths for the armored force between these safe points or using them as critical waypoints. These paths are ultimately output as a sequence of ordered geographic coordinate points, forming the tactical evasive path instructions.

[0154] See also Figure 5 , battlefield situation update module includes:

[0155] The trajectory interpolation submodule obtains the movement trajectory parameters in the tactical evasion path instruction, establishes the control point selection rules of the cubic Bezier curve, sets the curvature radius threshold to 1.2 times the minimum turning radius of the armored vehicle, calculates the coordinates of the intermediate control point by the difference between the coordinates of adjacent trajectory points, and uses the piecewise interpolation method to make the discrete trajectory points continuous and generate the trajectory smoothness coefficient;

[0156] The trajectory interpolation submodule obtains the tactical evasion path instructions generated in the previous steps. The core content of the instruction is a series of discrete two-dimensional or three-dimensional coordinate points, representing the path waypoints that the armored vehicle should follow, such as {(wp 1x ,wp 1y ),(wp 2x ,wp 2y ),(wp 3x ,wp 3y ),...,(wp kx ,wp ky The broken line path formed by directly connecting these waypoints may have sharp corners, which is not consistent with the actual maneuverability of the armored vehicle.

[0157] In order to generate a smooth and executable trajectory, the module first establishes the control point selection rules of the cubic Bezier curve. ix ,wp iy ) and P3=(wp (i+1)x ,wp (i+1)y ) defines a path segment, and needs to determine two control points P1 and P2 between the two waypoints to generate a smooth cubic Bezier curve segment. The parametric equation of the curve segment is B(t) = (1-t) 3 P0+3t(1-t) 2 P1+3t 2 (1-t)P2+t 3 P3, where the parameter t ranges from [0,1]. The selection rules of control points P1 and P2 must comprehensively consider the overall smoothness of the path (C 1 or C 2 continuity) and the dynamic and kinematic constraints of the vehicle. A method to ensure C 1 The method of selecting the continuous control points is: for the middle waypoint P in the waypoint sequence i (as P3 of the previous Bezier curve and P0 of the next one), the two control points before and after it (i.e. P2 of the previous one and P1 of the next one) must be aligned with P i Collinear, and P i The line segment connecting the two control points can be divided equally or distributed according to a specific ratio. For example, for a segment from P0 to P3, control point P1 can be set at a position extending from P0 along the P0P3 chord vector by a certain ratio (such as 1 / 3 of the chord length), taking into account the end tangent of the previous segment; control point P2 is set back from P3, also taking into account the starting tangent of the next segment to ensure a smooth transition at the connection.

[0158] The module sets a critical path curvature radius threshold. This threshold is the armored vehicle's minimum turning radius R minDifferent types of armored vehicles have different minimum turning radius, which is part of their design specifications. For example, the minimum turning radius R of a certain type of tracked main battle tank is min When turning in place at low speed (such as 5 km / h), it is 10 meters. The corresponding path curvature radius threshold R thresh = 1.2 × 10 meters = 12 meters. This 1.2 times magnification factor was determined through a series of targeted vehicle dynamics simulations and simulated driving experiments. The experiments showed that when the curvature radius of a point on the planned path is lower than R min When the curvature radius is between R min and R thresh (12 meters), although the vehicle can pass, it usually needs to significantly reduce the driving speed (for example, to below 50% of the normal cruising speed, such as from 30 km / h to below 15 km / h) and perform delicate operations, which will seriously affect the maneuvering efficiency and passenger comfort; when the path curvature radius is equal to or slightly higher than R thresh For example, within the range of 12 to 15 meters, the vehicle can pass smoothly at 70-85% of its normal tactical cruising speed (e.g., 20-25 km / h), and the driver's operating margin and reaction time to unexpected situations are also moderate. Therefore, 12 meters is used as a lower limit reference to ensure trajectory quality.

[0159] When calculating the coordinates of the intermediate control points by the difference of the coordinates of the adjacent track points (original waypoints), for example, for the segment P i P i+1 , whose previous waypoint is P i-1 , the next waypoint is P i+2 . It is used to generate P i to P i+1 The first control point P of the Bezier curve 1,i (Close to P i ) and the second control point P 2,i (Close to P i+1 ) position, needs to meet the requirements of P i The point is the same as the previous curve (P i-1 to P i ) are in the same tangent direction, at P i+1 The point and the next curve (P i+1 to P i+2 ) are aligned with the tangent direction. This is usually achieved by making P i-1 ,P i ,P 1,i The three points are collinear, and P 2,i ,P i+1 ,P i+2 (If P i+2If the first control point of the next segment is known, it should be P 2,i ,P i+1 ,P 1,i+1 ) The three points are collinear to achieve C 1 Continuous. The distance from the control point to the corresponding waypoint affects the tightness of the curve.

[0160] The module uses the segmented interpolation method to calculate the value of each pair of adjacent waypoints wp in the original path instruction. i ,wp i+1 ) Apply the aforementioned cubic Bezier curve generation algorithm to generate a smooth curve sub-segment. Connect all these curve sub-segments precisely (by ensuring that the waypoints coincide and the tangent vectors at the waypoints are continuous) to form an overall C 1 Continuous maneuvering trajectory.

[0161] After generating a smooth trajectory, the module calculates a trajectory smoothness coefficient S coeff This coefficient is used to quantify the degree of "smoothness" of the entire trajectory and its adaptability to the vehicle's maneuverability. One calculation method is: first calculate the curvature radius ρ(s) of all points on the smooth trajectory (where s is the arc length parameter). Then, count the number of points on the trajectory whose curvature radius is less than a preset threshold R. thresh The total length of the road section (12 meters) bad_curvature . Trajectory smoothness coefficient S coeff Defined as Among them L total is the total length of the entire smooth trajectory. S coeff The value range of is [0,1]. The closer the value is to 1, the smoother the trajectory is and the smaller the proportion of sections that do not meet the curvature requirements is. For example, if a smooth trajectory with a total length of 1000 meters is found to have a total of 50 meters of sections with a local curvature radius less than 12 meters, then the smoothness coefficient S of the trajectory is coeff =1-(50 /

[0162] 1000) = 1 - 0.05 = 0.95. Finally, the module outputs the calculated trajectory smoothness coefficient.

[0163] The suppression range reconstruction submodule uses the trajectory smoothness coefficient to extract the polar coordinate parameters of the discrete point set of the fire suppression range. When the difference between the convex hull perimeter and the original point set perimeter exceeds 15%, the convex hull reconstruction is triggered. The Graham scan algorithm is used to construct the convex hull vertex sequence and generate the suppression range coverage rate.

[0164] The suppression range reconstruction submodule calls the trajectory smoothness coefficient S generated by the aforementioned "trajectory interpolation submodule" coeff This calling relationship is that when S coeffFalling below a preset acceptance threshold (e.g., 0.90, indicating that more than 10% of the trajectory has low curvature) may indirectly indicate flaws in the quality of the originally planned path, necessitating a reassessment of the battlefield situation. This assessment could further trigger a reexamination and dynamic reconstruction of the fire suppression ranges of relevant fire units. However, the core function of this module is to process and optimize the discrete boundary point sets of the comprehensive fire suppression ranges formed by specific fire unit groups or areas of interest on the battlefield, independent of these triggering conditions.

[0165] The source of these discrete point sets describing the boundaries of the fire suppression range can be the real-time detection results of the enemy fire activity area by battlefield sensor networks (such as radar and optoelectronic reconnaissance equipment), or the enemy situation information reported by friendly units through battlefield information sharing systems (such as data links), or the boundary sampling points of the effective suppression area calculated by the fire simulation model based on the performance parameters of one's own fire units (such as weapon range, kill probability, sector limit) and deployment location. Imagine obtaining a set of discrete points P that describe the boundaries of a certain fire suppression area in a Cartesian plane coordinate system. set =

[0166] {(px1,py1),(px2,py2),...,(px m ,py m )}.

[0167] The module first determines whether it is necessary to trigger the convex hull reconstruction operation on the point set. The basis for judgment is: the current point set P set If they are connected in some initial order (for example, in the order of collection time or in the rough order of space) to form a simple polygon, calculate its perimeter L orig Then, a standard convex hull algorithm (such as Graham scan algorithm or JarvisMarch algorithm) is used to calculate the convex hull of these discrete points P. set The convex hull CH(P set ), and calculate the perimeter L of the convex hull ch The specific execution steps of the Graham scanning algorithm are as follows: Step 1, in the point set P set Find the point with the smallest Y coordinate as the starting point P b ; If there are multiple points with the smallest Y coordinate, select the one with the smallest X coordinate. The second step is to move all the remaining points relative to the base point P b Sort by polar angle (P b The origin is the positive direction of the X axis, which is the reference axis at 0 degrees. The distance between each point and P is calculated in the counterclockwise direction. b The polar angle formed by the connecting line), the angles are arranged from small to large; if there are points with the same polar angle, then the distance from the base point P bThe closer points are placed in front. The third step is to initialize an empty stack and put the first two points after sorting (base point P b The fourth step is to start from the third point after sorting and traverse all the remaining points in turn. For the current point P curr , check its compatibility with the second point P on the top of the stack stack_second and the first point P on the stack stack_top The steering relationship formed. If (P stack_second →P stack_top →P curr ) constitutes a "left turn" (or strictly "non-right turn", the specific judgment is based on the sign of the vector cross product, which depends on the selection of the coordinate system and the definition of the vertex order. It is usually required that the convex hull vertices are arranged counterclockwise), then the current point P curr Push it into the stack. If it forms a "right turn", it means the point P at the top of the stack stack_top Is a concave point, not a convex hull vertex, pop it from the stack, and then repeat the checking process (that is, use the new stack top and the second point on the stack top and P curr Compare), until the stack is empty or a left turn occurs, then P curr Push on the stack. After traversing all the sorted points, the sequence of points left on the stack (from the bottom to the top) is the point set P set The convex hull vertex sequence of .

[0168] When the calculated convex hull perimeter L ch and the perimeter L of the original point set orig The ratio difference between them is calculated as Diff ratio =|L ch -L orig | / L orig , when it exceeds 15%, Diff ratio >0.15, convex hull reconstruction is triggered. The setting of this 15% threshold is based on the morphological analysis of various battlefield target reconnaissance data and fire impact area simulation data. Statistical studies have shown that when the boundary described by the original point cloud is due to sensor noise, sparse data sampling, or the target area itself has many deep concave structures, resulting in its perimeter L orig The corresponding convex hull perimeter L ch Compared with the ratio L orig / L ch More than 1.176 (approximately corresponding to |L ch -L orig | / L origWhen the data is more than 15%, the convex hull can effectively filter out the irregularities caused by incomplete data or overly complex morphology, thereby forming a more robust, generalized, and easy-to-use outline of the suppression area, while better preserving the range characteristics of the core firepower coverage. For example, the perimeter L formed by a set of discrete points connected in sequence is orig = 250 meters, calculate its convex hull using the Graham scan algorithm and get the convex hull perimeter L ch = 200 meters. The ratio difference is |200-250| / 250=50 / 250=0.20=20%. Since 20%>15%, convex hull reconstruction of the original point set is triggered.

[0169] Under the condition of triggering reconstruction, the module uses the aforementioned Graham scanning algorithm to set Construct its convex hull vertex sequence and get the new vertex sequence {(chx1,chy1),(chx2,chy2),...,(chx q ,chy q )}. The polygonal area enclosed by this new convex hull vertex sequence is the reconstructed fire suppression range.

[0170] The module then calculates the area A of this convex hull region ch (You can use the shoelace formula.) And the convex hull area A ch A preset or dynamically calculated "expected effective suppression area reference value" A ref Compare and get the suppression range coverage CR coverage =A ch / A ref Here A ref The coverage ratio CR is 4,000 square meters. This value is set based on the minimum effective fire coverage area that the commander expects to achieve for a specific target area (such as an enemy assembly area or a key passage) in the current scenario. It takes into account the target value, threat level, and available firepower resources. coverage It reflects the degree to which the current friendly firepower (or enemy firepower) can effectively cover the target area. ch =3000 square meters, then the pressing range coverage CR coverage =3000 square meters /

[0171] 4000 square meters = 0.75. The module finally generates this suppression range coverage.

[0172] The parameter integration submodule performs a difference operation on the trajectory smoothness coefficient and the curvature threshold, and performs a ratio operation on the suppression range coverage and the area threshold. The dynamic weighted summation formula W = 0.6ΔS + 0.4R is used for calculation, where ΔS represents the difference in trajectory smoothness coefficient and R represents the suppression range coverage ratio, to generate updated parameters.

[0173] The core task of the parameter integration submodule is to comprehensively evaluate the dynamic changes of the battlefield situation. Its input comes from the key parameters calculated by the previous module. Specifically, it receives the smoothness coefficient S of the actual maneuver trajectory generated by the "trajectory interpolation submodule". coeff (In the aforementioned embodiment, S coeff =0.95), and the fire suppression range coverage rate CR for a certain area of ​​interest generated by the "suppression range reconstruction submodule" coverage (In the aforementioned embodiment, CR coverage =0.75).

[0174] The module uses a dynamic weighted summation formula W = α·ΔS + β·R to calculate a comprehensive situation update parameter W. In this formula: ΔS represents the evaluation value related to trajectory smoothness. In this embodiment, the trajectory smoothness coefficient S is directly used. coeff As the value of ΔS, that is, ΔS=S coeff =0.95. The higher this value is, the better the quality of the planned maneuver trajectory is and the more it conforms to the vehicle's motion characteristics. R represents the evaluation value related to the fire suppression effect. In this embodiment, the suppression range coverage CR is directly used. coverage As the value of R, that is, R = CR coverage = 0.75. A higher value indicates more effective fire coverage of the target area. α and β are preset weighting coefficients, corresponding to the relative importance of ΔS and R, respectively, in the comprehensive situation assessment, satisfying the requirement of α + β = 1. In this embodiment, α is set to 0.6 and β is set to 0.4. These weighting coefficients are set based on a priority analysis of specific tactical scenarios (e.g., rapid penetration and fire suppression of the enemy). In such scenarios, the smoothness of the maneuver trajectory (which directly affects maneuver speed, concealment, and troop energy consumption) is considered slightly more critical than whether the suppression range coverage reaches the theoretical maximum. Therefore, the trajectory smoothness evaluation index ΔS is assigned a weight of 0.6, and the fire suppression effectiveness evaluation index R is assigned a weight of 0.4. These weighting values ​​were ultimately determined through multiple rounds of evaluation by military operations experts (for example, using the Analytic Hierarchy Process (AHP) to compare and score each factor pairwise), combined with statistical analysis of the results of over 50 simulation exercises with similar tactical settings.

[0175] Substituting the various parameter values ​​into the formula for calculation: W = 0.6 × ΔS + 0.4 × R = 0.6 × 0.95 + 0.4 × 0.75 = 0.570 + 0.300 = 0.870. The benefit of this formula lies in its objective integration of two distinct yet crucial factors influencing the current battlefield situation—the quality of the maneuver trajectory of our own combat units (reflected in smoothness) and the effectiveness of our fire suppression on the enemy or target area (reflected in coverage)—into a single, quantitative, comprehensive evaluation parameter, W, through a structured, dynamic, weighted summation approach. This parameter, W, can more comprehensively and dynamically reflect the overall favorableness of the current battlefield situation or its changing trend relative to the previous moment. The calculated value of W is 0.870. Since both ΔS and R range from [0, 1] and their weights sum to 1, the value of W also ranges from [0, 1]. A higher value of W generally indicates a more favorable current battlefield situation (smooth maneuvering and effective firepower). The calculated W value is the final situation update parameter generated in this cycle, which can be used to update the status of the battlefield situation display system, dynamically adjust subsequent action plans, or trigger specific tactical decision-making processes.

[0176] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A battle drill simulation integrated system based on a local area network, characterized in that: The system comprises: The node status synchronization module is used to obtain the position coordinates, ammunition reserves, and armor integrity parameter sets of each combat unit through the UDP multicast protocol, perform weighted fusion of the elevation mutation rate matrix and the terrain barrier frequency factor, input the parameter set into the real-time data distribution service for packaging, and generate battlefield snapshot data to pass to the confrontation parameter correction module; A confrontation parameter correction module is used to perform spatial superposition operations on the fire coverage parameters using the battlefield snapshot data, convert the spatial coordinates into metric units, and then calculate the geometric intersection area. When the area of ​​the overlapping area exceeds a threshold, a Voronoi diagram algorithm is used to perform deployment density segmentation, generate the coordinates of the fire conflict area, and transmit them to the tactical event arbitration module; The tactical event arbitration module is used to sort the engagement distance, unit density, and terrain cover parameters in the coordinates of the fire conflict area through the TOPSIS decision model, optimize the sorting weights through the gradient descent method, establish a threat gradient distribution model, and generate tactical avoidance path instructions to pass to the battlefield situation update module.

2. The LAN-based battle drill simulation integrated system according to claim 1, characterized in that: The battlefield snapshot data includes the combat unit position coordinates, ammunition reserves, and armor integrity parameters. The fire conflict area coordinates specifically include coverage overlap, density gradient, and boundary vertex coordinates. The tactical avoidance path instructions include path priority queue, turning angle threshold, and bunker utilization coefficient.

3. The LAN-based battle drill simulation integrated system according to claim 2, characterized in that: The node status synchronization module includes: The data acquisition submodule receives combat unit status data packets through the UDP multicast protocol, extracts position coordinates, ammunition reserves, and armor integrity parameters, filters out abnormal data units that exceed the 200ms delay threshold, and generates a combat unit status parameter set; The data integration submodule calls the combat unit state parameter set, converts the coordinates to a standard spatial reference system through Gauss-Krüger projection, standardizes the ammunition reserve to a percentage format, calibrates the armor integrity value range, integrates the spatial position, resource status, and protection status data, and generates a standardized state matrix; The snapshot generation submodule constructs a spatial topological relationship based on the standardized state matrix, calculates the weighted fusion value of ammunition reserves and armor integrity, performs binary serialization operations and appends MD5 checksums through real-time data distribution services, and generates battlefield snapshot data.

4. The LAN-based battle drill simulation integrated system according to claim 3, characterized in that: The adversarial parameter correction module includes: The spatial superposition operation submodule obtains fire coverage parameters from the battlefield snapshot data, performs spatial superposition operations on multiple sets of parameters, calculates the geometric intersection area of ​​the coverage areas of multiple fire units, sets a tactical conflict threshold of 50 square meters based on armored vehicle mobility performance experimental data, compares the intersection area with a preset tactical conflict threshold, and generates an overlapping area value; The density profiling submodule is based on the overlapping area value. When the tactical conflict threshold is exceeded, the formula is used: Calculate and obtain the deployment density correction coefficient, call the Voronoi diagram algorithm to spatially divide the firepower deployment points, and generate the deployment density distribution value; Among them, A t represents the tactical conflict area threshold, which is 50 square meters. i Represents the Euclidean distance between the ith deployment point and the conflict boundary, in meters, N represents the total number of deployment points on the current battlefield, S c is the density correction coefficient, which is used to adjust the Voronoi diagram subdivision weight; The conflict coordinate generation submodule extracts the boundary coordinates of the area where the density gradient change rate exceeds the critical value according to the deployment density distribution value, uses a second-order continuously differentiable cubic spline interpolation function to perform interpolation operation on adjacent boundaries, and generates the coordinates of the firepower conflict area.

5. The LAN-based battle drill simulation integrated system according to claim 4, characterized in that: The tactical event arbitration module includes: The battlefield situation acquisition submodule detects the engagement distance, unit density, and terrain cover parameters in the coordinates of the fire conflict area, uses a 100m×100m grid division method to count the number of combat units within a unit area, extracts the meter-level value of the engagement distance through spatial coordinate analysis technology, and uses a terrain elevation model to calculate the cover coverage rate to generate a dynamic parameter set; The conflict situation quantification submodule performs inverse proportional normalization on the engagement distance based on the dynamic parameter set, converts the unit density into the number of combat units per square kilometer, calculates the weight coefficients of terrain shelter parameters using the entropy weight method, uses the standard deviation method to determine the indicator dispersion, and uses the range normalization method to eliminate dimensional differences to establish a situation coefficient matrix; The path decision generation submodule calls the situation coefficient matrix and uses the TOPSIS model to calculate the Euclidean distance between multiple coordinate points and the ideal solution. Based on the armored forces' actual combat exercise data, a 20% threshold is set as the safe coordinate point screening ratio. The sorting priority is determined by the ratio of positive and negative ideal solution distances, and tactical avoidance path instructions are generated.

6. The LAN-based battle drill simulation integrated system according to claim 5, characterized in that: The system further comprises: A battlefield situation update module is used to perform Bezier curve interpolation on movement trajectory parameters according to the tactical avoidance path instruction, perform convex hull reconstruction operation on fire suppression range parameters, and input the updated parameters into the real-time data distribution service.

7. The LAN-based battle drill simulation integrated system according to claim 6, characterized in that: The update parameters specifically refer to the movement trajectory control point set, the fire suppression boundary vertex sequence, and the path curvature radius parameter.

8. The LAN-based battle drill simulation integrated system according to claim 7, characterized in that: The Bezier curve control point selection rule is to set an intermediate control point at 1 / 3 of the difference in coordinates between adjacent trajectory points.

9. The LAN-based battle drill simulation integrated system according to claim 8, characterized in that: The battlefield situation update module includes: The trajectory interpolation submodule obtains the movement trajectory parameters in the tactical evasion path instruction, establishes a cubic Bezier curve control point selection rule, sets the curvature radius threshold to 1.2 times the minimum turning radius of the armored vehicle, calculates the coordinates of the intermediate control point by the difference between the coordinates of adjacent trajectory points, and uses the piecewise interpolation method to perform continuity processing on the discrete trajectory points to generate a trajectory smoothness coefficient; The suppression range reconstruction submodule calls the trajectory smoothness coefficient to extract the polar coordinate parameters of the discrete point set of the fire suppression range. When the difference between the convex hull perimeter and the original point set perimeter exceeds 15%, the convex hull reconstruction is triggered, and the Graham scan algorithm is used to construct the convex hull vertex sequence to generate the suppression range coverage. The parameter integration submodule performs a difference operation on the trajectory smoothness coefficient and the curvature threshold, and performs a ratio operation on the suppression range coverage and the area threshold, and adopts a dynamic weighted summation formula Calculation is performed using W=0.6ΔS+0.4R, where ΔS represents the difference in trajectory smoothness coefficient and R represents the suppression range coverage ratio, to generate updated parameters.

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