Laser strike decision-making method and system based on environment similarity matching and dynamic database

Abstract

The invention discloses a laser strike decision-making method and system based on environment similarity matching and a dynamic database, and the method comprises the steps: collecting multi-dimensional atmospheric environment parameters in real time, and carrying out the quantification of the multi-dimensional atmospheric environment parameters to form a real-time environment feature vector representing a current environment state; calculating laser to-target power density based on the real-time environment feature vector and laser transmission related parameters; calling a scene-based damage database, calculating the similarity between the real-time environment feature vector and each historical environment feature vector in the database, and screening a plurality of pieces of historical data with the highest similarity to form an optimal reference data set; executing interpolation calculation based on the optimal reference data set, and generating a laser strike decision instruction; and controlling the laser equipment to execute striking operation according to the decision instruction. According to the method, the problem of insufficient prediction precision caused by disjunction of a damaged database and a real-time environment of an existing laser strike decision-making system is solved, and the defect that a static database query method is poor in applicability when the environment changes is overcome.

Description

Technical Field

[0001] This invention belongs to the field of laser application technology, and in particular relates to a laser strike decision-making method and system based on environmental similarity matching and dynamic database. Background Technology

[0002] With the rapid development of high-energy laser technology, laser strike systems are increasingly being used in various fields such as national defense and industrial processing due to their advantages of fast response speed, high strike accuracy, and low combat cost. The intelligent decision-making level of the system has become the core key to determining the effectiveness of laser strikes.

[0003] Traditional laser strike decision-making schemes often employ static database queries or calculation methods based on fixed formulas. These methods fail to adequately consider the critical impact of environmental factors on laser strike effectiveness. For the same target material, the laser transmission attenuation, beam spread characteristics, and target damage effects vary significantly under different environmental conditions, including visibility, humidity, temperature, and atmospheric turbulence intensity. Furthermore, the global static databases relied upon in traditional schemes only store damage data under fixed environments, failing to establish a correlation between environmental parameters and damage results. This leads to significant discrepancies between decision results obtained through static data queries or fixed formula interpolation in complex and variable real-world application scenarios, such as breakdown time and required power density, and actual conditions. This severely impacts the accuracy and reliability of laser strikes.

[0004] The prior art, as disclosed in Chinese patent application CN113221235B, is a method for establishing a real-time joint strike optimization model, comprising: acquiring a dataset for establishing the joint strike optimization model, the dataset including weather and environmental data, target feature data, interception equipment data, and the obstruction relationship between the target and the interception equipment, the interception equipment including laser equipment, radio equipment, and flexible net equipment; establishing spatial dimension constraints, time dimension constraints, resource dimension constraints, weather dimension constraints, and environmental dimension constraints for each interception equipment based on the dataset; establishing interception weight factors based on these constraints; and establishing the joint strike optimization model based on the interception weight factors.

[0005] This existing technology targets multi-equipment collaborative interception scenarios for "low, slow, and small" targets such as illegal drones. It integrates laser equipment, radio equipment, and flexible network equipment. By establishing constraints in five dimensions—space, time, resources, weather, and environment—it constructs a joint strike optimization model to address the problem of insufficient defense capabilities of single equipment.

[0006] The core of the aforementioned existing technologies is the coordinated allocation of multiple equipment and the verification of the feasibility of the scheme. However, they do not address key issues such as environmental attenuation calculation during laser transmission and the scenario-based correlation of historical damage data. They cannot solve the problem of insufficient decision-making accuracy caused by the disconnect between the static database and the real-time environment of a single laser equipment. Summary of the Invention

[0007] The purpose of this invention is to provide a laser strike decision-making method and system based on environmental similarity matching and dynamic database, which solves the problem of insufficient prediction accuracy caused by the disconnect between the damage database and the real-time environment in existing laser strike decision-making systems, and overcomes the shortcomings of static database query methods in terms of poor applicability when the environment changes.

[0008] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution: A first aspect of the present invention is to provide a laser strike decision-making method based on environmental similarity matching and a dynamic database, comprising: Real-time acquisition of multi-dimensional atmospheric environmental parameters, and quantification to form a real-time environmental feature vector characterizing the current environmental state; The laser power density to the target is calculated based on real-time environmental feature vectors and laser transmission-related parameters. The scenario-based damage database is invoked, which is constructed based on historical strike test data; the similarity between the real-time environmental feature vector and each historical environmental feature vector in the database is calculated, and the most similar historical data are selected to form the optimal reference dataset; Interpolation calculations are performed based on the optimal reference dataset to generate laser strike decision instructions. The decision instructions include forward prediction of the penetration time based on the power density to the target and / or reverse recommendation of the power density and the optimal strike distance based on the preset penetration time. The laser equipment is controlled to perform strike operations according to the decision-making instructions.

[0009] Furthermore, the multidimensional atmospheric environmental parameters include at least visibility, humidity, temperature, and atmospheric turbulence intensity.

[0010] Furthermore, the elements in the scenario-based damage database data record include environmental feature vectors, target material, power density, and breakdown time.

[0011] Furthermore, methods for calculating laser power density to the target include: Using the formula: ;

[0012] Calculate the target power; where P is the target power and P0 is the initial input laser power. The attenuation coefficient is... Where λ is the laser wavelength, V is the visibility, q is the correction factor, and L is the transmission distance of the laser from the emitter to the target. Using the formula:

[0013] Calculate the target spot radius; where r is the spot radius, M2 is the beam quality, μ ATP is the system tracking and aiming accuracy, D is the laser transmission aperture, and r0 is the atmospheric coherence length; Use the formula:

[0014] Calculate the on-target power density; where W is the on-target power density.

[0015] Furthermore, the value rule of the correction coefficient q is: When V > 50km, q = 1.6; When 6km < V ≤ 50km, q = 1.3; When 1km < V ≤ 6km, q = 0.16V + 0.34; When 0.5km < V ≤ 1km, q = V - 0.5; When V ≤ 0.5km, q = 0.

[0016] Furthermore, use the cosine similarity algorithm to calculate the similarity between the real-time environmental feature vector and the historical environmental feature vector.

[0017] Furthermore, the method for predicting the breakdown time forward according to the on-target power density includes: Arrange the data records in the optimal reference dataset in ascending order according to the power density value; Locate the interval where the on-target power density W is located in the sequence, so that the two data points (W a , T a ) and (W b , T b ) satisfy W a < W < W b ; Use the formula:

[0018] Calculate the breakdown time; where T is the breakdown time, W is the on-target power density, W a is the target power density of the first data point, T a is the breakdown time of the first data point, W b is the target power density of the second data point, T b is the breakdown time of the second data point.

[0019] Furthermore, the method for recommending the power density and the best strike distance backward according to the preset breakdown time includes: Arrange the data records in the optimal reference dataset in ascending order according to the power density value; The preset breakdown time T is located in the sequence. target The interval in which the data point (W) lies makes the two data points before and after it (W) c ,T c ) and (W d ,T d Satisfying T c <T target <T d ; Using the formula:

[0020] Calculate the power density at the target location; where W target For the power density at the target location, T target To preset the breakdown time, W c T represents the target power density at the third data point. c W represents the breakdown time for the third data point. d T represents the target power density at the fourth data point. d The breakdown time for the fourth data point; Power density W at the target location target By substituting the atmospheric transport model, the optimal strike distance that meets the power density requirement can be calculated.

[0021] Furthermore, after controlling the laser equipment to perform the strike operation according to the decision-making instructions, the data of this strike is recorded and stored in the scenario-based damage database.

[0022] This invention also provides a laser strike decision-making system based on environmental similarity matching and a dynamic database, comprising: The multidimensional sensing module is used to collect multidimensional atmospheric environmental parameters in real time and quantify them to form a real-time environmental feature vector that characterizes the current environmental state. The data processing module is used to calculate the laser power density to the target based on real-time environmental feature vectors and laser transmission-related parameters. The environment similarity matching module is used to call the scenario-based damage database, which is constructed based on historical strike test data; calculate the similarity between the real-time environment feature vector and each historical environment feature vector in the database, and select the most similar historical data to form the optimal reference dataset; The dynamic interpolation decision module is used to perform interpolation calculations based on the optimal reference dataset and generate laser strike decision instructions. The decision instructions include forward prediction of the penetration time based on the power density to the target and / or reverse recommendation of the power density and the optimal strike distance based on the preset penetration time. The control and execution module is used to control the laser equipment to perform strike operations according to the decision instructions.

[0023] The advantages of this invention are: This invention abandons the global interpolation mode of traditional static databases. Instead, it uses a cosine similarity algorithm to select a subset of historical data that highly matches the current environment, performing linear interpolation calculations only based on locally similar data. This logic, which strongly binds environment, data, and decision-making, eliminates the mutual interference between data from different environments at its source. This results in significantly lower errors in breakdown time prediction and power density recommendation compared to traditional methods, ensuring the precision and controllability of laser strikes.

[0024] This invention utilizes a multi-dimensional environmental perception module to collect core parameters such as visibility, humidity, temperature, and atmospheric turbulence intensity in real time. The system can automatically identify different environmental types, including rain, fog, clear skies, and turbulence, and call upon corresponding historical scene data for decision-making. It maintains high reliability regardless of whether the environment is coastal with high humidity, low visibility, or strong turbulence. Furthermore, after each strike, the data is cleaned and verified, and then stored as standardized tuples in a scenario-based damage database, enabling dynamic incremental updates to the database. As the number of uses increases, the database covers richer environmental scenarios and target types, provides more similar matching reference samples, and continuously optimizes the system's decision-making accuracy and adaptability.

[0025] This invention also supports two modes: forward prediction of penetration time and reverse recommendation of power density strike distance, which can meet the needs of different combat scenarios. It is suitable for scenarios where the laser power is fixed and the strike duration needs to be predicted, as well as scenarios such as emergency interception that require preset penetration time and reverse adjustment of strike parameters. Its flexibility far exceeds that of traditional single-function decision-making systems. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0027] Figure 1 This is a flowchart of Embodiment 1 of the present invention; Figure 2 This is a structural diagram of Embodiment 2 of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0030] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0031] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0032] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0033] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0034] Example 1: like Figure 1 As shown, this embodiment provides a laser strike decision-making method based on environmental similarity matching and a dynamic database, the specific steps of which include: S1 collects multi-dimensional atmospheric environmental parameters in real time and quantifies them to form a real-time environmental feature vector that characterizes the current environmental state.

[0035] Specifically, the multi-dimensional atmospheric environment parameters in this embodiment at least include visibility, humidity, temperature, and atmospheric turbulence intensity. These four types of parameters are the key environmental factors that affect laser transmission attenuation, spot characteristics, and target damage effects. Visibility directly determines the scattering attenuation degree of the laser in the atmosphere; humidity affects the absorption effect of water vapor in the atmosphere on the laser; temperature changes the atmospheric refractive index distribution, thereby affecting the diffusion of the laser beam; the atmospheric turbulence intensity determines the jitter and expansion amplitude of the laser spot. The four types of parameters together cover the main influence dimensions of the atmosphere on the energy and spatial distribution of the laser during the process from emission to the target material.

[0036] The above-mentioned four types of quantified parameters are combined into a one-dimensional vector in a fixed order, and the real-time environmental feature vector E is obtained current = [visibility, humidity, temperature, atmospheric turbulence intensity].

[0037] S2 Calculate the laser-to-target power density based on the real-time environmental feature vector and the laser transmission-related parameters.

[0038] In this embodiment, based on the Beer-Lambert law, the attenuation of the laser over the transmission distance L is calculated according to the real-time collected atmospheric environment parameters, and the power P to the target is obtained, that is, using the formula: ;

[0039] Calculate the power to the target; where P is the power to the target, P0 is the initial input laser power, is the attenuation coefficient, is the laser wavelength, V is the visibility, q is the correction coefficient, and L is the transmission distance of the laser from the emission end to the target material; At the same time, calculate the radius of the spot to the target according to the beam quality, ATP tracking accuracy, atmospheric turbulence intensity, and transmission distance, that is, using the formula:

[0040] Calculate the radius of the spot to the target; where r is the spot radius, M2 is the beam quality, μ ATP is the system tracking accuracy, D is the transmission aperture of the laser, and r0 is the atmospheric coherence length.

[0041] Among them, the value rule of the correction coefficient q is: , that is: When V > 50km, q = 1.6; When 6km < V ≤ 50km, q = 1.3; When 1km < V ≤ 6km, q = 0.16V + 0.34; When 0.5 km < V ≤ 1 km, q = V - 0.5; When V ≤ 0.5 km, q = 0.

[0042] Finally, use the formula:

[0043] Calculate the power density to the target; where W is the power density to the target.

[0044] S3 calls the scenario damage database; calculates the similarity between the real-time environment feature vector and each historical environment feature vector in the database, and screens out several historical data with the highest similarity to form an optimal reference data set.

[0045] In this implementation, the scenario damage database is constructed based on standardized historical strike experiment data. Each data record is in a quadruple structure, and its elements include: the environment feature vector, whose dimension and quantization rule are the same as those of the real-time environment feature vector; the target material type, including attributes such as the material and thickness of the target material; it also includes the power density to the target and the corresponding breakdown time.

[0046] In this step, the cosine similarity algorithm is used to calculate the real-time environment feature vector E current and the historical environment feature vector E historical in each piece of technology for similarity. After completing the similarity calculation, it is necessary to screen out several historical data that are most similar to the current environment to form an optimal reference data set D reference .

[0047] Specifically, sort the historical data of the same target material according to the similarity with E current from high to low; select the top N pieces of data with the highest similarity (N usually takes 5 - 10, and 5 is taken in this embodiment), or select all data with a similarity ≥ 0.95; if there are records with exactly the same power density or breakdown time in the screened data set, only keep the one with the highest similarity to avoid the influence of redundant data on the interpolation accuracy.

[0048] S4 performs interpolation calculation based on the optimal reference data set to generate a laser strike decision instruction, and the decision instruction includes forward predicting the breakdown time according to the power density to the target or / and backward recommending the power density and the best strike distance according to the preset breakdown time.

[0049] In this step, by performing linear interpolation calculation on the optimal reference data set D reference that highly matches the current environment, two decision modes of forward predicting the breakdown time and backward recommending the power density and strike distance are realized, providing accurate and executable control instructions for laser strikes. This step is based on the power density W calculated in step S2 to the target or the preset breakdown time T targetThe input is the decision result that directly guides the operation of laser equipment.

[0050] Optimal reference dataset D reference Composed of historical data that is highly similar to the current environment, the power density and breakdown time mapping relationship within the data is approximately linearly distributed; and the linear interpolation calculation is highly efficient, which can meet the real-time decision-making requirements of laser strikes and avoid the delay caused by complex algorithms.

[0051] The following section details two decision-making models.

[0052] S41 decision mode one is a forward prediction of breakdown time, i.e., knowing W to calculate T. This mode is suitable for scenarios where the output power of the laser equipment is fixed and the time for the target to be broken down needs to be predicted.

[0053] S411 arranges the data records in the optimal reference dataset into an ascending sequence according to their power density values.

[0054] First, the optimal reference dataset D is... reference All historical data records are sorted in ascending order of target power density to obtain an ordered dataset. D sorted =[(W1,T1),(W2,T2),...,(W N ,T N [N], where N is the number of historical data entries selected; in this embodiment, N=5. The purpose of sorting is to ensure that the correspondence between power density and breakdown time is distributed in an orderly manner, facilitating quick location of the current power density's numerical range and ensuring the accuracy of interpolation calculations.

[0055] S412 locates the interval containing the target power density W in the sequence, such that the two data points before and after it (W) a ,T a ) and (W b ,T b Satisfying W a <W<W b .

[0056] Based on the previously calculated current target power density W, in the ordered dataset D sorted Find two adjacent data points W that satisfy the following conditions a <W<W b The first data point (Wa, Ta) represents historical data points to the left of the current power density, exhibiting lower power density and longer breakdown time. The second data point (W... b ,T b The data point to the right of the current power density is a historical data point with a higher power density and a shorter breakdown time.

[0057] S413 utilizes the formula:

[0058] Calculate the breakdown time; where T is the breakdown time, W is the power density to the target, and W a T represents the target power density at the first data point. a W represents the breakdown time for the first data point. b T represents the target power density at the second data point. b This represents the breakdown time for the second data point.

[0059] S42 Decision Mode 2 is a reverse recommendation of power density and optimal strike distance. This mode is suitable for targets that need to be penetrated within a preset time, based on a preset penetration time T. target First, interpolate to obtain the desired target power density W. target Then, work backwards to determine the optimal strike distance that satisfies this power density.

[0060] Specifically, methods for recommending power density and optimal strike distance based on a preset breakdown time include: S421 arranges the data records in the optimal reference dataset into an ascending sequence according to their power density values.

[0061] It is the same as step S411, and will not be repeated here.

[0062] S422 locates the preset breakdown time T in the sequence. target The interval in which the data point (W) lies makes the two data points before and after it (W) c ,T c ) and (W d ,T d Satisfying T c <T target <T d .

[0063] Based on the user-preset breakdown time T target In D sorted Find two adjacent data points T that satisfy the following conditions c <Ttarget<T d The third data point (W) c ,T c The fourth data point (W) represents historical data points to the left of the preset time, with longer breakdown times and lower power densities; d ,T d The data points to the right of the preset time are historical data points with shorter breakdown times and higher power densities.

[0064] S423 uses the formula:

[0065] Calculate the power density at the target location; where W target For the power density at the target location, T target To preset the breakdown time, W c T represents the target power density at the third data point. c W represents the breakdown time for the third data point. d T represents the target power density at the fourth data point. d This represents the breakdown time for the fourth data point.

[0066] S424 will target the power density W at the location. target By substituting the atmospheric transport model, the optimal strike distance that meets the power density requirement can be calculated.

[0067] In this step, the target power density formula from step S2 and the laser transmission attenuation model are used to deduce the condition that W satisfies... target The optimal striking distance L.

[0068] S5 controls the laser equipment to perform strike operations based on decision-making instructions.

[0069] In the forward prediction mode, the current power density to the target and the estimated penetration time in the analysis command are used to determine the power output level that the equipment needs to maintain and the duration of continuous strike.

[0070] In reverse recommendation mode, the target power density and optimal strike distance in the analysis command are used to determine the power output value that the equipment needs to adjust and the target distance parameters of the aiming system.

[0071] The control execution module sends control signals to each core component of the laser equipment, completes parameter configuration, and initiates the strike operation.

[0072] S6 will record the data from this attack and store it in a scenario-based damage database.

[0073] In this step, the complete scenario, execution parameters, and damage results data of this laser strike are standardized and organized, and then stored in the scenario-based damage database to upgrade the database. This will supplement the decision-making of strikes in similar environments with new and effective samples, allowing the database to be continuously enriched with the number of uses, and the system's decision-making accuracy to be gradually optimized.

[0074] Example 2: like Figure 2 As shown, this embodiment provides a laser strike decision-making system based on environmental similarity matching and a dynamic database, including: The multidimensional sensing module is used to collect multidimensional atmospheric environmental parameters in real time and quantify them to form a real-time environmental feature vector that characterizes the current environmental state. The data processing module is used to calculate the laser power density to the target based on real-time environmental feature vectors and laser transmission-related parameters. The environment similarity matching module is used to call the scenario-based damage database, which is constructed based on historical strike test data; calculate the similarity between the real-time environment feature vector and each historical environment feature vector in the database, and select the most similar historical data to form the optimal reference dataset; The dynamic interpolation decision module is used to perform interpolation calculations based on the optimal reference dataset and generate laser strike decision instructions. The decision instructions include forward prediction of the penetration time based on the power density to the target and / or reverse recommendation of the power density and the optimal strike distance based on the preset penetration time. The control and execution module is used to control the laser equipment to perform strike operations according to the decision instructions.

[0075] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0077] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A laser strike decision-making method based on environmental similarity matching and a dynamic database, characterized in that... include: Real-time acquisition of multi-dimensional atmospheric environmental parameters, and quantification to form a real-time environmental feature vector characterizing the current environmental state; The laser power density to the target is calculated based on real-time environmental feature vectors and laser transmission-related parameters. The scenario-based damage database is invoked to calculate the similarity between the real-time environmental feature vector and the historical environmental feature vectors in the database. The most similar historical data are then selected to form the optimal reference dataset. Interpolation calculations are performed based on the optimal reference dataset to generate laser strike decision instructions. The decision instructions include forward prediction of the penetration time based on the power density to the target and / or reverse recommendation of the power density and the optimal strike distance based on the preset penetration time. The laser equipment is controlled to perform strike operations according to the decision-making instructions.

2. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that: The multidimensional atmospheric environmental parameters include at least visibility, humidity, temperature, and atmospheric turbulence intensity.

3. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that: The elements in the scenario-based damage database record include environmental feature vectors, target material, power density, and breakdown time.

4. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that... Methods for calculating laser power density to a target include: Using the formula: ； Calculate the target power; where P is the target power and P0 is the initial input laser power. The attenuation coefficient is... Where λ is the laser wavelength, V is the visibility, q is the correction factor, and L is the transmission distance of the laser from the emitter to the target. Using the formula: Calculate the target spot radius; where r is the spot radius, M2 is the beam quality, and μ ATP For system aiming accuracy, D is the laser transmission aperture, and r0 is the atmospheric coherence length; Using the formula: The power density to the target is calculated; where W is the power density to the target.

5. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 4, characterized in that... The rules for determining the value of the correction coefficient q are as follows: When V > 50km, q = 1.6; When 6km < V ≤ 50km, q = 1.3; When 1km < V ≤ 6km, q = 0.16V + 0.34; When 0.5km < V ≤ 1km, q = V - 0.5; When V ≤ 0.5km, q = 0.

6. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that: The cosine similarity algorithm is used to calculate the similarity between the real-time environmental feature vector and the historical environmental feature vector.

7. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that... Methods for predicting breakdown time based on target power density include: Arrange the data records in the optimal reference dataset into an ascending sequence based on their power density values; Locate the interval containing the target power density W in the sequence, such that the two data points before and after it (W) a ,T a ) and (W b ,T b Satisfying W a <W<W b ; Using the formula: Calculate the breakdown time; where T is the breakdown time, W is the power density to the target, and W a T represents the target power density at the first data point. a W represents the breakdown time for the first data point. b T represents the target power density at the second data point. b This represents the breakdown time for the second data point.

8. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that... Methods that recommend power density and optimal impact distance based on preset breakdown time include: Arrange the data records in the optimal reference dataset into an ascending sequence based on their power density values; The preset breakdown time T is located in the sequence. target The interval in which the data point (W) lies makes the two data points before and after it (W) c ,T c ) and (W d ,T d Satisfying T c <T target <T d ; Using the formula: Calculate the power density at the target location; where W target For the power density at the target location, T target To preset the breakdown time, W c T represents the target power density at the third data point. c W represents the breakdown time for the third data point. d T represents the target power density at the fourth data point. d The breakdown time for the fourth data point; Power density W at the target location target By substituting the atmospheric transport model, the optimal strike distance that meets the power density requirement can be calculated.

9. The laser strike decision-making method based on environmental similarity matching and dynamic database according to claim 1, characterized in that: After controlling the laser equipment to carry out the strike operation according to the decision-making instructions, the data of this strike is recorded and stored in the scenario-based damage database.

10. A laser strike decision-making system based on environmental similarity matching and a dynamic database, characterized in that... include: The multidimensional sensing module is used to collect multidimensional atmospheric environmental parameters in real time and quantify them to form a real-time environmental feature vector that characterizes the current environmental state. The data processing module is used to calculate the laser power density to the target based on real-time environmental feature vectors and laser transmission-related parameters. The environment similarity matching module is used to call the scenario-based damage database, which is constructed based on historical strike test data; calculate the similarity between the real-time environment feature vector and each historical environment feature vector in the database, and select the most similar historical data to form the optimal reference dataset; The dynamic interpolation decision module is used to perform interpolation calculations based on the optimal reference dataset to generate laser strike decision instructions. The decision instructions include forward prediction of the penetration time based on the target power density and / or reverse recommendation of the power density and the optimal strike distance based on the preset penetration time. The control and execution module is used to control the laser equipment to perform strike operations according to the decision instructions.

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