Combat decision-making method and device of combat entity, readable storage medium and equipment

Abstract

The invention relates to the technical field of intelligent decision making, and provides a combat decision making method of a combat entity, a combat decision making device of the combat entity, a computer readable storage medium and electronic equipment. The combat decision-making method of the combat entity comprises the following steps: generating an initial probability distribution matrix (the number of rows is equal to the total number N of combat weapons deployed on the combat entity, and the number of columns is equal to the total number M of incoming attacking targets for the combat entity) representing an initial combat strategy; acquiring first state information of each combat weapon and second state information of each incoming attack target; the initial probability distribution matrix is used for describing the attack probability of each combat weapon for each incoming target; and performing conflict resolution processing on the initial probability distribution matrix based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy. The method can guarantee the reasonability of the strategy, and maximizes the defense capability of weapons.

Description

Background Art

[0002] Under digital conditions, the battlefield of military operations is highly transparent, the entities involved in the combat are complex and exist in multi-dimensional spaces such as sea, land, air and space, and participate in combat operations in a coordinated manner, making the war process more complicated. This puts higher demands on the command and decision-making personnel's global information acquisition capabilities, global observation and operational capabilities, and the control and coordination capabilities of participating entities in multi-dimensional spaces.

[0003] In the related technology, it is generally necessary to rely on pre-set strategies and rules to deal with various incoming targets. However, this solution may have unreasonable decisions or fail to maximize the defense capabilities of weapons.

[0004] In view of this, there is an urgent need in this field to develop a new combat decision-making method and device for combat entities.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure. Summary of the invention

[0006] The purpose of the present disclosure is to provide a combat decision-making method for a combat entity, a combat decision-making device for a combat entity, a computer-readable storage medium and an electronic device, thereby at least to a certain extent overcoming the technical problems of unreasonable decisions caused by the limitations of related technologies, or the inability to maximize the defense capabilities of weapons.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0008] According to a first aspect of the present disclosure, a combat decision method for a combat entity is provided, wherein the surrounding space of the combat entity is divided into a plurality of defense areas, N combat weapons are deployed on the combat entity, and each of the combat weapons is deployed in at least one defense area on the combat entity, and the method comprises:

[0009] After generating an initial probability distribution matrix representing an initial combat strategy, obtaining first state information of each combat weapon and second state information of each incoming target; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target;

[0010] The initial probability distribution matrix is ​​subjected to conflict resolution processing based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

[0011] In an exemplary embodiment of the present disclosure, the conflict resolution process is performed on the initial probability distribution matrix based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy, including:

[0012] Generate a first conflict resolution matrix; the first conflict resolution matrix has the same dimension as the initial probability distribution matrix, and each element in the first conflict resolution matrix is ​​equal to a first preset value;

[0013] Adjusting the first conflict resolution matrix based on the first state information and the second state information to obtain a second conflict resolution matrix;

[0014] The initial probability distribution matrix is ​​subjected to conflict resolution processing using the second conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0015] In an exemplary embodiment of the present disclosure, the first conflict resolution matrix includes a 1*N column vector corresponding to each of the M incoming targets;

[0016] The adjusting the first conflict resolution matrix based on the first state information in combination with the second state information to obtain a second conflict resolution matrix includes:

[0017] Adjust the 1*N column vector corresponding to each of the incoming targets based on the second state information to obtain the adjusted first column vector of each 1*N;

[0018] The adjusted M 1*N first column vectors are adjusted again based on the first state information to form the second conflict resolution matrix.

[0019] In an exemplary embodiment of the present disclosure, adjusting the 1*N column vector corresponding to each incoming target based on the second state information to obtain the adjusted first column vector of each 1*N includes:

[0020] Determining whether each of the incoming targets has a restriction condition of specifying combat weapons according to the second state information;

[0021] If the restriction condition exists, updating the first target element in the 1*N column vector from the first preset value to the second preset value to obtain an adjusted first column vector of each 1*N column vector;

[0022] Among them, the first target element is an element corresponding to other combat weapons except the designated combat weapon.

[0023] In an exemplary embodiment of the present disclosure, the adjusting the adjusted M 1*N first column vectors based on the first state information to form the second conflict resolution matrix includes:

[0024] Determine whether each of the N combat weapons is in a usable state according to the first state information;

[0025] If the target combat weapon is in an unavailable state, the second target element corresponding to the target combat weapon in the adjusted M 1*N first column vectors is updated from the first preset value to the second preset value to form the second conflict resolution matrix.

[0026] In an exemplary embodiment of the present disclosure, judging whether each of the N combat weapons is in a usable state according to the first state information includes:

[0027] Determining whether the remaining ammunition of each of the combat weapons meets the normal use conditions according to the first state information, and determining that the combat weapon is in the unusable state if the remaining ammunition does not meet the normal use conditions; and

[0028] determining whether the deactivation interval of each of the combat weapons satisfies a preset interval duration condition according to the first status information, and determining that the combat weapon is in the unavailable state if the deactivation interval does not satisfy the interval duration condition; and

[0029] It is determined based on the first status information whether the occupied frequency domain of each combat weapon conflicts with the occupied frequency of any combat weapon among the other N-1 combat weapons; if there is a conflict, it is determined that the combat weapon is in the unavailable state.

[0030] In an exemplary embodiment of the present disclosure, the using the second conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix to obtain a target probability distribution matrix representing the optimized combat strategy includes:

[0031] Generate a replica matrix of the initial probability distribution matrix, and multiply the replica matrix and the second conflict resolution matrix by corresponding elements to obtain a first product matrix; the first product matrix includes a 1*N second column vector corresponding to each of the M incoming targets;

[0032] Determine Y groups of joint attack strategies according to the first product matrix, and generate a final joint attack strategy based on the Y groups of joint attack strategies; the joint attack strategy is used to describe the corresponding relationship between each of the combat weapons and its corresponding attack target;

[0033] updating the second conflict resolution matrix according to the final joint attack strategy to obtain a third conflict resolution matrix;

[0034] The initial probability distribution matrix is ​​subjected to conflict resolution processing using the third conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0035] In an exemplary embodiment of the present disclosure, determining the Y group joint attack strategy according to the first product matrix includes:

[0036] Selecting the characteristic element with the largest value from the second column vector of 1*N of each incoming target, and determining the combat weapon corresponding to the characteristic element as the preferred combat weapon for each incoming target;

[0037] The preferred combat weapons for the M incoming targets are combined respectively to obtain the Y groups of joint attack strategies; Y is an integer greater than or equal to 1.

[0038] In an exemplary embodiment of the present disclosure, the step of selecting the characteristic element with the largest value from the second column vector of 1*N of each incoming target, and determining the combat weapon corresponding to the characteristic element as the preferred combat weapon for each incoming target includes:

[0039] If the number of combat weapons corresponding to the characteristic element with the largest value is multiple, a pre-configured expert rule is called; the expert rule is used to indicate the order of the preferred combat weapons for each of the incoming targets;

[0040] According to the expert rules, a preferred combat weapon for each incoming target is selected from the plurality of combat weapons.

[0041] In an exemplary embodiment of the present disclosure, after obtaining the Y group joint attack strategy, the method further includes:

[0042] The first product matrix is ​​copied Y-1 times to obtain Y first product matrices; each group of the joint attack strategies is associated with a first product matrix;

[0043] In the first product matrix associated with each group of the joint attack strategies, the characteristic element corresponding to the preferred combat weapon associated with each incoming target is updated to a third preset value to obtain Y second product matrices.

[0044] In an exemplary embodiment of the present disclosure, generating a final joint attack strategy based on the Y group joint attack strategy includes:

[0045] Detect whether there are S conflicting weapons occupying the same frequency band in each group of the joint attack strategies; S is an integer greater than 1;

[0046] If there are the S conflicting weapons, re-deciding the attack targets corresponding to the S conflicting weapons based on the second product matrix associated with each group of the joint attack strategies, so as to adjust each group of the joint attack strategies;

[0047] The joint attack strategy obtained by first adjustment and without the conflicting weapons is determined as the final joint attack strategy.

[0048] In an exemplary embodiment of the present disclosure, re-deciding the attack targets corresponding to the S conflicting weapons based on the second product matrix associated with each group of the joint attack strategies includes:

[0049] Determine a 1*M row vector of each of the conflicting weapons for the M incoming targets from a second product matrix associated with the joint attack strategy;

[0050] A specific incoming target associated with the maximum element value is selected from the 1*M row vector, and the specific incoming target is determined as the attack target corresponding to the conflict weapon.

[0051] In an exemplary embodiment of the present disclosure, the step of selecting a specific incoming target associated with a maximum element value from the 1*M row vector and determining the specific incoming target as an attack target corresponding to the conflict weapon includes:

[0052] If there are multiple incoming targets associated with the maximum element value, then an incoming target that meets a preset condition among the multiple incoming targets is determined as the specific incoming target;

[0053] Among them, the preset conditions include: in the joint attack strategy, there is no preferred combat weapon for the incoming target.

[0054] In an exemplary embodiment of the present disclosure, the method further includes:

[0055] If there is no incoming target satisfying the preset condition among the multiple incoming targets corresponding to the multiple maximum element values, the specific incoming target is randomly selected from the multiple incoming targets.

[0056] In an exemplary embodiment of the present disclosure, the updating of the second conflict resolution matrix according to the final joint attack strategy to obtain a third conflict resolution matrix includes:

[0057] Determine the target position of the characteristic element corresponding to the preferred combat weapon for each incoming target in the final joint attack strategy; the target position is determined by the row and column number of the characteristic element;

[0058] The element corresponding to the target position in the second conflict resolution matrix is ​​updated to the first preset value, and the other elements except the target position are updated to the second preset value, so as to obtain the third conflict resolution matrix.

[0059] In an exemplary embodiment of the present disclosure, the using the third conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix to obtain a target probability distribution matrix representing the optimized combat strategy includes:

[0060] Multiplying the third conflict resolution matrix and the initial probability distribution matrix by corresponding elements to obtain a third product matrix;

[0061] Normalizing each row vector in the third product matrix to obtain a normalized matrix;

[0062] Constructing a custom matrix; the custom matrix has the same dimension as the normalized matrix, and there is a preset correlation relationship between the element value at each position in the custom matrix and the element value at the corresponding position in the normalized matrix;

[0063] The normalized matrix and the custom matrix are summed to obtain a target probability distribution matrix representing the optimized combat strategy.

[0064] According to a second aspect of the present disclosure, a combat decision-making device of a combat entity is provided, wherein the surrounding space of the combat entity is divided into a plurality of defense areas, N combat weapons are deployed on the combat entity, and each of the combat weapons is deployed in at least one defense area on the combat entity, and the device comprises:

[0065] An information acquisition module, used for acquiring the first state information of each combat weapon and the second state information of each incoming target after generating an initial probability distribution matrix representing the initial combat strategy; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target;

[0066] The conflict resolution processing module is used to perform conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

[0067] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the combat decision-making method of the combat entity described in the first aspect above is implemented.

[0068] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the combat decision-making method of the combat entity described in the first aspect above by executing the executable instructions.

[0069] It can be seen from the above technical solutions that the combat decision method of the combat entity, the combat decision device of the combat entity, the computer-readable storage medium and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:

[0070] In the technical solutions provided in some embodiments of the present disclosure, after generating an initial probability distribution matrix representing the initial combat strategy (the number of rows of the initial probability distribution matrix is ​​equal to the total number N of combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target), the first state information of each combat weapon and the second state information of each incoming target are obtained; based on the first state information and the second state information, the initial probability distribution matrix is ​​subjected to conflict resolution processing to obtain a target probability distribution matrix representing the optimized combat strategy. On the one hand, it can solve problems such as multiple weapon systems aiming at the same target at the same time, avoid waste of resources, ensure that each target has the most suitable weapon to attack, improve the practicality and effectiveness of the final output strategy, and improve the combat success rate and overall combat efficiency; on the other hand, it can enhance the coordinated combat capability of multiple weapons and maximize the defense effect and strike capability of the weapons.

[0071] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0073] Figure 1A schematic diagram showing a flow chart of a combat decision-making method of a combat entity in an embodiment of the present disclosure;

[0074] Figure 2 A flow chart showing how to perform conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy;

[0075] Figure 3 A schematic diagram showing a flow chart of how to adjust the 1*N column vector corresponding to each incoming target based on the second state information in an embodiment of the present disclosure to obtain the adjusted first column vector of each 1*N;

[0076] Figure 4 A schematic diagram showing a flow chart of how to adjust the adjusted M 1*N first column vectors again based on the first state information to form a second conflict resolution matrix in an embodiment of the present disclosure;

[0077] Figure 5 A flow chart showing how to use the second conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy;

[0078] Figure 6 A schematic diagram showing a flow chart of how to determine the joint attack strategy of group Y according to the first product matrix in an embodiment of the present disclosure;

[0079] Figure 7 A flowchart showing how to generate a final joint attack strategy based on the Y group joint attack strategy in an embodiment of the present disclosure;

[0080] Figure 8 A flowchart showing how to re-determine the attack targets corresponding to S conflicting weapons based on the second product matrix associated with each group of joint attack strategies in the embodiment of the present disclosure to adjust each group of joint attack strategies;

[0081] Fig. 9 A flow chart showing how to use the third conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy;

[0082] Fig.10 A schematic diagram showing the structure of a combat decision-making device of a combat entity in an exemplary embodiment of the present disclosure;

[0083] Fig.11 A schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0084] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0085] The terms "a", "an", "the" and "said" are used in this specification to indicate the presence of one or more elements / components / etc.; the terms "including" and "having" are used to express an open-ended inclusion and mean that additional elements / components / etc. may exist in addition to the listed elements / components / etc.; the terms "first" and "second" etc. are used only as labels and are not intended to limit the quantity of their objects.

[0086] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0087] In the embodiments of the present disclosure, a combat decision-making method for a combat entity is first provided, which at least to a certain extent overcomes the defects of the related technologies such as unreasonable decision-making or failure to maximize the defense capability of weapons.

[0088] Figure 1 A flow chart of a combat decision-making method of a combat entity in an embodiment of the present disclosure is shown. The executor of the combat decision-making method of the combat entity may be the combat entity that makes the combat decision.

[0089] refer to Figure 1 According to an embodiment of the present disclosure, a combat decision-making method of a combat entity includes the following steps:

[0090] Step S110, after generating an initial probability distribution matrix representing the initial combat strategy, obtain the first state information of each combat weapon and the second state information of each incoming target; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target.

[0091] Step S120, performing conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

[0092] exist Figure 1 In the technical solution provided by the illustrated embodiment, after generating an initial probability distribution matrix representing the initial combat strategy (the number of rows of the initial probability distribution matrix is ​​equal to the total number N of combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target), the first state information of each combat weapon and the second state information of each incoming target are obtained; based on the first state information and the second state information, the initial probability distribution matrix is ​​subjected to conflict resolution processing to obtain a target probability distribution matrix representing the optimized combat strategy. On the one hand, it can solve problems such as multiple weapon systems aiming at the same target at the same time, avoid waste of resources, ensure that each target has the most suitable weapon to attack, improve the practicality and effectiveness of the final output strategy, and improve the combat success rate and overall combat efficiency; on the other hand, it can enhance the coordinated combat capability of multiple weapons and maximize the defense effect and strike capability of the weapons.

[0093] The following Figure 1 The specific implementation process of each step is described in detail:

[0094] Before step S110, it should be noted that the combat entity in the present disclosure refers to a unit or system that can perform combat missions. For example, it can be a ship, a fighter, a bomber, a tank, an infantry fighting vehicle, etc., which can be set according to actual conditions. The present disclosure does not make any special limitations on this. The following embodiments are described using the combat entity as a ship as an example.

[0095] In an exemplary embodiment of the present disclosure, the space surrounding a ship can be divided into multiple defense areas (also referred to as defense sectors), and the multiple defense sectors can completely cover the entire airspace outside the ship, and there is a certain overlapping angle between two adjacent defense sectors.

[0096] The following is a description of how to define each defense sector in the present disclosure:

[0097] First, establish the global NED coordinate system (North East Down, also known as the navigation coordinate system): the center of the ship is the origin O(0,0,0), the N axis is forward along the ship's heading, the E axis is perpendicular to the N axis and points to the right, and the D axis points to the center of the earth. Let the length and width of the ship be L and W respectively.

[0098] Second, establish a local polar coordinate system for each corner:

[0099] Assume that the four corners of the ship are set to A, B, C and D respectively, then the coordinates of point A are (-L / 2, -W / 2), the coordinates of point B are (L / 2, -W / 2), the coordinates of point C are (L / 2, W / 2), and the coordinates of point D are (-L / 2, W / 2).

[0100] Third, establish a local polar coordinate system for each corner:

[0101] A local polar coordinate system is established at each corner of A, B, C, and D, with each corner as the origin. A point in each local polar coordinate system is represented by (r, φ), where r is the distance to the origin (a fixed point at the center of the coordinate system, i.e., any of ABCD above), and φ is the polar angle (measured from the polar axis to the line from the pole to the point).

[0102] Fourth, define the defense sector:

[0103] The defense sectors at each corner of A, B, C, and D have a central angle θ (that is, the angle of each sector's defense), and there is an overlap angle Δθ between adjacent sectors. Assume that the defense sector at point A has an angle range of: The minimum radius of each defense sector is denoted as r mim , the maximum radius is represented by r max .

[0104] After defining the above multiple defense sectors, assume that the position of an incoming target is expressed as (N t ,E t ), then the defense sector in which the incoming target is located can be determined based on the following steps:

[0105] First, transform the global NED coordinates into the local rectangular coordinate system:

[0106] For the target position (N t ,E t ), it is necessary to convert it from the global NED coordinate system to the local rectangular coordinate system of each corner. Take point A as an example, the local rectangular coordinates of point A are:

[0107] Second, calculate the coordinates in the local rectangular coordinate system:

[0108] Assume that the coordinates of the target position in the local rectangular coordinate system of point A are (x A ,y A ), the conversion formula is as follows:

[0109] Third, calculate the polar angle φ t :

[0110] Calculate the polar angle φ in the local rectangular coordinate system t :

[0111] Fourth, determine whether the target location is within a certain defense sector:

[0112] Check the polar angle φ of the target position t Is it within the angle range of the defense sector? Also check if the target location is within the radius of the defense sector: If r min ≤r t ≤r max , the target position is within the defense sector.

[0113] It should be noted that the present invention can deploy combat weapons in each defense sector. Exemplarily, the combat weapons can include four types. Exemplarily, the types of combat weapons can include missiles, cannons, decoys, and electronic jamming (ew), among which missiles and cannons are hard weapons, and decoys and electronic jamming are soft weapons.

[0114] More specifically, the missile can include two attack modes (also called two guidance modes), which can be radar mode and infrared mode; the machine gun can include two attack modes (also called two auxiliary aiming modes), which can be radar mode and infrared mode; the decoy bomb can include three attack modes (also called three interference modes), which can be active infrared decoy bomb, outboard active decoy and passive infrared jammer; the electronic jamming can include two attack modes (also called two interference modes), which can be passive chaff interference and active noise interference.

[0115] Exemplarily, the above four types of combat weapons can be deployed in each defense sector. Given that each type of combat weapon can include multiple attack modes, when determining the specific number of combat weapons deployed on the ship, it is necessary to jointly determine the number of types of combat weapons, the number of combat weapons, and the number of attack modes associated with each combat weapon. For example, assuming that two types of combat weapons (missiles and machine guns) are deployed in a certain defense sector, one of each type of combat weapon is deployed, and the missile includes two attack modes, and the machine gun also includes two attack modes, then the number of combat weapons deployed in the defense sector can be 1*2+1*2=4. After calculating the number of combat weapons deployed in each defense sector one by one, the numbers of combat weapons corresponding to the above multiple defense sectors can be accumulated to determine the specific number N of combat weapons deployed on the ship.

[0116] In view that combat weapons may be located in different defense sectors and contain different attack modes, different combat weapons can be distinguished according to the identification of the defense sector and the identification of the attack mode. Specifically, the weapon configuration on the ship can refer to Table 1:

[0117] Table 1

[0118]

[0119]

[0120] Before step S110, it should also be noted that the present disclosure can pre-train a combat decision model, which can include a strategy network, which can include a coding layer, a decision layer and a strategy mask layer, and the strategy network is used to output a target probability distribution matrix that represents the optimized combat strategy.

[0121] Specifically, the characteristic parameters of the M incoming targets of the combat entity at the current moment can be obtained, and the overall combat effectiveness parameters of the combat entity for the M incoming targets at the current moment can be obtained, and the overall combat effectiveness parameters are input into the coding layer, and the overall combat effectiveness parameters are encoded by the coding layer to obtain the coding matrix; the decision layer decides the initial combat strategy (i.e., the initial probability distribution matrix) for the M incoming targets based on the coding matrix and the above-mentioned characteristic parameters; thereafter, the above-mentioned initial combat strategy can be subjected to conflict resolution processing by the above-mentioned strategy mask layer to obtain the target probability distribution matrix of the above-mentioned combat strategy after characterization optimization.

[0122] Among them, the characteristic parameters of each incoming target can be composed of a set of attributes describing its current state (i.e., characteristic variables of multiple dimensions). These attributes are combined into a vector, which includes the distance, azimuth, pitch, speed, speed (moving direction relative to a certain reference direction), x, y, z (x, y, z correspond to the N-axis coordinates of the NED coordinate system, the E-axis coordinates of the NED coordinate system, and the D-axis coordinates of the NED coordinate system), Vx, Vy, Vz (Vx, Vy, Vz correspond to the N-axis speed of the NED coordinate system, the E-axis speed of the NED coordinate system, and the D-axis speed of the NED coordinate system), absolute heading, absolute speed, etc., as well as electronic reconnaissance parameters such as threat level, credibility, etc. These dimensions include sufficient information about the incoming target itself to support comprehensive evaluation and decision-making of the incoming target.

[0123] Exemplarily, the dimension of the above characteristic parameter may be G. Referring to Table 2, Table 2 shows the specific contents of the dimension included in the above characteristic parameter:

[0124] Table 2

[0125]

[0126]

[0127]

[0128] The overall combat effectiveness parameter of the combat entity against M incoming targets at the current moment can be called the overall combat effectiveness parameter. The overall combat effectiveness parameter is composed of the combat effectiveness parameter of each combat weapon against each incoming target at the current moment, and the combat effectiveness parameter of each combat weapon against each incoming target at the current moment can include parameters from two aspects, namely, the expected combat effect characterization parameter of each combat weapon against each incoming target and the performance parameter of each combat weapon at the current moment.

[0129] Specifically, referring to Table 3, the combat effectiveness parameters of each combat weapon against each incoming target at the current moment may include the following L (L is an integer greater than 1) dimensions:

[0130] Table 3

[0131]

[0132]

[0133]

[0134] It should be noted that in the above Table 3, Corresponding to the performance parameters of each of the above-mentioned combat weapons at the current moment, the rest correspond to the parameters characterizing the expected combat effects of each of the above-mentioned combat weapons against each incoming target.

[0135] Given that the combat effectiveness parameters of each combat weapon against each incoming target at the current moment correspond to L dimensions, while the total number of combat weapons is N, and the total number of incoming targets is M, the above-mentioned overall combat effectiveness parameters correspond to N*M*L dimensions, that is, the above-mentioned overall combat effectiveness parameters are composed of M N*L first matrices.

[0136] The number of rows of the initial probability distribution matrix output by the decision layer is equal to the total number N of combat weapons deployed on the combat entity, and the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity. The initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target, that is, the initial probability distribution matrix corresponds to N*M dimensions.

[0137] After the decision layer outputs the above initial combat strategy, the strategy mask layer can perform the following steps S110-S120.

[0138] In step S110, after generating an initial probability distribution matrix representing the initial combat strategy, first state information of each combat weapon and second state information of each incoming target are obtained.

[0139] In this step, after generating the initial probability distribution matrix representing the initial combat strategy, the first state information of each combat weapon and the second state information of each incoming target can be obtained from the cache library (which can dynamically update the information of the weapon and each incoming target).

[0140] The current first status information of each combat weapon may include the following information:

[0141] Last used time: helps determine whether the weapon is in the cooldown period;

[0142] Ammunition remaining: Determine if the weapon has enough ammunition to fight;

[0143] Weapon frequency domain usage: records the frequency range used by enabled weapons, used to detect frequency domain conflicts;

[0144] Weapon time domain information: records the time window in which the weapon has been enabled to avoid time domain conflicts;

[0145] Weapon airspace information: records the space area where enabled weapons are acting. Although this part is complex to process, it helps manage space resources.

[0146] Exemplarily, each combat weapon may correspond to a data record, and each data record may contain the above-mentioned related information.

[0147] The second status information of each incoming target mentioned above may be the airspace information of the incoming target, which is used to record the current spatial area of ​​the incoming target that is still alive, and is helpful for managing space resources. Exemplarily, the second status information may also include whether there is a designated combat weapon for each incoming target, which can be set according to actual conditions, and the present disclosure does not make any special limitation on this.

[0148] In step S120, conflict resolution processing is performed on the initial probability distribution matrix based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

[0149] In this step, refer to Figure 2 , Figure 2 A flow chart showing how to perform conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy includes steps S201 to S203:

[0150] In step S201, a first conflict resolution matrix is ​​generated.

[0151] In this step, a first conflict resolution matrix MASK can be generated. The first conflict resolution matrix has the same dimension as the initial probability distribution matrix (that is, it also corresponds to N*M dimension). Each element in the first conflict resolution matrix is ​​equal to a first preset value. Exemplarily, the first preset value can be 1, that is, the first conflict resolution matrix is ​​an N*M dimensional matrix with all element values ​​​​being 1. It can also be understood that the first conflict resolution matrix contains a 1*N column vector corresponding to each of the M incoming targets.

[0152] Since each element of the first conflict resolution matrix corresponds to the actual feasible state of each "incoming target-combat weapon" pair in the initial probability distribution matrix, the first conflict resolution matrix indicates that no "incoming target-combat weapon" pair is shielded.

[0153] In step S202, the first conflict resolution matrix is ​​adjusted based on the first state information in combination with the second state information to obtain a second conflict resolution matrix.

[0154] In this step, specifically, given that the first conflict resolution matrix contains a 1*N column vector corresponding to each of the M incoming targets, the present disclosure can adjust the 1*N column vector corresponding to each incoming target based on the second state information to obtain the adjusted first column vector of each 1*N; thereafter, the adjusted M 1*N first column vectors are adjusted again based on the first state information to form a second conflict resolution matrix.

[0155] refer to Figure 3 , Figure 3 A flow chart showing how to adjust the 1*N column vector corresponding to each incoming target based on the second state information to obtain the adjusted first column vector of each 1*N in the embodiment of the present disclosure includes steps S301-S302:

[0156] In step S301, it is determined whether each incoming target has restriction conditions for designated combat weapons according to the second state information.

[0157] In this step, it can be determined based on the second status information whether each incoming target has a restriction condition of a designated combat weapon, that is, whether only the designated combat weapon can successfully destroy the incoming target.

[0158] In step S302, if there is a restriction condition, the first target element in the 1*N column vector is updated from the first preset value to the second preset value to obtain an adjusted first column vector of each 1*N.

[0159] In this step, if the incoming target has the restriction condition of the above-mentioned designated combat weapon, the first target element in the 1*N column vector (which may be the element corresponding to other combat weapons except the above-mentioned designated combat weapon) can be updated from the first preset value 1 to the second preset value 0 to obtain the adjusted first column vector of each 1*N.

[0160] After obtaining the adjusted M 1*N first column vectors, you can refer to Figure 4 , Figure 4 A flow chart showing how to adjust the adjusted M 1*N first column vectors again based on the first state information to form a second conflict resolution matrix in an embodiment of the present disclosure includes steps S401-S402:

[0161] In step S401, it is determined whether each of the N combat weapons is in an available state according to the first state information.

[0162] In this step, the specific implementation method of how to determine whether each combat weapon among the N combat weapons is in an available state according to the first state information is shown below:

[0163] In an optional implementation, it can be determined whether the remaining ammunition of each combat weapon meets the normal use conditions based on the first status information. If the remaining ammunition does not meet the normal use conditions, it is determined that the combat weapon is in an unusable state.

[0164] In another optional implementation, it can be determined whether the deactivation interval of each combat weapon meets a preset interval duration condition based on the first status information; if the deactivation interval does not meet the interval duration condition, it is determined that the combat weapon is in an unusable state.

[0165] In another optional implementation, it can be determined based on the first status information whether the occupied frequency domain of each combat weapon conflicts with the occupied frequency domain of any combat weapon among the other N-1 combat weapons. If there is a conflict, it is determined that the combat weapon is in an unusable state.

[0166] In step S402, if the target combat weapon is in an unavailable state, the second target element corresponding to the target combat weapon in the adjusted M 1*N first column vectors is updated from the first preset value to the second preset value to form a second conflict resolution matrix.

[0167] In this step, if it is determined that the target combat weapon is in an unusable state, the second target elements of the entire row where the target combat weapon is located in the M*N matrix can be updated from the first preset value 1 to the second preset value 0 to form the above-mentioned second conflict resolution matrix.

[0168] Next reference Figure 2 In step S203, the initial probability distribution matrix is ​​subjected to conflict resolution processing using the second conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0169] In this step, after obtaining the second conflict resolution matrix, refer to Figure 5 , Figure 5 A flow chart showing how to use the second conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy includes steps S501 to S504:

[0170] In step S501, a copy matrix of the initial probability distribution matrix is ​​generated, and the copy matrix and the second conflict resolution matrix are multiplied by corresponding elements to obtain a first product matrix.

[0171] In this step, a copy matrix of the initial probability distribution matrix may be copied, and the copy matrix is ​​multiplied by corresponding elements with the second conflict resolution matrix to obtain a first product matrix.

[0172] The first product matrix includes a 1*N second column vector corresponding to each of the M incoming targets.

[0173] In step S502, Y groups of joint attack strategies are determined according to the first product matrix, and a final joint attack strategy is generated based on the Y groups of joint attack strategies.

[0174] In this step, you can refer to Figure 6 , Figure 6 A flow chart showing how to determine the joint attack strategy of group Y according to the first product matrix in an embodiment of the present disclosure includes steps S601 and S602:

[0175] In step S601, the characteristic element with the largest value is selected from the second column vector of 1*N of each incoming target, and the combat weapon corresponding to the characteristic element is determined as the preferred combat weapon for each incoming target.

[0176] In this step, the characteristic element with the largest value can be screened out from the second column vector of 1*N associated with each incoming target, and the combat weapon corresponding to the characteristic element can be determined as the preferred combat weapon for each incoming target.

[0177] It should be noted that, assuming that there are multiple combat weapons corresponding to the characteristic element with the largest value, the pre-configured expert rules can be called (the expert rules are used to indicate the order of the preferred combat weapons for each incoming target), and according to the expert rules, a specific preferred combat weapon for each incoming target can be screened out from the multiple combat weapons corresponding to the multiple characteristic elements with the largest values.

[0178] It should be noted that when the above-mentioned expert rules are not provided, or when a specific preferred combat weapon for each incoming target cannot be screened out according to the above-mentioned expert rules, multiple combat weapons corresponding to the characteristic elements with the largest values ​​can be retained.

[0179] In step S602, the preferred combat weapons for the M incoming targets are combined respectively to obtain Y groups of joint attack strategies.

[0180] In this step, the preferred combat weapons for the M incoming targets can be combined respectively to obtain Y (Y is an integer greater than or equal to 1) groups of joint attack strategies, which are used to describe the corresponding relationship between each combat weapon and its corresponding attack target.

[0181] For example, taking the value of M as 2 and the value of N as 3, assuming that the preferred combat weapon for incoming target 1 is combat weapon 1, and the preferred combat weapons for incoming target 2 are combat weapon 2 and combat weapon 3, the first set of joint attack strategies obtained can be: combat weapon 1-incoming target 1, combat weapon 2-incoming target 2; the other set of joint attack strategies can be: combat weapon 1-incoming target 1, combat weapon 3-incoming target 2.

[0182] After obtaining the above-mentioned Y groups of joint attack strategies, it should be noted that the present disclosure can also copy the above-mentioned first product matrix Y-1 times to obtain Y first product matrices, so that each group of joint attack strategies is associated with a first product matrix. After that, the characteristic element corresponding to the preferred combat weapon associated with each incoming target in the first product matrix associated with each group of joint attack strategies (the incoming target can be located by the column number, and the preferred combat weapon can be located by the row number, so that a characteristic element can be located) can be updated to a third preset value -1 to obtain Y second product matrices.

[0183] In addition, reference Figure 7 , Figure 7 A flowchart showing how to generate a final joint attack strategy based on the joint attack strategy of group Y in an embodiment of the present disclosure includes steps S701 to S703:

[0184] In step S701, it is detected whether there are S conflicting weapons occupying the same frequency band in each group of joint attack strategies.

[0185] In this step, it can be detected whether there are S conflicting weapons occupying the same frequency band in each group of joint attack strategies, where S is an integer greater than 1, and S is greater than or equal to 2.

[0186] In step S702, if there are S conflicting weapons, the attack targets corresponding to the S conflicting weapons are re-determined based on the second product matrix associated with each group of joint attack strategies to adjust each group of joint attack strategies.

[0187] In this step, if there are S conflicting weapons in any set of joint attack strategies, you can refer to Figure 8 , Figure 8 A flow chart showing how to re-determine the attack targets corresponding to S conflicting weapons based on the second product matrix associated with each group of joint attack strategies in the embodiment of the present disclosure to adjust each group of joint attack strategies includes steps S801-step S802:

[0188] In step S801 , a 1*M row vector of each conflict weapon for M incoming targets is determined from a second product matrix associated with the joint attack strategy.

[0189] In this step, a 1*M row vector of each conflict weapon for the M incoming targets can be determined from the second product matrix associated with the joint attack strategy.

[0190] In step S802, the specific incoming target associated with the maximum element value is screened out from the 1*M row vector, and the specific incoming target is determined as the attack target corresponding to the conflict weapon.

[0191] In this step, the specific incoming target associated with the maximum element value can be screened out from the row vector of 1*M, and the specific incoming target can be determined as the attack target corresponding to the conflict weapon. It should be noted that if the number of incoming targets associated with the maximum element value is multiple, the incoming target that meets the preset conditions (the preset conditions include: in the joint attack strategy, there is no preferred combat weapon for the incoming target) among the multiple incoming targets can be determined as the specific incoming target. If there is no incoming target that meets the above preset conditions among the multiple incoming targets corresponding to the multiple maximum element values, the specific incoming target can be randomly selected from the multiple incoming targets.

[0192] Similarly, you can refer to the above Figure 8 Each set of joint attack strategies is optimized and adjusted according to the steps in , until a set of joint attack strategies without conflicting weapons is obtained.

[0193] Next reference Figure 7 In step S703, the first adjusted joint attack strategy without conflicting weapons is determined as the final joint attack strategy.

[0194] In this step, the first adjusted joint attack strategy without conflicting weapons can be determined as the final joint attack strategy, and other joint attack strategies and their corresponding first product matrices and second product matrices can be deleted.

[0195] It should be noted that if a joint attack strategy without conflicting weapons is not obtained after adjustment, a group of joint attack strategies can be randomly selected, and the conflicting weapons can be removed to obtain the final joint attack strategy.

[0196] Next reference Figure 5 , in step S503, the second conflict resolution matrix is ​​updated according to the final joint attack strategy to obtain a third conflict resolution matrix.

[0197] In this step, the target position of the characteristic element corresponding to the preferred combat weapon for each incoming target in the final joint attack strategy can be determined (the incoming target can determine the column number of the characteristic element, and the preferred combat weapon can determine the row number of the characteristic element, thereby locating a target position); thereafter, the element corresponding to the target position in the second conflict resolution matrix can be updated to the first preset value 1, and the other elements except the target position can be updated to the second preset value 0, to obtain the third conflict resolution matrix.

[0198] In step S504, the initial probability distribution matrix is ​​subjected to conflict resolution processing using the third conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0199] In this step, after obtaining the third conflict resolution matrix, you can refer to Fig. 9 , Fig. 9 A flow chart showing how to use the third conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix in the embodiment of the present disclosure to obtain a target probability distribution matrix representing the optimized combat strategy includes steps S901 to S904:

[0200] In step S901, the third conflict resolution matrix is ​​multiplied by the initial probability distribution matrix at corresponding elements to obtain a third product matrix.

[0201] In this step, the third conflict resolution matrix and the initial probability distribution matrix may be multiplied by corresponding elements to obtain a third product matrix.

[0202] In step S902, each row vector in the third product matrix is ​​normalized to obtain a normalized matrix.

[0203] In this step, each row vector in the third product matrix may be normalized to obtain a normalized matrix to ensure that each row of the matrix is ​​still a legal probability distribution.

[0204] In step S903, a custom matrix is ​​constructed.

[0205] In this step, a custom matrix can be constructed. The dimension of the custom matrix is ​​the same as that of the normalized matrix, and there is a preset correlation between the element values ​​at each position in the custom matrix and the element values ​​at the corresponding position in the normalized matrix. Specifically, for the position where the element in the normalized matrix is ​​0, the value in the custom matrix is ​​-1, and the other positions are all assigned 0 in the custom matrix.

[0206] In step S904, the normalized matrix and the custom matrix are summed to obtain a target probability distribution matrix representing the optimized combat strategy.

[0207] In this step, the normalized matrix and the custom matrix can be summed to obtain a target probability distribution matrix that represents the optimized combat strategy. The target probability distribution matrix is ​​also an N*M matrix, the number of rows of which is equal to the total number N of combat weapons deployed on the combat entity, and the number of columns of which is equal to the total number M of at least one incoming target for the combat entity. Each element in the target probability distribution matrix is ​​used to describe the final attack probability of each combat weapon against each incoming target.

[0208] In the present disclosure, the mask operation is essentially to directly set the selection probability of certain actions to 0, which means that the influence of these actions is completely ignored. Therefore, the above technical solution has at least the following technical effects:

[0209] On the one hand, making preliminary decisions based on algorithms can reduce the uncertainty of human factors, making combat decisions more objective and scientific, and helping to improve the accuracy and reliability of decisions; further, by using the action mask layer to resolve conflicts in the initial combat strategies decided by the decision-making layer, it can solve problems such as multiple weapon systems aiming at the same target at the same time, avoid waste of resources, ensure that each target has the most appropriate weapon to attack, improve the practicality and effectiveness of the final output strategy, and improve the combat success rate and overall combat efficiency; on the other hand, it can enhance the coordinated combat capabilities of multiple weapons and maximize the defensive effects and strike capabilities of weapons.

[0210] The present disclosure also provides a combat decision-making device for a combat entity, wherein the surrounding space of the combat entity is divided into a plurality of defense areas, and N combat weapons are deployed on the combat entity, each of the combat weapons being deployed in at least one defense area on the combat entity. Fig.10 A schematic diagram showing the structure of a combat decision-making device of a combat entity in an exemplary embodiment of the present disclosure; Fig.10 As shown, the combat decision-making device 1000 of the combat entity may include an information acquisition module 1010 and a conflict resolution processing module 1020. Among them:

[0211] The information acquisition module 1010 is used to acquire the first state information of each combat weapon and the second state information of each incoming target after generating an initial probability distribution matrix representing the initial combat strategy; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target;

[0212] The conflict resolution processing module 1020 is used to perform conflict resolution processing on the initial probability distribution matrix based on the first state information and the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

[0213] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 performs conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy, including:

[0214] Generate a first conflict resolution matrix; the first conflict resolution matrix has the same dimension as the initial probability distribution matrix, and each element in the first conflict resolution matrix is ​​equal to a first preset value;

[0215] Adjusting the first conflict resolution matrix based on the first state information and the second state information to obtain a second conflict resolution matrix;

[0216] The initial probability distribution matrix is ​​subjected to conflict resolution processing using the second conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0217] In an exemplary embodiment of the present disclosure, the first conflict resolution matrix includes a 1*N column vector corresponding to each of the M incoming targets;

[0218] The conflict resolution processing module 1020 adjusts the first conflict resolution matrix based on the first state information and the second state information to obtain a second conflict resolution matrix, including:

[0219] Adjust the 1*N column vector corresponding to each of the incoming targets based on the second state information to obtain the adjusted first column vector of each 1*N;

[0220] The adjusted M 1*N first column vectors are adjusted again based on the first state information to form the second conflict resolution matrix.

[0221] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 adjusts the 1*N column vector corresponding to each incoming target based on the second state information to obtain the adjusted first column vector of each 1*N, including:

[0222] Determining whether each of the incoming targets has a restriction condition of specifying combat weapons according to the second state information;

[0223] If the restriction condition exists, updating the first target element in the 1*N column vector from the first preset value to the second preset value to obtain an adjusted first column vector of each 1*N column vector;

[0224] Among them, the first target element is an element corresponding to other combat weapons except the designated combat weapon.

[0225] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 further adjusts the adjusted M 1*N first column vectors based on the first state information to form the second conflict resolution matrix, including:

[0226] Determine whether each of the N combat weapons is in a usable state according to the first state information;

[0227] If the target combat weapon is in an unavailable state, the second target element corresponding to the target combat weapon in the adjusted M 1*N first column vectors is updated from the first preset value to the second preset value to form the second conflict resolution matrix.

[0228] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 determines whether each of the N combat weapons is in an available state according to the first state information, including:

[0229] Determining whether the remaining ammunition of each of the combat weapons meets the normal use conditions according to the first state information, and determining that the combat weapon is in the unusable state if the remaining ammunition does not meet the normal use conditions; and

[0230] determining whether the deactivation interval of each of the combat weapons satisfies a preset interval duration condition according to the first status information, and determining that the combat weapon is in the unavailable state if the deactivation interval does not satisfy the interval duration condition; and

[0231] It is determined based on the first status information whether the occupied frequency domain of each combat weapon conflicts with the occupied frequency of any combat weapon among the other N-1 combat weapons; if there is a conflict, it is determined that the combat weapon is in the unavailable state.

[0232] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 uses the second conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix to obtain a target probability distribution matrix representing the optimized combat strategy, including:

[0233] Generate a replica matrix of the initial probability distribution matrix, and multiply the replica matrix and the second conflict resolution matrix by corresponding elements to obtain a first product matrix; the first product matrix includes a 1*N second column vector corresponding to each of the M incoming targets;

[0234] Determining Y groups of joint attack strategies according to the first product matrix, and generating a final joint attack strategy based on the Y groups of joint attack strategies; the joint attack strategy is used to describe the corresponding relationship between each of the combat weapons and its corresponding attack target;

[0235] updating the second conflict resolution matrix according to the final joint attack strategy to obtain a third conflict resolution matrix;

[0236] The initial probability distribution matrix is ​​subjected to conflict resolution processing using the third conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

[0237] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 determines the Y group joint attack strategy according to the first product matrix, including:

[0238] Selecting the characteristic element with the largest value from the second column vector of 1*N of each incoming target, and determining the combat weapon corresponding to the characteristic element as the preferred combat weapon for each incoming target;

[0239] The preferred combat weapons for the M incoming targets are combined respectively to obtain the Y groups of joint attack strategies; Y is an integer greater than or equal to 1.

[0240] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 selects the feature element with the largest value from the second column vector of 1*N of each incoming target, and determines the combat weapon corresponding to the feature element as the preferred combat weapon for each incoming target, including:

[0241] If the number of combat weapons corresponding to the characteristic element with the largest value is multiple, a pre-configured expert rule is called; the expert rule is used to indicate the order of the preferred combat weapons for each incoming target;

[0242] According to the expert rules, a preferred combat weapon for each incoming target is selected from the plurality of combat weapons.

[0243] In an exemplary embodiment of the present disclosure, after obtaining the Y group joint attack strategy, the conflict resolution processing module 1020 is configured to:

[0244] The first product matrix is ​​copied Y-1 times to obtain Y first product matrices; each group of the joint attack strategies is associated with a first product matrix;

[0245] In the first product matrix associated with each group of the joint attack strategies, the characteristic element corresponding to the preferred combat weapon associated with each incoming target is updated to a third preset value to obtain Y second product matrices.

[0246] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 generates a final joint attack strategy based on the Y group joint attack strategy, including:

[0247] Detect whether there are S conflicting weapons occupying the same frequency band in each group of the joint attack strategies; S is an integer greater than 1;

[0248] If there are the S conflicting weapons, re-deciding the attack targets corresponding to the S conflicting weapons based on the second product matrix associated with each group of the joint attack strategies, so as to adjust each group of the joint attack strategies;

[0249] The first adjusted joint attack strategy without the conflicting weapons is determined as the final joint attack strategy.

[0250] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 re-decides the attack targets corresponding to the S conflicting weapons based on the second product matrix associated with each group of the joint attack strategies, including:

[0251] Determine a 1*M row vector of each of the conflicting weapons for the M incoming targets from a second product matrix associated with the joint attack strategy;

[0252] A specific incoming target associated with the maximum element value is selected from the 1*M row vector, and the specific incoming target is determined as the attack target corresponding to the conflict weapon.

[0253] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 selects a specific incoming target associated with the maximum element value from the 1*M row vector, and determines the specific incoming target as the attack target corresponding to the conflict weapon, including:

[0254] If there are multiple incoming targets associated with the maximum element value, then an incoming target that meets a preset condition among the multiple incoming targets is determined as the specific incoming target;

[0255] Among them, the preset conditions include: in the joint attack strategy, there is no preferred combat weapon for the incoming target.

[0256] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 is configured to:

[0257] If there is no incoming target satisfying the preset condition among the multiple incoming targets corresponding to the multiple maximum element values, the specific incoming target is randomly selected from the multiple incoming targets.

[0258] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 updates the second conflict resolution matrix according to the final joint attack strategy to obtain a third conflict resolution matrix, including:

[0259] Determine the target position of the characteristic element corresponding to the preferred combat weapon for each incoming target in the final joint attack strategy; the target position is determined by the row and column number of the characteristic element;

[0260] The element corresponding to the target position in the second conflict resolution matrix is ​​updated to the first preset value, and the other elements except the target position are updated to the second preset value, so as to obtain the third conflict resolution matrix.

[0261] In an exemplary embodiment of the present disclosure, the conflict resolution processing module 1020 uses the third conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix to obtain a target probability distribution matrix representing the optimized combat strategy, including:

[0262] Multiplying the third conflict resolution matrix and the initial probability distribution matrix by corresponding elements to obtain a third product matrix;

[0263] Normalizing each row vector in the third product matrix to obtain a normalized matrix;

[0264] Constructing a custom matrix; the custom matrix has the same dimension as the normalized matrix, and there is a preset correlation relationship between the element value at each position in the custom matrix and the element value at the corresponding position in the normalized matrix;

[0265] The normalized matrix and the custom matrix are summed to obtain a target probability distribution matrix representing the optimized combat strategy.

[0266] The specific details of each module in the combat decision-making device of the above-mentioned combat entity have been described in detail in the combat decision-making method of the corresponding combat entity, so they will not be repeated here.

[0267] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0268] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0269] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0270] The present disclosure also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist independently without being assembled into the electronic device.

[0271] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0272] Computer-readable storage media can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0273] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0274] In addition, an electronic device capable of implementing the above method is also provided in an embodiment of the present disclosure.

[0275] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0276] Refer to the following Fig.11 1100 according to this embodiment of the present disclosure is described. Fig.11 The electronic device 1100 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0277] like Fig.11 As shown, the electronic device 1100 is in the form of a general computing device. The components of the electronic device 1100 may include, but are not limited to: at least one processor 1110, at least one memory 1120, a bus 1130 connecting different system components (including the memory 1120 and the processor 1110), and a display 1140.

[0278] The memory stores program codes, which can be executed by the processor 1110, so that the processor 1110 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processor 1110 can execute the following steps: Figure 1 As shown in: Step S110, after generating an initial probability distribution matrix representing the initial combat strategy, obtaining the first state information of each combat weapon and the second state information of each incoming target; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target; Step S120, based on the first state information combined with the second state information, the initial probability distribution matrix is ​​subjected to conflict resolution processing to obtain a target probability distribution matrix representing the optimized combat strategy.

[0279] The memory 1120 may include a readable medium in the form of a volatile storage, such as a random access memory (RAM) 11201 and / or a cache memory 11202 , and may further include a read-only memory (ROM) 11203 .

[0280] Memory 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0281] The bus 1130 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0282] The electronic device 1100 may also communicate with one or more external devices 1200 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1100, and / or communicate with any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1150. Furthermore, the electronic device 1100 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1160. As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 via a bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0283] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A combat decision-making method for a combat entity, characterized in that: The space around the combat entity is divided into a plurality of defense areas, N combat weapons are deployed on the combat entity, and each combat weapon is deployed in at least one defense area on the combat entity. The method includes: After generating an initial probability distribution matrix representing an initial combat strategy, obtaining first state information of each combat weapon and second state information of each incoming target; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target; The initial probability distribution matrix is ​​subjected to conflict resolution processing based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

2. The method according to claim 1, characterized in that The performing conflict resolution processing on the initial probability distribution matrix based on the first state information in combination with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy includes: Generate a first conflict resolution matrix; the first conflict resolution matrix has the same dimension as the initial probability distribution matrix, and each element in the first conflict resolution matrix is ​​equal to a first preset value; Adjusting the first conflict resolution matrix based on the first state information and the second state information to obtain a second conflict resolution matrix; The initial probability distribution matrix is ​​subjected to conflict resolution processing using the second conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

3. The method according to claim 2, characterized in that The first conflict resolution matrix includes a 1*N column vector corresponding to each of the M incoming targets; The adjusting the first conflict resolution matrix based on the first state information in combination with the second state information to obtain a second conflict resolution matrix includes: Adjust the 1*N column vector corresponding to each of the incoming targets based on the second state information to obtain the adjusted first column vector of each 1*N; The adjusted M 1*N first column vectors are adjusted again based on the first state information to form the second conflict resolution matrix.

4. The method according to claim 3, characterized in that The adjusting the 1*N column vector corresponding to each of the incoming targets based on the second state information to obtain each adjusted 1*N first column vector includes: Determining whether each of the incoming targets has a restriction condition of specifying combat weapons according to the second state information; If the restriction condition exists, updating the first target element in the 1*N column vector from the first preset value to the second preset value to obtain an adjusted first column vector of each 1*N column vector; Among them, the first target element is an element corresponding to other combat weapons except the designated combat weapon.

5. The method according to claim 4, characterized in that The step of re-adjusting the adjusted M 1*N first column vectors based on the first state information to form the second conflict resolution matrix includes: Determine whether each of the N combat weapons is in a usable state according to the first state information; If the target combat weapon is in an unavailable state, the second target element corresponding to the target combat weapon in the adjusted M 1*N first column vectors is updated from the first preset value to the second preset value to form the second conflict resolution matrix.

6. The method according to claim 5, characterized in that The step of judging whether each of the N combat weapons is in an available state according to the first state information includes: Determining whether the remaining ammunition of each of the combat weapons meets the normal use conditions according to the first state information, and determining that the combat weapon is in the unusable state if the remaining ammunition does not meet the normal use conditions; and determining whether the deactivation interval of each of the combat weapons satisfies a preset interval duration condition according to the first status information, and determining that the combat weapon is in the unavailable state if the deactivation interval does not satisfy the interval duration condition; and It is determined based on the first status information whether the occupied frequency domain of each of the combat weapons conflicts with the occupied frequency domain of any of the other N-1 combat weapons; if there is a conflict, it is determined that the combat weapon is in the unavailable state.

7. The method according to claim 2, characterized in that The using the second conflict resolution matrix to perform conflict resolution processing on the initial probability distribution matrix to obtain a target probability distribution matrix representing the optimized combat strategy includes: Generate a replica matrix of the initial probability distribution matrix, and multiply the replica matrix and the second conflict resolution matrix by corresponding elements to obtain a first product matrix; the first product matrix includes a 1*N second column vector corresponding to each of the M incoming targets; Determine Y groups of joint attack strategies according to the first product matrix, and generate a final joint attack strategy based on the Y groups of joint attack strategies; the joint attack strategy is used to describe the corresponding relationship between each of the combat weapons and its corresponding attack target; updating the second conflict resolution matrix according to the final joint attack strategy to obtain a third conflict resolution matrix; The initial probability distribution matrix is ​​subjected to conflict resolution processing using the third conflict resolution matrix to obtain a target probability distribution matrix representing the optimized combat strategy.

8. A combat decision-making device of a combat entity, characterized in that: The space around the combat entity is divided into a plurality of defense areas. N combat weapons are deployed on the combat entity. Each combat weapon is deployed in at least one defense area on the combat entity. The device includes: An information acquisition module, used for acquiring the first state information of each combat weapon and the second state information of each incoming target after generating an initial probability distribution matrix representing the initial combat strategy; the number of rows of the initial probability distribution matrix is ​​equal to the total number N of the combat weapons deployed on the combat entity, the number of columns of the initial probability distribution matrix is ​​equal to the total number M of at least one incoming target for the combat entity, and the initial probability distribution matrix is ​​used to describe the attack probability of each combat weapon against each incoming target; The conflict resolution processing module is used to perform conflict resolution processing on the initial probability distribution matrix based on the first state information combined with the second state information to obtain a target probability distribution matrix representing the optimized combat strategy.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the combat decision-making method of the combat entity described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the combat decision-making method of the combat entity described in any one of claims 1 to 7 by executing the executable instructions.

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