Action scheme recommendation method and system based on action dependence partial order

Abstract

The invention discloses an action scheme recommendation method and system based on action dependency partial order. The method comprises the following steps: based on an opponent unit set determined by a user, analyzing a sequence relationship of actions which should be taken by opponent units so as to construct a partial sequence relationship set; inputting a target set, analyzing and determining other opponent units having a partial order relationship with the opponent units in the target set based on the partial order relationship set, sorting the other opponent units and the opponent units in the target set according to the partial order relationship, and updating to obtain a target extension set; based on the target extension set, searching a historical scheme which is consistent with the opponent units in the target extension set in order in a historical scheme library; and recommending the searched historical scheme as a current action scheme. Therefore, the method can deeply analyze the features of the opponent system based on the user input, excavate the support relationship between the opponent units in a fine-grained manner, expand the opponent unit set, more fully and effectively perform historical scheme matching, and improve the scheme recommendation accuracy.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to technical fields such as databases and graph databases, and more particularly to a method and system for recommending an action plan based on a partial order of action dependencies. Background Art

[0002] A confrontation action plan refers to a targeted action plan or strategy developed in a specific confrontational environment based on our goals and the opponent's behavior and capabilities to help ourselves or the team gain an advantage or achieve a specific goal in the confrontation. In the long-term business process, plan makers often accumulate a large number of action plans, among which valuable plans are often saved to form a plan library for subsequent reference and reuse.

[0003] When formulating action plans for new scenarios, it is very necessary to recommend solutions based on the solution library. ① Decision support: Decision-making in a confrontational environment is highly complex, and the solutions in the solution library are often tested in practice and saved for subsequent reuse. Recommendations based on this can help users make wise choices; ② Efficiency improvement: The formulation of action plans in a confrontational environment often faces a large amount of data and information. Solution recommendations based on the solution library can help users quickly locate applicable solutions and save time and energy; ③ Competitive advantage: The recommendation function can help users better understand the environment and opponents, thereby gaining a competitive advantage.

[0004] The existing solution recommendations based on the solution library can be roughly divided into two categories. ① Keyword query matching: query the solution library based on the keywords entered by the user. Keywords include solution name, solution ID, solution scenario, etc. For example, in a strategy game, the player may enter the name of the current confrontation scenario and the approximate solution name to quickly filter the required solution from the historical solution library. According to the specific matching technology, it can be divided into keyword precise matching and keyword fuzzy matching. The former is fast and accurate, while the latter has good fault tolerance and strong adaptability. In general, the advantages of the keyword query method are fast and efficient, and the technology is mature; ② Solution fine-grained information query matching: query from the more fine-grained information such as tasks, actions, and strategies contained in the solution. Compared with keyword query, the query expression ability is enhanced, so that the user's query needs can be more accurately expressed. For example, in a strategy game, the user may be concerned about the confrontation with several key targets of the opponent, so the information of these targets can be used as input to quickly filter solutions that have hit similar targets from the solution library.

[0005] However, the above methods have deficiencies to varying degrees. In the keyword-based query matching method, the expressive power of keywords is limited and cannot reflect the solution features that users are most concerned about at the moment, thus affecting the recommendation effect; the solution fine-grained information query matching method improves this problem, but its technical approach is mostly based on set or list comparison, such as matching the target set input by the user with the solution one by one, and does not dig deeper into the relationship between these targets in the current scenario. However, since confrontation is often a confrontation between systems, the different units of the opponent are not isolated, and there is a mutual support relationship. Simple set comparison lacks analysis of this relationship, and thus cannot extract more accurate matching features, affecting the solution recommendation effect. Summary of the invention

[0006] The present application provides an action plan recommendation method and system based on action dependency partial ordering to solve the problem that existing recommendation plans cannot extract more accurate matching features, affecting the effect of plan recommendation.

[0007] The technical solution is as follows: In the first aspect, a method for recommending an action plan based on a partial order of action dependencies is provided, comprising: Based on the opponent unit set determined by the user, analyzing the order relationship of actions that should be taken against each opponent unit in the opponent unit set to construct a partial order relationship set; Input a target set, analyze and determine, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sort the other opponent units and the opponent units in the target set according to the partial order relationship to update and obtain a target extension set; Based on the target extended set, searching a historical solution library for a historical solution that is consistent with the order of the opponent unit in the target extended set; the historical solution library contains multiple action plans for the opponent unit; The historical plan found is recommended as the current action plan.

[0008] In a possible implementation, based on the set of opponent units determined by the user, the order relationship of actions to be taken against each opponent unit in the set of opponent units is analyzed to construct a partial order relationship set, specifically including: Analyzing the sequential relationship of actions that should be taken between the opponent units in the opponent unit set, and determining that the sequential relationship is a unidirectional support relationship between different opponent units; Based on the sequential relationship of the actions taken by each opponent unit, each unidirectional support relationship is counted to construct a partial order relationship set.

[0009] In a possible implementation, the unidirectional support relationship is defined as , represents the opponent unit Opponent Unit There is a sequential relationship in which actions are taken; or, in a directed graph, there is a path from the opponent unit to the Point to opponent unit The directed edge of .

[0010] In a possible implementation, the unidirectional support relationship satisfies the following properties: reflexivity, antisymmetry, and transitivity; Among them, reflexivity means that each opponent unit in the partial order relation set forms a support relationship with itself; Antisymmetry means that since the one-way support relationship is one-way, if the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is no sequential relationship for taking actions; Transitivity means that the support relationship between the opponent units can be transferred. Opponent Unit There is a sequential relationship in which actions are taken, and at the same time, the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is a sequential relationship in which actions are taken.

[0011] In a possible implementation scheme, based on the partial order relationship set, analyzing and determining, in addition to the partial order relationship between the opponent units in the target set, other opponent units that have a partial order relationship with the opponent units in the target set, and sorting the other opponent units and the opponent units in the target set according to the partial order relationship, and updating the target extended set, specifically includes: Determining at least two opponent units included in the target set; For each opponent unit, searching from the partial order relationship set for other opponent units that have a partial order relationship with the opponent unit, wherein the other opponent units do not include the opponent units in the target set; The partial order relationship between each opponent unit and other opponent units, and the partial order relationship between the at least two opponent units in the target set are combined to form a target extended set.

[0012] In a possible implementation, based on the target extended set, searching a historical solution library for a historical solution that is consistent with the order of opponent units in the target extended set specifically includes: Based on the target extended set, searching a historical solution library for a historical solution that contains the ordering of opponent units in the target extended set; or, Based on the target extended set, a historical solution library is searched for a historical solution with the same order as the opponent unit in the target extended set.

[0013] In a possible implementation, the action plan is an attack plan implemented by the two opposing parties against each other's units in a game mission.

[0014] In a second aspect, a computing device is provided, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements any of the above methods when executing the computer program.

[0015] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any of the above methods is implemented.

[0016] In a fourth aspect, an action plan recommendation system based on action dependency partial order is provided, comprising: A construction module, configured to analyze, based on a set of opponent units determined by a user, a sequential relationship of actions to be taken against each opponent unit in the set of opponent units, so as to construct a set of partial order relations; An expansion module is used to input a target set, analyze and determine, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sort the other opponent units with the opponent units in the target set according to the partial order relationship to update and obtain a target expansion set; A search module, configured to search, based on the target extension set, a historical solution library for a historical solution that is consistent with the order of the opponent unit in the target extension set; the historical solution library contains a plurality of action plans for the opponent unit; The recommendation module is used to recommend the found historical plan as the current action plan.

[0017] The beneficial effects of the technical solution provided by this application include at least: It can be seen from the above technical solution that, based on the opponent unit set determined by the user, the order relationship of the actions to be taken against each opponent unit in the opponent unit set is analyzed to construct a partial order relationship set; the target set is input, and based on the partial order relationship set, other opponent units that have partial order relationships with the opponent units in the target set are analyzed and determined, in addition to the partial order relationship between the opponent units in the target set, and the other opponent units are sorted with the opponent units in the target set according to the partial order relationship, and the target extension set is updated; based on the target extension set, the historical solution library is searched for the historical solution library that is consistent with the opponent unit in the target extension set; the historical solution library contains multiple action plans for the opponent units; the historical solution found is recommended as the current action plan. Therefore, it is possible to deeply analyze the characteristics of the opponent system based on user input, finely mine the supporting relationship between the opponent units, expand the opponent unit set, more fully and effectively match the historical solutions, and improve the accuracy of solution recommendation.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a schematic diagram of the steps of a method for recommending an action plan based on a partial order of action dependencies provided in an embodiment of the present application.

[0021] Figure 2 It is a schematic diagram of the algorithm code for expanding and sorting the opponent unit set provided in an embodiment of the present application.

[0022] Figure 3 It is a schematic diagram of the opponent units in the game task scenario provided by the embodiment of the present application being various virtual devices.

[0023] Figure 4 It is the action plans contained in the historical plan library provided in the embodiment of the present application and the action sequence in each action plan.

[0024] Figure 5 It is the guarantee relationship between the virtual devices corresponding to each opponent unit in the opponent provided in the embodiment of the present application.

[0025] Figure 6 It is a structural block diagram of an action plan recommendation system based on action dependency partial order provided in an embodiment of the present application.

[0026] Figure 7 It is a block diagram of a computing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0028] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0029] Taking into account the need to recommend action plans based on a plan library, this application proposes an action plan recommendation technology based on action dependency partial order. Compared with previous methods, it can mine user input in a fine-grained manner and deeply analyze the characteristics of the opponent's system, thereby making more effective use of user input information for historical plan matching and improving the plan recommendation effect.

[0030] The action plan recommendation method based on action dependency partial order has the following core ideas: based on the opponent unit set determined by the user, the order relationship of the actions to be taken against each opponent unit in the opponent unit set is analyzed to construct a partial order relationship set; the target set is input, and based on the partial order relationship set, other opponent units with partial order relationships with the opponent units in the target set are analyzed and determined, in addition to the partial order relationship between the opponent units in the target set, and the other opponent units are sorted with the opponent units in the target set according to the partial order relationship, and the target extension set is updated; based on the target extension set, the historical plan library is searched for the historical plan library with the same order as the opponent units in the target extension set; the historical plan library contains multiple action plans for the opponent units; the historical plan found is recommended as the current action plan. In this way, based on the opponent unit set input by the user, the order relationship of the actions to be taken against these opponent units is analyzed. The order relationship of these actions corresponds to the dependency or support relationship between the opponent units, that is, in addition to the units that the user cares about, which opponent units have a supporting role for them, and they should be considered in sequence according to the support relationship. This relationship refers to different things in different fields. In strategy games, it can refer to the support and coordination relationship between different opposing units. For example, in the game scene, the virtual oil depot forms a fuel support relationship with the virtual equipment (such as virtual tanks, virtual airplanes, virtual airports, etc.) cluster. When the user is concerned about how to destroy the virtual tank of the opponent unit, the virtual oil depot should be taken into consideration and attacked first. In sports competitions, it can refer to the cover relationship between different players in the opponent unit. For example, in basketball, the center forms a pick-and-roll relationship with the guard. When the user is concerned about how to destroy the opponent's guard's shot, the opponent's center should be taken into consideration and the pick-and-roll relationship should be blocked first, thereby affecting the opponent's guard's shot.

[0031] Reference Figure 1 FIG. 1 is a schematic diagram of a method for recommending an action plan based on a partial order of action dependency provided by an embodiment of the present application. The execution subject of the recommendation method may be a software module with computing and data processing capabilities, or a computing device integrated with a similar software module.

[0032] The action plan recommendation method based on action dependency partial order may include the following steps: Step 102: Based on the opponent unit set determined by the user, the order relationship of actions to be taken against each opponent unit in the opponent unit set is analyzed to construct a partial order relationship set.

[0033] In the present application, the action plan recommendation method can be applied to various confrontation scenarios, such as strategy games, sports competitions, etc. Depending on the confrontation scenario, there are slight differences in the set of opponent units. It should be understood that there are two opposing parties in the confrontation scenario, and the roles can be exchanged to use the action plan recommendation method to strike or attack the opponent unit. In other words, the action plan recommendation method is applicable to both opposing parties. For example, role A can use the action plan recommendation method to attack role B, and role B can also use the action plan recommendation method to strike role A.

[0034] The opponent unit set may include multiple opponent units of the opponent. When analyzing the sequential relationship of actions to be taken for each opponent unit in the opponent unit set to construct a partial order relationship set, the sequential relationship of actions to be taken between each opponent unit in the opponent unit set can be specifically analyzed, and the sequential relationship can be determined to be a unidirectional support relationship between different opponent units; based on the sequential relationship of actions taken by each opponent unit, each unidirectional support relationship is counted to construct a partial order relationship set.

[0035] Optionally, the one-way support relationship is defined as , represents the opponent unit Opponent Unit There is a sequential relationship in which actions are taken; or, in a directed graph, there is a path from the opponent unit to the Point to opponent unit The directed edge of .

[0036] Optionally, the unidirectional support relationship satisfies the following properties: reflexivity, antisymmetry and transitivity; wherein reflexivity means that each opponent unit in the partial order relationship set forms a support relationship with itself; antisymmetry means that since the unidirectional support relationship is unidirectional, if the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is no sequential relationship for taking actions; Transitivity means that the support relationship between the opponent units can be transferred. Opponent Unit There is a sequential relationship in which actions are taken, and at the same time, the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is a sequential relationship in which actions are taken.

[0037] In the specific implementation, the support relationship is first formally modeled. Assume that the one-way support relationship between different units of the opponent is , the set of opponent units is , if the opponent unit For Unit There is a supporting relationship, which can be formally expressed as , which is represented on a directed graph as a directed edge from point to ; A unidirectional support relationship refers to a relationship that only has point to , but not from point to The directed edge of . is a partial order relation that defines a set ,Right now Satisfies the properties of reflexivity, antisymmetry, and transitivity: ① Since any opponent unit They all form a supporting relationship with themselves, so , that is, it satisfies reflexivity; ② Due to is one-way, so when hour, , that is, it satisfies antisymmetry; ③ The support relationship is transitive, support , support ,but support , so when and Sometimes, there are , which satisfies transitivity. Then the partial order relation is constructed, that is, for the opponent unit set ,for Any two units and ,if right There is a supporting relationship, that is If true, the tuple Add to collection , thus constructing a partially ordered relation set .

[0038] Step 104: Input a target set, analyze and determine based on the partial order relationship set, in addition to the partial order relationship between the opponent units in the target set, other opponent units that have a partial order relationship with the opponent units in the target set, and sort the other opponent units and the opponent units in the target set according to the partial order relationship to update the target extended set.

[0039] Optionally, when updating the target extended set, step 104 can specifically determine at least two opponent units included in the target set; for each opponent unit, search other opponent units that have a partial order relationship with the opponent unit from the partial order relationship set, and the other opponent units do not include the opponent units in the target set; the partial order relationship between each opponent unit and other opponent units, and the partial order relationship between the at least two opponent units in the target set, are combined to form a target extended set.

[0040] Based on the partial order relation set obtained by analysis and the input target set , analyze and determine the The opponent units in the support relationship exist in the opponent units, and are sorted according to the support relationship. When implementing it specifically, you can refer to Figure 2 The algorithm shown is implemented in code.

[0041] Step 106: Based on the target extension set, search for historical solutions in a historical solution library that are consistent with the order of the opponent units in the target extension set; the historical solution library contains multiple action plans for the opponent units.

[0042] Optionally, when searching for historical solutions in the historical solution library that are consistent with the order of opponent units in the target extended set based on the target extended set, it is specifically possible to search for historical solutions in the historical solution library that contain the order of opponent units in the target extended set based on the target extended set; or, based on the target extended set, search for historical solutions in the historical solution library that are the same as the order of opponent units in the target extended set.

[0043] Step 108: recommending the historical plan found as the current action plan.

[0044] In specific implementation, you can use a collection As input, traverse the historical solution library, and for each solution, check whether the order of actions it contains is consistent with The order of the goals in the solution is consistent. If they are consistent, this solution will be recommended.

[0045] Optionally, the action plan is an attack plan implemented by the two opposing parties against each other's units in the game mission.

[0046] The following is a detailed description of the above action plan recommendation method using the attack plan implemented by the two opposing parties in the game mission as an example. Among them, the firepower agent involved is mainly explained by taking the drone as an example.

[0047] Assume that the game task is to attack certain targets of the opponent. The opponent units include detection agents, firepower agents (such as flying equipment such as drones), defense agents, airports and oil depots in the virtual scene, such as Figure 3 It should be understood that these opponent units are all virtual devices in the game scene, and the virtual devices can be configured with corresponding functional modules to perform specific confrontation functions.

[0048] Among them, the main targets of this mission are the defense agents and airports. Assume that there are already 5 plans in the history plan library, and their action sequence is as follows: Figure 4 shown.

[0049] In order to match an accurate action plan, a partial order relationship can be first constructed according to the action plan recommendation method of this application. It should be understood that the opponent units are not isolated, but support each other. Here we consider the guarantee relationship between the opponent units. Figure 5 As shown in the figure, for example, the main capability of the detection agent is reconnaissance and detection, which can provide information guarantee for the defense agent and the fire agent (such as drones); the main capability of the defense agent is air interception, which can provide defense protection for the airport; the airport is mainly used for aircraft takeoff and landing and replenishment, providing logistical support for the fire agent (such as drones). Therefore, according to the support between each opponent unit, a partial order relationship set is constructed. .

[0050] Then, the target opponents are expanded and sorted. The target opponent set entered by the user , if we follow the traditional set comparison method, based on and Figure 4 Comparing all the schemes in the above one by one, it is found that the attack targets of schemes 3, 4, and 5 all contain , but Scheme 4 is more accurate, so Scheme 4 is recommended, that is, the airport will be attacked first and then the defense agent. However, since the defense agent has a protective relationship with the airport, the attack launched by us has a great risk of being intercepted, so the attack effect is not ideal. According to the method proposed in this application, it is necessary to and ,according to Figure 2 The algorithm shown analyzes the expansion and sorting of targets. After analysis, it can be seen that the defense agent has a protection relationship with the airport, and the detection agent has an information guarantee relationship with the defense agent. Therefore, the detection agent is expanded as a necessary attack target, and according to the guarantee order, the target opponent set is finally obtained. .

[0051] Finally, the scheme matching is performed based on the expanded target opponent set. As input, Figure 4After comparing the above schemes one by one, we found that the actions in Scheme 3 conform to this order, so Scheme 3 is recommended. The reason why this scheme is more reasonable than Scheme 4 is that if the detection agent is destroyed first, the enemy's defense agent will lose information guarantee and cannot lock on the attack launched by us, and its interception capability will be greatly reduced. After destroying its defense agent, the airport will lose protection, and the success rate of its attack will naturally increase significantly.

[0052] Through the above technical solution, it is possible to mine user input in a fine-grained manner and deeply analyze the characteristics of the opponent's system, so as to make more effective use of user input information for historical solution matching and improve the solution recommendation effect.

[0053] It should be noted that the specific implementation process provided in this implementation can be combined with the various specific implementation processes provided in the aforementioned implementation to implement the method for recommending action plans based on the partial order of action dependency in this embodiment. For a detailed description, please refer to the relevant content in the aforementioned implementation, which will not be repeated here.

[0054] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0055] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0056] Figure 6 FIG. 1 shows a structural block diagram of an action plan recommendation system based on action dependency partial order provided by an embodiment of the present application. Figure 6As shown. The action plan recommendation system 600 based on action dependency partial order of this embodiment may include a construction module 601, an expansion module 602, a search module 603 and a recommendation module 604. Among them, the construction module 601 is used to analyze the order relationship of actions to be taken for each opponent unit in the opponent unit set based on the opponent unit set determined by the user, so as to construct a partial order relationship set; the expansion module 602 is used to input a target set, analyze and determine, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sort the other opponent units with the opponent units in the target set according to the partial order relationship, and update the target extension set; the search module 603 is used to search for historical plans in the historical plan library that are consistent with the order of the opponent units in the target extension set based on the target extension set; the historical plan library contains multiple action plans for the opponent units; the recommendation module 604 is used to recommend the found historical plan as the current action plan.

[0057] It should be noted that part or all of the action plan recommendation system based on action dependency partial order of this embodiment may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in a control platform on the network side, etc. This embodiment does not specifically limit this.

[0058] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may also be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0059] Optionally, in a possible implementation of the present embodiment, when the construction module 601 analyzes the sequential relationship of actions to be taken for each opponent unit in the opponent unit set based on the opponent unit set determined by the user to construct a partial order relationship set, it can be specifically used to analyze the sequential relationship of actions to be taken between each opponent unit in the opponent unit set, and determine that the sequential relationship is a unidirectional support relationship between different opponent units; based on the sequential relationship of the actions taken by each opponent unit, each unidirectional support relationship is counted to construct a partial order relationship set.

[0060] Optionally, in a possible implementation of this embodiment, the unidirectional support relationship is defined as: , represents the opponent unit Opponent Unit There is a sequential relationship in which actions are taken; or, in a directed graph, there is a path from the opponent unit to the Point to opponent unit The directed edge of .

[0061] Optionally, in a possible implementation of this embodiment, the unidirectional support relationship satisfies the following properties: reflexivity, antisymmetry and transitivity; wherein reflexivity means that each opponent unit in the partial order relationship set forms a support relationship with itself; antisymmetry means that since the unidirectional support relationship is unidirectional, if the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is no order relationship for taking actions; transitivity means that the support relationship between the opponent units can be transferred. Opponent Unit There is a sequential relationship in which actions are taken, and at the same time, the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is a sequential relationship in which actions are taken.

[0062] Optionally, in a possible implementation of the present embodiment, the expansion module 602 analyzes and determines, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sorts the other opponent units and the opponent units in the target set according to the partial order relationship, and when updating the target extended set, it can be specifically used to determine at least two opponent units included in the target set; for each opponent unit, search the partial order relationship set for other opponent units that have a partial order relationship with the opponent unit, and the other opponent units do not include the opponent units in the target set; the partial order relationship between each opponent unit and other opponent units, as well as the partial order relationship between the at least two opponent units in the target set, are combined to form a target extended set.

[0063] Optionally, in a possible implementation of the present embodiment, when the search module 603 searches for a historical solution in the historical solution library that is consistent with the order of opponent units in the target extended set based on the target extended set, it is specifically used to search for a historical solution in the historical solution library that contains the order of opponent units in the target extended set based on the target extended set; or, based on the target extended set, search for a historical solution in the historical solution library that is the same as the order of opponent units in the target extended set.

[0064] Optionally, in a possible implementation of this embodiment, the action plan is an attack plan implemented by the two opposing parties in the game task against each other's unit.

[0065] An embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the action plan recommendation method based on the action dependency partial order as described above.

[0066] An embodiment of the present application provides a computing device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the action plan recommendation method based on action dependency partial order as described above.

[0067] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0068] Figure 7 A schematic block diagram of an example computing device 700 that can be used to implement an embodiment of the present application is shown. The computing device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The computing device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0069] like Figure 7 As shown, the computing device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 to a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the computing device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0070] A number of components in the computing device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the computing device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0071] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the action plan recommendation method based on the action dependency partial order. For example, in some embodiments, the action plan recommendation method based on the action dependency partial order may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the computing device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the action plan recommendation method based on the action dependency partial order described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured in any other appropriate manner (eg, by means of firmware) to execute the action plan recommendation method based on the action dependency partial order.

[0072] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0073] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0074] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 of the foregoing.

[0075] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0076] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0077] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0078] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0079] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for recommending action plans based on partial order of action dependency, characterized in that: include: Based on the opponent unit set determined by the user, analyzing the order relationship of actions that should be taken against each opponent unit in the opponent unit set to construct a partial order relationship set; Input a target set, analyze and determine, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sort the other opponent units and the opponent units in the target set according to the partial order relationship to update and obtain a target extension set; Based on the target extended set, searching a historical solution library for a historical solution that is consistent with the order of the opponent unit in the target extended set; the historical solution library contains multiple action plans for the opponent unit; The historical plan found is recommended as the current action plan.

2. The method according to claim 1, characterized in that Based on the set of opponent units determined by the user, the order relationship of actions to be taken against each opponent unit in the set of opponent units is analyzed to construct a partial order relationship set, specifically including: Analyzing the sequential relationship of actions that should be taken between the opponent units in the opponent unit set, and determining that the sequential relationship is a unidirectional support relationship between different opponent units; Based on the sequential relationship of the actions taken by each opponent unit, each unidirectional support relationship is counted to construct a partial order relationship set.

3. The method according to claim 2, characterized in that The one-way support relationship is defined as , represents the opponent unit Opponent Unit There is a sequential relationship for taking actions; Or, on a directed graph, there is a path from the opponent unit Point to opponent unit The directed edge of .

4. The method according to claim 3, characterized in that The one-way support relationship satisfies the following properties: reflexivity, antisymmetry and transitivity; Among them, reflexivity means that each opponent unit in the partial order relation set forms a support relationship with itself; Antisymmetry means that since the one-way support relationship is one-way, if the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is no sequential relationship for taking actions; Transitivity means that the support relationship between the opponent units can be transferred. Opponent Unit There is a sequential relationship in which actions are taken, and at the same time, the opponent unit Opponent Unit There is a sequential relationship in which the opponent unit takes action. Opponent Unit There is a sequential relationship in which actions are taken.

5. The method according to claim 1, characterized in that Based on the partial order relationship set, analyzing and determining, in addition to the partial order relationship between the opponent units in the target set, other opponent units that have a partial order relationship with the opponent units in the target set, and sorting the other opponent units and the opponent units in the target set according to the partial order relationship, and updating to obtain a target extended set, specifically includes: Determining at least two opponent units included in the target set; For each opponent unit, searching from the partial order relationship set for other opponent units that have a partial order relationship with the opponent unit, wherein the other opponent units do not include the opponent units in the target set; The partial order relationship between each opponent unit and other opponent units, and the partial order relationship between the at least two opponent units in the target set are combined to form a target extended set.

6. The method according to claim 1, characterized in that Based on the target extended set, searching the historical solution library for a historical solution that is consistent with the order of the opponent units in the target extended set specifically includes: Based on the target extended set, searching a historical solution library for a historical solution that contains the ordering of opponent units in the target extended set; or, Based on the target extended set, a historical solution library is searched for a historical solution with the same order as the opponent unit in the target extended set.

7. The method according to any one of claims 1 to 6, characterized in that: The action plan is an attack plan implemented by the two opposing parties against each other's units in the game mission.

8. A computing device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the method described in any one of claims 1 to 7 when executing the computer program.

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 method described in any one of claims 1 to 7 is implemented.

10. An action plan recommendation system based on partial order of action dependency, characterized in that: include: A construction module, configured to analyze, based on a set of opponent units determined by a user, a sequential relationship of actions to be taken against each opponent unit in the set of opponent units, so as to construct a set of partial order relations; An expansion module is used to input a target set, analyze and determine, based on the partial order relationship set, other opponent units that have a partial order relationship with the opponent units in the target set in addition to the partial order relationship between the opponent units in the target set, and sort the other opponent units with the opponent units in the target set according to the partial order relationship to update and obtain a target expansion set; A search module, configured to search, based on the target extension set, a historical solution library for a historical solution that is consistent with the order of the opponent unit in the target extension set; the historical solution library contains a plurality of action plans for the opponent unit; The recommendation module is used to recommend the found historical plan as the current action plan.

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