Data processing method and device for behavior modeling

Abstract

The invention discloses a data processing method and device for behavior modeling. The method comprises the following steps: acquiring behavior modeling task information; the behavior modeling task information comprises a plurality of pieces of to-be-modeled behavior information; processing the behavior modeling task information and the basic atomic model information to obtain structured atomic model information; the structured atomic model information comprises atomic condition model information and / or atomic state model information; the atomic condition model information comprises a plurality of pieces of basic atomic condition model information; the atomic state model information comprises a plurality of pieces of basic atomic state model information; the basic atomic model information comprises a plurality of pieces of entity action behavior information; and performing model construction processing on the structured atomic model information and the behavior node task information to obtain target behavior model information.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a data processing method and device for behavior modeling. Background Art

[0002] Behavior modeling is action rule modeling, and action rules are the means and methods of action taken by an action entity during the action process. The classification method of behavior modeling is consistent with the classification of the entity model system. The system mainly creates behavior models for equipment and personnel, so the action rule model includes equipment behavior model and personnel command behavior model. Due to the complexity and diversity of operational behaviors and command behaviors in entity actions, and the requirements of simulation tasks are usually personalized, it is usually difficult to achieve efficient and fast behavior modeling. Therefore, a data processing method and device for behavior modeling are provided to structure the behavior model construction process, improve the efficiency and accuracy of behavior modeling, and thereby improve the modeling requirements of complex behavior rule models. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a data processing method and device for behavior modeling that is conducive to the process of constructing a structured behavior model, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses a data processing method for behavior modeling, the method comprising:

[0005] Obtaining behavior modeling task information; the behavior modeling task information includes information on a plurality of behaviors to be modeled;

[0006] Processing the behavior modeling task information and the basic atomic model information to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a plurality of basic atomic condition model information; the atomic state model information includes a plurality of basic atomic state model information; the basic atomic model information includes a plurality of entity action behavior information;

[0007] Model building processing is performed on the structured atomic model information and the behavior node task information to obtain target behavior model information.

[0008] A second aspect of an embodiment of the present invention discloses a data processing device for behavior modeling, the device comprising:

[0009] An acquisition module is used to acquire behavior modeling task information; the behavior modeling task information includes information on a plurality of behaviors to be modeled;

[0010] A first processing module is configured to process the behavior modeling task information and the basic atomic model to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a plurality of basic atomic condition model information; the atomic state model information includes a plurality of basic atomic state model information; the basic atomic model includes a plurality of entity action behavior information;

[0011] The second processing module is used to perform model building processing on the structured atomic model information and the behavior node task information to obtain target behavior model information.

[0012] A third aspect of the present invention discloses another data processing device for behavior modeling, the device comprising:

[0013] a memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the data processing method for behavior modeling disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the data processing method for behavior modeling disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a scenario of a data processing system for behavior modeling provided by an embodiment of the present invention;

[0019] Figure 2 This is a flow chart of a data processing method for behavior modeling disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural diagram of a data processing device for behavior modeling disclosed in an embodiment of the present invention;

[0021] Figure 4It is a structural diagram of another data processing device for behavior modeling disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0024] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is given to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that one of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0026] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, the corresponding data exist for the computer device to process. The details will not be repeated here.

[0027] The embodiments of the present application provide a data processing method, apparatus, computer device, and computer-readable storage medium for behavior modeling, which are described in detail below.

[0028] See also Figure 1 , Figure 1 Schematic diagram of a data processing system for behavior modeling provided in an embodiment of the present application. The data processing system for behavior modeling may include a computer device 100, in which a data processing device for behavior modeling is integrated, such as Figure 1 Computer equipment in.

[0029] In the embodiment of the present application, the computer device 100 is mainly used to obtain behavior modeling task information; the behavior modeling task information includes a plurality of behavior information to be modeled;

[0030] The behavior modeling task information and the basic atomic model information are processed to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a plurality of basic atomic condition model information; the atomic state model information includes a plurality of basic atomic state model information; the basic atomic model information includes a plurality of entity action behavior information;

[0031] The structured atomic model information and behavior node task information are model-built to obtain the target behavior model information.

[0032] It can structure the behavior model building process, improve the efficiency and accuracy of behavior modeling, and thus improve the modeling requirements of complex behavior rule models.

[0033] In the embodiments of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiments of the present application includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. A cloud server is composed of a large number of computers or network servers based on cloud computing.

[0034] It is understood that the computer device 100 used in the embodiments of the present application can be a device that includes both receiving and transmitting hardware, that is, a device that has receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a device may include: a cellular or other communication device that has a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. The specific computer device 100 can be a desktop terminal or a mobile terminal. The computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

[0035] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or fewer computer devices as shown in Figure 1 Only one computer device is shown in the figure. It can be understood that the data processing system for behavior modeling can also include one or more other services, which are not limited here.

[0036] In addition, if Figure 1 As shown, the data processing system for behavior modeling may further include a memory 200 for storing data, such as image data, position information, and the like.

[0037] It should be noted that Figure 1 The scenario diagram of the data processing system for behavior modeling shown is merely an example. The data processing system and scenario for behavior modeling described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person skilled in the art will appreciate that, with the evolution of the data processing system for behavior modeling and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0038] The present invention discloses a data processing method and device for behavior modeling that facilitates the process of constructing structured behavior models, improves the efficiency and accuracy of behavior modeling, and thereby addresses the modeling requirements of complex behavior rule models. These are described in detail below.

[0039] Example 1

[0040] See also Figure 2 , Figure 2 This is a flow chart of a data processing method for behavior modeling disclosed in an embodiment of the present invention. Figure 2 The data processing method for behavior modeling described is applied to a management system, such as a local server or a cloud server for management, and is not limited in the embodiments of the present invention. Figure 2 As shown, the data processing method for behavior modeling may include the following operations:

[0041] 101. Obtain behavior modeling task information.

[0042] In the embodiment of the present invention, the behavior modeling task information includes information on a plurality of behaviors to be modeled.

[0043] 102. Process the behavior modeling task information and the basic atomic model information to obtain structured atomic model information.

[0044] In an embodiment of the present invention, the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes several basic atomic condition model information; the atomic state model information includes several basic atomic state model information; the basic atomic model information includes several entity action behavior information.

[0045] 103. Perform model construction processing on the structured atomic model information and the behavior node task information to obtain the target behavior model information.

[0046] It should be noted that the data processing method for behavioral modeling of the present application can form a tactic into an "atomic tactic" through aggregation technology. The aggregated "atomic tactic" can be combined with other "atomic tactic" or atomic states and atomic conditions to construct a more complex tactic, which is not limited in the embodiments of the present invention.

[0047] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0048] In an optional embodiment, the above-mentioned model building process is performed on the structured atomic model information and the behavior node task information to obtain the target behavior model information, including:

[0049] Based on the structured atomic model information and the behavior node task information, the target behavior node task information is determined; the target behavior node task information includes several sub-target behavior node task information;

[0050] Based on the target behavior node task information, target behavior model information is determined; the target behavior model information includes a plurality of sub-target behavior model information.

[0051] It should be noted that the above-mentioned determination of the target behavior node task information based on the structured atomic model information and the behavior node task information is based on the name information and / or number information of the two to match and filter, thereby determining the corresponding

[0052] It should be noted that the aforementioned behavior node task information can be user-entered or automatically generated by the system based on historical task information, and this is not limited in the present embodiment. Furthermore, the aforementioned sub-goal behavior node task information represents the task model corresponding to each task node, and can represent the conditions for executing the task node and the related model requirements, and this is not limited in the present embodiment.

[0053] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0054] In another optional embodiment, the target behavior model information is determined based on the target behavior node task information, including:

[0055] Get the behavior sequence diagram;

[0056] Setting the logical order of the logical sequence axis of the behavior sequence diagram to obtain the logical sequence information corresponding to the behavior sequence diagram;

[0057] Filling the sub-target behavior node task information in the target behavior node task information into the behavior sequence diagram to obtain first behavior model information; the first behavior model information includes a plurality of first sub-behavior model information;

[0058] Performing model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain second behavior model information; the second behavior model information includes a plurality of second sub-behavior model information; the second sub-behavior model information includes model parameter information and / or an associated behavior model;

[0059] Based on the logical sequence information, the second sub-behavior model information in the second behavior model information is subjected to sequence position adjustment processing to obtain target behavior model information.

[0060] It should be noted that the above-mentioned behavior sequence diagram can be constructed based on a Gantt chart, or can be a matrix module for constructing execution order and execution content based on a matrix, which is not limited in the embodiment of the present invention.

[0061] It should be noted that the above-mentioned logical sequence axis can be a sequence axis representing time or a sequence axis based on numbers, which is not limited in the embodiments of the present invention. Furthermore, the above-mentioned logical sequence can be an absolute sequence relationship or a relative sequence relationship, which is not limited in the embodiments of the present invention.

[0062] It should be noted that the above-mentioned model parameter information includes basic parameter information and / or operation parameter information, which is not limited in the implementation of the present invention.

[0063] It should be noted that the above basic parameter information includes the model name, model operating conditions, etc., which are not limited in this embodiment of the present invention. Furthermore, the above operating parameter information includes the execution order (such as the start time) and the model running time, which are not limited in this embodiment of the present invention. Furthermore, the accuracy of the above model running time is in seconds, which is not limited in this embodiment of the present invention.

[0064] It should be noted that the above-mentioned association behavior model may be a model constructed based on an atomic state model and / or an atomic condition model, which is not limited in the embodiment of the present invention.

[0065] It should be noted that the above-mentioned adjustment of the order position of the second sub-behavior model information in the second behavior model information is performed based on the logical order information.

[0066] It should be noted that the above-mentioned sequential position adjustment processing of the second sub-behavior model information in the second behavior model information based on the logical sequence information is to adjust the actual logical order of the second sub-behavior model information according to the corresponding logical order in the behavior sequence diagram, such as adjusting the execution start time, or adjusting the end time according to the duration, and adaptively adjusting the starting position of the next second sub-behavior model information, which is not limited in the embodiments of the present invention.

[0067] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0068] In yet another optional embodiment, performing model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain the second behavior model information includes:

[0069] For any first sub-behavior model information in the first behavior model information, perform model parameter adjustment processing on the first sub-behavior model information to obtain model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0070] Determine whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models in the behavior management rule model information, and obtain a first matching determination result;

[0071] When the first matching judgment result is yes, the target behavior management rule model is associated with the first sub-behavior model information to obtain an associated behavior model of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0072] When the first matching judgment result is no, detecting whether a model change signal is received to obtain a first signal detection result; the model change signal is triggered when the behavior management rule model in the behavior management rule model information changes;

[0073] When the first signal detection result is yes, triggering execution to determine whether there is a target behavior management rule model that matches the first sub-behavior model information in all the behavior management rule models of the behavior management rule model information, and obtaining a first matching determination result;

[0074] When the first signal detection result is negative, obtaining a time coefficient;

[0075] The time interval model is used to calculate the time coefficient and the basic interval time to obtain the target interval time;

[0076] Among them, the time interval model is:

[0077]

[0078] Where, X is the target interval time; Y is the basic interval time; Z is the time coefficient;

[0079] The target interval time triggers the execution to determine whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information, and obtains a first matching judgment result.

[0080] It should be noted that the time coefficient can be set by the user or automatically given by the system based on historical time coefficients, which is not limited in the embodiment of the present invention. Furthermore, the time coefficient is a positive number greater than 0, which is not limited in the embodiment of the present invention.

[0081] It should be noted that the above basic interval time may be set by the user, or may be automatically given by the system based on a historical time coefficient, and the embodiment of the present invention does not limit this.

[0082] It should be noted that the aforementioned management rule model information representation uses a finite state machine to describe the behavior model and command behavior model, which is not limited in this embodiment of the present invention. Furthermore, the equipment platform and personnel take a series of actions to complete a specific task (sampling, disinfection, etc.), and this series of actions is represented by a series of states, which is not limited in this embodiment of the present invention.

[0083] It should be noted that the above-mentioned judgment of whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information can be implemented based on the consistency of the name information or based on the consistency of the numbering information, and the embodiment of the present invention does not limit this.

[0084] It should be noted that the above-mentioned associating the target behavior management rule model with the first sub-behavior model information is to form the two into one data body, thereby achieving a direct association between the two, which is not limited in the embodiment of the present invention.

[0085] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0086] In yet another optional embodiment, after performing sequence position adjustment processing on the second sub-behavior model information in the second behavior model information based on the logical sequence information to obtain the target behavior model information, the method further includes:

[0087] After a basic interval, detecting whether a modeling task change signal is received, and obtaining a second signal detection result;

[0088] When the second signal detection result is yes, the target behavior model information is updated using the modeling task change signal;

[0089] When the second signal detection result is negative, the detection process corresponding to the second signal detection result is terminated.

[0090] It should be noted that the above-mentioned modeling task change signal is when the user determines the target behavior model information and makes appropriate modifications to it. Therefore, when the user inputs the modification information, the corresponding target behavior model information is generated, and the system automatically performs information modification and fusion according to the modification information input by the user, thereby realizing the use of the modeling task change signal to update the target behavior model information to further improve the accuracy of behavior modeling. The embodiments of the present invention do not limit this.

[0091] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0092] In an optional embodiment, the above-mentioned processing of the behavior modeling task information and the basic atomic model to obtain structured atomic model information includes:

[0093] For any behavior information to be modeled in the behavior modeling task information, entity action behavior information corresponding to the behavior information to be modeled is screened out from the basic atomic model as target entity action behavior information;

[0094] Determine whether the target entity action behavior information matches the first behavior attribute information to obtain a second matching judgment result;

[0095] When the second matching judgment result is yes, determining the basic atomic condition model information corresponding to the behavior information to be modeled based on the first behavior attribute information and the target entity action behavior information;

[0096] When the second matching judgment result is no, the basic atomic state model information corresponding to the behavior information to be modeled is determined based on the second behavior attribute information and the target entity action behavior information; the attribute number of the first behavior attribute information is inconsistent with the attribute number of the second behavior attribute information.

[0097] It should be noted that the above entity action behavior information includes action behavior name information, action behavior object, action behavior number information (including an attribute number), action behavior duration, and action behavior sequence relationship, which is not limited in the embodiment of the present invention.

[0098] It should be noted that the above-mentioned first behavior attribute information includes behavior name information, attribute number information (including one attribute number), action behavior duration, and execution sequence position information, which is not limited in the embodiment of the present invention.

[0099] It should be noted that the second task attribute direction information includes behavior name information, action behavior duration, attribute number information (including one attribute number), and execution sequence position information, which is not limited in the embodiment of the present invention.

[0100] It should be noted that the above attribute numbers can be constructed based on numbers or English letters, and this is not limited in the embodiment of the present invention.

[0101] It should be noted that the above-mentioned screening of entity action behavior information corresponding to the behavior information to be modeled from the basic atomic model is performed based on name information or number information to improve the efficiency and accuracy of screening and matching, which is not limited in the embodiment of the present invention.

[0102] It should be noted that each of the above target entity action behavior information has a corresponding unique behavior name information corresponding thereto, which is not limited in the embodiment of the present invention.

[0103] It should be noted that the basic atomic state model information representation combines a series of atomic actions to form different behavioral states. Different behavioral states combined with atomic conditions can form behavioral rules specific to different equipment platforms and models. Furthermore, the system utilizes dynamic nesting technology to combine and nest behavioral rules with behavioral states and atomic conditions to form new, complex behavioral rules. Atomic states are the fundamental elements of the basic atomic state model information; they are the most basic elements describing the CGF's behavior. Atomic states are categorized into different types. For example, actions primarily include maneuver decisions, target decisions, sensor decisions, and communication schemes: 1) Maneuver decisions. Maneuver decision actions define the CGF's maneuvering method in the current state. For example, "maneuver at 5 kts" means the CGF will move at 5 kts. 2) Target selection. Target selection defines how to select a target. For example, "nearest target" means the CGF will select the target closest to it. 3) Sensor schemes. Sensor schemes determine which sensors the CGF will activate and how they operate. For example, "activate all active sensors" means the CGF will activate all its active sensor systems. 4) Communication schemes. The communication scheme determines the reports that the CGF should send and the activation scheme of the communication equipment, for example: "turn off all communication systems".

[0104] It should be noted that the above-mentioned basic atomic condition model information represents the conversion rules for the target object to perform tasks, which is a series of basic "if" conditions, which form a predicate to realize the change of the current action state of CGF. These conditions can be combined using "and" and "or" logical operations. When the judgment conditions must be established at the same time, it is "and" logic; and when the judgment conditions only need to meet a group of conditions, it is "or" logic. When the conditions defined by the transfer rule are met, the CGF state is converted to a new state (the state indicated by the arrow of the rule line) according to the logic defined in the state machine. The basic element of the rule is "condition". Its representation can be shown in the following condition type table:

[0105] Condition Type Table

[0106]

[0107] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0108] In another optional embodiment, determining basic atomic condition model information corresponding to the behavior information to be modeled based on the first behavior attribute information and the target entity action behavior information includes:

[0109] Constructing and processing a mapping relationship between the first behavior attribute information and the target entity action behavior information to obtain first candidate behavior relationship information;

[0110] Based on the original execution sequence point information corresponding to the behavior information to be modeled and the execution sequence position information corresponding to the first behavior attribute information, the first candidate behavior relationship information is subjected to execution sequence adjustment processing to obtain the basic atomic condition model information corresponding to the behavior information to be modeled.

[0111] It should be noted that the above-mentioned first candidate behavior relationship information includes first target execution sequence information, second target execution sequence information, behavior name information, action behavior duration, and action behavior object, which is not limited in the embodiment of the present invention.

[0112] It should be noted that the above original execution sequence point information represents the execution sequence generated by the system when generating the behavior information to be modeled, and the embodiment of the present invention does not limit this.

[0113] It should be noted that the above-mentioned execution sequence adjustment processing of the first alternative behavior relationship information based on the original execution sequence point information corresponding to the behavior information to be modeled and the execution sequence position information corresponding to the first behavior attribute information is to adjust the execution sequence corresponding to the first alternative behavior relationship information to the corresponding sequence position when it is inconsistent with the execution sequence generated by the system when generating the behavior information to be modeled, thereby improving the sequence relationship of subsequent models and improving the model construction efficiency and accuracy. The embodiments of the present invention do not limit this.

[0114] It should be noted that the above-mentioned construction and processing of the mapping relationship between the first behavior attribute information and the target entity action behavior information can be achieved by forming the first target execution sequence information and the second target execution sequence information corresponding to the execution sequence position information of the first behavior attribute information. At the same time, the first behavior attribute information is associated with the action behavior object information to obtain the corresponding first alternative behavior relationship information, which is not limited in the embodiment of the present invention.

[0115] It should be noted that the second target execution sequence information represents the sequential position of the action behavior object in the logical relationship of the action behavior execution sequence corresponding to the first behavior attribute information, and the embodiment of the present invention does not limit this.

[0116] It should be noted that the above-mentioned first target execution sequence information includes the execution sequence position information corresponding to the first behavior attribute information and the previous target entity action behavior information or the next target entity action behavior information in the action behavior sequence, which is not limited in the embodiment of the present invention.

[0117] It can be seen that implementing the data processing method for behavior modeling described in the embodiment of the present invention is beneficial to the structured behavior model construction process, improving the efficiency and accuracy of behavior modeling, and thus improving the modeling requirements of complex behavior rule models.

[0118] Example 2

[0119] See also Figure 3 , Figure 3 Schematic diagram of a data processing device for behavior modeling disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 3 As shown, the device may include:

[0120] The acquisition module 201 is used to acquire behavior modeling task information; the behavior modeling task information includes information on a number of behaviors to be modeled;

[0121] The first processing module 202 is configured to process the behavior modeling task information and the basic atomic model to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a plurality of basic atomic condition model information; the atomic state model information includes a plurality of basic atomic state model information; and the basic atomic model includes a plurality of entity action behavior information;

[0122] The second processing module 203 is used to perform model building processing on the structured atomic model information and the behavior node task information to obtain target behavior model information.

[0123] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0124] In another optional embodiment, Figure 3 As shown, the second processing module 203 performs model building processing on the structured atomic model information and the behavior node task information to obtain target behavior model information, including:

[0125] Based on the structured atomic model information and the behavior node task information, the target behavior node task information is determined; the target behavior node task information includes several sub-target behavior node task information;

[0126] Based on the target behavior node task information, target behavior model information is determined; the target behavior model information includes a plurality of sub-target behavior model information.

[0127] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0128] In another optional embodiment, Figure 3 As shown, the second processing module 203 determines target behavior model information based on the target behavior node task information, including:

[0129] Get the behavior sequence diagram;

[0130] Setting the logical order of the logical sequence axis of the behavior sequence diagram to obtain the logical sequence information corresponding to the behavior sequence diagram;

[0131] Filling the sub-target behavior node task information in the target behavior node task information into the behavior sequence diagram to obtain first behavior model information; the first behavior model information includes a plurality of first sub-behavior model information;

[0132] Performing model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain second behavior model information; the second behavior model information includes a plurality of second sub-behavior model information; the second sub-behavior model information includes model parameter information and / or an associated behavior model;

[0133] Based on the logical sequence information, the second sub-behavior model information in the second behavior model information is subjected to sequence position adjustment processing to obtain target behavior model information.

[0134] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0135] In another optional embodiment, Figure 3 As shown, the second processing module 203 performs model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain second behavior model information, including:

[0136] For any first sub-behavior model information in the first behavior model information, perform model parameter adjustment processing on the first sub-behavior model information to obtain model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0137] Determine whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models in the behavior management rule model information, and obtain a first matching determination result;

[0138] When the first matching judgment result is yes, the target behavior management rule model is associated with the first sub-behavior model information to obtain an associated behavior model of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0139] When the first matching judgment result is no, detecting whether a model change signal is received to obtain a first signal detection result; the model change signal is triggered when the behavior management rule model in the behavior management rule model information changes;

[0140] When the first signal detection result is yes, triggering execution to determine whether there is a target behavior management rule model that matches the first sub-behavior model information in all the behavior management rule models of the behavior management rule model information, and obtaining a first matching determination result;

[0141] When the first signal detection result is negative, obtaining a time coefficient;

[0142] The time interval model is used to calculate the time coefficient and the basic interval time to obtain the target interval time;

[0143] Among them, the time interval model is:

[0144]

[0145] Where, X is the target interval time; Y is the basic interval time; Z is the time coefficient;

[0146] The target interval time triggers the execution to determine whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information, and obtains a first matching judgment result.

[0147] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0148] In another optional embodiment, Figure 3 As shown, after the second processing module 203 performs sequence position adjustment processing on the second sub-behavior model information in the second behavior model information based on the logical sequence information to obtain the target behavior model information, the method further includes:

[0149] After a basic interval, detecting whether a modeling task change signal is received, and obtaining a second signal detection result;

[0150] When the second signal detection result is yes, the target behavior model information is updated using the modeling task change signal;

[0151] When the second signal detection result is negative, the detection process corresponding to the second signal detection result is terminated.

[0152] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0153] In another optional embodiment, Figure 3 As shown, the first processing module 202 processes the behavior modeling task information and the basic atomic model to obtain structured atomic model information, including:

[0154] For any behavior information to be modeled in the behavior modeling task information, entity action behavior information corresponding to the behavior information to be modeled is screened out from the basic atomic model as target entity action behavior information;

[0155] Determine whether the target entity action behavior information matches the first behavior attribute information to obtain a second matching judgment result;

[0156] When the second matching judgment result is yes, determining the basic atomic condition model information corresponding to the behavior information to be modeled based on the first behavior attribute information and the target entity action behavior information;

[0157] When the second matching judgment result is no, the basic atomic state model information corresponding to the behavior information to be modeled is determined based on the second behavior attribute information and the target entity action behavior information; the attribute number of the first behavior attribute information is inconsistent with the attribute number of the second behavior attribute information.

[0158] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0159] In another optional embodiment, Figure 3 As shown, the first processing module 202 determines the basic atomic condition model information corresponding to the behavior information to be modeled based on the first behavior attribute information and the target entity action behavior information, including:

[0160] Constructing and processing a mapping relationship between the first behavior attribute information and the target entity action behavior information to obtain first candidate behavior relationship information;

[0161] Based on the original execution sequence point information corresponding to the behavior information to be modeled and the execution sequence position information corresponding to the first behavior attribute information, the first candidate behavior relationship information is subjected to execution sequence adjustment processing to obtain the basic atomic condition model information corresponding to the behavior information to be modeled.

[0162] It can be seen that implementation Figure 3 The described data processing device for behavior modeling is beneficial to the structured behavior model building process, improves the efficiency and accuracy of behavior modeling, and thus improves the modeling requirements of complex behavior rule models.

[0163] Example 3

[0164] See also Figure 4 , Figure 4 This is a structural diagram of another data processing device for behavior modeling disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., and the embodiment of the present invention does not limit this. Figure 4 As shown, the device may include:

[0165] A memory 301 storing executable program code;

[0166] a processor 302 coupled to the memory 301;

[0167] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the data processing method for behavior modeling described in the first embodiment.

[0168] Example 4

[0169] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the data processing method for behavior modeling described in the first embodiment.

[0170] Example 5

[0171] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the data processing method for behavior modeling described in the first embodiment.

[0172] The device embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0173] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus the necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the above technical solution, in essence, or the portion that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0174] Finally, it should be noted that the data processing method and device for behavior modeling disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A data processing method for behavior modeling, characterized in that: The method comprises: Acquire behavior modeling task information; the behavior modeling task information includes information of a plurality of behaviors to be modeled; Processing the behavior modeling task information and the basic atomic model information to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a number of basic atomic condition model information; the atomic state model information includes a number of basic atomic state model information; the basic atomic model information includes a number of entity action behavior information; Model building processing is performed on the structured atomic model information and the behavior node task information to obtain target behavior model information.

2. The data processing method for behavior modeling according to claim 1, characterized in that: The step of performing model building processing on the structured atomic model information and the behavior node task information to obtain target behavior model information includes: Based on the structured atomic model information and the behavior node task information, target behavior node task information is determined; the target behavior node task information includes a plurality of sub-target behavior node task information; Based on the target behavior node task information, target behavior model information is determined; the target behavior model information includes a plurality of sub-target behavior model information.

3. The data processing method for behavior modeling according to claim 2, characterized in that: The step of determining target behavior model information based on the target behavior node task information includes: Get the behavior sequence diagram; Setting the logical order of the logical order axis of the behavior sequence diagram to obtain the logical order information corresponding to the behavior sequence diagram; Filling the sub-target behavior node task information in the target behavior node task information into the behavior sequence diagram to obtain first behavior model information; the first behavior model information includes a plurality of first sub-behavior model information; Performing model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain second behavior model information; the second behavior model information includes a plurality of second sub-behavior model information; the second sub-behavior model information includes model parameter information and / or associated behavior models; Based on the logical sequence information, the second sub-behavior model information in the second behavior model information is subjected to sequence position adjustment processing to obtain target behavior model information.

4. The data processing method for behavior modeling according to claim 3, characterized in that: The performing model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain second behavior model information includes: For any of the first sub-behavior model information in the first behavior model information, perform model parameter adjustment processing on the first sub-behavior model information to obtain model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information; Determine whether there is a target behavior management rule model that matches the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information, and obtain a first matching determination result; When the first matching judgment result is yes, the target behavior management rule model is associated with the first sub-behavior model information to obtain an associated behavior model of the second sub-behavior model information corresponding to the first sub-behavior model information; When the first match judgment result is no, detecting whether a model change signal is received to obtain a first signal detection result; the model change signal is triggered when the behavior management rule model in the behavior management rule model information changes; When the first signal detection result is yes, trigger the execution of the determination of whether there is a target behavior management rule model matching the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information, and obtain a first matching judgment result; When the first signal detection result is no, obtaining a time coefficient; The time coefficient and the basic interval time are calculated and processed by using the time interval model to obtain the target interval time; Wherein, the time interval model is: Wherein, X is the target interval time; Y is the basic interval time; Z is the time coefficient; The target interval time is used to trigger the execution of the judgment of whether there is a target behavior management rule model matching the first sub-behavior model information among all the behavior management rule models of the behavior management rule model information, and obtain a first matching judgment result.

5. The data processing method for behavior modeling according to claim 3, characterized in that: After performing sequence position adjustment processing on the second sub-behavior model information in the second behavior model information based on the logical sequence information to obtain target behavior model information, the method further includes: At a basic interval, detecting whether a modeling task change signal is received, and obtaining a second signal detection result; When the second signal detection result is yes, using the modeling task change signal to update the target behavior model information; When the second signal detection result is no, the detection process corresponding to the second signal detection result is terminated.

6. The data processing method for behavior modeling according to claim 1, characterized in that: The processing of the behavior modeling task information and the basic atomic model to obtain structured atomic model information includes: For any of the to-be-modeled behavior information in the behavior modeling task information, selecting entity action behavior information corresponding to the to-be-modeled behavior information from the basic atomic model as target entity action behavior information; Determine whether the target entity action behavior information matches the first behavior attribute information to obtain a second matching judgment result; When the second matching judgment result is yes, based on the first behavior attribute information and the target entity action behavior information, determining the basic atomic condition model information corresponding to the behavior information to be modeled; When the second matching judgment result is no, the basic atomic state model information corresponding to the behavior information to be modeled is determined based on the second behavior attribute information and the target entity action behavior information; the attribute number of the first behavior attribute information is inconsistent with the attribute number of the second behavior attribute information.

7. The data processing method for behavior modeling according to claim 6, characterized in that: The determining, based on the first behavior attribute information and the target entity action behavior information, basic atomic condition model information corresponding to the behavior information to be modeled includes: Constructing and processing a mapping relationship between the first behavior attribute information and the target entity action behavior information to obtain first candidate behavior relationship information; Based on the original execution sequence point information corresponding to the behavior information to be modeled and the execution sequence position information corresponding to the first behavior attribute information, the execution sequence of the first candidate behavior relationship information is adjusted to obtain the basic atomic condition model information corresponding to the behavior information to be modeled.

8. A data processing device for behavior modeling, characterized in that: The device comprises: An acquisition module is used to acquire behavior modeling task information; the behavior modeling task information includes a plurality of behaviors to be modeled; A first processing module is used to process the behavior modeling task information and the basic atomic model to obtain structured atomic model information; the structured atomic model information includes atomic condition model information and / or atomic state model information; the atomic condition model information includes a number of basic atomic condition model information; the atomic state model information includes a number of basic atomic state model information; the basic atomic model includes a number of entity action behavior information; The second processing module is used to perform model building processing on the structured atomic model information and the behavior node task information to obtain target behavior model information.

9. A data processing device for behavior modeling, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the data processing method for behavior modeling as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the data processing method for behavior modeling as described in any one of claims 1-7.

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