A data processing method and device for behavior modeling

Abstract

The present invention discloses a data processing method and device for behavior modeling. The method includes: obtaining behavior modeling task information; the behavior modeling task information includes a plurality of behavior information to be modeled; processing the behavior modeling task information and 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; performing model construction processing on the structured atomic model information and 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 particularly to a data processing method and device for behavior modeling. Background Art

[0002] Behavior modeling is the modeling of action rules. Action rules are the action means and methods adopted by action entities during the action process. The classification method of behavior modeling is consistent with that classified by the entity model system. The system mainly creates behavior models for equipment and personnel. Therefore, the action rule model includes an equipment behavior model and a personnel command behavior model. Due to the complexity and diversity of operation behaviors and command behaviors in entity actions, and the requirements of simulation tasks are usually personalized, it is usually difficult to achieve efficient and rapid behavior modeling. Therefore, a data processing method and device for behavior modeling are provided to structure the process of constructing a behavior model, improve the efficiency and accuracy of behavior modeling, and further meet 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, which are beneficial to structuring the process of constructing a behavior model, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models.

[0004] To solve the above technical problem, in the first aspect, an embodiment of the present invention discloses a data processing method for behavior modeling, and the method includes:

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

[0006] Process the behavior modeling task information and 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] Perform model construction processing on the structured atomic model information and behavior node task information to obtain target behavior model information.

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

[0009] An obtaining module, configured to obtain behavior modeling task information; the behavior modeling task information includes a plurality of behaviors to be modeled;

[0010] A first processing module, 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] A second processing module, configured to perform model construction 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 includes:

[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 and executes some or all of the steps in the data processing method for behavior modeling disclosed in the first aspect of the embodiments of the present invention.

[0016] A fourth aspect of the present invention discloses a computer-readable storage medium, the computer-readable storage medium stores computer instructions, and 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 the embodiments 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

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

[0019] Figure 2 It is a schematic flowchart of a data processing method for behavior modeling disclosed by the embodiments of the present invention;

[0020] Figure 3 It is a schematic structural diagram of a data processing device for behavior modeling disclosed by the embodiments of the present invention;

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

[0022] In order to enable those skilled in the art to better understand the solution 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 in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0023] The terms "first", "second", etc. in the description and claims of the present invention and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or equipment.

[0024] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment can 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 is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0025] In this application, the term "exemplary" is used to mean "serving as an example, illustration, or description". Any embodiment described as "exemplary" in this application is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use this application, the following description is given. In the following description, details are set forth for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that this application can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to 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, which is actually time information. It can be understood that if dimensions, quantities, positions, etc. are mentioned in the subsequent embodiments, they are all corresponding data existences for the computer device to process, and specific details are not elaborated here.

[0027] The embodiment of the present application provides a data processing method, device, computer device, and computer-readable storage medium for behavior modeling, which will be described in detail below.

[0028] Please refer to Figure 1 , Figure 1 , which is a schematic diagram of the scenario of the data processing system for behavior modeling provided by the embodiment of the present application. The data processing system for behavior modeling may include a computer device 100, and a data processing device for behavior modeling is integrated in the computer device 100, such as Figure 1 the computer device 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 several pieces of behavior information to be modeled;

[0030] Process 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 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;

[0031] Perform model construction processing on the structured atomic model information and the behavior node task information to obtain target behavior model information.

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

[0033] In the embodiment 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 embodiment 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. Among them, the cloud server is composed of a large number of computers or network servers based on cloud computing (Cloud Computing).

[0034] It can be 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 with receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such devices can include: cellular or other communication devices, which have a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 can be a desktop terminal or a mobile terminal, and the computer device 100 can also be a mobile phone, a tablet computer, a laptop computer, etc.

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

[0036] In addition, as Figure 1 shown, the data processing system for behavior modeling can also include a memory 200 for storing data, such as image data, location information, etc.

[0037] It should be noted that Figure 1 the scenario schematic diagram of the data processing system for behavior modeling shown is only an example. The data processing system and scenario described in the embodiments of the present application are for more clearly explaining the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art know that with the evolution of the data processing system for behavior modeling and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0038] The present invention discloses a data processing method and device for behavior modeling, which is beneficial to structuring the process of building a behavior model, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models. The following will be described in detail respectively.

[0039] Embodiment 1

[0040] Please refer to Figure 2 , Figure 2 which is a flowchart of a data processing method for behavior modeling disclosed in an embodiment of the present invention. Among them, Figure 2 the described data processing method for behavior modeling is applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. AsFigure 2 As shown, the data processing method for behavior modeling may include the following operations:

[0041] 101. Obtain behavior modeling task information.

[0042] In an embodiment of the present invention, the behavior modeling task information includes several pieces of behavior information 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 pieces of basic atomic condition model information; the atomic state model information includes several pieces of basic atomic state model information; the basic atomic model information includes several pieces of entity action behavior information.

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

[0046] It should be noted that by using the data processing method for behavior modeling of the present application, a combat method can be formed into an "atomic combat method" form through an aggregation technique. The aggregated "atomic combat method" and other "atomic combat methods" or atomic states and atomic conditions are used together to construct more complex combat methods, which are 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 embodiments of the present invention is beneficial to the structured behavior model construction process, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

[0048] In an optional embodiment, the above-mentioned performing model construction processing on the structured atomic model information and the behavior node task information to obtain target behavior model information includes:

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

[0050] Based on the target behavior node task information, determine the target behavior model information; the target behavior model information includes several sub-target behavior model information.

[0051] It should be noted that the above-mentioned determining the target behavior node task information based on the structured atomic model information and the behavior node task information is based on matching and screening their name information and / or number information to determine the corresponding

[0052] It should be noted that the above-mentioned behavioral node task information can be the behavioral task information input by the user or automatically generated by the system according to historical task information, which is not limited in the embodiments of the present invention. Further, the above-mentioned sub-goal behavioral node task information represents the task model situation corresponding to each task node, and can be the conditions corresponding to the execution of the task node and related model requirements, which are not limited in the embodiments of the present invention.

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

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

[0055] Obtain a behavior sequence diagram;

[0056] Set 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;

[0057] Fill the sub-goal behavioral node task information in the target behavioral node task information into the behavior sequence diagram to obtain the first behavior model information; the first behavior model information includes several first sub-behavior model information;

[0058] Perform model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain the second behavior model information; the second behavior model information includes several second sub-behavior model information; the second sub-behavior model information includes model parameter information and / or associated behavior models;

[0059] Based on the logical order information, perform sequential position adjustment processing on the second sub-behavior model information in the second behavior model information to obtain the 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 a matrix module that constructs the execution order and execution content based on a matrix, which is not limited in the embodiments of the present invention.

[0061] It should be noted that the above-mentioned logical order axis can be a time-based order axis or an order axis composed of numbers, which is not limited in the embodiments of the present invention. Further, the above-mentioned logical order can be an absolute order relationship or a relative order 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 operating parameter information, which is not limited in the present invention.

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

[0064] It should be noted that the above associated behavior model can be a model constructed based on the atomic state model and / or the atomic condition model, which is not limited in the embodiments of the present invention.

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

[0066] It should be noted that the above-mentioned sequential position adjustment process 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 sequence of the second sub-behavior model information according to the corresponding logical sequence 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 start 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 embodiments of the present invention is beneficial to the construction process of the structured behavior model, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

[0068] In another optional embodiment, performing a model information adjustment process 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, performing a model parameter adjustment process on the first sub-behavior model information to obtain the model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0070] Judging 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 to obtain a first matching judgment result;

[0071] When the first matching judgment result is yes, associating the target behavior management rule model with the first sub-behavior model information to obtain the 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 negative, it is detected 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 positive, it is triggered to execute a judgment on 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 a first matching judgment result is obtained;

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

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

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

[0077]

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

[0079] Trigger the execution of a judgment on 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 at the target interval time, and a first matching judgment result is obtained.

[0080] It should be noted that the above time coefficient can be set by the user or automatically given by the system based on historical time coefficients, and the embodiments of the present invention do not make limitations. Further, the above time coefficient is a positive number greater than 0, and the embodiments of the present invention do not make limitations.

[0081] It should be noted that the above basic interval time can be set by the user or automatically given by the system based on historical time coefficients, and the embodiments of the present invention do not make limitations.

[0082] It should be noted that the above management rule model information represents a model that describes the behavior model and commands the behavior in the form of a finite state machine, and the embodiments of the present invention do not make limitations. Further, the equipment platform and personnel take a series of actions to complete certain tasks (sampling, disinfection, etc.), and these series of actions are represented by individual states, and the embodiments of the present invention do not make limitations.

[0083] It should be noted that the judgment on 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 can be realized based on the consistency of name information or the consistency of number information, and the embodiments of the present invention do not make limitations.

[0084] It should be noted that, the above-mentioned association of the target behavior management rule model with the first sub-behavior model information forms a data entity from the two, so as to achieve the direct association between the two. The embodiments of the present invention do not make any limitations in this regard.

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

[0086] In another optional embodiment, after performing sequential 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 an interval of a basic interval time, detect whether a modeling task change signal is received to obtain a second signal detection result;

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

[0089] When the second signal detection result is no, end the detection process corresponding to the second signal detection result.

[0090] It should be noted that the above-mentioned modeling task change signal is generated when the user needs to make appropriate modifications to the target behavior model information after determining it. Therefore, when the user inputs modification information, the corresponding target behavior model information is generated, and the system automatically modifies and integrates the information according to the modification information input by the user, so as to update the target behavior model information by using the modeling task change signal to further improve the accuracy of behavior modeling. The embodiments of the present invention do not make any limitations in this regard.

[0091] It can be seen that implementing the data processing method for behavior modeling described in the embodiments of the present invention is beneficial to the structured behavior model construction process, improves the efficiency and accuracy of behavior modeling, and further meets 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 the structured atomic model information includes:

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

[0094] Judge 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 determination result is yes, based on the first-line attribute information and the target entity action behavior information, determine the basic atomic condition model information corresponding to the behavior information to be modeled;

[0096] When the second matching determination result is no, based on the second-line attribute information and the target entity action behavior information, determine the basic atomic state model information corresponding to the behavior information to be modeled; The attribute numbers of the first-line attribute information and the second-line attribute information are inconsistent.

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

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

[0099] It should be noted that the above-mentioned second task attribute direction information includes behavior name information, action behavior duration, attribute number information (including an attribute number), execution sequence position information, which are not limited in the embodiments of the present invention.

[0100] It should be noted that the above-mentioned attribute number can be constructed based on numbers or based on English letters, which are not limited in the embodiments of the present invention.

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

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

[0103] It should be noted that the above-mentioned basic atomic state model information representation combines a series of different atomic actions and can be assembled into different behavioral states. Different behavioral states combined with atomic conditions can form behavioral rules for different equipment platform models. At the same time, the system adopts dynamic nesting technology, which can combine and nest behavioral rules with behavioral states and atomic conditions to form new complex behavioral rules. Among them, the atomic state is the basic element of the basic atomic state model information, that is, the atomic state is the most basic element for describing how the CGF behaves. The atomic state is divided into different types. Taking the action type as an example, it mainly includes maneuver decision-making, target decision-making, sensor decision-making, communication scheme, etc.: 1) Maneuver decision-making. The maneuver decision-making action defines the maneuver mode of the CGF in the current state. For example: "Maneuver at a speed of 5Kts", that is, the CGF will move at a speed of 5Kts. 2) Target selection. The target selection defines the way to select the target. For example: "The nearest target", that is, the CGF will select the target with the shortest distance from it. 3) Sensor scheme. The sensor scheme determines which sensors the CGF will activate and the working mode of the sensors. For example: "Activate all active sensors", that is, the CGF will activate all its active sensor systems. 4) Communication scheme. 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 execute tasks. It is a series of basic "if" conditions, which form a predicate for changing the current action state of the CGF. These conditions can be combined using "and" and "or" logical operations. When the judgment conditions must hold simultaneously, that is, the "and" logic; while as long as a set of conditions is satisfied for the judgment conditions, that is, the "or" logic. When the conditions defined by the transfer rules are met, the CGF state is converted to a new state (the state pointed to by the rule line arrow) according to the logic defined in the state machine. The basic element of the rule is the "condition". Its representation form 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 embodiments of the present invention is beneficial to the construction process of the structured behavior model, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

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

[0109] Construct and process the mapping relationship between the attribute information of the first line and the action behavior information of the target entity to obtain the first alternative 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, perform execution sequence adjustment processing on the first alternative behavior relationship information to obtain the basic atomic condition model information corresponding to the behavior information to be modeled.

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

[0113] It should be noted that the above 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, so as to improve the sequence relationship of the subsequent model and improve the construction efficiency and accuracy of the model, which is not limited in the embodiments of the present invention.

[0114] It should be noted that the above construction and processing of the mapping relationship between the first behavior attribute information and the action behavior information of the target entity may be to form the first target execution sequence information through the execution sequence position information corresponding to the first behavior attribute information, and form the second target execution sequence information. At the same time, associate the first behavior attribute information with the action behavior object information to obtain the corresponding first alternative behavior relationship information, which is not limited in the embodiments of the present invention.

[0115] It should be noted that the above second target execution sequence information represents the sequence position situation of the action behavior object in the action behavior execution sequence logical relationship corresponding to the above first behavior attribute information, which is not limited in the embodiments of the present invention.

[0116] It should be noted that the above 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 of the action behavior sequence, which is not limited in the embodiments of the present invention.

[0117] It can be seen that implementing the data processing method for behavior modeling described in the embodiments of the present invention is conducive to the construction process of a structured behavior model, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

[0118] Embodiment 2

[0119] Please refer to Figure 3 , Figure 3 which is a schematic structural diagram of a data processing device for behavior modeling disclosed in the embodiments of the present invention. Among them, Figure 3 the described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. As Figure 3 shown, the device may include:

[0120] An acquisition module 201, configured to acquire behavior modeling task information; the behavior modeling task information includes a plurality of behavior information to be modeled;

[0121] A first processing module 202, configured to process the behavior modeling task information and a 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;

[0122] A second processing module 203, configured to perform model construction processing on the structured atomic model information and behavior node task information to obtain target behavior model information.

[0123] It can be seen that implementing Figure 3 the data processing device for behavior modeling described is conducive to the construction process of a structured behavior model, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

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

[0125] Based on the structured atomic model information and behavior node task information, determining target behavior node task information; the target behavior node task information includes a plurality of sub-target behavior node task information;

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

[0127] It can be seen that implementing Figure 3 the data processing device for behavior modeling described is conducive to structuring the behavior model construction process, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models.

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

[0129] Obtain the behavior sequence diagram;

[0130] Set 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;

[0131] Fill the sub-target behavior node task information in the target behavior node task information into the behavior sequence diagram to obtain the first behavior model information; the first behavior model information includes several first sub-behavior model information;

[0132] Perform model information adjustment processing on the first sub-behavior model information in the first behavior model information to obtain the second behavior model information; the second behavior model information includes several second sub-behavior model information; the second sub-behavior model information includes model parameter information, and / or, associated behavior models;

[0133] Based on the logical order information, perform sequential position adjustment processing on the second sub-behavior model information in the second behavior model information to obtain the target behavior model information.

[0134] It can be seen that implementing Figure 3 the data processing device for behavior modeling described is conducive to structuring the behavior model construction process, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models.

[0135] In yet another alternative embodiment, as Figure 3 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 the 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 the model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information;

[0137] Judge whether there is a target behavior management rule model in all the behavior management rule models of the behavior management rule model information that matches the first sub-behavior model information to obtain the first matching judgment result;

[0138] When the first matching judgment result is positive, associate the target behavior management rule model 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 negative, detect whether a model change signal is received to obtain a first signal detection result; the model change signal is triggered and generated when the behavior management rule model in the behavior management rule model information changes;

[0140] When the first signal detection result is positive, trigger and execute a judgment on 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 to obtain a first matching judgment result;

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

[0142] Use the time interval model to calculate and process the time coefficient and the base interval time to obtain a target interval time;

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

[0144]

[0145] In the formula, X is the target interval time; Y is the base interval time; Z is the time coefficient;

[0146] Trigger and execute a judgment on 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 at an interval of the target interval time to obtain a first matching judgment result.

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

[0148] In another alternative embodiment, as Figure 3 shown, after the second processing module 203 adjusts the sequential positions of 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] At an interval of a base interval time, detect whether a modeling task change signal is received to obtain a second signal detection result;

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

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

[0152] It can be seen that implementing Figure 3 the described data processing device for behavior modeling is conducive to structuring the behavior model construction process, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models.

[0153] In yet another alternative embodiment, as Figure 3 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, screen out the entity action behavior information corresponding to the behavior information to be modeled from the basic atomic model as the target entity action behavior information;

[0155] Judge 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 positive, based on the first behavior attribute information and the target entity action behavior information, determine the basic atomic condition model information corresponding to the behavior information to be modeled;

[0157] When the second matching judgment result is negative, based on the second behavior attribute information and the target entity action behavior information, determine the basic atomic state model information corresponding to the behavior information to be modeled; the attribute numbers of the first behavior attribute information and the second behavior attribute information are inconsistent.

[0158] It can be seen that implementing Figure 3 the described data processing device for behavior modeling is conducive to structuring the behavior model construction process, improving the efficiency and accuracy of behavior modeling, and further meeting the modeling requirements of complex behavior rule models.

[0159] In yet another alternative embodiment, as Figure 3 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] Perform a construction process on the mapping relationship between the first behavior attribute information and the target entity action behavior information to obtain the first alternative 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, perform execution sequence adjustment processing on the first alternative behavior relationship information to obtain the basic atomic condition model information corresponding to the behavior information to be modeled.

[0162] It can be seen that implementing Figure 3 the data processing device for behavior modeling described above is beneficial to the process of constructing a structured behavior model, improves the efficiency and accuracy of behavior modeling, and further meets the modeling requirements of complex behavior rule models.

[0163] Embodiment III

[0164] Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of another data processing device for behavior modeling disclosed in the embodiments of the present invention. Among them, Figure 4 the described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. As Figure 4 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 in the data processing method for behavior modeling described in Embodiment I.

[0168] Embodiment IV

[0169] The embodiments of the present invention disclose a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps in the data processing method for behavior modeling described in Embodiment I.

[0170] Embodiment V

[0171] The embodiments of the present invention disclose 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 in the data processing method for behavior modeling described in Embodiment I.

[0172] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0173] Through the above specific descriptions of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium that can be used to carry or store 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 only disclose the 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 foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method for behavior modeling, characterized in that The method includes: Obtaining behavior modeling task information; the behavior modeling task information includes a number of behaviors to be modeled; Processing the behavior modeling task information and 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; Performing model construction processing on the structured atomic model information and behavior node task information to obtain target behavior model information; Among them, the performing model construction processing on the structured atomic model information and behavior node task information to obtain target behavior model information includes: Based on the structured atomic model information and behavior node task information, determining target behavior node task information; the target behavior node task information includes a number of sub-target behavior node task information; Based on the target behavior node task information, determining target behavior model information; the target behavior model information includes a number of sub-target behavior model information; Among them, the determining target behavior model information based on the target behavior node task information includes: Obtaining a 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 number 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 number 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 order information, performing sequential position adjustment processing on the second sub-behavior model information in the second behavior model information to obtain target behavior model information.

2. The data processing method for behavior modeling according to claim 1, wherein 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 one of the first sub-behavior model information in the first behavior model information, performing model parameter adjustment processing on the first sub-behavior model information to obtain the model parameter information of the second sub-behavior model information corresponding to the first sub-behavior model information; Judging 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 to obtain a first matching judgment result; When the first matching judgment result is yes, associating the target behavior management rule model with the first sub-behavior model information to obtain the associated behavior model of the second sub-behavior model information corresponding to the first sub-behavior model information; When the first matching judgment result is negative, it is detected whether a model change signal is received to obtain a first signal detection result; the model change signal is triggered and generated when the behavior management rule model in the behavior management rule model information changes; When the first signal detection result is positive, it is triggered to execute a judgment on 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, to obtain a first matching judgment result; When the first signal detection result is negative, a time coefficient is obtained; The time coefficient and a base interval time are calculated and processed by using a time interval model to obtain a target interval time; Wherein, the time interval model is: In the formula, X is the target interval time; Y is the base interval time; Z is the time coefficient; After an interval of the target interval time, it is triggered to execute a judgment on 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, to obtain a first matching judgment result.

3. The data processing method for behavior modeling according to claim 1, wherein After the sequential position adjustment process is performed 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: After an interval of a base interval time, it is detected whether a modeling task change signal is received to obtain a second signal detection result; When the second signal detection result is positive, the target behavior model information is updated by using the modeling task change signal; When the second signal detection result is negative, the detection process corresponding to the second signal detection result is ended.

4. The data processing method for behavior modeling according to claim 1, wherein The processing of the behavior modeling task information and the base atomic model to obtain structured atomic model information includes: For any of the to-be-modeled behavior information in the behavior modeling task information, the entity action behavior information corresponding to the to-be-modeled behavior information is screened out from the base atomic model as the target entity action behavior information; It is judged 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 positive, based on the first behavior attribute information and the target entity action behavior information, the base atomic condition model information corresponding to the to-be-modeled behavior information is determined; When the second matching judgment result is negative, based on the second behavior attribute information and the target entity action behavior information, the base atomic state model information corresponding to the to-be-modeled behavior information is determined; the attribute number of the first behavior attribute information is inconsistent with the attribute number of the second behavior attribute information.

5. The data processing method for behavior modeling according to claim 4, wherein The determining the base atomic condition model information corresponding to the to-be-modeled behavior information based on the first behavior attribute information and the target entity action behavior information includes: Performing a construction process on the mapping relationship between the first behavior attribute information and the target entity action behavior information to obtain first alternative behavior relationship information; Based on the original execution sequence point information corresponding to the to-be-modeled behavior information and the execution sequence position information corresponding to the first behavior attribute information, perform execution sequence adjustment processing on the first alternative behavior relationship information to obtain the basic atomic condition model information corresponding to the to-be-modeled behavior information.

6. A data processing device for behavior modeling, characterized in that, The device includes: An acquisition module, configured to acquire behavior modeling task information; the behavior modeling task information includes a plurality of to-be-modeled behavior information; A first processing module, 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; A second processing module, configured to perform model construction processing on the structured atomic model information and the behavior node task information to obtain target behavior model information; Among them, the performing model construction 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, determine target behavior node task information; the target behavior node task information includes a plurality of sub-target behavior node task information; Based on the target behavior node task information, determine target behavior model information; the target behavior model information includes a plurality of sub-target behavior model information; Among them, the determining target behavior model information based on the target behavior node task information includes: Obtain a behavior sequence diagram; Set the logical sequence of the logical sequence axis of the behavior sequence diagram to obtain the logical sequence information corresponding to the behavior sequence diagram; Fill 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; Perform 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, perform sequence position adjustment processing on the second sub-behavior model information in the second behavior model information to obtain target behavior model information.

7. A data processing device for behavior modeling, characterized in that, The device includes: A memory storing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory and executes the data processing method for behavior modeling according to any one of claims 1-5.

8. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which are used to execute the data processing method for behavior modeling according to any one of claims 1-5 when called.

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