CN115471554A - Method, device and storage medium for acquiring user behavior data of metauniverse space - Google Patents
Method, device and storage medium for acquiring user behavior data of metauniverse space Download PDFInfo
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Abstract
The invention discloses a method, a device and a storage medium for acquiring user behavior data of a meta-space. The method comprises the following steps: acquiring behavioral expression data of a target user in a preset time period from a meta universe real-time database according to identity information of the target user; determining a plurality of analysis moments with time sequence in the preset time period; extracting a plurality of types of body part static expression data corresponding to the analysis time from the behavior expression data; combining the static expression data of various body parts at each analysis moment to obtain the static action characteristic data at each analysis moment; analyzing the change of each body part static performance data of a plurality of continuous analysis moments in the preset time period to obtain the dynamic performance data of the body part; and combining the plurality of static motion characteristic data and the corresponding body part dynamic expression data to obtain the behavior data of the user. The embodiment disclosed by the invention can be used for acquiring the user behavior data from the meta-space.
Description
Technical Field
The invention relates to the technical field of virtual worlds, in particular to a method and a device for acquiring user behavior data of a meta-space, electronic equipment and a computer-readable storage medium.
Background
The meta universe, or called virtual world, is an open and shared online platform that integrates information technology, communication technology, AR, VR and other virtual technologies, and is a huge and developing virtual universe. The user's behavior in the meta universe is not limited by the action prescribed by the system, and has high degree of freedom, and the meta universe, as a multi-user shared platform and activity space, must have certain behavior rules to standardize the user's behavior in the meta universe space to maintain the order of the meta universe space, so in the aspect of social governance, the user's behavior needs to be analyzed, and further, some group characteristics need to be obtained to better manage the social order. For some commercial applications of users in the meta-space, for example, it is necessary to analyze user behavior to obtain their habit preferences and further obtain user images. The behavior analysis applied in various aspects is based on obtaining the user behavior data of the metastic space, however, no solution for obtaining the user behavior data from the metastic space exists at present.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, an electronic device, and a computer-readable storage medium for acquiring user behavior data of a meta-space, which are used to acquire user behavior data from the meta-space and provide basic data for subsequent user behavior analysis.
In a first aspect, the present invention provides a method for acquiring meta-space user behavior data, comprising the steps of:
acquiring behavioral expression data of a target user in a preset time period from a metastic real-time database according to identity information of the target user;
determining a plurality of analysis moments with time sequences in the preset time period;
extracting a plurality of types of body part static expression data corresponding to the analysis time from the behavior expression data;
combining the static expression data of various body parts at each analysis moment to obtain the static action characteristic data at each analysis moment;
analyzing the change of each body part static performance data of a plurality of continuous analysis moments in the preset time period to obtain the dynamic performance data of the body part; and
and combining the plurality of static motion characteristic data and the corresponding body part dynamic expression data to obtain the behavior data of the user.
In a second aspect, an embodiment of the present invention provides an apparatus for acquiring meta-space user behavior data, where the apparatus includes a data acquisition module, a time slicing module, a static data extraction module, a static action feature module, a dynamic performance data module, and a behavior data determination module, where the data acquisition module is configured to acquire, from a meta-space real-time database, behavior performance data of a target user in a preset time period according to identity information of the target user; the time division module is connected with the data acquisition module and is used for determining a plurality of analysis times with time sequence in the preset time period; the static data extraction module is connected with the time segmentation module and used for extracting multiple body part static expression data corresponding to the analysis time from the behavior expression data; the static action characteristic module is connected with the static data extraction module and used for combining the static expression data of various body parts at each analysis moment to obtain the static action characteristic data at each analysis moment; the dynamic expression data module is connected with the static data extraction module and is used for analyzing the change of the static expression data of each body part at a plurality of continuous analysis moments in the preset time period to obtain the dynamic expression data of the body part; the behavior data determining module is connected with the static action characteristic module and the dynamic expression data module and is used for combining a plurality of static action characteristic data and corresponding body part dynamic expression data to obtain behavior data of the user.
In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, performs the steps of the method of acquiring meta-cosmic space user behavior data as described above.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the steps of the method for acquiring meta-cosmic space user behavior data as described above.
In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes computer program instructions, and when the computer program instructions are executed by a processor, the computer program instructions implement the steps of the method for acquiring meta-space user behavior data as described above.
The invention provides a solution for acquiring user behavior data from a metacosmic space, which extracts and integrates various characteristics based on personal behavior characteristics in the metacosmic digital space, thereby encoding the user behavior data, describing the user behavior in the metacosmic space by using an encoded mode, further forming a characteristic knowledge base of the personal behavior of the metacosmic space, and providing basic data for subsequent data processing related to the user behavior.
Drawings
In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings in the embodiment of the present invention are briefly described below.
FIG. 1 is a schematic diagram of a metasystem architecture according to an embodiment of the present invention.
FIG. 2 is a flow diagram of a method of obtaining meta-cosmic space user behavior data according to one embodiment of the invention;
FIG. 3 is a diagram of a data structure according to one embodiment of the invention;
FIG. 4 is a schematic diagram of a series of user actions, according to one embodiment of the invention;
FIG. 5 is a flow diagram of a method for obtaining data on a dynamic representation of a body part, in accordance with one embodiment of the present invention;
FIG. 6 is a functional block diagram of an apparatus for obtaining meta-space user behavior data according to one embodiment of the present invention;
FIG. 7 is a functional block diagram of a data acquisition module according to one embodiment of the present invention;
FIG. 8 is a functional block diagram of a static action feature module according to one embodiment of the invention;
FIG. 9 is a functional block diagram of a dynamic representation data module according to one embodiment of the present invention; and
fig. 10 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is to be understood that these embodiments are provided so that the principles and spirit of the invention will be clear and thorough, and will be understood by those skilled in the art and will fully convey the principles and spirit of the invention to those skilled in the art. The exemplary embodiments provided herein are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive step, are within the scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, computer-readable storage medium, or computer program product. Accordingly, the present disclosure may be embodied in at least one of the following forms: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or a combination of hardware and software.
According to embodiments of the present invention, a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for acquiring meta-space user behavior data are claimed.
In this document, terms such as first and second are used only to distinguish one entity (or operation) from another entity (or operation), and do not require or imply any order or relationship between the entities (or operations). In this document, the elements (e.g., parts, components, processes, steps) defined by the phrases "comprising 8230 \8230;" comprising 8230; "do not exclude the presence of other elements in addition to those listed, i.e., may also include other elements not expressly listed. Any elements and numbers thereof in the figures are meant to be illustrative and not limiting in nature, and any nomenclature in the figures is used solely for distinction, and not in any limiting sense.
The principles and spirit of the present invention are explained in detail below with reference to a number of exemplary or representative embodiments of the invention.
FIG. 1 is a schematic diagram of a metasystem architecture according to one embodiment of the present invention. The metastables system includes a plurality of decentralized data processing nodes for generating and maintaining metastables and various activities therein. Each data processing node is connected with a database, wherein the data of the meta space and the data of the processing process for maintaining the internal activities of the space are stored. Any entity in the metacosmic space, including a user in the metacosmic space, is characterized in one embodiment by a three-dimensional point cloud array describing the spatial geometry, which is a dataset characterizing the physical three-dimensional space structure, and a texture image. In one embodiment, the metasystem updates the spatial data at a refresh rate and records the data after each refresh. Thus, when a user is engaged in various activities in the metaspace space, such as walking, standing, running, the three-dimensional spatial structure data set of the user's body in the metaspace space at each refresh is recorded in the database, and these three-dimensional spatial structure data sets representing the user's behaviors are referred to as behavior performance data, which is a data set describing the corresponding behaviors of the user engaged in these activities, and is a time-series data set.
FIG. 2 is a flow diagram of a method of obtaining meta-cosmic space user behavior data according to one embodiment of the invention. FIG. 3 is a diagram of a data structure according to one embodiment of the invention. With reference to fig. 2 and fig. 3, the method for acquiring the user behavior data of the meta space in this embodiment includes:
step S1, acquiring the behavior expression data of a target user in a preset time period from a meta universe real-time database according to the identity information of the target user.
Step S2, determining a plurality of analysis moments T with time sequence in the preset time period 1 、T 2 ……T n . In one embodiment, the analysis time may be at or multiple of the time of the metaspace refreshAnd (4) counting.
Step S3, extracting various body part static expression data M corresponding to analysis time from the behavior expression data X . Wherein the content of the first and second substances, X representing body parts, e.g. M h Representing the head. For the analysis time T 1 、T 2 ……T n The head static expression data are respectively M h1 、M h2 、M hn 。M b Represents the trunk, M al Representing the left arm, M ar Representing the right arm, M ll And M lr Respectively representing the left and right legs. Thus, each body part static expression data M X ＝[M X1 ,M X2 ,M X3 ……M Xi ……M Xn ]I represents the number of analysis times, and n is the number of analysis times.
Step S4, combining the static expression data of various body parts at each analysis moment to obtain static action characteristic data S at each analysis moment Ti . Combining the data of the head, the trunk, the left arm, the right arm, the left leg and the right leg at each analysis moment to form static motion characteristic data S at each analysis moment Ti And i is the serial number of the analysis time. The motion diagram shown in fig. 4 is a motion image restored from the three-dimensional spatial structure data set. From the combined data (three-dimensional spatial structure data) of the head, the torso, the left arm, the right arm, the left leg, and the right leg at time T1, it can be determined that the motion of the user at time T1 is as shown in fig. 2. Thus the static motion characteristic data S at each analysis time Ti ＝[M hi ,M bi ,M ali ,……M lri ]Representing the static motion characteristics at that time.
Step S5, based on the change of each body part static expression data of a plurality of continuous analysis moments in the preset time period to obtain the dynamic expression data M of the body part xd . Namely: m is a group of xd ＝[M xd1 ，M xd2 ……M xd(n-1) ]. For example, the head static representation data M based on the time T1 and the time T2 h1 、M h2 To obtain T (1-2) Dynamic table of header of time periodPresent data M hd1 ＝M h2 -M h1 And the data M of head dynamic representation is obtained by analogy hdi Namely: head dynamic representation data M hd ＝[M hd1 ，M hd2 ……M hd(n-1) ]Wherein M is hd(n-1) ＝M hdn -M hd(n-1) 。
In the method, the body part static representation data is a three-dimensional space structure data set, that is, a three-dimensional coordinate set of a plurality of points, so that when the dynamic representation data is calculated, the difference between the coordinates of three dimensions is calculated respectively to obtain the dynamic representation data. The degree of positional change in three-dimensional space can be obtained from the dynamic representation data. For example, for one piece of dynamic expression data (0, 15, 1), assuming that the meta-space coordinate system (x, y, z) corresponds to the x-axis and the y-axis for determining the ground surface plane, respectively, and corresponds to the z-axis in the height direction, it is known from the dynamic expression data (0, 15, 1) that the spatial positions at two times differ by 0 and a slight 1, respectively, in the x-axis and the y-axis, and by 15 in the height direction, and thus it can be determined that the user is in a state of almost moving vertically upward.
Step S6, combining a plurality of static motion characteristic data S Ti And dynamic performance data M of corresponding body parts xd And obtaining the behavior data X of the user. Wherein, behavior data X = [ S, A ]]，S＝[S T1 ，S T2 ……S Tn ]For static motion feature data corresponding to n analysis times, a = [ M = hd ,M bd ,……M lrd ]The dynamic feature data of all body parts corresponding to n analysis times are obtained.
Through the processing process, the embodiment of the invention describes the user behaviors in the meta-space by the encoded multidimensional matrix, has a simple and clear data structure, and can comprehensively express the user behaviors. Based on the activities of the user in the meta universe, the method provided by the invention records the action behavior data of the user engaged in the activities in the form of data of coding structures, and the corresponding time period of each coding structure can be long or short and is adaptive to the change degree of the action behavior of the user. When the behavior data codes of the user describing the behavior of the user in the meta-space are integrated, the behavior characteristic knowledge base of the user is obtained, and the behavior data of the user is a multidimensional matrix with codes, so that the behavior of the user can be comprehensively expressed, and the user behavior can be conveniently used as data in other applications.
As shown in fig. 4, based on a plurality of static motion characteristic data S Ti The spatial position of each body part in the meta-space at the time of analysis can be determined, and the data M is expressed according to the dynamic state of the body part xd The degree of change of the same body part at two different spatial positions can be determined, and the behavior and the type of the current user in the time period can be obtained based on the two data. The behavior of the user "standing long jump" can be obtained by the static motion characteristic data and the corresponding dynamic performance data of the body part from the time T1 to the time T12 in fig. 4.
In order to reduce the data processing amount, in one embodiment, when the plurality of types of body part static representation data corresponding to the analysis time are extracted in step S3, only a three-dimensional space structure data set representative of each type of body part and capable of representing one part or a plurality of parts of the whole body part is extracted as the static representation data of the body part. For example, the head is a data set composed of a plurality of spatial points, and after being reduced, the head static expression data can be represented by three-dimensional coordinates of only one central point, and for parts with a length in a certain dimension, such as the trunk and limbs, the parts can be represented by three-dimensional coordinates of a plurality of points, so that the calculation amount is simplified. Correspondingly, the dynamic representation data of each body part is a set of static difference data obtained from the three-dimensional space structure data set of one part or a plurality of sets of static difference data obtained from the three-dimensional space structure data sets of a plurality of parts respectively.
Further, when the static motion characteristic data of each analysis time is obtained in step S4, the difference between the static motion characteristic data of two adjacent analysis times is compared, and when the difference meets the data combination requirement, if the difference is smaller than a threshold value, the static motion characteristic data of the two analysis times are combined, and the combined result data is used as the static motion characteristic data of one analysis time, thereby further reducing the data volume.
Furthermore, when the dynamic representation data of the body part is obtained in step S5, data reduction may be performed as well. As shown in fig. 5, fig. 5 is a flowchart of a method for analyzing a change of static body part performance data at a plurality of consecutive analysis time points within the preset time period to obtain dynamic body part performance data according to an embodiment of the present invention, which specifically includes the following steps:
step S51, calculating the difference between the static expression data of the same body part at two adjacent analysis times to obtain the static difference data of the body part, for example, calculating the head static expression data M at time T1 and time T2 h1 、M h2 Obtaining dynamic head expression data M of the time segment T1-2 by the difference hd1 ＝M h2 -M h1 Calculating the head static expression data M at the time T2 and the time T3 h2 、M h3 The difference between the two values to obtain T 2-3 Header dynamic representation data M of time slot hd2 ＝M h3 M h2 。
And S52, comparing the two adjacent static difference data. For example, calculate M hd1 And M hd2 Difference Δ M hd1 As a result of comparison.
Step S53, determining whether the difference between two adjacent static difference data meets the data merging requirement. For example, setting a merge threshold, M is compared hd1 And M hd2 Difference Δ M hd1 And the size of the set threshold. If Δ M hd1 If the difference is smaller than the threshold, it is determined that the data merging requirement is met, and the two static difference data are merged in step S54, and the merged result data is regarded as one static difference data. If Δ M hd1 If the data merging requirement is larger than or equal to the threshold value, the data merging requirement is not met, and merging processing is not carried out.
Step S55, determining whether all static difference data have been processed, if not, returning to step S52, and if so, combining the plurality of static difference data to form dynamic representation data of the body part in step S56.
The threshold set in step S53 is a three-dimensional threshold, that is, it is determined that the data merging requirement is met when the two pieces of static difference data meet the threshold of each dimension in three dimensions. The three-dimensional threshold values can be the same or different according to the scene where the user behavior is located, and are set according to the precision requirement on each dimension. The merging of the two static difference data comprises discarding one data and keeping the other data, or calculating the average value of the two data, and taking the average value as the merged static difference data.
Through the data reduction processing flow of the step S3-5, the data storage space is saved, and the subsequent data processing amount in use is reduced.
In another embodiment of the invention, the dynamic performance data of the body-part as described in the foregoing fig. 2 and 5 is inertial indicator data characterizing a change in the spatial position of the body-part, such as velocity and/or acceleration in one or more dimensions in three-dimensional space. Taking the dynamic performance data obtained after the processing by the method shown in fig. 5 as an example, the inertia index data, i.e., the speed, is obtained by dividing the static difference data in the dynamic performance data by the time difference of the static performance data at two times of obtaining the static difference data. Or further dividing the difference between the two speeds by the time difference to obtain the inertia index data of the acceleration. Still further, the dynamic performance data may be expressed in one or more functions according to a law of change in the dynamic performance data. Statically representing data M with header h1 、M h2 ……M hn For example, when each head static representation data is a three-dimensional space structure data, the velocity/acceleration or the function of the velocity/acceleration of three dimensions can be obtained according to time and a plurality of coordinates of each dimension. Wherein, when the speed/acceleration of a certain dimension is 0, the dynamic representation data M can be simplified hdi Speed/acceleration in a single dimension. For example, the head, the skeleton, of the two data T1 and T2 shown in FIG. 4The arms, torso, and legs all have certain velocities/accelerations in three dimensions, for example, in the x-axis, the velocity/acceleration of the arms is significantly greater than the velocity/acceleration of the head, torso, and legs, and the velocity/acceleration of the legs can be roughly classified into two types: the foot has zero velocity/acceleration, the closer to the torso the greater the velocity/acceleration. In the embodiment, the dynamic expression data is converted into the index with more use significance, so that the use of the subsequent data is facilitated.
In another aspect, the present invention further provides an apparatus for acquiring meta-space user behavior data, as shown in fig. 6, which is a schematic block diagram of an apparatus for acquiring meta-space user behavior data, and the apparatus includes a data acquisition module 1, a time segmentation module 2, a static data extraction module 3, a static action characteristic module 4, a dynamic performance data module 5, and a behavior data determination module 6, where the data acquisition module 1 acquires behavior performance data of a target user in a preset time period from a meta-space real-time database according to identity information of the target user. Wherein the performance data is a three-dimensional spatial structure data set representing the user's behavior, which is a time-series data set.
The time slicing module 2 is connected to the data acquiring module 1, and after the data acquiring module 1 acquires the behavioral performance data within a preset time period, the time slicing module 2 determines a plurality of analysis times with time sequence within the preset time period, where the analysis times may be the meta-space refreshing time or multiples thereof, and then notifies the static data extracting module 3.
The static data extraction module 3 is connected to the time slicing module 2, and extracts a plurality of types of body part static expression data corresponding to each analysis time from the behavior expression data according to the determined analysis time after receiving the notification sent by the time slicing module 2. For example, each body part static representation data M X ＝[M X1 ,M X2 ,M X3 ……M Xn ]Wherein, in the process, X represents a body part, and i represents an analysis time number. Notifying the static action feature module 4 andthe dynamic performance data module 5.
The static motion characteristic module 4 is connected to the static data extraction module 3, and combines the various body part static expression data at each analysis time to obtain the static motion characteristic data at each analysis time after receiving the notification from the static data extraction module 3. For example, the static motion characteristic data S at each analysis time Ti ＝[M hi ,M bi ,M ali ,……M lri ,……]Where i is the analysis time, M hi ,M bi ,M ali ,……M lri The static performance data at the analysis time are respectively. The static action characteristics module 4 notifies the behavior data determination module 6 after processing.
The dynamic expression data module 5 is connected to the static data extraction module 3, and after receiving the notification from the static data extraction module 3, analyzes the change of the static expression data of each body part at a plurality of continuous analysis times within the preset time period to obtain the dynamic expression data of the body part. In one embodiment, the dynamic representation data M of the body part xd ＝[M xd1 ，M xd2 ……M xdi ……M xd(n-1) ]. Wherein, M xd Data for dynamic representation of a body part, M xdi Is based on T (i-1) Time of day and T i Time of day static performance data M of the body part X(i-1) 、M Xi To obtain T (1-2) Dynamic performance data for the time period. The dynamic performance data module 5 notifies the behavior data determination module 6 after processing.
The behavior data determining module 6 is connected to the static action characteristic module 4 and the dynamic expression data module 5, and obtains behavior data of the user by combining a plurality of static action characteristic data and corresponding body part dynamic expression data after receiving the notifications of the static action characteristic module 4 and the dynamic expression data module 5. For example, the obtained behavior data X = [ S, a =]，S＝[S T1 ，S T2 ……S Tn ]For the static motion characteristic data corresponding to the n analysis moments,A＝[M hdi ,M bdi ,……M lrdi ]the data are dynamic feature data of all body parts corresponding to n analysis times.
FIG. 7 is a functional block diagram of a data acquisition module according to one embodiment of the present invention. The data acquisition module 1 further comprises an identity information unit 11, a data reading unit 12 and a data reduction unit 13. The identity information unit 11 is configured to obtain identity information of a target user, and send the obtained identity information of the target user to the data reading unit 12. The data reading unit 12 is connected to the identity information unit 11, and is configured to obtain, from the metastic real-time database, a three-dimensional spatial structure data set of multiple body parts of the target user in the metastic space within a preset time period according to the identity information of the target user, and notify the data reduction unit 13. The data reduction unit 13 is connected to the data reading unit 12, and acquires, from the three-dimensional spatial structure data set of each body part after receiving the acquired data, a three-dimensional spatial structure data set representing one part or a plurality of parts of the whole body part as static expression data of the body part. For example, the amount of calculation in each subsequent step is simplified by setting the three-dimensional coordinates representing the center point of the head as head static expression data and the three-dimensional coordinates representing a plurality of points such as the trunk and the limbs as head static expression data.
FIG. 8 is a functional block diagram of a static action feature module according to one embodiment of the invention. In this embodiment, the static motion characteristic module 4 includes a first data combining unit 41, a motion comparison unit 42, and a first merging unit 43. The first data combining unit 41 is configured to combine multiple types of body part static expression data at each analysis time to obtain static motion feature data at each analysis time, and then send a notification to the motion comparing unit 42. The action comparison unit 42 is connected to the data combination unit 41, and compares the difference between the static action characteristic data of two adjacent analysis moments after receiving the notification sent by the data combination unit 41; when the difference meets the data merging requirement, a merging notification is sent to the first merging unit 43. The first merging unit 43 is connected to the motion comparison unit 42, and merges the static motion feature data at the two analysis times when receiving the merge notification sent by the motion comparison unit 42, and uses the merged result data as the static motion feature data at one analysis time. In the embodiment, when the static motion characteristic data of two characteristic moments are not greatly different, the two characteristic moments are combined, so that the data size is further reduced.
FIG. 9 is a functional block diagram of a dynamic presentation data module, according to one embodiment of the present invention. In the present embodiment, the dynamic expression data module 5 includes a difference calculating unit 51, a difference comparing unit 52, a second merging unit 53, and a second data combining unit 54. After receiving the notification from the static data extraction module 3, the difference calculation unit 51 calculates the difference between the static expression data of the same body part at two adjacent analysis times to obtain the static difference data of the body part, and notifies the difference comparison unit 52 after the calculation. The difference comparing unit 52 is connected to the difference calculating unit 51, and is configured to compare two adjacent static difference data, and send a merging notification to the second merging unit 53 when the difference between the two adjacent static difference data meets the data merging requirement. The second merging unit 53 is connected to the difference comparing unit 51, and merges the two static difference data after receiving the merge notification, and takes the merged result data as one static difference data. The second data combining unit 54 is connected to the second merging unit 53, and is configured to combine a plurality of static difference data to form dynamic representation data of the body part. In the present embodiment, the data amount is further reduced by merging the static difference data.
The invention also provides an electronic device comprising a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the method for acquiring meta-cosmic space user behavior data of any of the above embodiments. The electronic device in the embodiment of the invention can be a server or other computing devices, and can also be a cloud server.
Fig. 10 shows a hardware structure diagram of an embodiment of the electronic device provided by the present invention.
As shown in fig. 10, the electronic device may include a processor 601 and a memory 602 that stores computer program instructions.
Specifically, the processor 601 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions that, when read and executed by the processor 601, implement any of the above-described embodiments of methods for retrieving meta-space user behavior data by reading and executing computer program instructions stored in the memory 602.
In one example, the electronic device may also include a communication interface 603 and a bus 610. As shown in fig. 10, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 to complete communication therebetween.
The communication interface 603 is mainly used for implementing communication between various modules, apparatuses, units and/or devices in the embodiments of the present invention.
In addition, in combination with the method for acquiring the user behavior data of the meta space in the foregoing embodiment, an embodiment of the present invention may provide a computer storage medium to implement the method. The computer storage medium has computer program instructions stored thereon. The computer program instructions, when executed by a processor, implement any of the above-described embodiments of methods for obtaining meta-cosmic space user behavior data.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps, after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. The machine-readable medium may include non-transitory computer-readable storage media such as electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed at the same time.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based computer instructions which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present invention.
Claims (15)
1. A method of obtaining meta-space user behavior data, comprising:
acquiring behavioral expression data of a target user in a preset time period from a metastic real-time database according to identity information of the target user;
determining a plurality of analysis moments with time sequences in the preset time period;
extracting a plurality of types of body part static expression data corresponding to the analysis time from the behavior expression data;
combining the static expression data of various body parts at each analysis moment to obtain the static action characteristic data at each analysis moment;
analyzing the change of each body part static performance data of a plurality of continuous analysis moments in the preset time period to obtain the dynamic performance data of the body part; and
and combining the plurality of static motion characteristic data and the corresponding body part dynamic performance data to obtain the behavior data of the user.
2. The method of claim 1, wherein analyzing changes in the static body part performance data for each of the plurality of successive analysis moments over the preset time period to obtain dynamic body part performance data comprises:
calculating the difference of static performance data of the same body part at two adjacent analysis moments to obtain static difference data of the body part;
comparing two adjacent static difference data;
when the difference of two adjacent static difference data meets the data merging requirement, merging the two static difference data, and taking the merging result data as one static difference data; and
combining the plurality of static difference data to form dynamic performance data for the body part.
3. The method according to claim 1 or 2, wherein the body part static representation data is a three-dimensional spatial structure data set of body parts in metacosmic space.
4. The method of claim 3, wherein the dynamic performance data of the body part is inertial index data characterizing changes in spatial position of the body part.
5. The method of claim 4, wherein the inertial index data is velocity and/or acceleration in one or more dimensions in three-dimensional space.
6. The method of claim 3, wherein in extracting the plurality of body part static performance data corresponding to the analysis time, further comprising:
acquiring a three-dimensional spatial structure data set for representing one part or a plurality of parts of the whole body part from the three-dimensional spatial structure data set of each body part as static expression data of the body part;
correspondingly, the dynamic representation data of each body part is a set of static difference data obtained from the three-dimensional space structure data set of one part or a plurality of sets of static difference data obtained from the three-dimensional space structure data sets of a plurality of parts respectively.
7. The method of claim 3, wherein obtaining static motion feature data for each analysis time further comprises:
comparing the difference of the static action characteristic data of two adjacent analysis moments; and
and when the difference meets the data combination requirement, combining the static action characteristic data of the two analysis moments, and taking the combined result data as the static action characteristic data of one analysis moment.
8. The method according to claim 2 or 7, wherein the merging is to arbitrarily determine one of two data to be merged as merged result data, or to calculate an average of the two data to be merged as merged result data.
9. An apparatus for acquiring meta-space user behavior data, comprising:
the data acquisition module is configured to acquire the behavior data of the target user in a preset time period from a metastic real-time database according to the identity information of the target user;
a time slicing module connected with the data acquisition module and configured to determine a plurality of analysis times with time sequence within the preset time period;
a static data extraction module, connected to the time segmentation module, configured to extract a plurality of body part static performance data corresponding to an analysis time from the performance data;
a static motion feature module connected with the static data extraction module and configured to combine the plurality of body part static expression data at each analysis time to obtain static motion feature data at each analysis time;
a dynamic performance data module connected with the static data extraction module and configured to analyze the change of each body part static performance data of a plurality of continuous analysis moments in the preset time period to obtain the dynamic performance data of the body part; and
a behavior data determination module, coupled to the static motion characterization module and the dynamic representation data module, configured to combine a plurality of static motion characterization data and corresponding body part dynamic representation data to obtain behavior data of the user.
10. The apparatus of claim 9, wherein the data acquisition module further comprises:
an identity information unit configured to acquire identity information of a target user;
the data reading unit is connected with the identity information unit and is configured to acquire a three-dimensional space structure data set of a plurality of body parts of a target user in a metacosmic space within a preset time period from a metacosmic real-time database according to identity information of the target user; and
a data reduction unit connected with the data reading unit and configured to acquire a three-dimensional spatial structure data set for representing one part or a plurality of parts of the whole body part from the three-dimensional spatial structure data set of each body part as static expression data of the body part.
11. The apparatus of claim 9, wherein the static action feature module comprises:
the first data combination unit is configured to combine the static expression data of the various body parts at each analysis moment to obtain static action characteristic data at each analysis moment;
the action comparison unit is connected with the data combination unit and is configured to compare the difference of the static action characteristic data of two adjacent analysis moments; and
and the first merging unit is connected with the action comparison unit and is configured to merge the static action characteristic data of the two analysis moments when the difference meets the data merging requirement, and the merged result data is used as the static action characteristic data of one analysis moment.
12. The apparatus of claim 9, wherein the dynamic performance data module comprises:
a difference calculating unit configured to calculate a difference between static performance data of the same body part at two adjacent analysis time points to obtain static difference data of the body part;
a difference comparison unit connected with the difference calculation unit and configured to compare two adjacent static difference data;
a second merging unit, connected to the difference comparison unit, configured to merge two adjacent static difference data as one static difference data in response to a difference between the two static difference data meeting a data merging requirement; and
a second data combining unit, connected to the second merging unit, configured to combine a plurality of static difference data to form dynamic performance data of the body part.
13. An electronic device, comprising: a processor and a memory storing computer program instructions; the electronic device, when executing the computer program instructions, implements the method of any of claims 1-8.
14. A computer-readable storage medium, wherein computer program instructions are stored thereon, which when executed by a processor, implement the method of any one of claims 1-8.
15. A computer program product comprising computer program instructions which, when executed by a processor, implement the method of any one of claims 1-8.
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| CN117786438A (en) * | 2024-02-26 | 2024-03-29 | 广东奥飞数据科技股份有限公司 | Meta-universe digital twin method and system |
| CN117786438B (en) * | 2024-02-26 | 2024-05-10 | 广东奥飞数据科技股份有限公司 | Meta-universe digital twin method and system |
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