CN116271737A - Speed control method and system for running crawler belt of VR running machine - Google Patents
Speed control method and system for running crawler belt of VR running machine Download PDFInfo
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Abstract
According to the speed control method and system for the running track of the VR running machine, the eccentric probability of matching the falling point cloud space attitude vector with the target historical falling point cloud space attitude vector in at least one historical falling point cloud space attitude vector can be accurately deduced, running state analysis is conducted on the historical falling point cloud space attitude vector matched with the falling point cloud space attitude vector according to the eccentric probability, running state analysis results of falling foot sensing detection data of the running machine to be analyzed are accurately obtained, targeted running track speed control and adjustment can be conducted on the target running machine based on the running state analysis results of the falling foot sensing detection data of the running machine to be analyzed, and due to the fact that historical space attitude vector clusters and control decision correlation data are integrated when the running state analysis results are determined, not only can the determination efficiency of the running state analysis results be guaranteed, but also running track speed control adjustment continuity can be guaranteed through the running state analysis results.
Description
Technical Field
The invention relates to the technical field of VR (virtual reality) running machines, in particular to a speed control method and a speed control system for a running track of a VR running machine.
Background
With the continuous development of VR technology, the combination of a treadmill and VR can bring more interesting and high-end exercise experience to users. In view of the characteristics of scene immersion and the like of the VR running machine, the speed control of the VR running machine is different from that of the traditional running machine, the pertinence of the speed control of the VR running machine is difficult to ensure in the implementation of the related technology, and the problems of low efficiency and resource waste exist in the preamble processing of the speed control.
Disclosure of Invention
In order to improve the technical problems in the related art, the invention provides a speed control method and a speed control system for a running track of a VR running machine.
The embodiment of the invention provides a speed control method of a running track of a VR running machine, which is applied to a VR running machine control system, and comprises the following steps:
acquiring a falling point cloud space attitude vector and a historical space attitude vector cluster corresponding to falling point sensing detection data of a running machine to be analyzed, wherein the historical space attitude vector cluster comprises at least one historical falling point cloud space attitude vector;
extracting target control decision correlation data between the falling point cloud space attitude vector and each historical falling point cloud space attitude vector, and historical control decision correlation data between the historical falling point cloud space attitude vectors;
creating a control decision correlation profile representing a deduction relationship between the target control decision correlation data and the historical control decision correlation data;
performing data decentration probability analysis through the control decision correlation distribution to obtain decentration probability of matching of the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector;
determining a historical falling point cloud space posture vector matched with the falling point cloud space posture vector from the target historical falling point cloud space posture vector according to the eccentricity probability;
and determining running state analysis results of the running machine falling foot sensing detection data to be analyzed according to the matched historical falling foot point cloud space attitude vectors, and controlling the running track speed of the target running machine by using the running state analysis results.
Preferably, the acquiring the space attitude vector and the historical space attitude vector cluster of the falling point cloud corresponding to the falling sensing detection data of the running machine to be analyzed includes:
acquiring a falling point cloud space attitude vector and a state analysis vector set corresponding to falling point sensing detection data of a running machine to be analyzed, wherein the state analysis vector set comprises at least one set falling point cloud space attitude vector;
determining the control decision correlation of the falling point cloud space attitude vector and each set falling point cloud space attitude vector in the state analysis vector set;
and acquiring a set foot drop point cloud space posture vector with a control decision correlation position before the set queue is ranked from the state analysis vector set as a historical foot drop point cloud space posture vector so as to generate the historical space posture vector cluster.
Preferably, the extracting target control decision correlation data between the falling point cloud space pose vector and each of the historical falling point cloud space pose vectors, and the historical control decision correlation data between the historical falling point cloud space pose vectors, includes:
determining control decision correlation factors between the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector to obtain target control decision correlation data;
and determining a control decision correlation factor between the historical falling point cloud space attitude vectors to obtain the historical control decision correlation data.
Preferably, the extracting target control decision correlation data between the falling point cloud space pose vector and each of the historical falling point cloud space pose vectors, and the historical control decision correlation data between the historical falling point cloud space pose vectors, includes:
extracting the space posture change between the falling point cloud space posture vector and each historical falling point cloud space posture vector to obtain the target control decision correlation data;
and extracting the spatial attitude change among the spatial attitude vectors of the historical foothold cloud to obtain the historical control decision correlation data.
Preferably, said creating a control decision correlation distribution representing a deduced relationship between said target control decision correlation data and said historical control decision correlation data comprises:
acquiring control decision correlation of the falling point cloud space attitude vector and each historical falling point cloud space attitude vector;
sequentially sorting the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector according to the control decision correlation;
and creating a correlation distribution of the target control decision correlation data and the historical control decision correlation data according to the sequence arrangement, and generating the control decision correlation distribution.
Preferably, the performing data decent probability analysis through the control decision correlation distribution to obtain the decent probability that the footprint cloud space gesture vector matches with a target historical footprint cloud space gesture vector in at least one historical footprint cloud space gesture vector includes:
performing feature mining processing on the control decision correlation distribution to obtain a control decision description vector;
and performing eccentric probability analysis through the control decision description vector to obtain eccentric probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector.
Preferably, the performing the eccentric probability analysis by using the control decision description vector to obtain the eccentric probability that the falling point cloud space gesture vector matches with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector includes:
performing feature integration processing on the control decision description vector to obtain a control decision integration vector;
and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching the space gesture vector of the falling point cloud with the space gesture vector of the target historical falling point cloud in the at least one historical falling point cloud space gesture vector.
Preferably, the target historical falling point cloud space attitude vector comprises one historical falling point cloud space attitude vector with highest control decision correlation with the falling point cloud space attitude vector; grouping operation is performed through the control decision integration vector, so as to obtain the eccentric probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, and the method comprises the following steps:
and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching the historical falling point cloud space attitude vector with the highest control decision correlation with the falling point cloud space attitude vector.
Preferably, the target historical footdrop point cloud spatial pose vector comprises at least two historical footdrop point cloud spatial pose vectors with highest control decision correlation with the footdrop point cloud spatial pose vector; grouping operation is performed through the control decision integration vector, so as to obtain the eccentric probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, and the method comprises the following steps:
and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching at least two historical falling point cloud space attitude vectors with highest control decision correlation with the falling point cloud space attitude vectors.
Preferably, the feature mining processing is performed on the control decision correlation distribution to obtain a control decision description vector, which includes:
and performing sliding filtering operation on the control decision correlation distribution to generate the control decision description vector.
Preferably, the feature mining processing is performed on the control decision correlation distribution to obtain a control decision description vector, which includes:
and carrying out local feature focusing mining on the control decision correlation distribution to generate the control decision description vector.
Preferably, the determining, according to the decent probability, a historical falling point cloud space pose vector matched with the falling point cloud space pose vector from the target historical falling point cloud space pose vector includes:
determining a historical foot drop point cloud space attitude vector with eccentric probability meeting a matching requirement from the target historical foot drop point cloud space attitude vector;
and taking the historical falling point cloud space posture vector with the eccentric probability meeting the matching requirement as the historical falling point cloud space posture vector matched with the falling point cloud space posture vector.
The embodiment of the invention also provides a VR running machine control system, which comprises a processor and a memory; the processor is in communication with the memory, and the processor is configured to read and execute a computer program from the memory to implement the method described above.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when run implements the method described above.
By applying the embodiment of the invention, not only the target control decision correlation data of the falling point cloud space posture vector of the running machine falling point sensing detection data to be analyzed and each historical falling point cloud space posture vector are considered, but also the historical control decision correlation data between the historical falling point cloud space posture vectors is introduced, the deviation of the falling point cloud space posture vector and each historical falling point cloud space posture vector when the control decision correlation is matched can be deduced by establishing the control decision correlation distribution representing the deduction relation between the target control decision correlation data and the historical control decision correlation data, for example, when the detection error exists in the running machine falling point cloud space posture vector to be analyzed, the historical falling point cloud space posture vector which does not meet the matching requirement is judged to be matched, the error of the global control decision correlation matrix is caused, so that the decent probability of the matching of the foot drop point cloud space gesture vector and the target historical foot drop point cloud space gesture vector in at least one historical foot drop point cloud space gesture vector can be accurately deduced, the running state analysis can be carried out on the historical foot drop point cloud space gesture vector matched with the foot drop point cloud space gesture vector according to the decent probability, the running state analysis result of the foot drop sensing detection data of the running machine to be analyzed can be accurately obtained, the targeted running track speed control and adjustment can be carried out on the target running machine based on the running state analysis result of the foot drop sensing detection data of the running machine to be analyzed, and the running state analysis result can be ensured not only due to the fact that the historical space gesture vector cluster and the control decision correlation data are integrated when the running state analysis result is determined, saving system resources and ensuring the continuity of the control and adjustment of the running track speed through running state analysis results.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flow chart of a speed control method for a running track of a VR running machine according to an embodiment of the present invention.
Description of the embodiments
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided by the embodiments of the present invention may be implemented in a VR treadmill control system, a computer device, or similar computing device. Taking the example of operating on a VR treadmill control system, the VR treadmill control system may include one or more processors (which may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and memory for storing data, and optionally, transmission means for communication functions.
The memory may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a speed control method of a running track of a VR running machine in an embodiment of the present invention, and the processor executes the computer program stored in the memory to perform various functional applications and data processing, that is, to implement the above-mentioned method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory may further include memory remotely located with respect to the processor, which may be connected to the VR treadmill control system via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the VR treadmill control system. In one example, the transmission means comprises a network adapter (Network Interface Controller, simply referred to as NIC) that can be connected to other network devices via a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Referring to fig. 1, fig. 1 is a flow chart of a speed control method of a running track of a VR running machine according to an embodiment of the invention, where the method is applied to a VR running machine control system, and further the method may at least include the following steps S10-S60.
S10, acquiring a falling point cloud space posture vector and a historical space posture vector cluster corresponding to falling point sensing detection data of the running machine to be analyzed, wherein the historical space posture vector cluster comprises at least one historical falling point cloud space posture vector.
In the embodiment of the invention, the foot drop sensing detection data of the running machine to be analyzed can be data obtained by detecting the foot drop position of a user/user of the running machine through a mechanical sensor arranged on the running machine, wherein the data comprise foot drop area data, foot drop force data and the like of the user/user of the running machine on a running track of the running machine, and based on the data, the foot drop point cloud space posture vector can reflect the position characteristics, the posture characteristics, the stress characteristics and the like of the foot drop position/area of the user/user on the running track of the running machine. In some examples, the force signature may be recorded by point cloud density, while the position signature, the attitude signature may be recorded by signature variables. A historical spatial pose vector cluster can be understood as a set of spatial pose vectors that serve as references.
And S20, extracting target control decision correlation data between the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector and historical control decision correlation data between the historical falling point cloud space attitude vectors.
In the embodiment of the invention, the control decision correlation data can represent the similarity or the similarity between different foot drop point cloud space attitude vectors at the speed control adjustment level of the running machine.
S30, creating a control decision correlation distribution representing a deduction relation between the target control decision correlation data and the historical control decision correlation data.
In the embodiment of the invention, the deduction relation can be understood as the association relation between the target control decision correlation data and the historical control decision correlation data, and the control decision correlation distribution can be understood as a correlation matrix or a correlation list.
And S40, carrying out data decentration probability analysis through the control decision correlation distribution to obtain decentration probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector.
In the embodiment of the present invention, the decent probability may be understood as a confidence coefficient or a confidence coefficient, which is used to reflect the possibility that the space gesture vector of the falling point cloud matches with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector.
S50, determining a historical falling point cloud space gesture vector matched with the falling point cloud space gesture vector from the target historical falling point cloud space gesture vector according to the eccentricity probability.
S60, determining running state analysis results of the running machine falling foot sensing detection data to be analyzed through the matched historical falling foot point cloud space attitude vectors, and controlling the running track speed of the target running machine by using the running state analysis results.
According to the embodiment of the invention, the running state analysis result of the running machine falling sensing data to be analyzed can be rapidly and accurately determined through the matched historical falling point cloud space attitude vector, for example, deduction analysis is carried out based on the historical running state analysis result of the historical falling point cloud space attitude vector, so that the running state analysis result is obtained, and the state change and gait requirements of a user can be determined through the running state analysis result, so that the speed control adjustment of the running track can be realized in a targeted and differentiated mode.
It can be seen that, when applied to S10-S60, not only the target control decision correlation data of the falling point cloud space posture vector of the running machine falling point sensing detection data to be analyzed and each historical falling point cloud space posture vector is considered, but also the historical control decision correlation data between the historical falling point cloud space posture vectors is introduced, the data eccentric probability analysis is performed by creating the control decision correlation distribution representing the deduction relation between the target control decision correlation data and the historical control decision correlation data, the deviation of the falling point cloud space posture vector and each historical falling point cloud space posture vector when the control decision correlation is matched, for example, when the running machine falling point sensing detection data to be analyzed has a detection error, the historical falling point cloud space posture vectors which do not meet the matching requirement individually are determined to be matched, the error of the global control decision correlation matrix is caused, so that the decent probability of the matching of the foot drop point cloud space gesture vector and the target historical foot drop point cloud space gesture vector in at least one historical foot drop point cloud space gesture vector can be accurately deduced, the running state analysis can be carried out on the historical foot drop point cloud space gesture vector matched with the foot drop point cloud space gesture vector according to the decent probability, the running state analysis result of the foot drop sensing detection data of the running machine to be analyzed can be accurately obtained, the targeted running track speed control and adjustment can be carried out on the target running machine based on the running state analysis result of the foot drop sensing detection data of the running machine to be analyzed, and the running state analysis result can be ensured not only due to the fact that the historical space gesture vector cluster and the control decision correlation data are integrated when the running state analysis result is determined, saving system resources and ensuring the continuity of the control and adjustment of the running track speed through running state analysis results.
In addition, in the embodiment of the invention, the running machine can be a VR running machine, and the coverage range of the running track is obviously larger than that of a conventional running machine, so that the running state analysis result can be determined comprehensively and abundantly as possible, and the running track speed control and adjustment can be matched with the current VR scene as much as possible.
In some possible examples, the step of obtaining the space attitude vector of the falling point cloud and the historical space attitude vector cluster corresponding to the falling sensing detection data of the running machine to be analyzed in the step of S10 includes steps of S11-S13.
S11, acquiring a falling point cloud space attitude vector and a state analysis vector set corresponding to falling point cloud space attitude sensing data of the running machine to be analyzed, wherein the state analysis vector set comprises at least one set falling point cloud space attitude vector.
S12, determining the control decision correlation of the falling point cloud space attitude vector and each set falling point cloud space attitude vector in the state analysis vector set.
S13, acquiring a set foot drop point cloud space posture vector with a control decision correlation position before the set queue is arranged from the state analysis vector set as a historical foot drop point cloud space posture vector so as to generate the historical space posture vector cluster.
Therefore, the historical space attitude vector clusters are determined by acquiring a plurality of historical foothold cloud space attitude vectors with the control decision correlation arranged at the front, so that the correlation between the historical space attitude vector clusters and the running machine foothold sensing detection data to be analyzed can be ensured to be maximized.
In some possible embodiments, extracting the target control decision correlation data between the falling point cloud spatial pose vector and each of the historical falling point cloud spatial pose vectors and the historical control decision correlation data between the historical falling point cloud spatial pose vectors in S20 includes: determining control decision correlation factors between the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector to obtain target control decision correlation data; and determining a control decision correlation factor between the historical falling point cloud space attitude vectors to obtain the historical control decision correlation data. Wherein the control decision correlation factor can be understood as a similarity score.
In some possible embodiments, extracting the target control decision correlation data between the falling point cloud spatial pose vector and each of the historical falling point cloud spatial pose vectors and the historical control decision correlation data between the historical falling point cloud spatial pose vectors in S20 includes: extracting the space posture change between the falling point cloud space posture vector and each historical falling point cloud space posture vector to obtain the target control decision correlation data; and extracting the spatial attitude change among the spatial attitude vectors of the historical foothold cloud to obtain the historical control decision correlation data.
It can be seen that the target control decision correlation data as well as the historical control decision correlation data can be flexibly determined based on the control decision correlation factor or the spatial pose change.
Further, creating a control decision correlation distribution representing a deductive relationship between the target control decision correlation data and the historical control decision correlation data in S30 includes S31-S33.
S31, acquiring the control decision correlation of the falling point cloud space attitude vector and each historical falling point cloud space attitude vector.
S32, sequentially sorting the space attitude vectors of the falling point clouds and the space attitude vectors of the historical falling point clouds according to the control decision correlation.
S33, creating the association distribution of the target control decision correlation data and the historical control decision correlation data according to the sequence arrangement, and generating the control decision correlation distribution.
Wherein, the association distribution can be understood as an association matrix of the target control decision correlation data and the historical control decision correlation data.
In some optional embodiments, the step S40 of performing data decent probability analysis through the control decision correlation distribution, to obtain a decent probability that the falling point cloud spatial pose vector matches a target historical falling point cloud spatial pose vector in at least one historical falling point cloud spatial pose vector, including step S41 and step S42.
S41, performing feature mining processing on the control decision correlation distribution to obtain a control decision description vector.
S42, performing eccentric probability analysis through the control decision description vector to obtain eccentric probability of matching of the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector.
Further, in S42, the eccentric probability analysis is performed by the control decision description vector, so as to obtain the eccentric probability that the footprint cloud space gesture vector matches with the target historical footprint cloud space gesture vector in the at least one historical footprint cloud space gesture vector, which includes S421 and S422.
S421, performing feature integration processing on the control decision description vector to obtain a control decision integration vector.
S422, performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching the falling point cloud space gesture vector with the target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector.
Further, the target historical footprint cloud space pose vector comprises a historical footprint cloud space pose vector having the highest control decision correlation with the footprint cloud space pose vector. Based on this, performing the grouping operation through the control decision integration vector in S422, to obtain an eccentric probability that the falling point cloud space gesture vector matches with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, including: and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching the historical falling point cloud space attitude vector with the highest control decision correlation with the falling point cloud space attitude vector.
In addition, the target historical footprint cloud space pose vector comprises at least two historical footprint cloud space pose vectors with highest control decision correlation with the footprint cloud space pose vector. Based on this, performing the grouping operation through the control decision integration vector in S422, to obtain an eccentric probability that the falling point cloud space gesture vector matches with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, including: and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching at least two historical falling point cloud space attitude vectors with highest control decision correlation with the falling point cloud space attitude vectors.
In some exemplary embodiments, the feature mining processing is performed on the control decision correlation distribution in S41 to obtain a control decision description vector, which may be implemented by any one of the following two kinds of ideas.
And (3) performing sliding filtering operation on the control decision correlation distribution to generate the control decision description vector.
And (2) carrying out local feature focusing mining on the control decision correlation distribution to generate the control decision description vector.
In some possible embodiments, determining a historical footprint point cloud space pose vector matching the footprint point cloud space pose vector from the target historical footprint point cloud space pose vector according to the decent probability in S50 includes S51 and S52.
S51, determining the historical falling point cloud space attitude vector with the eccentricity probability meeting the matching requirement from the target historical falling point cloud space attitude vector.
S52, taking the historical falling point cloud space posture vector with the eccentric probability meeting the matching requirement as the historical falling point cloud space posture vector matched with the falling point cloud space posture vector.
The embodiment of the invention also provides a VR running machine control system, which comprises a processor and a memory; the processor is in communication with the memory, and the processor is configured to read and execute a computer program from the memory to implement the method described above.
The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when run implements the method described above.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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 systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a network device, or the like) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of controlling the speed of a VR treadmill track, for use in a VR treadmill control system, the method comprising:
acquiring a falling point cloud space attitude vector and a historical space attitude vector cluster corresponding to falling point sensing detection data of a running machine to be analyzed, wherein the historical space attitude vector cluster comprises at least one historical falling point cloud space attitude vector;
extracting target control decision correlation data between the falling point cloud space attitude vector and each historical falling point cloud space attitude vector, and historical control decision correlation data between the historical falling point cloud space attitude vectors;
creating a control decision correlation profile representing a deduction relationship between the target control decision correlation data and the historical control decision correlation data;
performing data decentration probability analysis through the control decision correlation distribution to obtain decentration probability of matching of the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector;
determining a historical falling point cloud space posture vector matched with the falling point cloud space posture vector from the target historical falling point cloud space posture vector according to the eccentricity probability;
and determining running state analysis results of the running machine falling foot sensing detection data to be analyzed according to the matched historical falling foot point cloud space attitude vectors, and controlling the running track speed of the target running machine by using the running state analysis results.
2. The method according to claim 1, wherein the obtaining a set of space attitude vectors and a set of history space attitude vectors of a landing point cloud corresponding to the landing sensing data of the treadmill to be analyzed includes:
acquiring a falling point cloud space attitude vector and a state analysis vector set corresponding to falling point sensing detection data of a running machine to be analyzed, wherein the state analysis vector set comprises at least one set falling point cloud space attitude vector;
determining the control decision correlation of the falling point cloud space attitude vector and each set falling point cloud space attitude vector in the state analysis vector set;
and acquiring a set foot drop point cloud space posture vector with a control decision correlation position before the set queue is ranked from the state analysis vector set as a historical foot drop point cloud space posture vector so as to generate the historical space posture vector cluster.
3. The method of claim 1, wherein the extracting target control decision correlation data between the footprint point cloud spatial pose vector and each of the historical footprint point cloud spatial pose vectors, and historical control decision correlation data between the historical footprint point cloud spatial pose vectors, comprises:
determining control decision correlation factors between the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector to obtain target control decision correlation data;
and determining a control decision correlation factor between the historical falling point cloud space attitude vectors to obtain the historical control decision correlation data.
4. The method of claim 1, wherein the extracting target control decision correlation data between the footprint point cloud spatial pose vector and each of the historical footprint point cloud spatial pose vectors, and historical control decision correlation data between the historical footprint point cloud spatial pose vectors, comprises:
extracting the space posture change between the falling point cloud space posture vector and each historical falling point cloud space posture vector to obtain the target control decision correlation data;
and extracting the spatial attitude change among the spatial attitude vectors of the historical foothold cloud to obtain the historical control decision correlation data.
5. The method of claim 1, wherein the creating a control decision correlation profile representing a deduced relationship between the target control decision correlation data and the historical control decision correlation data comprises:
acquiring control decision correlation of the falling point cloud space attitude vector and each historical falling point cloud space attitude vector;
sequentially sorting the falling point cloud space attitude vectors and each historical falling point cloud space attitude vector according to the control decision correlation;
and creating a correlation distribution of the target control decision correlation data and the historical control decision correlation data according to the sequence arrangement, and generating the control decision correlation distribution.
6. The method of claim 1, wherein the performing the data decent probability analysis by the control decision correlation distribution to obtain the decent probability of the fit of the footprint cloud spatial pose vector to a target historical footprint cloud spatial pose vector of at least one historical footprint cloud spatial pose vector comprises: performing feature mining processing on the control decision correlation distribution to obtain a control decision description vector; performing eccentric probability analysis through the control decision description vector to obtain eccentric probability of matching of the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector;
the performing the eccentric probability analysis by using the control decision description vector to obtain the eccentric probability that the falling point cloud space gesture vector matches with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, including: performing feature integration processing on the control decision description vector to obtain a control decision integration vector; and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching the space gesture vector of the falling point cloud with the space gesture vector of the target historical falling point cloud in the at least one historical falling point cloud space gesture vector.
7. The method of claim 6, wherein the target historical footprint point cloud spatial pose vector comprises one historical footprint point cloud spatial pose vector that has highest control decision correlation with the footprint point cloud spatial pose vector; grouping operation is performed through the control decision integration vector, so as to obtain the eccentric probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, and the method comprises the following steps: grouping operation is carried out through the control decision integration vector, so that the eccentric probability of matching of the historical falling point cloud space attitude vector with the highest control decision correlation with the falling point cloud space attitude vector is obtained;
or the target historical falling point cloud space attitude vector comprises at least two historical falling point cloud space attitude vectors with highest control decision correlation with the falling point cloud space attitude vector; grouping operation is performed through the control decision integration vector, so as to obtain the eccentric probability of matching the falling point cloud space gesture vector with a target historical falling point cloud space gesture vector in at least one historical falling point cloud space gesture vector, and the method comprises the following steps: and performing grouping operation through the control decision integration vector to obtain the eccentric probability of matching at least two historical falling point cloud space attitude vectors with highest control decision correlation with the falling point cloud space attitude vectors.
8. The method of claim 6, wherein the feature mining the control decision correlation distribution to obtain a control decision description vector comprises: performing sliding filtering operation on the control decision correlation distribution to generate the control decision description vector; or performing local feature focus mining on the control decision correlation distribution to generate the control decision description vector.
9. The method of claim 1, wherein the determining a historical footprint point cloud spatial pose vector that matches the footprint point cloud spatial pose vector from the target historical footprint point cloud spatial pose vectors according to the decent probability comprises:
determining a historical foot drop point cloud space attitude vector with eccentric probability meeting a matching requirement from the target historical foot drop point cloud space attitude vector;
and taking the historical falling point cloud space posture vector with the eccentric probability meeting the matching requirement as the historical falling point cloud space posture vector matched with the falling point cloud space posture vector.
10. A VR treadmill control system comprising a processor and a memory; the processor being communicatively connected to the memory, the processor being adapted to read a computer program from the memory and execute it to carry out the method of any of the preceding claims 1-9.
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