CN116208986A - Wireless network optimization system and method based on meta universe - Google Patents
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Abstract
The invention provides a wireless network optimization system and a method based on metauniverse, which can complete acquisition of real-time base station state information, construct a base station twin model through a metauniverse simulation center, quickly perform base station test adjustment based on the twin model, when a wireless network problem is found, management operators can perform virtual scene operation through VR equipment, and after the operation is completed, an adjustment instruction is issued to a base station network manager through an instruction operation execution module and is executed at a base station provided with an antenna attitude meter, so that the problems that part of base stations cannot go to a station, adjustment is difficult and quick implementation is impossible are solved, and the working efficiency of wireless network optimization is improved.
Description
Technical Field
The invention relates to the technical field of wireless networks, in particular to a wireless network optimization system and method based on metauniverse.
Background
In the daily work of wireless network optimization, because the most real field environment information of the wireless network cannot be obtained in real time, when a network problem occurs, it is generally difficult to find a proper and accurate network optimization method to complete network optimization; in the daily wireless network optimization work, part of base stations are difficult to adjust on site due to factors such as beautifying antennas (exhaust pipes and beautifying covers), site property sensitivity and the like; when an urgent wireless network optimization requirement is met, for example: large-scale activities guarantee traffic diversion, emergency VIP complaints and the like, are limited by factors such as base station distance, property procedures, night safety and the like, and cannot be rapidly carried out on the station.
Disclosure of Invention
The invention aims to provide a wireless network optimization system and method based on metauniverse, so as to solve the problems in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the invention provides a meta-universe-based wireless network optimization system, which comprises a perception layer, an application layer and an interaction layer;
the sensing layer comprises a base station provided with an antenna attitude meter and an electric tuning antenna remote control unit, and is used for monitoring the running state of the base station in real time and transmitting the running state of the base station to the application layer, receiving a remote control instruction sent by the application layer, and adjusting the state of the antenna attitude meter through the electric tuning antenna remote control unit;
the application layer comprises a meta-universe data processing center, an instruction operation execution module and a base station network manager, wherein the meta-universe data processing center is used for receiving, processing and storing base station running state data transmitted by the perception layer, and the base station network manager sends a remote control instruction to a base station based on the processed base station running state data;
the interaction layer comprises a metauniverse scene simulation center, wherein the metauniverse scene simulation center constructs a metauniverse scene based on the base station running state data stored in the metauniverse, the metauniverse scene is displayed in VR equipment, the base station running state data is adjusted and updated through operation of the VR equipment, and the adjusted instruction is transmitted to the application layer.
Preferably, the meta space data processing center comprises a three-dimensional vector model library, a three-dimensional vector model data processing module, a vector diagram processing module, a base station and an environment three-dimensional model database, wherein the three-dimensional vector model data processing module comprises a text decoder, a return data decoder, a feature processor and a space-time consistency processor, and is used for processing base station return data, combining the base station real object and environment real object processed by the vector diagram processing module and the text model library, processing the base station return data into base station and environment three-dimensional model data, and storing the base station return data in the base station and environment three-dimensional model database.
Preferably, the three-dimensional vector model data processing module processes the base station feedback data, combines the base station real object and the environment real object processed by the vector diagram processing module with the text model library, and processes the base station feedback data into the base station and environment three-dimensional model data, and specifically comprises the following steps:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generating by the base station attitude position data of the c group, D representing vector point multiplication operation, lambda representing an adjustable constant variable for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i The result of text position consistent calculation;
3) Space-time coincidence calculation: intercepting the position-text description pair in a given S second according to the result of consistent text position obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,
lambda represents an adjustable constant variable, then{g n And the three-dimensional model data of the base station and the environment of the target are obtained.
Preferably, the metauniverse scene simulation center comprises VR equipment, an image synthesizer, an image display interface, an image decoder and an instruction decoder, wherein the image synthesizer is used for rendering the environment three-dimensional model data processed by the metauniverse data processing center into an image, and the image is displayed in the VR equipment through the image display interface; and adjusting the displayed picture by controlling the VR equipment, converting the displayed picture into image variation data by the image decoder, and finally converting the image variation data into an adjustment instruction by the instruction decoder.
Another object of the present invention is to provide a meta-universe-based wireless network optimization method, implemented based on the meta-universe-based wireless network optimization system described in the first aspect, including the following steps:
s1, a base station transmits the latest data of the base station state back to a metadata processing center for processing;
s2, the metadata processing center processes the received feedback data into base station and environment three-dimensional model data by combining a text model database through a three-dimensional vector model data processing module, and stores the base station and environment three-dimensional model data in a base station and environment three-dimensional model database;
s3, an image synthesizer in the meta-universe scene simulation center renders the base station and the environment three-dimensional model data in the base station and the environment three-dimensional model database into a virtual scene, and the virtual scene is presented in an image display interface and is presented in VR equipment;
s4, a management operator adjusts the display picture by operating the VR equipment, the image decoder converts the display picture into image change data, and finally the instruction decoder converts the image change data into an adjustment instruction;
s5, the adjustment instruction enters an instruction operation execution module, and the instruction operation execution module converts the adjustment instruction into a standard network management execution message and pushes the standard network management execution message to the base station network management;
s6, the base station network manager issues a network management execution message to the base station, adjusts the state of the antenna attitude meter through the electric adjustment antenna remote control unit to complete adjustment of the real base station, and repeats the step S1.
Preferably, step S2 specifically includes:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generating by the base station attitude position data of the c group, D representing vector point multiplication operation, lambda representing an adjustable constant variable for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i The result of text position consistent calculation;
3) Space-time coincidence calculation: intercepting the position-text description pair in a given S second according to the result of consistent text position obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,
lambda represents an adjustable constant variable, then { g }, is obtained n And the three-dimensional model data of the base station and the environment of the target are obtained.
Preferably, the step S3 specifically includes:
s31, mapping the three-dimensional model data to rays: the image synthesizer emits a ray r=o+td from the origin o of the new view of the object through a point in the three-dimensional model data, where t represents the distance between the sampling points along the ray, and the origin is denoted o;
s32, inquiring ray characteristics: for each distance t to origin o k Of (c) at a position o+t k d and direction d are both sent as inputs to the MLP model (o+t k d,d)→(σ k ，c k ) The model outputs a corresponding density sigma k And RGB color c k As an extraction feature of the specific point;
s33, color rendering: and integrating the characteristics of each point on the ray r, and calculating the color C (r) of the corresponding pixel of the emergent ray r, wherein the expression is as follows:
wherein />
N represents the number of sampling points along ray r, t k To the point o+t along the ray r k d, i.e. the probability that the ray reaches that point without encountering any other point. In order to render an image with resolution H×W, the above steps are repeated H×W times, corresponding to the number of queries H×W×N of the MLP model, then the scene picture can be displayed on the image presentation interface;
s34, change adjustment: the user can change the image display interface by operating the VR device. The image decoder maps the pixels of the changed picture onto the rays in turn, uniformly samples all candidate points along the rays, then identifies pre-existing points through a query process based on occupied grids, the method is that the occupied grids are tiled into eight subspaces, a non-zero cube in the subspace closest to the origin of the target view is selected as the pre-existing point, and the steps are repeated to output image change data;
s35, instruction decoding: the image variation data output in S34 is subjected to instruction decoding by an instruction decoder, and the decoding process is as follows: the image variation data is translated through the GPT-2 language model, and the result is output to the style adapter, wherein for each style from j=1 to m, a subset of β is first selected, wherein
Equal to the j-th pattern, training set S j And training by using GPT-2 language model parameters, and outputting a result after training to be a required adjustment instruction.
Preferably, in step S6, the state of the antenna attitude meter is adjusted by the electrically-adjustable antenna remote control unit to complete the adjustment of the real base station, specifically adopting: the remote control unit of the electrically-controlled antenna measures and rotates between two clamping points of the minimum value and the maximum value of the electronic downtilt angle of the supporting angle, so that the reading of the downtilt angle degree and the accurate adjustment are realized.
Preferably, the latest data of the base station state in step S1 includes the latest parameter data of the base station antenna, mainly including a device model, a height, a pitch angle, an azimuth angle, and a position longitude and latitude.
The beneficial effects of the invention are as follows:
the invention provides a wireless network optimization system and a method based on metauniverse, which can complete acquisition of real-time base station state information, construct a base station twin model through a metauniverse simulation center, quickly perform base station test adjustment based on the twin model, and when a wireless network problem is found, management operators can perform virtual scene operation through VR equipment, and an adjustment instruction is issued to a base station network manager through an instruction operation execution module after the operation is completed and is executed at a base station equipped with an antenna attitude meter. Therefore, the problems that part of base stations cannot get on the station, adjustment is difficult and quick implementation cannot be achieved are solved, and the working efficiency of wireless network optimization is improved.
Drawings
FIG. 1 is a schematic diagram of a metauniverse-based wireless network optimization system provided in example 1;
fig. 2 is a schematic diagram of a base station principle of the antenna attitude meter equipped in embodiment 1;
FIG. 3 is a schematic diagram of the metauniverse data processing center provided in example 1;
FIG. 4 is a schematic diagram of the data principle of the three-dimensional vector model data processing module in embodiment 1;
fig. 5 is a schematic diagram of the meta-cosmic scene simulation center principle in embodiment 1.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the invention.
Example 1
The embodiment provides a wireless network optimization system based on metauniverse, which comprises a perception layer, an application layer and an interaction layer, wherein the structure is shown in the accompanying figure 1:
the sensing layer comprises a base station provided with an antenna attitude meter and an electric tuning antenna remote control unit, and is used for monitoring the running state of the base station in real time and transmitting the running state of the base station to the application layer, receiving a remote control instruction sent by the application layer, and adjusting the state of the antenna attitude meter through the electric tuning antenna remote control unit;
the application layer comprises a meta-universe data processing center, an instruction operation execution module and a base station network manager, wherein the meta-universe data processing center is used for receiving, processing and storing base station running state data transmitted by the perception layer, and the base station network manager sends a remote control instruction to a base station based on the processed base station running state data;
the interaction layer comprises a metauniverse scene simulation center, wherein the metauniverse scene simulation center constructs a metauniverse scene based on the base station running state data stored in the metauniverse, the metauniverse scene is displayed in VR equipment, the base station running state data is adjusted and updated through operation of the VR equipment, and the adjusted instruction is transmitted to the application layer.
The base station provided with the antenna attitude meter comprises the antenna attitude meter and a base station provided with an electrically-controlled antenna remote control unit, as shown in figure 2;
the antenna attitude meter is equipment capable of monitoring the running condition of the base station antenna in real time in all weather. The base station provided with the remote control unit of the electrically-controlled antenna means that the remote control unit of the electrically-controlled antenna is arranged between the RRU and the antenna, and the rotation of the remote control unit of the electrically-controlled antenna can be remotely controlled by using a network manager. When a calibration command is received from a network manager, the remote control unit of the electrically-controlled antenna measures and rotates between two clamping points of the minimum value and the maximum value of the electronic downtilt angle of the supporting angle, so that the reading and the accurate adjustment of the downtilt angle degree are realized;
the meta-universe data processing center is a data processing center system built on a meta-universe server, and the principle is shown in figure 3;
the data processing center system comprises a three-dimensional vector model library, a three-dimensional vector model data processing module, a vector diagram processing module, a base station and an environment three-dimensional model database, wherein the three-dimensional vector model data processing module comprises a text decoder, a feedback data decoder, a characteristic processor and a space-time consistency processor, the three-dimensional vector model data processing module can process base station feedback data, and the base station entity, the environment entity and the text model library processed by the vector diagram processing module are combined to process the data into base station and environment three-dimensional model data, and the base station and environment three-dimensional model data are stored in the base station and environment three-dimensional model database, and the processing process is as shown in figure 4, and comprises the following steps:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generating by the base station attitude position data of the c group, D representing vector point multiplication operation, lambda representing an adjustable constant variable for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i The result of text position consistent calculation;
3) And (3) calculating space-time consistency: intercepting the position-text description pair in a given S second from the result of the text position coincidence calculation obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,
lambda represents an adjustable constant variable, then { g }, is obtained n And the three-dimensional model data of the base station and the environment of the target are obtained. />
The meta-space scene simulation center in this embodiment is shown in fig. 5, and includes a VR device, an image synthesizer, an image display interface, an image decoder, and an instruction decoder. VR equipment refers to equipment capable of seeing a virtual scene, and simultaneously, the VR equipment can feed back to an image display interface to change the state of the virtual scene through voice or physical entity keys; the image synthesizer refers to a functional module for rendering the three-dimensional model data into an image; the image display module is a functional module with color and shape display; the image decoder is a module capable of converting a picture into image variation data; the instruction decoder is a module that can convert image change data into adjustment instructions. The treatment process is as follows:
1. mapping three-dimensional model data onto rays: the image synthesizer emits a ray r=o+td from the origin o of the new view of the object through a point in the three-dimensional model data, where t represents the distance between the sampling points along the ray, and the origin is denoted o;
2. query ray characteristics: for each point to o at a distance tk, its position o+t k d and direction d are both sent as inputs to the MLP model (o+t k d,d)→(σ k ，c k ) The model outputs a corresponding density sigma k And RGB color c k As an extraction feature of the specific point;
3. color rendering: and integrating the characteristics of each point on the ray r, and calculating the color C (r) of the corresponding pixel of the emergent ray r, wherein the expression is as follows:
wherein />
N represents the number of sampling points along ray r, t k To the point o+t along the ray r k d, i.e. the probability that the ray reaches that point without encountering any other point. In order to render an image with resolution H×W, the above steps are repeated H×W times, corresponding to the number of queries H×W×N of the MLP model, then the scene picture can be displayed on the image presentation interface;
4. and (3) change adjustment: the user can change the image display interface by operating the VR device. The image decoder maps the pixels of the changed picture onto the rays in turn, uniformly samples all candidate points along the rays, then identifies pre-existing points through a query process based on occupied grids, the method is that the occupied grids are tiled into eight subspaces, a non-zero cube in the subspace closest to the origin of the target view is selected as the pre-existing point, and the steps are repeated to output image change data;
5. instruction decoding: s4, outputting the image variation numberAccording to the instruction decoding by the instruction decoder, the decoding process is as follows: the image variation data is translated through the GPT-2 language model, and the result is output to the style adapter, wherein for each style from j=1 to m, a subset of β is first selected, wherein
Equal to the j-th pattern, training set S j And training by using GPT-2 language model parameters, and outputting a result after training to be a required adjustment instruction.
The instruction operation execution module described in the embodiment can convert the adjustment instruction sent by the meta-universe scene simulation center into a standard network management execution message, and has the functions of instruction exception monitoring, automatic calibration, intelligent evaluation of network management quota and historical instruction backtracking. The base station network manager is a platform for reasonably distributing and controlling the base stations to meet the requirements of service providers and network users. The base station network manager can collect the working parameters and working state information of the base station, display the information to management operators and receive the processing of the information and the working state information, send control instructions (changing working states or working parameters) to the base station from the processing results, monitor the execution results of the instructions and ensure that the base station works according to the requirements of the base station network manager.
Example 2
The embodiment provides a wireless network optimization method based on metauniverse, which is implemented by adopting the wireless network optimization system based on metauniverse described in the embodiment 1, and comprises the following steps:
s1, a base station provided with an antenna attitude meter according to the description of the figure 2 transmits the latest data of the state of the base station back to a meta-universe data processing center;
the latest data of the base station state comprises the latest industrial parameter data of the base station antenna, and mainly comprises equipment model, height, pitch angle, azimuth angle and position longitude and latitude.
S2, the metadata processing center can process base station feedback data, and process the base station feedback data into base station and environment three-dimensional model data by combining the base station real object, environment real object and text model library processed by the three-dimensional vector diagram processing module, and store the base station feedback data in the base station and environment three-dimensional model database;
the base station physical object comprises a base station equipment picture, a base station equipment model, a base station equipment manufacturer and an asset number; the environment real object comprises a surrounding real environment picture of the base station, a common building and an environment picture; a three-dimensional vector-related voice text description model stored in a text model library; the three-dimensional vector model data processing module converts the text description and the gesture data into three-dimensional model data of the base station and the environment through a text decoder, a feedback data decoder, a feature processor and a space-time consistency processor; the base station and environment three-dimensional model database stores processed base station and environment three-dimensional model data.
The three-dimensional vector model data processing module adopted in the embodiment converts the text description and the gesture data into the three-dimensional model data of the base station and the environment through the text decoder, the feedback data decoder, the feature processor and the space-time consistency processor, and the specific principle is as follows:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generated from group c base station attitude and position data, D representing a vectorThe dot multiplication operation, lambda represents an adjustable constant variable, which is used for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i The result of text position consistent calculation;
3) Space-time coincidence calculation: intercepting the position-text description pair in a given S second according to the result of consistent text position obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,
lambda represents an adjustable constant variable, then { g }, is obtained n And the three-dimensional model data of the base station and the environment of the target are obtained.
S3, an image synthesizer in a meta-universe scene simulation center renders base station and environment three-dimensional model data in a base station and environment three-dimensional model database into virtual scenes to be presented in an image display interface, and corresponding pictures are presented in VR equipment, wherein the specific steps are as follows;
s31, mapping the three-dimensional model data to rays: the image synthesizer emits a ray r=o+td from the origin o of the new view of the object through a point in the three-dimensional model data, where t represents the distance between the sampling points along the ray, and the origin is denoted o;
s32, inquiring ray characteristics: for each distance t to origin o k Of (c) at a position o+t k d and direction d are both sent as inputs to the MLP model (o+t k d,d)→(σ k ，c k ) The model outputs a corresponding density sigma k And RGB color c k As an extraction feature of the specific point;
s33, color rendering: and integrating the characteristics of each point on the ray r, and calculating the color C (r) of the corresponding pixel of the emergent ray r, wherein the expression is as follows:
wherein />
N represents the number of sampling points along ray r, t k To the point o+t along the ray r k d, i.e. the probability that the ray reaches that point without encountering any other point. In order to render an image with resolution H×W, the above steps are repeated H×W times, corresponding to the number of queries H×W×N of the MLP model, then the scene picture can be displayed on the image presentation interface;
s34, change adjustment: the user can change the image display interface by operating the VR device. The image decoder maps the pixels of the changed picture onto the rays in turn, uniformly samples all candidate points along the rays, then identifies pre-existing points through a query process based on occupied grids, the method is that the occupied grids are tiled into eight subspaces, a non-zero cube in the subspace closest to the origin of the target view is selected as the pre-existing point, and the steps are repeated to output image change data;
s35, instruction decoding: the image variation data output in S34 is subjected to instruction decoding by an instruction decoder, and the decoding process is as follows: the image variation data is translated through the GPT-2 language model, and the result is output to the style adapter, wherein for each style from j=1 to m, a subset of β is first selected, wherein
Equal to the j-th pattern, training set S j And training by using GPT-2 language model parameters, and outputting a result after training to be a required adjustment instruction.
S4, a management operator adjusts the display picture by operating the VR equipment, the image decoder converts the display picture into image change data, and finally the instruction decoder converts the image change data into an adjustment instruction;
the adjustment instruction mentioned in this embodiment refers to a chinese description of adjustment, for example, "adjust the azimuth of the a base station from 30 degrees to 90 degrees".
S5, the adjustment instruction enters an instruction operation execution module, and the instruction operation execution module converts the adjustment instruction into a standard network management execution message and pushes the standard network management execution message to the base station network management;
the standard network management execution message refers to a string of json character strings which are universal to each equipment manufacturer or are compliant with the restful design standard according to the http protocol after being adapted according to the actual network management conditions of each manufacturer;
s6, the base station network manager executes the message, sends the message to the base station provided with the antenna attitude meter to complete adjustment of the real base station, and returns to the step S1 to repeat the steps S1-S6.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
before the invention, when a wireless network problem needs to go to a problem site to adjust the base station, a work order permission is required to be applied for arranging vehicles, reservation personnel (including optimization personnel and tower work) acquire a parameter entering permission to enter a machine room by a contact property, and adjustment is completed. The general flow circulation needs 1 day, the coordination entrance needs 1 day, and the work output cost of vehicles and personnel is several.
If the invention is applied to the problem site, when a wireless network problem is found, management operators can perform virtual scene operation through VR equipment, and after the operation is completed, an adjustment instruction is issued to a base station network manager through an instruction operation execution module and is executed at a base station equipped with an antenna attitude instrument. The whole process lasts for a few minutes, and only 1 part of work fee is paid for management operators. Compared with the method before and after the implementation of the invention, the time and the labor cost can be saved by times. The invention can complete the acquisition of the real-time base station state information, solves the problems that part of base stations cannot get on the station, adjustment is difficult and quick implementation cannot be realized, and improves the working efficiency of wireless network optimization.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which is also intended to be covered by the present invention.
Claims (9)
1. The wireless network optimization system based on the meta universe is characterized by comprising a perception layer, an application layer and an interaction layer;
the sensing layer comprises a base station provided with an antenna attitude meter and an electric tuning antenna remote control unit, and is used for monitoring the running state of the base station in real time and transmitting the running state of the base station to the application layer, receiving a remote control instruction sent by the application layer, and adjusting the state of the antenna attitude meter through the electric tuning antenna remote control unit;
the application layer comprises a meta-universe data processing center, an instruction operation execution module and a base station network manager, wherein the meta-universe data processing center is used for receiving, processing and storing base station running state data transmitted by the perception layer, and the base station network manager sends a remote control instruction to a base station based on the processed base station running state data;
the interaction layer comprises a metauniverse scene simulation center, wherein the metauniverse scene simulation center constructs a metauniverse scene based on the base station running state data stored in the metauniverse, the metauniverse scene is displayed in VR equipment, the base station running state data is adjusted and updated through operation of the VR equipment, and the adjusted instruction is transmitted to the application layer.
2. The metauniverse-based wireless network optimization system of claim 1 wherein the metauniverse data processing center comprises a three-dimensional vector model library, a three-dimensional vector model data processing module, a vector graph processing module and a base station and environment three-dimensional model database, the three-dimensional vector model data processing module comprises a text decoder, a return data decoder, a feature processor and a space-time consistency processor, the three-dimensional vector model data processing module is used for processing base station return data, and processing the base station return data into base station and environment three-dimensional model data in combination with the base station real object and environment real object and the text model library processed by the vector graph processing module, and storing the base station return data in the base station and environment three-dimensional model database.
3. The meta-universe based wireless network optimization system of claim 2 wherein the process includes the steps of:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generating by the base station attitude position data of the c group, D representing vector point multiplication operation, lambda representing an adjustable constant variable for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i The result of text position consistent calculation;
3) Space-time coincidence calculation: intercepting the position-text description pair in a given S second according to the result of consistent text position obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,/>
/>
Lambda represents an adjustable constant variable, then { g }, is obtained n And the three-dimensional model data of the base station and the environment of the target are obtained.
4. The metauniverse-based wireless network optimization system of claim 3 wherein the metauniverse scene simulation center comprises a VR device, an image synthesizer, an image presentation interface, an image decoder and an instruction decoder, the image synthesizer is used for rendering the environmental three-dimensional model data processed by the metauniverse data processing center into an image, and the image presentation interface is presented in the VR device; and adjusting the displayed picture by controlling the VR equipment, converting the displayed picture into image variation data by the image decoder, and finally converting the image variation data into an adjustment instruction by the instruction decoder.
5. A meta-universe based wireless network optimization method, characterized in that the meta-universe based wireless network optimization system according to any one of claims 1-4 is implemented, comprising the following steps:
s1, a base station transmits the latest data of the base station state back to a metadata processing center for processing;
s2, the metadata processing center processes the received feedback data into base station and environment three-dimensional model data through a three-dimensional vector model data processing module by combining a text model database, and stores the base station and environment three-dimensional model data in the base station and environment three-dimensional model database;
s3, an image synthesizer in the meta-universe scene simulation center renders the base station and the environment three-dimensional model data in the base station and the environment three-dimensional model database into a virtual scene, and the virtual scene is presented in an image display interface and is presented in VR equipment;
s4, a management operator adjusts the display picture by operating the VR equipment, the image decoder converts the display picture into image change data, and finally the instruction decoder converts the image change data into an adjustment instruction;
s5, the adjustment instruction enters an instruction operation execution module, and the instruction operation execution module converts the adjustment instruction into a standard network management execution message and pushes the standard network management execution message to the base station network management;
s6, the base station network manager issues a network management execution message to the base station, adjusts the state of the antenna attitude meter through the electric adjustment antenna remote control unit to complete adjustment of the real base station, and repeats the step S1.
6. The meta-universe-based wireless network optimization method of claim 5, wherein step S2 specifically includes:
1) And (3) decoding data: the base station feedback data comprises a group of text description and base station attitude position data, and the text description t is obtained through mapping of a text decoder and a feedback data decoder respectively i And base station attitude and position data x i The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the base station entity obtains the vector characteristic p through a vector diagram processing module i The text description, the base station attitude position data and the vector features generated in a short time are expressed as
2) Text position coincidence calculation: in the feature processor, for the position-word description pair { x }, obtained at the same time i ，t i Construction of an inverse pair { x }, of samples i ，t j And (i +.j) are input together to the objective function
wherein xi C represents x i Generating by the base station attitude position data of the c group, D representing vector point multiplication operation, lambda representing an adjustable constant variable for dynamically adjusting the threshold; according to the result output by the result objective function, the data with consistent text will be converged, the data with inconsistent text will be pushed away, the final processed position-text description pair { x } i ，t i "i.e.)Results of the text position consistent calculation;
3) Space-time coincidence calculation: intercepting the position-text description pair in a given S second according to the result of consistent text position obtained in the step 2)
Through objective function->
Wherein>
Representing position-vector pairs->
Within the nth vector diagram, h n Is a semantically guided cross-modal fusion feature, and its expression is: />
wherein ,
lambda represents an adjustable constant variable, then { g }, is obtained n And the three-dimensional model data of the base station and the environment of the target are obtained.
7. The meta-universe-based wireless network optimization method of claim 6, wherein step S3 specifically includes:
s31, mapping the three-dimensional model data to rays: the image synthesizer emits a ray r=o+td from the origin o of the new view of the object through a point in the three-dimensional model data, where t represents the distance between the sampling points along the ray, and the origin is denoted o;
s32, inquiring ray characteristics: for each distance t to origin o k Of (c) at a position o+t k d and direction d are both sent as inputs to the MLP model (o+t k d,d)→(σ k ，c k ) The model outputs a corresponding density sigma k And RGB color c k As an extraction feature of the specific point;
s33, color rendering: and integrating the characteristics of each point on the ray r, and calculating the color C (r) of the corresponding pixel of the emergent ray r, wherein the expression is as follows:
wherein />
N represents the number of sampling points along ray r, t k To the point o+t along the ray r k d, i.e. the probability that the ray reaches that point without encountering any other point. In order to render an image with resolution H×W, the above steps are repeated H×W times, corresponding to the number of queries H×W×N of the MLP model, then the scene picture can be displayed on the image presentation interface;
s34, change adjustment: the user can change the image display interface by operating the VR device. The image decoder maps the pixels of the changed picture onto the rays in turn, uniformly samples all candidate points along the rays, then identifies pre-existing points through a query process based on occupied grids, the method is that the occupied grids are tiled into eight subspaces, a non-zero cube in the subspace closest to the origin of the target view is selected as the pre-existing point, and the steps are repeated to output image change data;
s35, instruction decoding: the image variation data output in S34 is subjected to instruction decoding by an instruction decoder, and the decoding process is as follows: image change data passing GPT-2The language model performs data translation, outputs the result to the style adapter, and for each style from j=1 to m, first selects a subset of β, where
Equal to the j-th pattern, training set S j And training by using GPT-2 language model parameters, and outputting a result after training to be a required adjustment instruction.
8. The meta-universe-based wireless network optimization method according to claim 5, wherein in step S6, the adjustment of the real base station is completed by adjusting the state of the antenna attitude meter by the electrically-tunable antenna remote control unit, specifically comprising: the remote control unit of the electrically-controlled antenna measures and rotates between two clamping points of the minimum value and the maximum value of the electronic downtilt angle of the supporting angle, so that the reading of the downtilt angle degree and the accurate adjustment are realized.
9. The meta-universe-based wireless network optimization method of claim 5 wherein the latest data of the base station state in step S1 includes the latest parameter data of the base station antenna, including equipment model, altitude, pitch angle, azimuth angle and position longitude and latitude.
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