CN117353788B - Intelligent control method and system applied to antenna gain system - Google Patents

Intelligent control method and system applied to antenna gain system Download PDF

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CN117353788B
CN117353788B CN202311351962.7A CN202311351962A CN117353788B CN 117353788 B CN117353788 B CN 117353788B CN 202311351962 A CN202311351962 A CN 202311351962A CN 117353788 B CN117353788 B CN 117353788B
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CN117353788A (en
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龚尚坤
汪娇艳
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Shenzhen Qiantang Technology Co ltd
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    • H—ELECTRICITY
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    • H04B7/14—Relay systems
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    • H04B7/155—Ground-based stations
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/155—Ground-based stations
    • H04B7/15528—Control of operation parameters of a relay station to exploit the physical medium
    • H04B7/1555—Selecting relay station antenna mode, e.g. selecting omnidirectional -, directional beams, selecting polarizations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/155—Ground-based stations
    • H04B7/15564—Relay station antennae loop interference reduction
    • H04B7/15578—Relay station antennae loop interference reduction by gain adjustment
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Abstract

The embodiment of the invention relates to the technical field of artificial intelligence, and provides an intelligent control method and system applied to an antenna gain system. By using the embodiment of the invention, the adaptive and intelligent antenna control can be realized by utilizing the target multi-mode antenna signal description and combining analysis and optimization of the antenna characteristics and association relation and identification and optimization of quantitative characterization knowledge, thereby improving the system performance and adapting to different communication scenes.

Description

Intelligent control method and system applied to antenna gain system
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an intelligent control method and system applied to an antenna gain system.
Background
An antenna gain system is an important device for wireless communication, which enhances the receiving and transmitting effects of signals by increasing the gain of an antenna. In wireless communications, an antenna plays a role in bridging between a user equipment and a network, and plays a vital role. Antenna gain refers to the ratio of the radiated power of an antenna in a certain direction relative to an ideal point source. The higher the gain, the more powerful the antenna system is in signal receiving and transmitting in a particular direction. Therefore, the antenna gain is critical for improving the communication quality, expanding the coverage and increasing the signal transmission distance.
The primary goal of antenna gain systems is to improve the reliability, coverage, and data transmission rate of wireless communications. By enhancing the gain of the antenna, the strength of the signal can be increased, thereby reducing the attenuation and interference of the signal and improving the receiving quality of the signal. Meanwhile, the antenna gain system can also increase the transmission distance of signals, expand the coverage area of a network and enable users to communicate in more distant places. In addition, the antenna gain system can also improve the data transmission rate. By increasing the strength of the signal and reducing the noise of the signal, the transmission rate and capacity of the signal can be increased, thereby increasing the efficiency and throughput of the communication system. In general, the antenna gain system is an essential component of wireless communication. Through optimizing the antenna structure, adjusting the antenna parameters and intelligently controlling the antenna gain, better signal receiving and transmitting effects can be realized, the communication quality and coverage range are improved, and further development and application of the wireless communication technology are promoted. Therefore, high quality regulation and control of the antenna gain system is important, but the traditional antenna gain system regulation and control technology has the problem of poor intelligent degree.
Disclosure of Invention
In order to improve the technical problems in the related art, the invention provides an intelligent control method and system applied to an antenna gain system.
In a first aspect, an embodiment of the present invention provides an intelligent control method applied to an antenna gain system, and applied to an AI intelligent control system, where the method includes: obtaining multi-mode antenna signal description generated by a u-1 th order signal description processing component in a signal description recognition algorithm; the signal description recognition algorithm comprises an X-order signal description processing component with a cascade relation, wherein the multi-mode antenna signal is described as a quantized representation knowledge set obtained after signal description mining is carried out on an antenna signal set to be analyzed, the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment in the antenna signal set to be analyzed respectively, X is an integer greater than 1, and u is an integer greater than 1 and not more than X;
V linkage signal description knowledge corresponding to a u-th order signal description processing component is obtained; the v linkage signal description knowledge is used for reflecting the linkage characteristics among the quantitative characterization knowledge in the multi-mode antenna signal description, and v is an integer greater than 1;
Sequentially obtaining description knowledge commonality coefficients of each quantized representation knowledge in the multi-mode antenna signal description and each linkage signal description knowledge in the v linkage signal description knowledge, and sequentially adjusting each quantized representation knowledge into quantized representation optimization knowledge based on the v description knowledge commonality coefficients pointing to the same quantized representation knowledge;
Determining a quantized representation optimization knowledge set obtained by integrating the quantized representation optimization knowledge as a target multi-mode antenna signal description obtained by identification;
and regulating and controlling an antenna gain system by utilizing the target multi-mode antenna signal description.
In some aspects, the sequentially obtaining the descriptive knowledge commonality coefficients of each of the multi-modal antenna signal descriptions and each of the v linkage signal description knowledge, and sequentially adjusting each of the quantitative characterization knowledge to a quantitative characterization optimization knowledge based on the v descriptive knowledge commonalities pointing to the same quantitative characterization knowledge includes:
The following scheme is circularly implemented until each quantitative characterization knowledge in the multi-mode antenna signal description is completely walked:
Obtaining a quantized characterization knowledge from the multi-mode antenna signal description as a current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the current quantized characterization knowledge;
p pieces of front and rear sequence quantization characterization knowledge continuous with the current quantization characterization knowledge and a second description knowledge commonality coefficient between the current quantization characterization knowledge are respectively obtained; the difference between the position labels of the front and rear quantitative characterization knowledge between the first position label in the quantitative characterization knowledge set and the second position label of the current quantitative characterization knowledge accords with the front and rear judgment requirement, and P is an integer greater than 1;
And changing the current quantitative characterization knowledge into the quantitative characterization optimization knowledge based on the v first description knowledge commonality coefficients and the P second description knowledge commonality coefficients.
In some aspects, the altering the current quantitative characterization knowledge to the quantitative characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients includes:
Determining first characteristic strengthening factors corresponding to the linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients;
determining second characteristic strengthening factors corresponding to the respective front and rear quantitative characterization knowledge based on the P second description knowledge commonality coefficients respectively;
Determining first description characteristic strengthening knowledge according to v linkage signal description knowledge and the corresponding first characteristic strengthening factors, and determining second description characteristic strengthening knowledge according to P previous and subsequent quantitative characterization knowledge and the corresponding second characteristic strengthening factors;
And determining the aggregate result of the first descriptive feature enhancement knowledge and the second descriptive feature enhancement knowledge as the quantitative characterization optimization knowledge.
In some aspects, the obtaining v first description knowledge commonality coefficients corresponding to the current quantization characterization knowledge includes:
Performing characteristic multiplication operation on the current quantitative characterization knowledge and the v linkage signal description knowledge respectively, and determining v first characteristic multiplication results as v first description knowledge commonality coefficients;
the obtaining P pieces of previous and subsequent quantized representation knowledge continuous with the current quantized representation knowledge, and the second descriptive knowledge commonality coefficient between the current quantized representation knowledge, respectively, includes:
And respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the P previous and subsequent quantitative characterization knowledge, and determining P second characteristic multiplication results as P second description knowledge commonality coefficients.
In some aspects, determining second feature reinforcement factors corresponding to each of the preceding and following quantitative characterization knowledge based on the P second descriptive knowledge commonality coefficients, respectively, includes:
determining a first eigenvalue based on the v first descriptive knowledge commonality coefficients and the P second descriptive knowledge commonality coefficients;
Determining v second eigenvalues corresponding to the v linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients, obtaining first comparison variables of the v second eigenvalues and the first eigenvalues respectively, and determining the v first comparison variables as the first characteristic strengthening factors corresponding to the v linkage signal description knowledge respectively;
And respectively determining P third eigenvalues corresponding to the P pieces of front and rear quantitative characterization knowledge based on the P pieces of second descriptive knowledge commonality coefficients, respectively obtaining second comparison variables of the P third eigenvalues and the first eigenvalues, and determining the P second comparison variables as the second characteristic strengthening factors corresponding to the P pieces of front and rear quantitative characterization knowledge.
In some aspects, before obtaining the v linkage signal description knowledge corresponding to the u-th order signal description processing component, the method further includes:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component;
the following scheme is circularly implemented until the linkage signal description knowledge of the v to-be-changed is moved out:
Acquiring one linkage signal description knowledge to be changed from the v linkage signal description knowledge to be changed as current linkage signal description knowledge;
respectively obtaining a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed;
respectively obtaining a fourth description knowledge commonality coefficient between the current linkage signal description knowledge and each quantized representation knowledge in the multi-mode antenna signal description;
And using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient to change the current linkage signal description knowledge.
In some aspects, the modifying the current linkage signal description knowledge using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient comprises:
Respectively determining third characteristic strengthening factors corresponding to the linkage signal description knowledge to be changed according to v-1 third description knowledge commonality coefficients;
determining fourth characteristic strengthening factors corresponding to the quantitative characterization knowledge according to the G fourth description knowledge commonality coefficients respectively; the multi-mode antenna signal description comprises G pieces of quantitative characterization knowledge, wherein G is an integer greater than 1;
Determining third description characteristic strengthening knowledge based on the v-1 linkage signal description knowledge to be changed and the corresponding third characteristic strengthening factors, and determining fourth description characteristic strengthening knowledge based on the G quantitative characterization knowledge and the corresponding fourth characteristic strengthening factors;
Determining the linkage signal description knowledge of the completed change based on the third description feature enrichment knowledge and the fourth description feature enrichment knowledge.
In some aspects, the obtaining the linkage signal description knowledge of v to-be-changed corresponding to the u-th order signal description processing component includes:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the v linkage signal description knowledge to be changed is the linkage signal description knowledge determined by the signal description recognition algorithm in the debugging process;
Or alternatively
And v pieces of linkage signal description knowledge corresponding to the u-1 th order signal description processing component are used as v pieces of linkage signal description knowledge to be changed corresponding to the u-1 th order signal description processing component.
In some aspects, after the determining the quantized characterization optimization knowledge set obtained by integrating the quantized characterization optimization knowledge set as identifying the obtained target multi-mode antenna signal description, the method further includes:
Performing interval numerical mapping processing on the target multi-modal antenna signal description and the multi-modal antenna signal description generated by the u-1 order signal description processing component to obtain multi-modal antenna signal mapping description;
When u is the same as X, determining a signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description;
and when the u is smaller than the X, inputting the derivative result of the multi-mode antenna signal mapping description into a (u+1) th order signal description processing component.
In some aspects, after the determining, based on the multi-mode antenna signal mapping description, a signal description recognition result generated by the signal description recognition algorithm when the u is the same as the X, the method further includes:
Performing first signal decoding on the signal description identification result to obtain an expected antenna signal set matched with the antenna signal set to be analyzed;
performing second signal decoding on the signal description identification result to determine key signal fragments included in the antenna signal set to be analyzed;
And performing third signal decoding on the signal description recognition result to obtain a signal optimization control decision type matched with the antenna signal set to be analyzed.
In a second aspect, the present invention further provides an AI intelligent control system, where the AI intelligent control system is configured to:
Collecting a to-be-analyzed antenna signal set of an antenna gain system;
obtaining multi-mode antenna signal description generated by a u-1 th order signal description processing component in a signal description recognition algorithm; the signal description recognition algorithm comprises an X-order signal description processing component with a cascade relation, wherein the multi-mode antenna signal is described as a quantized representation knowledge set obtained after signal description mining is carried out on an antenna signal set to be analyzed, the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment in the antenna signal set to be analyzed respectively, X is an integer greater than 1, and u is an integer greater than 1 and not more than X;
V linkage signal description knowledge corresponding to a u-th order signal description processing component is obtained; the v linkage signal description knowledge is used for reflecting the linkage characteristics among the quantitative characterization knowledge in the multi-mode antenna signal description, and v is an integer greater than 1;
Sequentially obtaining description knowledge commonality coefficients of each quantized representation knowledge in the multi-mode antenna signal description and each linkage signal description knowledge in the v linkage signal description knowledge, and sequentially adjusting each quantized representation knowledge into quantized representation optimization knowledge based on the v description knowledge commonality coefficients pointing to the same quantized representation knowledge;
Determining a quantized representation optimization knowledge set obtained by integrating the quantized representation optimization knowledge as a target multi-mode antenna signal description obtained by identification;
and regulating and controlling an antenna gain system by utilizing the target multi-mode antenna signal description.
In some aspects, the sequentially obtaining the descriptive knowledge commonality coefficients of each of the multi-modal antenna signal descriptions and each of the v linkage signal description knowledge, and sequentially adjusting each of the quantitative characterization knowledge to a quantitative characterization optimization knowledge based on the v descriptive knowledge commonalities pointing to the same quantitative characterization knowledge includes:
The following scheme is circularly implemented until each quantitative characterization knowledge in the multi-mode antenna signal description is completely walked:
Obtaining a quantized characterization knowledge from the multi-mode antenna signal description as a current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the current quantized characterization knowledge;
p pieces of front and rear sequence quantization characterization knowledge continuous with the current quantization characterization knowledge and a second description knowledge commonality coefficient between the current quantization characterization knowledge are respectively obtained; the difference between the position labels of the front and rear quantitative characterization knowledge between the first position label in the quantitative characterization knowledge set and the second position label of the current quantitative characterization knowledge accords with the front and rear judgment requirement, and P is an integer greater than 1;
changing the current quantized characterization knowledge into the quantized characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients;
Wherein the changing the current quantization characterization knowledge to the quantization characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients comprises: determining first characteristic strengthening factors corresponding to the linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients; determining second characteristic strengthening factors corresponding to the respective front and rear quantitative characterization knowledge based on the P second description knowledge commonality coefficients respectively; determining first description characteristic strengthening knowledge according to v linkage signal description knowledge and the corresponding first characteristic strengthening factors, and determining second description characteristic strengthening knowledge according to P previous and subsequent quantitative characterization knowledge and the corresponding second characteristic strengthening factors; determining an aggregate result of the first descriptive feature-enhanced knowledge and the second descriptive feature-enhanced knowledge as the quantitative characterization optimization knowledge;
Wherein determining second feature reinforcement factors corresponding to each of the preceding and following quantized representation knowledge based on the P second description knowledge commonality coefficients, respectively, comprises: determining a first eigenvalue based on the v first descriptive knowledge commonality coefficients and the P second descriptive knowledge commonality coefficients; determining v second eigenvalues corresponding to the v linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients, obtaining first comparison variables of the v second eigenvalues and the first eigenvalues respectively, and determining the v first comparison variables as the first characteristic strengthening factors corresponding to the v linkage signal description knowledge respectively; determining P third eigenvalues corresponding to the P preceding and following quantized representation knowledge based on the P second descriptive knowledge commonality coefficients respectively, obtaining second comparison variables of the P third eigenvalues and the first eigenvalues respectively, and determining the P second comparison variables as the second characteristic strengthening factors corresponding to the P preceding and following quantized representation knowledge respectively;
Wherein the obtaining v first description knowledge commonality coefficients corresponding to the current quantization characterization knowledge includes: performing characteristic multiplication operation on the current quantitative characterization knowledge and the v linkage signal description knowledge respectively, and determining v first characteristic multiplication results as v first description knowledge commonality coefficients; the obtaining P pieces of previous and subsequent quantized representation knowledge continuous with the current quantized representation knowledge, and the second descriptive knowledge commonality coefficient between the current quantized representation knowledge, respectively, includes: and respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the P previous and subsequent quantitative characterization knowledge, and determining P second characteristic multiplication results as P second description knowledge commonality coefficients.
In some aspects, before the obtaining the v linkage signal description knowledge corresponding to the u-th order signal description processing component, the AI intelligent control system is further configured to:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component;
the following scheme is circularly implemented until the linkage signal description knowledge of the v to-be-changed is moved out:
Acquiring one linkage signal description knowledge to be changed from the v linkage signal description knowledge to be changed as current linkage signal description knowledge;
respectively obtaining a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed;
respectively obtaining a fourth description knowledge commonality coefficient between the current linkage signal description knowledge and each quantized representation knowledge in the multi-mode antenna signal description;
Changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient;
Wherein the changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient includes:
Respectively determining third characteristic strengthening factors corresponding to the linkage signal description knowledge to be changed according to v-1 third description knowledge commonality coefficients;
determining fourth characteristic strengthening factors corresponding to the quantitative characterization knowledge according to the G fourth description knowledge commonality coefficients respectively; the multi-mode antenna signal description comprises G pieces of quantitative characterization knowledge, wherein G is an integer greater than 1;
Determining third description characteristic strengthening knowledge based on the v-1 linkage signal description knowledge to be changed and the corresponding third characteristic strengthening factors, and determining fourth description characteristic strengthening knowledge based on the G quantitative characterization knowledge and the corresponding fourth characteristic strengthening factors;
determining the linkage signal description knowledge of the completed change based on the third description feature enrichment knowledge and the fourth description feature enrichment knowledge;
The step of obtaining v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component comprises the following steps:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the v linkage signal description knowledge to be changed is the linkage signal description knowledge determined by the signal description recognition algorithm in the debugging process;
Or alternatively
And v pieces of linkage signal description knowledge corresponding to the u-1 th order signal description processing component are used as v pieces of linkage signal description knowledge to be changed corresponding to the u-1 th order signal description processing component.
In some aspects, after the determining the quantized characterization optimization knowledge set resulting from the integrating the quantized characterization optimization knowledge set as identifying the resulting target multi-modal antenna signal description, the AI intelligent control system is further configured to:
Performing interval numerical mapping processing on the target multi-modal antenna signal description and the multi-modal antenna signal description generated by the u-1 order signal description processing component to obtain multi-modal antenna signal mapping description;
When u is the same as X, determining a signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description;
When u is smaller than X, inputting the derivative result of the multi-mode antenna signal mapping description into a (u+1) -th order signal description processing component;
Wherein, after determining the signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description when the u is the same as the X, the AI intelligent control system is further configured to:
Performing first signal decoding on the signal description identification result to obtain an expected antenna signal set matched with the antenna signal set to be analyzed;
performing second signal decoding on the signal description identification result to determine key signal fragments included in the antenna signal set to be analyzed;
And performing third signal decoding on the signal description recognition result to obtain a signal optimization control decision type matched with the antenna signal set to be analyzed.
In a third aspect, the present invention also provides an AI intelligent control system, including 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.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a program which when executed by a processor implements the method described above.
With the embodiment of the invention, first, a multi-mode antenna signal description is generated by using a signal description processing component in a signal description identification algorithm. This process involves signal description processing components in a cascade relationship, where the X-th order signal description processing component is associated with the u-1 th order signal description processing component. A quantized representation knowledge set is obtained by performing signal description mining on the antenna signal set to be analyzed, wherein the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment. Next, v pieces of linkage signal description knowledge corresponding to the u-th order signal description processing component are acquired. These linkage signal description knowledge reflect the linkage characteristics between the quantitative characterization knowledge in the multi-modal antenna signal description. And then calculating the description knowledge commonality coefficient between the quantized characterization knowledge and the linkage signal description knowledge in the multi-mode antenna signal description one by one. These co-coefficients are used to measure the degree of similarity between the two descriptive knowledge. And adjusting each quantization characterization knowledge according to v description knowledge commonality coefficients pointing to the same quantization characterization knowledge to form quantization characterization optimization knowledge. And finally, taking the integrated quantitative characterization optimization knowledge as a target multi-mode antenna signal description. The antenna gain system can be regulated and controlled by utilizing the target multi-mode antenna signal description. Specifically, according to the information in the target multi-mode antenna signal description, antenna parameters such as gain and the like can be automatically adjusted so as to improve the system performance to the maximum extent. Therefore, by identifying and optimizing quantitative characterization knowledge and utilizing target multi-mode antenna signal descriptions to regulate and control an antenna gain system, self-adaptive and intelligent antenna control can be realized so as to adapt to different communication scenes and improve system performance.
Drawings
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 schematic flow chart of an intelligent control method applied to an antenna gain system according to an embodiment of the present invention.
Detailed Description
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.
It should be noted that the terms "first," "second," and the like in the description of the present invention and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiment provided by the embodiment of the invention can be executed in an AI intelligent control system, a computer device or a similar computing device. Taking the example of operation on an AI intelligent control system, the AI intelligent control system may include one or more processors (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA or the like) and a memory for storing data, and optionally, the AI intelligent control system may further include a transmission device for communication functions. It will be appreciated by those of ordinary skill in the art that the above-described configuration is merely illustrative, and is not intended to limit the configuration of the above-described AI intelligent control system. For example, the AI intelligent control system may also include more or fewer components than those shown above, or have a different configuration than those shown above.
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 an intelligent control method applied to an antenna gain system in an embodiment of the present invention, and the processor executes the computer program stored in the memory, thereby performing various functional applications and data processing, that is, implementing 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 AI intelligent 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 AI intelligent 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.
Based on this, referring to fig. 1, fig. 1 is a flowchart of an intelligent control method applied to an antenna gain system according to an embodiment of the present invention, where the method is applied to an AI intelligent control system, and further includes steps 101-105.
Step 101, obtaining the multi-mode antenna signal description generated by a u-1 order signal description processing component in the signal description recognition algorithm.
The signal description recognition algorithm comprises an X-order signal description processing component with a cascade relation, the multi-mode antenna signal is described as a quantized representation knowledge set obtained after signal description mining is carried out on a to-be-analyzed antenna signal set, the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment in the to-be-analyzed antenna signal set respectively, X is an integer greater than 1, and u is an integer greater than 1 and not exceeding X.
Step 102, v linkage signal description knowledge corresponding to the u-th order signal description processing component is obtained.
The v linkage signal description knowledge is used for reflecting the linkage characteristics among the quantitative characterization knowledge in the multi-mode antenna signal description, and v is an integer greater than 1.
Step 103, sequentially obtaining the description knowledge commonality coefficients of each quantized representation knowledge in the multi-mode antenna signal description and each linkage signal description knowledge in the v linkage signal description knowledge, and sequentially adjusting each quantized representation knowledge into quantized representation optimization knowledge based on the v description knowledge commonality coefficients pointing to the same quantized representation knowledge.
And 104, determining a quantized representation optimization knowledge set obtained by integrating the quantized representation optimization knowledge as a target multi-mode antenna signal description obtained by identification.
And 105, regulating and controlling an antenna gain system by using the target multi-mode antenna signal description.
Applying steps 101-105 described above, first, a multi-modal antenna signal description is generated using a signal description processing component in a signal description identification algorithm. This process involves signal description processing components in a cascade relationship, where the X-th order signal description processing component is associated with the u-1 th order signal description processing component. A quantized representation knowledge set is obtained by performing signal description mining on the antenna signal set to be analyzed, wherein the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment. Next, v pieces of linkage signal description knowledge corresponding to the u-th order signal description processing component are acquired. These linkage signal description knowledge reflect the linkage characteristics between the quantitative characterization knowledge in the multi-modal antenna signal description. And then calculating the description knowledge commonality coefficient between the quantized characterization knowledge and the linkage signal description knowledge in the multi-mode antenna signal description one by one. These co-coefficients are used to measure the degree of similarity between the two descriptive knowledge. And adjusting each quantization characterization knowledge according to v description knowledge commonality coefficients pointing to the same quantization characterization knowledge to form quantization characterization optimization knowledge. And finally, taking the integrated quantitative characterization optimization knowledge as a target multi-mode antenna signal description. The antenna gain system can be regulated and controlled by utilizing the target multi-mode antenna signal description. Specifically, according to the information in the target multi-mode antenna signal description, antenna parameters such as gain and the like can be automatically adjusted so as to improve the system performance to the maximum extent. Therefore, by identifying and optimizing quantitative characterization knowledge and utilizing target multi-mode antenna signal descriptions to regulate and control an antenna gain system, self-adaptive and intelligent antenna control can be realized so as to adapt to different communication scenes and improve system performance.
When referring to intelligent control in an antenna gain system, the following examples may be considered:
1) Antenna array adaptive beamforming: in a wireless communication system, an antenna array may form a beam of a particular direction by adjusting the phase and amplitude of each antenna element. The intelligent control can automatically adjust the direction and the width of the wave beam according to the received signal quality and the interference condition so as to improve the signal quality to the greatest extent and reduce the interference;
2) Multi-user interference cancellation: in a multi-user environment, the antenna gain system may utilize intelligent control techniques to reduce interference between users. By analyzing the received signal and interference conditions, the system can automatically select the optimal antenna configuration and beamforming strategy to minimize interference and increase the data transmission rate for each user;
3) Antenna resource optimization allocation: in some scenarios, the antenna resources may be limited, e.g. the number of antennas in a mobile communication base station is limited. The intelligent control can automatically adjust the distribution mode of antenna resources according to real-time communication requirements and a network topological structure so as to improve the overall system performance to the greatest extent;
4) Cross-band and cross-mode adaptation: the intelligent control can also enable the antenna gain system to adapt to different frequency bands and communication modes. By analyzing the received signal characteristics and communication requirements, the system can automatically select the optimal antenna configuration and parameter settings to accommodate different communication scenarios.
In summary, by using the target multi-mode antenna signal description, and combining analysis and optimization of antenna characteristics and association relationships, and identification and optimization of quantitative characterization knowledge, self-adaptive and intelligent antenna control can be realized, system performance is improved, and different communication scenes are adapted.
The following is an explanation of steps 101 to 105, respectively.
In step 101, a signal description recognition algorithm is an algorithm for analyzing and processing signals for extracting features and attributes of the signals. For example, algorithms based on time domain, frequency domain, or other fields may be used to extract information of amplitude, phase, frequency, etc. of the signal. The X-order signal description processing components with cascade relation refer to X processing components which are connected in a cascade mode in a signal description recognition algorithm, and each component is responsible for signal description processing of different stages. For example, the first stage may be filtering and denoising the original signal, the second stage may be extracting spectral features of the signal, and so on. The multi-mode antenna signal description is a quantized representation knowledge set obtained after signal description mining is carried out on the antenna signal set to be analyzed. These characterization knowledge may include characteristics, statistics, spectral distribution, etc. of the individual signal segments. The multi-modal representation means that different signal descriptions may describe the same antenna signal from different angles. The quantitative characterization knowledge set is used to describe a digitized knowledge set of signal features and attributes. For example, the energy value, spectral range, pulse width, etc. of a certain signal segment may be used as quantitative characterization knowledge. Definition of X and u: x is an integer greater than 1, representing the order of the signal description processing component; u is an integer greater than 1 and not exceeding X, representing the order of the current processing component.
An antenna gain system is assumed, which comprises a signal description recognition algorithm consisting of three signal description processing components (x=3) having a cascade relationship. In the processing component of the second stage (u=2), a multi-modal antenna signal description is obtained, which description contains various quantitative characterization knowledge extracted from the antenna signal set to be analyzed, such as spectral range, pulse width and energy values of the signal segments, etc. These quantitative characterization knowledge will be used for subsequent optimization and tuning steps.
Further, in step 102, linkage signal description knowledge (Interconnected Signal Description Knowledge) refers to knowledge that reflects the linkage characteristics between different quantitative characterization knowledge in the multi-modal antenna signal description. The linkage signal description knowledge may describe the association or interaction between different quantitative characterization knowledge. For example, in a radar system, linkage signal description knowledge may represent a correlation between a target signal and an interfering signal.
An adaptive antenna system is provided that adjusts the gain of the antenna based on the received signal to optimize performance. In this system, step 102 involves obtaining v linkage signal description knowledge corresponding to the u-th order signal description processing component. For example, when in step 101, a multi-modal antenna signal description has been generated by a signal description identification algorithm. These signal descriptions may include characteristics of signal strength, frequency distribution, etc. In step 102, v pieces of linkage signal description knowledge corresponding to the u-th order signal description processing component need to be obtained. The hypothetical multi-modal antenna signal description contains the following features: signal strength, frequency distribution, and signal phase. In this case, the u-th order signal description processing component may be a component that performs processing for signal strength. In step 102, v linkage signal description knowledge corresponding to the component needs to be obtained. These linkage signal descriptive knowledge may describe the relationship between signal strength and other characteristics such as frequency distribution and signal phase. For example, by analyzing a large number of data sets, it can be found that when the signal strength is higher, the frequency distribution is more concentrated and the signal phase is more stable. These associations constitute linkage signal description knowledge. These linkage signal descriptive knowledge can be obtained through step 102 and used in subsequent steps to optimize the performance of the antenna system.
In addition, in step 103, a description knowledge commonality coefficient (DESCRIPTIVE KNOWLEDGE COEFFICIENT) refers to the degree of commonality between the characterization knowledge and the linkage signal description knowledge. Describing knowledge commonality coefficients may be used to measure similarity or correlation between two knowledge. For example, in an antenna system, the commonality coefficients between the different quantitative characterization knowledge and the linkage signal description knowledge can be calculated to determine the degree of correlation between them.
An adaptive antenna control system is provided for optimizing radio communication quality. In the system, step 103 involves sequentially obtaining description knowledge commonality coefficients between each quantized characterization knowledge in the multi-mode antenna signal description and the linkage signal description knowledge, and adjusting each quantized characterization knowledge to quantized characterization optimization knowledge based on the commonality coefficients. The multi-modal antenna signal description is assumed to include characteristics such as signal strength, frequency distribution, and signal phase. In step 103, a description knowledge commonality coefficient between each quantized characterization knowledge and the linkage signal description knowledge needs to be calculated. For example, a commonality coefficient between the knowledge of the quantized representation of the signal strength and the knowledge of the linked signal description of the frequency distribution may be calculated to measure the correlation between them. By calculating the common coefficients between all the quantized characterization knowledge and the linkage signal description knowledge, the judgment of which quantized characterization knowledge and linkage signal description knowledge have higher common degree can be performed. The individual quantized characterization knowledge may then be sequentially adjusted to quantized characterization optimization knowledge based on these commonalities for use in subsequent steps. Step 103 involves calculating description knowledge commonality coefficients between each quantized characterization knowledge in the multi-modal antenna signal description and the linkage signal description knowledge, and adjusting each quantized characterization knowledge to quantized characterization optimization knowledge based on the commonality coefficients. This may improve the performance and adaptability of the antenna control system.
In step 104, the nouns quantifying the characterization optimization knowledge set are interpreted as follows: in step 103, the optimized knowledge set is obtained after adjustment according to the commonality coefficient between the quantized representation knowledge. This knowledge can be used to characterize the multi-modal antenna signal.
Such as: it is assumed that a multi-modal set of antenna signals is being processed, which includes signal segments of different frequencies, power levels and phases. By analyzing these signal segments and extracting relevant features, a set of quantitative characterization knowledge, such as frequency ranges, power values, phase differences, etc., can be obtained. Then, the adjustment is performed according to the common coefficient between the knowledge, so as to obtain a set of optimized knowledge sets, and the knowledge can better describe the characteristics of the multi-mode antenna signals.
And the term describing the target multi-mode antenna signal is explained as follows: and determining to identify the obtained target multi-mode antenna signal description through the integrated and optimized quantitative characterization optimization knowledge set. This description may be used to regulate the antenna gain system.
In the above example, a set of optimized knowledge sets is obtained via step 103, e.g. frequency range of 2GHz,4GHz, power values of-20 dBm, -10dBm, and phase difference of 45 degrees. The knowledge sets form target multi-mode antenna signal descriptions, which can be applied to an antenna gain system, and parameters such as gain, direction and the like of the antenna are regulated and controlled according to the descriptions so as to adapt to different communication scenes.
Step 104 obtains a set of target multi-mode antenna signal descriptions through integrating and optimizing quantitative characterization knowledge, and the descriptions can be used for regulating and controlling an antenna gain system so as to improve system performance and adapt to different communication scenes.
In step 105, the target multi-modal antenna signal description refers to the integrated and optimized quantized representation optimization knowledge set determined in step 104. This description may be used to regulate the antenna gain system. It is assumed that a set of target multi-mode antenna signal descriptions is obtained through step 104, including a frequency range of [2GHz,4GHz ], a power value of [ -20dBm, -10dBm ], a phase difference of 45 degrees, and the like. These descriptions reflect the desired antenna signal characteristics that can be used to regulate the antenna gain system.
Further, the regulation and control of the antenna gain system means that the parameters of the antenna gain system are controlled by utilizing the target multi-mode antenna signal description so as to realize performance optimization and adapt to different communication scenes. Assuming an antenna gain system, signal reception or transmission can be optimized by adjusting parameters such as gain, direction, etc. According to the target multi-mode antenna signal description obtained in step 104, the method can be applied to an antenna gain system, and according to the information such as the frequency range, the power value, the phase difference and the like in the description, the gain and the direction of the antenna are dynamically adjusted so as to adapt to different communication scenes. For example, in a high signal-to-noise environment, a higher gain and appropriate direction may be selected to enhance signal reception. While in low signal-to-noise environments, the gain may be reduced to reduce interference.
Step 105 utilizes the target multi-mode antenna signal description to regulate and control the antenna gain system, and dynamically adjusts antenna parameters according to the information in the description so as to improve the system performance and adapt to different communication scenes.
In some optional embodiments, sequentially obtaining the descriptive knowledge commonality coefficients of each of the quantized characterization knowledge in the multi-mode antenna signal description and each of the v coordinated signal description knowledge in step 103, and sequentially adjusting each of the quantized characterization knowledge to quantized characterization optimization knowledge based on v descriptive knowledge commonalities pointing to the same quantized characterization knowledge includes the steps of: the following substep 1031-substep 1033 is performed in a loop until each of the quantitative characterization knowledge in the multi-modal antenna signal description has been walked.
Sub-step 1031, obtaining a quantized characterization knowledge from the multi-mode antenna signal description as a current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the current quantized characterization knowledge.
Sub-step 1032, respectively obtaining P pieces of previous and subsequent quantized characterization knowledge continuous with the current quantized characterization knowledge, and a second descriptive knowledge commonality coefficient between the current quantized characterization knowledge; the difference between the position labels of the front and rear quantitative characterization knowledge between the first position label in the quantitative characterization knowledge set and the second position label of the current quantitative characterization knowledge meets the front and rear judgment requirement, and P is an integer greater than 1.
And 1033, changing the current quantitative characterization knowledge into the quantitative characterization optimization knowledge based on the v first description knowledge commonality coefficients and the P second description knowledge commonality coefficients.
In sub-step 1031, the quantitative characterization knowledge refers to the numerical expression used to represent a particular feature or attribute in the multi-modal antenna signal description. For example, power, frequency, direction, etc. may be used as quantitative characterization knowledge. The description knowledge commonality coefficient refers to the degree of correlation between linkage signal description knowledge and quantitative characterization knowledge. The strength of the association between the two can be determined by calculating the coefficient of commonality. For example, if a quantized representation knowledge is highly correlated with a linkage signal description knowledge, then its description knowledge commonality coefficient is large.
Assume a multi-modal antenna signal description that includes three different pieces of quantitative characterization knowledge: power, frequency and direction. There are also two linkage signal descriptive knowledge: a and B. For the quantized representation knowledge power, the quantized representation knowledge power has very high correlation with the linkage signal description knowledge A, and has a large commonality coefficient; and the correlation with the linkage signal description knowledge B is lower, and the commonality coefficient is smaller.
In sub-step 1032, the preceding and following quantized representation knowledge refers to the preceding or following quantized representation knowledge in the set of quantized representation knowledge that is arranged consecutively to the current quantized representation knowledge. According to the front-back sequence judging requirements, the difference between the position labels of the quantitative characterization knowledge and the position labels of the current quantitative characterization knowledge accords with a specific condition. The second descriptive knowledge commonality coefficient refers to a degree of correlation between the current quantitative characterization knowledge and the previous and subsequent quantitative characterization knowledge. The strength of the association between the second descriptive knowledge commonality coefficients may be determined by calculating them.
Assume that there are five quantitative characterization knowledge in the quantitative characterization knowledge set: A. b, C, D and E. The current quantization characterization knowledge is B. And selecting front and rear quantitative characterization knowledge which is continuously arranged with the B as front and rear quantitative characterization knowledge according to the front and rear judgment requirements. Let the front and back quantitative characterization knowledge be a and C, respectively. And calculating a second descriptive knowledge commonality coefficient of B and A and a second descriptive knowledge commonality coefficient of B and C to determine the association strength between the two.
In sub-step 1033, the characterization optimization knowledge is quantized: the current quantitative characterization knowledge is adjusted based on the descriptive knowledge commonality coefficient, so that the current quantitative characterization knowledge is more in line with the actual situation or the process of system requirements. The adjusted knowledge is referred to as quantitative characterization optimization knowledge.
Assume that there is a quantized representation knowledge set that contains five quantized representation knowledge: A. b, C, D and E. The current quantization characterization knowledge is B. According to the calculated first description knowledge commonality coefficient and the second description knowledge commonality coefficient, the current quantization characterization knowledge B can be adjusted to be more in line with the system requirement or actual situation, for example, the current quantization characterization knowledge B is adjusted to be B', so that quantization characterization optimization knowledge is obtained.
Applying substep 1031-substep 1033, first, selecting one quantized characterization knowledge from the multi-mode antenna signal descriptions as the current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the quantized characterization knowledge. In this way, the correlation between the current quantitative characterization knowledge and the different linkage signal description knowledge can be understood. And secondly, in the continuous front and back quantitative characterization knowledge, P pieces of quantitative characterization knowledge adjacent to the current quantitative characterization knowledge are respectively obtained, and a second descriptive knowledge commonality coefficient between the current quantitative characterization knowledge and the quantitative characterization knowledge is calculated. Through this step, the degree of association between the current and surrounding quantitative characterization knowledge can be further explored. And finally, adjusting the current quantitative characterization knowledge based on v first description knowledge commonality coefficients and P second description knowledge commonality coefficients to enable the current quantitative characterization knowledge to better meet the actual situation or system requirement, thereby obtaining quantitative characterization optimization knowledge. Through the process, quantitative characterization knowledge is gradually optimized to improve the performance of the antenna gain system and enable the antenna gain system to be better suitable for different communication scenes. Through the above process, the quantitative characterization knowledge can be effectively extracted and optimized, so that the performance of the antenna gain system is improved, and the actual requirements are better met.
In some examples, altering the current quantitative characterization knowledge to the quantitative characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients in substep 1033 includes steps 10331-10334.
Step 10331, determining first characteristic strengthening factors corresponding to the linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients.
Step 10332, determining second characteristic strengthening factors corresponding to the respective front and rear quantitative characterization knowledge based on the P second descriptive knowledge commonality coefficients.
Step 10333, determining first description feature enhancement knowledge according to v linkage signal description knowledge and the corresponding first feature enhancement factors, and determining second description feature enhancement knowledge according to P previous and subsequent quantized characterization knowledge and the corresponding second feature enhancement factors.
Step 10334, determining an aggregate result of the first descriptive feature enriched knowledge and the second descriptive feature enriched knowledge as the quantitative characterization optimization knowledge.
The first feature enhancement factor in step 10331 is determined based on v first descriptive knowledge commonality coefficients, which are used to enhance features of the linkage signal descriptive knowledge. For example, in a smart antenna system, there are multiple coordinated signal descriptions such as received signal strength, signal to noise ratio, etc. Each linkage signal description knowledge has a corresponding first characteristic enhancement factor that determines the importance of the linkage signal description knowledge in quantitatively characterizing the optimization knowledge.
The second feature enhancement factor in step 10332 is determined based on the P second descriptive knowledge commonality coefficients, which are used to enhance the features of the underlying quantitative characterization knowledge. For example, in a smart antenna system, there are multiple pieces of knowledge of the quantitative characterization of the front and back, such as antenna gain, directivity, etc. Each of the preceding and following quantized representation knowledge has a corresponding second feature enhancement factor that determines the importance of the preceding and following quantized representation knowledge in the quantized representation optimization knowledge.
The first profile enhancement knowledge in step 10333 is determined from the v linkage signal profile knowledge and the respective corresponding first profile enhancement factors. It is used to enhance the features of the linkage signal descriptive knowledge. For example, in a smart antenna system, there are three linkage signal description knowledge: received signal strength, signal-to-noise ratio, and multipath fading. Assuming that their first feature enhancement factors are 0.8, 0.6 and 0.9, respectively, a first descriptive feature enhancement knowledge can be calculated.
The quantized characterization optimization knowledge in step 10334 is a result of aggregating the first descriptive feature enhancement knowledge and the second descriptive feature enhancement knowledge. The importance of linkage signal description knowledge and the importance of the front and rear quantitative characterization knowledge are comprehensively considered. For example, in a smart antenna system, the quantized characterization optimization knowledge may be calculated based on the first descriptive feature enhancement knowledge and the second descriptive feature enhancement knowledge obtained in step 10333.
The purpose of steps 10331-10334 is to obtain the quantized representation optimization knowledge by determining the feature enhancement factor based on the commonality coefficients and applying it to the linkage signal description knowledge and the subsequent quantized representation knowledge. This can improve the processing power of the system for different signal descriptions and quantitative characterizations and optimize the system performance. For example, in a smart antenna system, antenna parameters can be dynamically adjusted according to different communication scenes and requirements by optimizing quantitative characterization knowledge, so that the performance and adaptability of the system are improved.
In some optional embodiments, obtaining v first description knowledge commonality coefficients corresponding to the current quantization characterization knowledge in step 1031 includes: and respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the v linkage signal description knowledge, and determining v first characteristic multiplication results as v first description knowledge commonality coefficients. Obtaining P successive pieces of quantized representation knowledge successive to the current quantized representation knowledge, and a second descriptive knowledge commonality coefficient between the current quantized representation knowledge in step 1032 includes: and respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the P previous and subsequent quantitative characterization knowledge, and determining P second characteristic multiplication results as P second description knowledge commonality coefficients.
In the above alternative embodiment, the first descriptive knowledge commonality coefficient refers to a coefficient for measuring a degree of association between the current quantized representation knowledge and the linkage signal descriptive knowledge. The method is obtained by carrying out characteristic multiplication operation on the current quantitative characterization knowledge and each linkage signal description knowledge. Such as: in a smart antenna system, the current knowledge of the quantitative characterization is the antenna gain, and the knowledge of the linkage signal description is the received signal strength, signal-to-noise ratio, and multipath fading. Assuming that the association degree of the antenna gain and the description knowledge of the three linkage signals is 0.8, 0.6 and 0.9 respectively, three first description knowledge commonality coefficients can be calculated.
The second descriptive knowledge commonality coefficient refers to a coefficient for measuring the degree of association between the current quantized representation knowledge and the previous and subsequent quantized representation knowledge. The method is obtained by carrying out characteristic multiplication operation on the current quantitative characterization knowledge and each preceding and following quantitative characterization knowledge. Such as: in a smart antenna system, the current quantitative characterization knowledge is the antenna gain, and the preceding and following quantitative characterization knowledge is directional and signal transmission rate. Assuming that the association degree of the antenna gain and the two pieces of front and rear quantitative characterization knowledge is 0.7 and 0.5 respectively, two second description knowledge commonality coefficients can be obtained through calculation.
In the technical scheme, v first description knowledge commonality coefficients corresponding to the current quantization characterization knowledge are obtained through characteristic multiplication operation. And then, P second description knowledge commonality coefficients between the P previous and subsequent quantized characterization knowledge continuous with the current quantized characterization knowledge and the current quantized characterization knowledge are obtained through characteristic multiplication operation.
The optional embodiment accurately evaluates the association degree between the current quantized representation knowledge and the linkage signal description knowledge and the previous and subsequent quantized representation knowledge by determining the commonality coefficient between the description knowledge. This allows more accurate optimization of the quantitative characterization knowledge and allows the system to better adapt to different communication scenarios and requirements. For example, in a smart antenna system, by accurately evaluating the degree of correlation between the antenna gain and the received signal strength, signal-to-noise ratio, multipath fading, directivity, signal transmission rate, etc., the antenna parameters can be more accurately adjusted, improving the performance and adaptability of the system.
Under some preferred design considerations, the determining of the second feature enhancement factors corresponding to the respective ones of the front and rear quantitative characterization knowledge based on the P second descriptive knowledge commonalities described in step 10332 includes steps 103321-103323.
And step 103321, determining a first eigenvalue based on the v first description knowledge commonality coefficients and the P second description knowledge commonality coefficients.
Step 103322, determining v second eigenvalues corresponding to the v linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients, obtaining first comparison variables of the v second eigenvalues and the first eigenvalues respectively, and determining v first comparison variables as the first characteristic strengthening factors corresponding to the v linkage signal description knowledge respectively; wherein, the comparison variable is the ratio.
And 103323, respectively determining P third eigenvalues corresponding to the P preceding and following quantized representation knowledge based on the P second description knowledge commonality coefficients, respectively obtaining second comparison variables of the P third eigenvalues and the first eigenvalues, and determining the P second comparison variables as the second characteristic strengthening factors corresponding to the P preceding and following quantized representation knowledge.
In step 103321, the first descriptive knowledge commonality factor refers to a factor determined from the commonalities of the different signal descriptive knowledge in the antenna gain system. Which represents the similarity between the knowledge of the individual signal descriptions. Such as: it is assumed that two signal description knowledge a and B are present, both of which are related to the performance of the antenna gain system. The first descriptive knowledge commonality coefficient may be determined by comparing similarities between a and B.
In step 103322, the linkage signal description knowledge refers to knowledge that correlates to the signal description knowledge in the antenna gain system. There may be some correlation between these knowledge, for example they may describe the same or similar signal characteristics. The second characteristic value refers to a value for measuring the importance of the linkage signal description knowledge relative to the first characteristic value. The first comparison variable refers to a variable for comparing the importance of the linkage signal descriptive knowledge relative to the first characteristic value. Typically by calculating the ratio of linkage signal descriptive knowledge to the first characteristic value. Such as: assume that three linkage signal descriptions, A, B and C, are present, all of which are related to the performance of the antenna gain system. The second characteristic values may be determined by comparing their importance with the first characteristic values. The first comparison variable may be obtained by calculating A, B and the ratio of C to the first eigenvalue.
In step 103323, the third eigenvalue refers to a value that measures the importance of the contextual quantitative characterization knowledge relative to the first eigenvalue. The second comparison variable refers to a variable for comparing the importance of the underlying quantitative characterization knowledge with respect to the first eigenvalue. Typically by calculating the ratio of the characterization knowledge to the first eigenvalue in a sequential quantization. Such as: it is assumed that there are two sequential quantitative characterization knowledge T1 and T2, both of which are related to the performance of the antenna gain system. The third characteristic values may be determined by comparing their importance with the first characteristic values. The second comparison variable may be obtained by calculating the ratio of T1 and T2 to the first characteristic value.
It is assumed that in one smart antenna system, there are 4 signal description knowledge A, B, C and D. These knowledge describe different antenna performance parameters, such as antenna gain, frequency response, etc., respectively. It is desirable to optimize the performance of the system based on this knowledge. First, in step 103321, a first descriptive knowledge commonality coefficient is calculated based on the degrees of commonality of the 4 signal descriptive knowledge. For example, assuming that the similarity between a and B is high and the similarity between a and C is low, a corresponding first descriptive knowledge commonality coefficient may be obtained. Then, in step 103322, a second eigenvalue of linkage signal description knowledge is calculated based on the first description knowledge commonality coefficient. Assume that there are two linked signal description knowledge T1 and T2, which are related to signal description knowledge A, B and C. The importance of T1 and T2 with respect to the first eigenvalue can be calculated and a corresponding first comparison variable obtained. Finally, in step 103323, a third eigenvalue of the front-to-back quantitative characterization knowledge is calculated based on the second descriptive knowledge commonality coefficients. Assume that there are two sequential quantitative characterization knowledge F1 and F2, which are related to signal description knowledge A, B and D. The importance of F1 and F2 with respect to the first characteristic value can be calculated and a corresponding second comparison variable can be obtained. Overall, through these steps, the importance of the different signal description knowledge and the quantitative characterization knowledge can be determined and the system parameters adjusted accordingly. Therefore, the performance and the adaptability of the intelligent antenna system can be improved, and the intelligent antenna system can better meet the requirements of different communication scenes.
In some alternative embodiments, the method further includes steps 201-202 before obtaining v linkage signal description knowledge corresponding to the u-th order signal description processing component as described in step 102.
Step 201, obtaining v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component.
Step 202, circularly implementing the following steps 2021 to 2024 until the v linkage signal description knowledge to be changed is walked.
Step 2021, obtaining one linkage signal description knowledge to be changed from the v linkage signal description knowledge to be changed as current linkage signal description knowledge.
Step 2022, respectively obtaining a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed.
Step 2023, obtaining fourth description knowledge commonality coefficients between the current linkage signal description knowledge and each of the quantized characterization knowledge in the multi-mode antenna signal description.
And step 2024, using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient to change the current linkage signal description knowledge.
In step 201, the u-th order signal description processing component refers to a component for processing the u-th order signal description in the antenna gain system. It may include filters, amplifiers, etc. The linkage signal description knowledge refers to a set of related knowledge corresponding to the u-th order signal description processing component, and is used for describing the characteristics and performance of the linkage signal. The linkage signal description knowledge to be changed refers to new knowledge obtained after the linkage signal description knowledge is adjusted or improved. Such as: it is assumed that in the antenna gain system, there is a2 nd order signal description processing component, and it is desirable to obtain knowledge of the linkage signal description corresponding to the component. Such knowledge may include frequency response, phase characteristics, etc. associated with the component. By adjusting or improving the knowledge, linkage signal description knowledge to be changed can be obtained.
In step 202, the current linkage signal description knowledge refers to the linkage signal description knowledge currently being processed selected in the loop. The third description knowledge commonality coefficient refers to a coefficient for measuring similarity between the current linkage signal description knowledge and other linkage signal description knowledge to be changed. The fourth description knowledge commonality coefficient refers to a coefficient for measuring similarity between the current linkage signal description knowledge and each quantization characterization knowledge in the multi-mode antenna signal description. Such as: in step 202, the loop processes the linkage signal description knowledge to be changed. Suppose that three linkage signals to be altered describe knowledge X, Y and Z. First, X is selected as the current linkage signal description knowledge. And then, calculating a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed, and a fourth description knowledge commonality coefficient between the current linkage signal description knowledge and each quantization characterization knowledge in the multi-mode antenna signal description.
Further, changing the current linkage signal description knowledge refers to adjusting or improving the current linkage signal description knowledge according to the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient. Such as: in the loop, X is selected as the current linkage signal description knowledge. And then, calculating a third description knowledge commonality coefficient between X and other linkage signal description knowledge to be changed and a fourth description knowledge commonality coefficient between X and each quantization characterization knowledge in the multi-mode antenna signal description. And finally, according to the commonality coefficients, adjusting or improving X to obtain changed current linkage signal description knowledge.
Through the above steps, a set of linkage signal description knowledge corresponding to the u-th order signal description processing component is first obtained. Then, in the cycle, the linkage signal description knowledge to be changed is processed one by one, and the current linkage signal description knowledge is adjusted or improved by calculating the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient. This can improve the processing power of the system for different signal descriptions and optimize the system performance.
In some alternative embodiments, the modifying the current linkage signal description knowledge in step 2024 using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient comprises steps 20241-20244.
Step 20241, determining third characteristic enhancement factors corresponding to the linkage signal description knowledge to be changed according to v-1 third description knowledge commonality coefficients respectively.
Step 20242, determining fourth characteristic strengthening factors corresponding to the quantized representation knowledge according to the G fourth description knowledge commonality coefficients; the multi-mode antenna signal description comprises G pieces of quantitative characterization knowledge, wherein G is an integer greater than 1.
And 20243, determining third description characteristic strengthening knowledge based on the v-1 linkage signal description knowledge to be changed and the corresponding third characteristic strengthening factors, and determining fourth description characteristic strengthening knowledge based on the G quantitative characterization knowledge and the corresponding fourth characteristic strengthening factors.
And step 20244, determining the linkage signal description knowledge with the change completed based on the third description characteristic strengthening knowledge and the fourth description characteristic strengthening knowledge.
In step 20241, the third description knowledge commonality coefficient refers to a coefficient in the smart antenna system that quantifies a correlation of the third feature in the linkage signal description knowledge. Which represents the degree of association between a third, different feature and the other features. For example, assume that there is a linked signal description knowledge base that includes characteristics of frequency, phase, and amplitude of different signals. The third descriptive knowledge commonality factor may measure the correlation between the third feature (e.g., amplitude) and the other features.
In step 20242, the fourth descriptive knowledge commonality coefficient refers to a coefficient in the smart antenna system that quantifies a degree of correlation of the fourth feature in the quantized representation knowledge. Which represents the degree of association between the fourth, different feature and the other features. For example, assume a knowledge base of quantitative characterizations that includes time domain features, frequency domain features, and power spectral density features of different signals. The fourth descriptive knowledge commonality factor may measure the correlation between the fourth feature (e.g., power spectral density) and other features.
In step 20243, the third characteristic-describing enhancement factor is a factor determined by weighting the third characteristic according to the linkage signal description knowledge to be changed and the corresponding third characteristic-enhancing factor. For example, assume that there is knowledge of the linkage signal description to be altered, which includes frequency, phase, and amplitude characteristics, and the third characteristic is amplitude. If the third characteristic enhancement factor is 0.8 for this linkage signal description knowledge to be altered, then the third characteristic enhancement factor is 0.8, meaning that the amplitude characteristic is multiplied by 0.8 for weighting processing when processing the linkage signal description knowledge. The fourth characterization feature enhancement factor is a factor determined to weight the fourth feature based on the quantized characterization knowledge and the corresponding fourth feature enhancement factor. For example, assume that there is a knowledge of the quantization characterization, which includes time domain features, frequency domain features, and power spectral density, etc., of the different signals, while the fourth feature is the power spectral density. If the fourth feature enhancement factor is 0.6 for this quantized characterization knowledge, then the fourth feature enhancement factor is 0.6, meaning that the power spectral density features are multiplied by 0.6 for weighting when processing the quantized characterization knowledge.
In step 20244, the linkage signal description knowledge of the modification is the final result obtained by integrating and optimizing the linkage signal description knowledge to be modified according to the third description feature enhancement knowledge and the fourth description feature enhancement knowledge. For example, assuming that the third description feature enhancement knowledge and the fourth description feature enhancement knowledge have been obtained in step 20243, then in step 20244, the third feature and the fourth feature are respectively weighted according to the corresponding enhancement factors to obtain the linkage signal description knowledge of which the modification is completed.
It is assumed that the smart antenna system needs to process signals of different frequency bands. First, in step 20241, a third characteristic enhancement factor corresponding to each frequency band signal is determined by calculating a third descriptive knowledge commonality coefficient for the different frequency band signals. For example, for a signal of band 1, its third characteristic enhancement factor is 0.9; for a signal of band 2, its third characteristic enhancement factor is 0.7. Next, in step 20242, a fourth characteristic enhancement factor corresponding to each frequency band signal is determined by calculating a fourth descriptive knowledge commonality coefficient for the different frequency band signals. For example, for a band 1 signal, its fourth characteristic enhancement factor is 0.8; for a band 2 signal, the fourth characteristic enhancement factor is 0.6. Then, in step 20243, the third feature enhancement factor and the fourth feature enhancement factor are applied to the corresponding linkage signal description knowledge and the quantized characterization knowledge, respectively, to obtain the third description feature enhancement knowledge and the fourth description feature enhancement knowledge. Finally, in step 20244, the third description feature reinforcement knowledge and the fourth description feature reinforcement knowledge are applied to the linkage signal description knowledge to be changed, that is, the third feature and the fourth feature are weighted according to the corresponding reinforcement factors, so as to obtain the linkage signal description knowledge with the change completed.
Overall, the beneficial effect of steps 20241-20244 is to increase the processing power of the system for different signal descriptions and quantitative characterizations and further optimize the system performance by quantifying and optimizing the degree of association between the third and fourth features and other features. For example, when the signal of the frequency band 1 is processed, the linkage signal description knowledge is weighted according to the third characteristic strengthening factor and the fourth characteristic strengthening factor, so that the characteristics of the signal can be more accurately described, and the processing effect of the system on the signal of the frequency band 1 is improved. Similarly, a similar effect can be achieved when processing the band 2 signal.
In some alternative embodiments, the step 201 of obtaining v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component includes a step 2011 or a step 2012.
2011, Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the v linkage signal description knowledge to be changed is the linkage signal description knowledge determined by the signal description recognition algorithm in the debugging process.
Step 2012, using v pieces of linkage signal description knowledge corresponding to the u-1 th order signal description processing component as v pieces of linkage signal description knowledge to be changed corresponding to the u-1 th order signal description processing component.
In step 2011, the u-th order signal description processing component refers to a component that describes and processes the u-th order signal in the smart antenna system. It may be an algorithm, model or other related technical means. For example, assume that in a smart antenna system, the first-order signal description processing component is a spectral analysis algorithm for performing spectral feature extraction on the signal; the second-order signal description processing component is a time domain analysis algorithm for performing time domain feature extraction on the signal.
The linkage signal description knowledge to be changed refers to v linkage signal description knowledge corresponding to the u-th order signal description processing component, and is used for describing and optimizing the characteristics of the corresponding signals. These linkage signal description knowledge can be determined during the debugging process according to a signal description recognition algorithm. For example, when the smart antenna system is debugged, by running a signal description recognition algorithm, linkage signal description knowledge to be changed corresponding to the first-order signal description processing component can be obtained, wherein the linkage signal description knowledge comprises spectrum characteristic information of different signals.
In step 2012, the u-1 th order signal describes the processing component: refer to the components in the smart antenna system that describe and process the u-1 order signal. It may be an algorithm, model or other related technical means. For example, in a smart antenna system, the first order signal description processing component is a spectral analysis algorithm and the second order signal description processing component is a time domain analysis algorithm.
The v linkage signal description knowledge refers to v linkage signal description knowledge corresponding to the u-1 order signal description processing component, and is used for describing and optimizing the characteristics of the corresponding signals. These linkage signal description knowledge are derived from the u-1 order signal description processing component. For example, when the first-order signal description processing component is a spectrum analysis algorithm, by running the algorithm, linkage signal description knowledge corresponding to the algorithm can be obtained, wherein the linkage signal description knowledge comprises spectrum characteristic information of different signals.
First, in step 2011, according to the result of the signal description recognition algorithm in the debugging process, linkage signal description knowledge to be changed corresponding to the second-order signal description processing component is obtained. In the debugging process, the signal description recognition algorithm determines 5 linkage signal description knowledge corresponding to the second-order signal description processing assembly, and the linkage signal description knowledge comprises information such as frequency spectrum characteristics and time domain characteristics. Then, in step 2012, the linkage signal description knowledge corresponding to the first-order signal description processing component is taken as the linkage signal description knowledge to be changed corresponding to the second-order signal description processing component. It is assumed that in the case that the first-order signal description processing component is a spectrum analysis algorithm, 3 pieces of linkage signal description knowledge are obtained, including spectrum feature information.
Overall, the beneficial effects of steps 2011-2012 are that by using the linkage signal description knowledge corresponding to the previous stage signal description processing assembly as the initial linkage signal description knowledge corresponding to the current stage signal description processing assembly, the knowledge is transferred and optimized. For example, where the first-order signal description processing component is a spectral analysis algorithm, linkage signal description knowledge derived from the algorithm may be used as initial knowledge of the second-order signal description processing component, thereby providing a more accurate and complete characterization, further optimizing system performance.
Under some optional design considerations, after determining the quantized characterization optimization knowledge set obtained by integrating the quantized characterization optimization knowledge set as the identified target multi-mode antenna signal description in step 104, steps 301-303 are further included.
Step 301, performing interval numerical mapping processing on the target multi-mode antenna signal description and the multi-mode antenna signal description generated by the u-1 order signal description processing component to obtain multi-mode antenna signal mapping description.
And 302, determining a signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description when u is the same as X.
And 303, inputting the derivative result of the multi-mode antenna signal mapping description into a (u+1) -th order signal description processing component when the u is smaller than the X.
In step 301, a target multi-modal antenna signal description refers to a set of quantized representations obtained by processing and analyzing the multi-modal antenna signal to describe the characteristics of the antenna signal. It may comprise a plurality of different modalities, each modality describing a different signal characteristic. Such as: assuming a smart antenna system, ambient electromagnetic signals can be perceived. By processing and analyzing the received signals, the system can obtain a target multi-mode antenna signal description. For example, for a wireless communication scenario, the target multi-modal antenna signal description may include characteristics of frequency, amplitude, phase, etc. of the signal.
In step 302, the multi-mode antenna signal mapping description refers to a new description result obtained after performing interval numerical mapping processing on the target multi-mode antenna signal description. The mapping process may convert the original descriptive results into a form that is more convenient to process and understand. Such as: the target multi-modal antenna signal description is assumed to include characteristics of the frequency range, amplitude variation, etc. of the signal. By performing an interval value mapping process on these features, it can be converted into a more convenient form to handle, such as mapping the frequency range to values between 0-1 and mapping the amplitude variation to values between-1 and 1.
In step 303, the (u+1) -th order signal description processing component refers to a component that further processes and analyzes the signal according to the derivative of the multi-mode antenna signal mapping description. It may generate a new signal description based on the derived result. Such as: it is assumed that a new set of signal characteristics is obtained by multi-modal antenna signal mapping, e.g. mapping the frequency range to a value between 0 and 1. The u+1st order signal description processing component may utilize these derivatives to further analyze and process the signal, such as generating new signal descriptions based on new characteristics, such as frequency change rates of the signal, etc.
Through step 301, the interval value mapping process is performed on the target multi-mode antenna signal description, so that the original description result can be converted into a form which is more convenient to process and understand. This helps to extract and identify important features of the signal, providing a better basis for subsequent processing and analysis. In step 302, a signal description recognition result is determined based on the multi-mode antenna signal mapping description, so that signal characteristics can be accurately recognized according to the mapped description result, and the recognition accuracy of the system on the signals is improved. In step 303, the derivative of the multi-mode antenna signal mapping description is input to the u+1st order signal description processing component, and these derivative results can be used for further analysis and processing, so as to further optimize the understanding and processing capability of the system on the signals.
In summary, steps 301 to 303 improve the description, identification and processing capabilities of the system on the signals by performing mapping processing on the target multi-mode antenna signal description and utilizing the derived results, thereby improving the performance and adaptability of the smart antenna system.
In further embodiments, after determining the signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description when the u is the same as the X, step 401, step 402 or step 403 is further included.
And step 401, performing first signal decoding on the signal description identification result to obtain a desired antenna signal set matched with the antenna signal set to be analyzed.
And step 402, performing second signal decoding on the signal description identification result to determine key signal fragments included in the antenna signal set to be analyzed.
And step 403, performing third signal decoding on the signal description identification result to obtain a signal optimization control decision type matched with the antenna signal set to be analyzed.
In step 401, the first signal decoding finger decodes and analyzes the signal description recognition result to determine a desired antenna signal set that matches the antenna signal set to be analyzed. This process may translate the signal description into a specific signal type or characteristic. Such as: it is assumed that the signal description recognition result includes characteristics such as frequency, amplitude, etc. of a certain wireless communication system. These features can be decoded into specific signal types, such as Wi-Fi signals, bluetooth signals, etc., by first signal decoding. In this way, the desired set of antenna signals, i.e. the signal type matching the set of antenna signals to be analyzed, can be determined.
In step 402, the second signal decoding means further decodes and analyzes the signal description recognition result to determine key signal segments contained in the antenna signal set to be analyzed. This process may extract important information or features in the signal. Such as: it is assumed that the signal description recognition result represents the spectral characteristics of an audio signal. Through the second signal decoding, the frequency spectrum characteristics of the signal can be further analyzed, and key frequency components or frequency bands in the signal can be extracted. In this way, it is possible to determine key signal segments, such as the treble or bass portions of sound, contained in the antenna signal set to be analyzed.
In step 403, the third signal decoding means performs final decoding and analysis on the signal description recognition result to determine a signal optimization control decision type matching the antenna signal set to be analyzed. This process may translate the signal description into a specific signal processing or optimization strategy. Such as: it is assumed that the signal description recognition result represents the pulse width, repetition frequency and other characteristics of a radar signal. By means of the third signal decoding, these features can be decoded into specific signal processing strategies, such as adjusting the operating mode or parameter settings of the radar. In this way, the kind of signal optimization control decision matching with the antenna signal set to be analyzed, such as selecting a suitable radar scan pattern, can be determined.
By performing the first signal decoding on the signal description identification result in step 401, the abstract signal description may be converted into a specific signal type, so as to accurately determine the desired antenna signal set. This facilitates further analysis and processing by the system for a particular signal type. In step 402, key signal segments in the antenna signal set to be analyzed may be extracted by the second signal decoding, thereby obtaining more detailed and accurate signal characteristics. This helps the system to perform deeper analysis and optimization of the signal. In step 403, the signal description may be converted into a specific signal processing or optimization strategy by means of a third signal decoding, so that an efficient control of the antenna signal set to be analyzed is achieved. This helps to improve system performance and adapt it to different communication scenarios.
In summary, steps 401-403 convert the abstract signal description into specific signal types, key signal segments and optimization strategies through the processes of signal decoding and optimization control decision, so as to improve the understanding, analysis and processing capabilities of the system on the signals and further improve the performance and adaptability of the smart antenna system.
Based on the same or similar inventive concept, the embodiment of the invention further provides an AI intelligent control system, which is used for:
Collecting a to-be-analyzed antenna signal set of an antenna gain system;
obtaining multi-mode antenna signal description generated by a u-1 th order signal description processing component in a signal description recognition algorithm; the signal description recognition algorithm comprises an X-order signal description processing component with a cascade relation, wherein the multi-mode antenna signal is described as a quantized representation knowledge set obtained after signal description mining is carried out on an antenna signal set to be analyzed, the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment in the antenna signal set to be analyzed respectively, X is an integer greater than 1, and u is an integer greater than 1 and not more than X;
V linkage signal description knowledge corresponding to a u-th order signal description processing component is obtained; the v linkage signal description knowledge is used for reflecting the linkage characteristics among the quantitative characterization knowledge in the multi-mode antenna signal description, and v is an integer greater than 1;
Sequentially obtaining description knowledge commonality coefficients of each quantized representation knowledge in the multi-mode antenna signal description and each linkage signal description knowledge in the v linkage signal description knowledge, and sequentially adjusting each quantized representation knowledge into quantized representation optimization knowledge based on the v description knowledge commonality coefficients pointing to the same quantized representation knowledge;
Determining a quantized representation optimization knowledge set obtained by integrating the quantized representation optimization knowledge as a target multi-mode antenna signal description obtained by identification;
and regulating and controlling an antenna gain system by utilizing the target multi-mode antenna signal description.
In some aspects, the sequentially obtaining the descriptive knowledge commonality coefficients of each of the multi-modal antenna signal descriptions and each of the v linkage signal description knowledge, and sequentially adjusting each of the quantitative characterization knowledge to a quantitative characterization optimization knowledge based on the v descriptive knowledge commonalities pointing to the same quantitative characterization knowledge includes: the following scheme is circularly implemented until each quantitative characterization knowledge in the multi-mode antenna signal description is completely walked: obtaining a quantized characterization knowledge from the multi-mode antenna signal description as a current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the current quantized characterization knowledge; p pieces of front and rear sequence quantization characterization knowledge continuous with the current quantization characterization knowledge and a second description knowledge commonality coefficient between the current quantization characterization knowledge are respectively obtained; the difference between the position labels of the front and rear quantitative characterization knowledge between the first position label in the quantitative characterization knowledge set and the second position label of the current quantitative characterization knowledge accords with the front and rear judgment requirement, and P is an integer greater than 1; changing the current quantized characterization knowledge into the quantized characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients;
Wherein the changing the current quantization characterization knowledge to the quantization characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients comprises: determining first characteristic strengthening factors corresponding to the linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients; determining second characteristic strengthening factors corresponding to the respective front and rear quantitative characterization knowledge based on the P second description knowledge commonality coefficients respectively; determining first description characteristic strengthening knowledge according to v linkage signal description knowledge and the corresponding first characteristic strengthening factors, and determining second description characteristic strengthening knowledge according to P previous and subsequent quantitative characterization knowledge and the corresponding second characteristic strengthening factors; determining an aggregate result of the first descriptive feature-enhanced knowledge and the second descriptive feature-enhanced knowledge as the quantitative characterization optimization knowledge;
Wherein determining second feature reinforcement factors corresponding to each of the preceding and following quantized representation knowledge based on the P second description knowledge commonality coefficients, respectively, comprises: determining a first eigenvalue based on the v first descriptive knowledge commonality coefficients and the P second descriptive knowledge commonality coefficients; determining v second eigenvalues corresponding to the v linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients, obtaining first comparison variables of the v second eigenvalues and the first eigenvalues respectively, and determining the v first comparison variables as the first characteristic strengthening factors corresponding to the v linkage signal description knowledge respectively; determining P third eigenvalues corresponding to the P preceding and following quantized representation knowledge based on the P second descriptive knowledge commonality coefficients respectively, obtaining second comparison variables of the P third eigenvalues and the first eigenvalues respectively, and determining the P second comparison variables as the second characteristic strengthening factors corresponding to the P preceding and following quantized representation knowledge respectively;
Wherein the obtaining v first description knowledge commonality coefficients corresponding to the current quantization characterization knowledge includes: performing characteristic multiplication operation on the current quantitative characterization knowledge and the v linkage signal description knowledge respectively, and determining v first characteristic multiplication results as v first description knowledge commonality coefficients; the obtaining P pieces of previous and subsequent quantized representation knowledge continuous with the current quantized representation knowledge, and the second descriptive knowledge commonality coefficient between the current quantized representation knowledge, respectively, includes: and respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the P previous and subsequent quantitative characterization knowledge, and determining P second characteristic multiplication results as P second description knowledge commonality coefficients.
In some aspects, before the obtaining the v linkage signal description knowledge corresponding to the u-th order signal description processing component, the AI intelligent control system is further configured to: acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the following scheme is circularly implemented until the linkage signal description knowledge of the v to-be-changed is moved out: acquiring one linkage signal description knowledge to be changed from the v linkage signal description knowledge to be changed as current linkage signal description knowledge; respectively obtaining a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed; respectively obtaining a fourth description knowledge commonality coefficient between the current linkage signal description knowledge and each quantized representation knowledge in the multi-mode antenna signal description; changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient;
Wherein the changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient includes: respectively determining third characteristic strengthening factors corresponding to the linkage signal description knowledge to be changed according to v-1 third description knowledge commonality coefficients; determining fourth characteristic strengthening factors corresponding to the quantitative characterization knowledge according to the G fourth description knowledge commonality coefficients respectively; the multi-mode antenna signal description comprises G pieces of quantitative characterization knowledge, wherein G is an integer greater than 1; determining third description characteristic strengthening knowledge based on the v-1 linkage signal description knowledge to be changed and the corresponding third characteristic strengthening factors, and determining fourth description characteristic strengthening knowledge based on the G quantitative characterization knowledge and the corresponding fourth characteristic strengthening factors; determining the linkage signal description knowledge of the completed change based on the third description feature enrichment knowledge and the fourth description feature enrichment knowledge;
The step of obtaining v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component comprises the following steps: acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the v linkage signal description knowledge to be changed is the linkage signal description knowledge determined by the signal description recognition algorithm in the debugging process; or v pieces of linkage signal description knowledge corresponding to the u-1 th order signal description processing component are used as v pieces of linkage signal description knowledge to be changed corresponding to the u-1 th order signal description processing component.
In some aspects, after the determining the quantized characterization optimization knowledge set resulting from the integrating the quantized characterization optimization knowledge set as identifying the resulting target multi-modal antenna signal description, the AI intelligent control system is further configured to: performing interval numerical mapping processing on the target multi-modal antenna signal description and the multi-modal antenna signal description generated by the u-1 order signal description processing component to obtain multi-modal antenna signal mapping description; when u is the same as X, determining a signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description; when u is smaller than X, inputting the derivative result of the multi-mode antenna signal mapping description into a (u+1) -th order signal description processing component;
Wherein, after determining the signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description when the u is the same as the X, the AI intelligent control system is further configured to: performing first signal decoding on the signal description identification result to obtain an expected antenna signal set matched with the antenna signal set to be analyzed; performing second signal decoding on the signal description identification result to determine key signal fragments included in the antenna signal set to be analyzed; and performing third signal decoding on the signal description recognition result to obtain a signal optimization control decision type matched with the antenna signal set to be analyzed.
Further, there is also provided a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the above-described method.
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 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 (7)

1. An intelligent control method applied to an antenna gain system is characterized in that the intelligent control method applied to an AI (analog to digital) intelligent control system comprises the following steps:
obtaining multi-mode antenna signal description generated by a u-1 th order signal description processing component in a signal description recognition algorithm; the signal description recognition algorithm comprises an X-order signal description processing component with a cascade relation, wherein the multi-mode antenna signal is described as a quantized representation knowledge set obtained after signal description mining is carried out on an antenna signal set to be analyzed, the quantized representation knowledge set comprises quantized representation knowledge corresponding to each signal segment in the antenna signal set to be analyzed respectively, X is an integer greater than 1, and u is an integer greater than 1 and not more than X;
V linkage signal description knowledge corresponding to a u-th order signal description processing component is obtained; the v linkage signal description knowledge is used for reflecting the linkage characteristics among the quantitative characterization knowledge in the multi-mode antenna signal description, and v is an integer greater than 1;
Sequentially obtaining description knowledge commonality coefficients of each quantized representation knowledge in the multi-mode antenna signal description and each linkage signal description knowledge in the v linkage signal description knowledge, and sequentially adjusting each quantized representation knowledge into quantized representation optimization knowledge based on the v description knowledge commonality coefficients pointing to the same quantized representation knowledge;
Determining a quantized representation optimization knowledge set obtained by integrating the quantized representation optimization knowledge as a target multi-mode antenna signal description obtained by identification;
and regulating and controlling an antenna gain system by utilizing the target multi-mode antenna signal description.
2. The method of claim 1, wherein the sequentially obtaining the descriptive knowledge commonality coefficients for each of the quantized representation knowledge in the multi-modal antenna signal description and each of the v linked signal description knowledge, and sequentially adjusting each of the quantized representation knowledge to quantized representation optimization knowledge based on v of the descriptive knowledge commonalities pointing to the same quantized representation knowledge comprises:
The following scheme is circularly implemented until each quantitative characterization knowledge in the multi-mode antenna signal description is completely walked:
Obtaining a quantized characterization knowledge from the multi-mode antenna signal description as a current quantized characterization knowledge, and obtaining v first description knowledge commonality coefficients corresponding to the current quantized characterization knowledge;
p pieces of front and rear sequence quantization characterization knowledge continuous with the current quantization characterization knowledge and a second description knowledge commonality coefficient between the current quantization characterization knowledge are respectively obtained; the difference between the position labels of the front and rear quantitative characterization knowledge between the first position label in the quantitative characterization knowledge set and the second position label of the current quantitative characterization knowledge accords with the front and rear judgment requirement, and P is an integer greater than 1;
And changing the current quantitative characterization knowledge into the quantitative characterization optimization knowledge based on the v first description knowledge commonality coefficients and the P second description knowledge commonality coefficients.
3. The method of claim 2, wherein the altering the current quantization characterization knowledge to the quantization characterization optimization knowledge based on the v first descriptive knowledge commonality coefficients and P second descriptive knowledge commonality coefficients comprises:
Determining first characteristic strengthening factors corresponding to the linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients;
determining second characteristic strengthening factors corresponding to the respective front and rear quantitative characterization knowledge based on the P second description knowledge commonality coefficients respectively;
Determining first description characteristic strengthening knowledge according to v linkage signal description knowledge and the corresponding first characteristic strengthening factors, and determining second description characteristic strengthening knowledge according to P previous and subsequent quantitative characterization knowledge and the corresponding second characteristic strengthening factors;
determining an aggregate result of the first descriptive feature-enhanced knowledge and the second descriptive feature-enhanced knowledge as the quantitative characterization optimization knowledge;
Wherein determining second feature reinforcement factors corresponding to each of the preceding and following quantized representation knowledge based on the P second description knowledge commonality coefficients, respectively, comprises:
determining a first eigenvalue based on the v first descriptive knowledge commonality coefficients and the P second descriptive knowledge commonality coefficients;
Determining v second eigenvalues corresponding to the v linkage signal description knowledge respectively based on the v first description knowledge commonality coefficients, obtaining first comparison variables of the v second eigenvalues and the first eigenvalues respectively, and determining the v first comparison variables as the first characteristic strengthening factors corresponding to the v linkage signal description knowledge respectively;
And respectively determining P third eigenvalues corresponding to the P pieces of front and rear quantitative characterization knowledge based on the P pieces of second descriptive knowledge commonality coefficients, respectively obtaining second comparison variables of the P third eigenvalues and the first eigenvalues, and determining the P second comparison variables as the second characteristic strengthening factors corresponding to the P pieces of front and rear quantitative characterization knowledge.
4. The method of claim 2, wherein the obtaining v first descriptive knowledge commonality coefficients for the current quantized representation knowledge comprises:
Performing characteristic multiplication operation on the current quantitative characterization knowledge and the v linkage signal description knowledge respectively, and determining v first characteristic multiplication results as v first description knowledge commonality coefficients;
the obtaining P pieces of previous and subsequent quantized representation knowledge continuous with the current quantized representation knowledge, and the second descriptive knowledge commonality coefficient between the current quantized representation knowledge, respectively, includes:
And respectively carrying out characteristic multiplication operation on the current quantitative characterization knowledge and the P previous and subsequent quantitative characterization knowledge, and determining P second characteristic multiplication results as P second description knowledge commonality coefficients.
5. The method of claim 1, wherein prior to obtaining v linkage signal description knowledge corresponding to the u-th order signal description processing component, further comprising:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component;
the following scheme is circularly implemented until the linkage signal description knowledge of the v to-be-changed is moved out:
Acquiring one linkage signal description knowledge to be changed from the v linkage signal description knowledge to be changed as current linkage signal description knowledge;
respectively obtaining a third description knowledge commonality coefficient between the current linkage signal description knowledge and other linkage signal description knowledge to be changed;
respectively obtaining a fourth description knowledge commonality coefficient between the current linkage signal description knowledge and each quantized representation knowledge in the multi-mode antenna signal description;
Changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient;
Wherein the changing the current linkage signal description knowledge by using the third description knowledge commonality coefficient and the fourth description knowledge commonality coefficient includes:
Respectively determining third characteristic strengthening factors corresponding to the linkage signal description knowledge to be changed according to v-1 third description knowledge commonality coefficients;
determining fourth characteristic strengthening factors corresponding to the quantitative characterization knowledge according to the G fourth description knowledge commonality coefficients respectively; the multi-mode antenna signal description comprises G pieces of quantitative characterization knowledge, wherein G is an integer greater than 1;
Determining third description characteristic strengthening knowledge based on the v-1 linkage signal description knowledge to be changed and the corresponding third characteristic strengthening factors, and determining fourth description characteristic strengthening knowledge based on the G quantitative characterization knowledge and the corresponding fourth characteristic strengthening factors;
determining the linkage signal description knowledge of the completed change based on the third description feature enrichment knowledge and the fourth description feature enrichment knowledge;
The step of obtaining v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component comprises the following steps:
Acquiring v linkage signal description knowledge to be changed corresponding to the u-th order signal description processing component; the v linkage signal description knowledge to be changed is the linkage signal description knowledge determined by the signal description recognition algorithm in the debugging process;
Or alternatively
And v pieces of linkage signal description knowledge corresponding to the u-1 th order signal description processing component are used as v pieces of linkage signal description knowledge to be changed corresponding to the u-1 th order signal description processing component.
6. The method of claim 1, wherein after the determining the set of quantized representation optimization knowledge integrated with the quantized representation optimization knowledge set to identify the resulting target multi-modal antenna signal description, further comprises:
Performing interval numerical mapping processing on the target multi-modal antenna signal description and the multi-modal antenna signal description generated by the u-1 order signal description processing component to obtain multi-modal antenna signal mapping description;
When u is the same as X, determining a signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description;
When u is smaller than X, inputting the derivative result of the multi-mode antenna signal mapping description into a (u+1) -th order signal description processing component;
Wherein when the u is the same as the X, after determining the signal description recognition result generated by the signal description recognition algorithm based on the multi-mode antenna signal mapping description, the method further includes:
Performing first signal decoding on the signal description identification result to obtain an expected antenna signal set matched with the antenna signal set to be analyzed;
performing second signal decoding on the signal description identification result to determine key signal fragments included in the antenna signal set to be analyzed;
And performing third signal decoding on the signal description recognition result to obtain a signal optimization control decision type matched with the antenna signal set to be analyzed.
7. An AI intelligent control system is characterized by comprising a processor and a memory; the processor is communicatively connected to the memory, the processor being configured to read a computer program from the memory and execute the computer program to implement the method of any of claims 1-6.
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