CN114158001A - Wireless self-adaptive communication method based on intelligent Internet of things equipment software algorithm - Google Patents
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Abstract
The invention discloses a wireless self-adaptive communication method based on an intelligent Internet of things equipment software algorithm, which comprises the following steps: collecting hardware interface information of each wireless interface, and selecting the same level for power supply and serial port communication to obtain an operable wireless module; respectively sorting AT instruction flows among the modules, storing wireless module information in a structural body, and matching the AT instructions by using a matching algorithm model; sending the AT instruction to an AT instruction identification module, and if a correct reply is received, continuing to send the AT instruction step by step to identify the communication module to which the AT instruction belongs; initializing the communication module and transmitting and receiving data. Aiming AT the problem of resource shortage of a CPU communication interface in an application scene of the Internet of things, the invention provides a plurality of wireless communication mode self-adaptive algorithms, adopts the AT instruction to identify a plurality of wireless communication modules, changes the traditional design idea that one set of hardware and software is required to be configured for wireless communication, saves the production cost and simplifies the field operation and maintenance.
Description
Technical Field
The invention relates to the technical field of application of the Internet of things, in particular to a wireless self-adaptive communication method based on an intelligent Internet of things device software algorithm.
Background
The technology of the internet of things is applied to various fields of social and economic development, and brings about and drives deep changes of social productivity, production modes and life modes. The communication technology is used as an important component of the internet of things, information data sensed by the internet of things can be efficiently transmitted and exchanged among different terminals, intercommunication and sharing of information resources are achieved, the communication technology is divided into wired communication and wireless communication according to the existing communication form division, and in view of the wide application scene of the internet of things, the wireless communication technology is mostly applied at present, such as NB-IoT, Bluetooth, LoRa, ZigBee, Wi-Fi and the like.
With the increase of the number of devices accessed by the internet of things, the accessed wireless communication modes are various, and the situation of coexistence of various wireless communication modes is gradually formed in the communication system of the internet of things. Therefore, in order to meet the requirements of various heterogeneous networks in different application scenarios, the internet of things terminal needs to use a high-performance CPU with rich interfaces, but a traditional wireless communication mode needs to match a set of communication hardware and software, a current CPU chip cannot support numerous wireless communication interfaces, and communication interfaces cannot coexist, so that interconnection and interoperability between devices are affected.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: the traditional method can not meet the requirements of various heterogeneous networks in different application scenes, a wireless communication mode needs to be matched with a set of communication hardware and software, the traditional CPU chip can not support numerous wireless communication interfaces, and the communication interfaces can not coexist, so that the interconnection and interoperability among equipment are influenced.
In order to solve the technical problems, the invention provides the following technical scheme: collecting hardware interface information of each wireless interface, and selecting the same level for power supply and serial port communication to obtain an operable wireless module; respectively sorting AT instruction flows among the modules, storing wireless module information in a memory, and matching the AT instructions by using a matching algorithm model; sending the AT instruction to an AT instruction identification module, and if a correct reply is received, continuing to send the AT instruction step by step to identify the communication module to which the AT instruction belongs; initializing the communication module and transmitting and receiving data.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: the AT instruction flow among the modules includes defining an NB-IoT AT flow as: AT, AT +1, AT +2, …, AT + 10; define the LoRaAT flow as: AT, AT +11, AT +12, …, AT + 20; define Wi-Fi AT procedure as: AT, AT +21, AT +22, …, AT + 30; defining the ZigBee AT process as follows: AT, AT +31, AT +32, …, AT + 40; … defines the nth module AT process as: AT, AT + N01, AT + N02, …, AT + NXX; n, XX is the number of AT commands for different modules.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: the matching algorithm model includes a set of one or more of,
wherein r is(n)(k) The self-correlation function is represented by a function,
the function of the match is represented by a function,
representing the sample characteristic value.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: the autocorrelation function r(n)(k) Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein k is a positive integer.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: calculating the usable probability of the AT command transmission link includes,
defining the average value of the usable state duration and the average value of the unusable state duration of the link as x1And x2The probability is:
where P represents the usable probability.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: the identification algorithm model in the AT identification module comprises an elastic matching algorithm model, an artificial neural network algorithm model, a support vector machine algorithm model and a principal component analysis algorithm model.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: the communication module comprises a module NB-IOT, a module LoRa, a module Wi-Fi and a module ZigBee.
As an optimal scheme of the wireless adaptive communication method based on the intelligent internet of things device software algorithm, the method comprises the following steps: sending AT +1 according to the optimal matching priority principle to see whether a correct reply is received, if the correct reply is received, continuing sending AT +2, if the correct reply is received, identifying the module NB-IOT, and recording that the module NB-IOT is identified; if no correct reply is received within the specified time, the AT +11 is sent according to the best matching priority principle, and if the correct reply is received, the identification module is LoRa; the subsequent processing is consistent with the module NB-IOT, if AT +11 does not receive the reply, the operation is finished, and whether the AT process arrangement is wrong or not is found out; if the AT +1 does not receive the correct reply, the AT +21 is continuously sent to identify the module Wi-Fi according to the optimal matching priority principle, and the AT +31 is sent to identify the ZiBee module according to the optimal matching priority principle.
The invention has the beneficial effects that: aiming AT the problem of resource shortage of a CPU communication interface in an application scene of the Internet of things, a plurality of wireless communication mode self-adaptive algorithms are provided, AT instructions are adopted to identify a plurality of wireless communication modules, the traditional design idea that one set of hardware and software needs to be configured for wireless communication is changed, the production cost is saved, and the field operation and maintenance are simplified.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a basic flow diagram of a wireless adaptive communication method based on an intelligent internet of things device software algorithm according to an embodiment of the present invention
Fig. 2 is a schematic diagram of a simulated experimental network topology structure of a wireless adaptive communication method based on an intelligent internet of things device software algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, an embodiment of the present invention provides a wireless adaptive communication method based on an intelligent internet of things device software algorithm, including:
s1: collecting hardware interface information of each wireless interface, and selecting the same level for power supply and serial port communication to obtain an operable wireless module;
it should be noted that the same level power supply and serial communication are selected: the TTL serial port interface can be selected to realize the communication between the CPU and the module, and the hardware can operate the wireless module.
S2: respectively sorting AT instruction flows among the modules, storing wireless module information in a structural body, and matching the wireless module information with the AT instructions by using a matching algorithm model;
it should be noted that the communication module includes wireless modules such as a module NB-IOT, a module LoRa, a module Wi-Fi, and a module ZigBee.
The AT instruction flow among the modules comprises the following steps:
the NB-IoT AT flow is defined as: AT, AT +1, AT +2, …, AT + 10;
define LoRa AT procedure as: AT, AT +11, AT +12, …, AT + 20;
define Wi-Fi AT procedure as: AT, AT +21, AT +22, …, AT + 30;
defining the ZigBee AT process as follows: AT, AT +31, AT +32, …, AT + 40.
…
Defining the AT process of the Nth module as follows: AT, AT + N01, AT + N02, …, AT + NXX;
n, XX arranges the number of AT commands for different modules.
Specifically, the adaptive AT recognition firstly carries out model matching, obtains the most possible wireless communication mode AT present, sends an AT command, screens out the most matching command in the next step if the reply is correct, recognizes the module until the sending is completed, and can continuously add a sample for updating through the algorithm if the module is changed and modified in the operation process, and selects the best matching module for configuration through the AT command.
Further, the matching algorithm model includes:
wherein r is(n)(k) The self-correlation function is represented by a function,
the function of the match is represented by a function,
representing the sample characteristic value.
Wherein the autocorrelation function r(n)(k) The method comprises the following steps:
wherein k is a positive integer.
S3: the AT instruction is sent to an AT instruction identification module, if the correct reply is received, the AT instruction is continuously sent step by step, and the communication module to which the AT instruction belongs is identified;
it should be noted that the recognition algorithm model in the AT recognition module includes an elastic matching algorithm model, an artificial neural network algorithm model, a support vector machine algorithm model, and a principal component analysis algorithm model.
Specifically, when the similarity among the similarities calculated by the elastic matching algorithm model is smaller than a preset threshold, the elastic matching algorithm model is not matched with the current recognition module scene, and AT the moment, the artificial neural network algorithm model is switched to the artificial neural network algorithm model, when the similarity among the similarities calculated by the artificial neural network algorithm model is larger than the preset threshold, the elastic matching algorithm model is matched with the current recognition module scene, and the artificial neural network algorithm model recognizes the current AT command and the communication module to which the AT command belongs, so that the adaptive matching of the AT command and the current communication module is realized, and the recognition accuracy is improved.
Further, an AT identification module is sent to check whether correct reply is received, otherwise, no module is used, the operation is finished, and if the reply is correct, the next step is carried out:
sending AT +1 according to the optimal matching priority principle to see whether a correct reply is received, if the correct reply is received, continuing sending AT +2, if the correct reply is received, identifying the module NB-IOT, and recording and identifying the module NB-IOT;
if no correct reply is received within the specified time, the AT +11 is sent according to the best matching priority principle, and if the correct reply is received, the identification module is LoRa;
the subsequent processing is consistent with the module NB-IOT, if AT +11 does not receive the reply, the operation is finished, and whether the AT process arrangement is wrong or not is found out;
if the AT +1 does not receive the correct reply, the AT +21 is continuously sent to identify the module Wi-Fi according to the optimal matching priority principle, and the AT +31 is sent to identify the ZiBee module according to the optimal matching priority principle.
S4: initializing the communication module and transmitting and receiving data.
It should be noted that calculating the usable probability of the AT command transmission link includes:
defining the average value of the duration of the usable state and the average value of the duration of the unusable state of the link as x1And x2The probability is:
where P represents the usable probability.
Specifically, P can be used to measure the recognition efficiency value of the algorithm to the module, where P approaches 1 indicates that the module can be recognized and the recognition efficiency to the module is extremely high, and P approaches 0 indicates that the module recognition efficiency is extremely low or the module cannot be successfully recognized.
Aiming at the problem of resource shortage of a CPU communication interface in an application scene of the Internet of things, the invention provides a self-adaptive algorithm of various wireless communication modes; the AT instruction is adopted to identify various wireless communication modules, the traditional design idea that one set of hardware and software is required to be configured for wireless communication is changed, the production cost is saved, and the field operation and maintenance are simplified.
Example 2
Referring to fig. 2, another embodiment of the present invention is different from the first embodiment in that a verification test of a wireless adaptive communication method based on an intelligent internet of things device software algorithm is provided, and in order to verify and explain technical effects adopted in the method, the embodiment adopts a conventional technical scheme and the method of the present invention to perform a comparison test, and compares test results by means of scientific demonstration to verify a real effect of the method.
A network topology diagram of a simulation process is shown in fig. 2, each data stream is defined to be of a wireless type, a transmission rate of a wireless channel is 2Mbps, sizes of first-class application and second-class application data packets of a real-time service CBR are 16Bytes and 100Bytes respectively, a size of a third-class application, namely a non-real-time service FTP data packet, is 500Bytes, a superframe length is 16ms, and simulation parameters of an adaptive mechanism, a DCF mechanism and a PCF mechanism are shown in tables 1 to 4:
table 1: and (4) a self-adaptive mechanism simulation parameter setting table.
Table 2: configuration parameter table for each site.
Table 3: and a DCF simulation parameter setting table.
| SlotTime | 20μs | RTSThreshold | 0bytes |
| SIFS | 10μs | MPDU Length | 1024bytes |
| PIFS | 30μs | RTS Length | 20bytes |
| DIFS | 50μs | CTS Length | 16bytes |
| CWmin | 31 | ACK Length | 16bytes |
| CWmax | 1023 | Bandwidth | 2Mbps |
| CCATime | 10μs | PreambleLength | 144bit |
| HeaderLength | 48bits |
Table 4: PCF simulation parameter setting table.
| SlotTime | 20μs | Beacon Length | 20bytes |
| SIFS | 10μs | CF-Poll Length | 16bytes |
| PIFS | 30μs | CF-Null Length | 16bytes |
| DIFS | 50μs | CF-End Length | 10bytes |
| Bandwidth | 2Mbps |
The simulation experiment results are shown in table 5:
table 5: the experimental results are shown in a comparison table.
| Experimental sample | Conventional methods | The method of the invention |
| Production cost | Height of | Is low in |
| Time delay | 15ms | 0.8ms |
| Efficiency of recognition | 83％ | 96％ |
From the above table, the method of the present invention has better robustness than the conventional method.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A wireless self-adaptive communication method based on an intelligent Internet of things equipment software algorithm is characterized by comprising the following steps:
collecting hardware interface information of each wireless interface, and selecting the same level for power supply and serial port communication to obtain an operable wireless module;
respectively sorting AT instruction flows among the modules, storing wireless module information in a memory, and matching the AT instructions by using a matching algorithm model;
sending the AT instruction to an AT instruction identification module, and if a correct reply is received, continuing to send the AT instruction step by step to identify the communication module to which the AT instruction belongs;
initializing the communication module and transmitting and receiving data.
2. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 1, wherein: the flow of AT commands between the various modules includes,
the NB-IoT AT flow is defined as: AT, AT +1, AT +2, …, AT + 10;
define LoRa AT procedure as: AT, AT +11, AT +12, …, AT + 20;
define Wi-Fi AT procedure as: AT, AT +21, AT +22, …, AT + 30;
defining the ZigBee AT process as follows: AT, AT +31, AT +32, …, AT + 40;
…
defining the AT process of the Nth module as follows: AT, AT + N01, AT + N02, …, AT + NXX;
n, XX is the number of AT commands for different modules.
3. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 1, wherein: the matching algorithm model includes a set of one or more of,
wherein r is(n)(k) The self-correlation function is represented by a function,
the function of the match is represented by a function,
representing the sample characteristic value.
4. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 1 or 3, characterized in that: the autocorrelation function r(n)(k) Comprises the steps of (a) preparing a mixture of a plurality of raw materials,
wherein k is a positive integer.
5. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 4, wherein: calculating the usable probability of the AT command transmission link includes,
defining the average value of the usable state duration and the average value of the unusable state duration of the link as x1And x2The probability is:
where P represents the usable probability.
6. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 5, wherein: the identification algorithm model in the AT identification module comprises an elastic matching algorithm model, an artificial neural network algorithm model, a support vector machine algorithm model and a principal component analysis algorithm model.
7. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 6, wherein: the communication module comprises a module NB-IOT, a module LoRa, a module Wi-Fi and a module ZigBee.
8. The intelligent internet of things device software algorithm-based wireless adaptive communication method according to claim 7, wherein: also comprises the following steps of (1) preparing,
sending AT +1 according to the optimal matching priority principle to see whether a correct reply is received, if the correct reply is received, continuing sending AT +2, if the correct reply is received, identifying the module NB-IOT, and recording and identifying the module NB-IOT;
if no correct reply is received within the specified time, the AT +11 is sent according to the best matching priority principle, and if the correct reply is received, the identification module is LoRa;
the subsequent processing is consistent with the module NB-IOT, if AT +11 does not receive the reply, the operation is finished, and whether the AT process arrangement is wrong or not is found out;
if the AT +1 does not receive the correct reply, the AT +21 is continuously sent to identify the module Wi-Fi according to the optimal matching priority principle, and the AT +31 is sent to identify the ZiBee module according to the optimal matching priority principle.
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