Wireless device intelligent pairing system and method based on Internet of Things

Abstract

The invention discloses a wireless device intelligent pairing system and method based on the Internet of Things, and relates to the technical field of wireless device intelligent pairing, and the method comprises the following steps: setting an interference analysis mechanism to analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals; setting a distance interference analysis mechanism to analyze a distance influence coefficient according to an actual distance; setting a pairing parameter adjustment mechanism for dynamically adjusting the optimal overtime threshold and the automatic matching frequency threshold in the current scene; setting a weight adjustment mechanism for judging whether to adjust the influence weight of the comprehensive interference intensity according to the analysis result of the comprehensive interference intensity; according to the invention, a single pairing duration threshold value and an automatic pairing frequency threshold value in an equipment pairing process are dynamically adjusted, and pairing parameters in the pairing process are adaptively adjusted according to interference intensity in a factory, so that the effect of considering both the pairing success rate and the equipment pairing efficiency is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent pairing technology for wireless devices, specifically to an intelligent pairing system and method for wireless devices based on the Internet of Things (IoT). Background Technology

[0002] With the rapid development of Industrial Internet of Things (IIoT) technology, the number of wireless devices deployed in factories, such as sensors, controllers, actuators, and gateways, has increased significantly, leading to increasingly frequent wireless pairing needs between devices. Factory scenarios differ significantly from ordinary civilian scenarios, containing numerous sources of strong electromagnetic interference such as motors, frequency converters, and welding machines. Furthermore, issues like concurrent pairing of multiple devices and metal structure obstructions pose significant challenges, resulting in severe interference during wireless device pairing. Currently, traditional wireless device pairing technologies often employ fixed timeout and matching count thresholds, failing to fully consider the interference characteristics of factory scenarios: when strong electromagnetic interference exists within a factory, wireless signals are prone to distortion and attenuation, leading to transmission difficulties in pairing commands. Increased transmission latency can lead to misjudgments of pairing failures if the timeout threshold is set too short, or if it is set too long, reducing the efficiency of concurrent pairing of multiple devices and increasing the likelihood of pairing conflicts between devices. Furthermore, a fixed threshold for the number of matches cannot adapt to different interference intensities and device distances. With strong interference or long device distances, the limited number of matches significantly increases the probability of pairing failures. Conversely, with weak interference and short device distances, excessive matching attempts waste resources and further exacerbate channel congestion during concurrent pairing. Existing technologies struggle to effectively address the issues of pairing failures and high error rates in scenarios with strong interference and multiple concurrent devices in factories, severely impacting the stability and deployment efficiency of factory IoT systems. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent pairing system and method for wireless devices based on the Internet of Things (IoT) to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a smart pairing method for wireless devices based on the Internet of Things, the method comprising the following steps: S1. Set up an interference analysis mechanism to collect wireless interference signals in the factory and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals; S2. Set up a distance interference analysis mechanism to detect the actual distance between the pairing initiator and the pairing response device, and analyze the distance influence coefficient based on the actual distance. S3. Set a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the obtained comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. S4. Set a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

[0005] Furthermore, in step S1: the interference signal analysis mechanism collects wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency acquisition device, and uses machine learning to analyze and extract the collected interference signals to obtain the type of interference signal; and obtains the strength of a single interference signal and the strength of a comprehensive interference signal.

[0006] Furthermore, the interference signal types include electromagnetic interference signals, multipath interference signals, and co-frequency interference signals. The individual interference intensities of different interference signals are analyzed in real time, with the interference intensity of the electromagnetic interference signal denoted as W1, the interference intensity of the multipath interference signal as W2, and the interference intensity of the co-frequency interference signal as W3. The collected mixed interference signals are then processed using signal power spectral density analysis to obtain the comprehensive interference intensity M. Based on the interference intensity, the superposition type of the interference signals is analyzed, and the analysis results are as follows: Where S represents the actual interference superposition coefficient; S0 represents the set reference interference superposition coefficient; Furthermore, in step S2: the distance interference analysis mechanism is used to detect the distance between the paired device and the device to be paired, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, use the metal obstruction coefficient to correct the distance between the devices to obtain the actual distance, and analyze the distance influence coefficient based on the actual distance.

[0007] Furthermore, the distance d between the pairing device and the device to be paired is collected, and the device distance is corrected according to the thickness of the obstructing metal to obtain the actual distance d′ between the devices; the distance influence coefficient is analyzed based on the actual distance between the devices, and the distance influence coefficient is calculated according to the following formula: Where Q represents the distance influence coefficient, d min Indicates the upper limit of the monitoring distance, d max Indicates the lower limit of distance detection; Because there are many metal devices in the factory, the distance between devices may be inaccurate due to obstruction by these devices. Therefore, by setting an actual obstruction coefficient, the distance between devices is corrected, thereby improving the accuracy of the distance influence coefficient and further improving the matching degree between the interference influence coefficient and the actual interference situation in the factory. To detect whether there is metal obstruction between the detection devices, if so, measure the thickness of the obstructing metal, record the thickness as L, and analyze the actual obstruction coefficient. ;in, Indicates the actual shading coefficient. This represents the set baseline shading coefficient, and L0 represents the set base metal shading thickness. Based on the metal shading analysis results, it is determined whether to correct the distance between devices. The analysis results are as follows: If there is no metal obstruction, no correction is made for the equipment distance, and the actual distance between the equipment is: d′=d; If there is obstruction between devices, the distance between the devices is corrected. The actual distance between the devices after correction is: d′=d×θ.

[0008] Furthermore, in step S3: the pairing parameter adjustment mechanism is used to analyze the interference influence coefficient by combining the comprehensive interference intensity, interference superposition coefficient and distance influence coefficient, and adjust the basic timeout threshold and automatic matching number for a single pairing according to the interference influence coefficient.

[0009] Furthermore, during the device pairing process, the interference impact coefficient is analyzed based on the acquired comprehensive interference intensity, interference superposition coefficient, and distance influence coefficient. The interference impact coefficient is calculated using the following formula: Where M0 represents the set comprehensive interference baseline threshold; This indicates the weight that influences the overall interference intensity. This indicates that the set interference superposition coefficient affects the weight; This indicates the influence weight of the distance-related coefficient. During wireless device pairing in a factory, various types of interference signals can cause wireless signals to distort and attenuate, leading to increased transmission delays in pairing commands. Setting the timeout threshold too short can cause false pairing failures. Conversely, setting the timeout threshold too long reduces the efficiency of concurrent pairing of multiple devices and increases the likelihood of pairing conflicts. Furthermore, a fixed pairing count threshold cannot adapt to different interference intensities and device distances. With strong interference or long device distances, the limited number of pairing counts significantly increases the probability of pairing failures. Conversely, with weak interference and short device distances, excessive pairing counts waste resources and further exacerbate channel congestion during concurrent pairing. Therefore, a pairing parameter adjustment mechanism is proposed to dynamically adjust the single pairing duration threshold and the automatic pairing count threshold. This adaptively adjusts the pairing parameters based on the interference intensity within the factory, achieving a balance between pairing success rate and device pairing efficiency. This solves the problem of significantly reduced device pairing success rates in traditional technologies with fixed pairing parameters, especially under strong interference or long distances. Set the basic timeout threshold T0 for a single pairing and the basic threshold K0 for the number of automatic matches; adjust the basic timeout threshold and the number of automatic matches for a single pairing based on the calculated interference impact coefficient. The adjusted single match duration threshold is as follows: The adjusted automatic matching threshold is: ; If a pairing attempt fails after the duration of a single pairing attempt reaches the adjusted threshold, a new pairing attempt will be restarted until the pairing is successful. If the number of pairing attempts reaches the automatic pairing attempt threshold and the pairing still fails, automatic pairing will stop, and staff will be prompted to perform manual pairing.

[0010] Furthermore, in step S4: the weight adjustment mechanism is used to compare and analyze the comprehensive interference intensity with the comprehensive interference intensity threshold, and to determine whether to adjust the weight affecting the comprehensive interference intensity based on the analysis results of the comprehensive interference intensity.

[0011] Furthermore, the actual acquired comprehensive interference intensity is compared with the set comprehensive interference intensity threshold. Based on the comparison analysis results, it is determined whether to adjust the influence weights. The analysis results are as follows: If M≤M0, then the weight of the overall interference intensity will not be adjusted; If M > M0, then the weighting of the overall interference intensity is adjusted, and the weighting of the interference superposition coefficient and the distance influence coefficient are adjusted simultaneously; the adjusted interference intensity influence weighting is: The adjusted interference superposition coefficient has the following impact weight: The adjusted influence weight of the distance influence coefficient is: ; The influence weights are adjusted based on whether the overall interference intensity exceeds a threshold. When the overall interference intensity is too high, the influence weights of the overall interference intensity and the superimposed interference coefficient are appropriately increased. This achieves the effect of obtaining an interference influence coefficient that is more consistent with the actual interference intensity when the overall interference is strong, and improves the matching effect between the adjusted pairing parameters and the actual interference situation.

[0012] The IoT-based intelligent pairing system for wireless devices includes an interference signal analysis module, a distance impact analysis module, a pairing parameter adjustment module, and a weight adjustment module. The interference signal analysis module is used to set up an interference analysis mechanism to collect wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency collector, analyze the extracted interference signal types, and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals. The distance impact analysis module is used to set up a distance interference analysis mechanism to detect the actual distance between the pairing initiating device and the pairing response device, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, and analyze the distance impact coefficient based on the actual distance. The pairing parameter adjustment module sets up a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the acquired comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. The weight adjustment module is used to set up a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention dynamically adjusts the single pairing duration threshold and automatic pairing count threshold during the device pairing process based on the monitored comprehensive interference intensity, interference superposition coefficient, and distance influence coefficient by setting a pairing parameter adjustment mechanism. Furthermore, during the calculation of the interference influence coefficient, it uses whether the comprehensive interference intensity exceeds the threshold to determine whether to adjust the influence weight. When the comprehensive interference intensity is too high, the influence weights of the comprehensive interference intensity and superposition interference coefficient are appropriately increased. This achieves a more accurate match between the obtained interference influence coefficient and the actual interference intensity when the comprehensive interference is strong, improving the effectiveness of the adjusted pairing parameters in matching the actual interference situation. The interference affects the pairing parameters during the pairing process. The system is adaptively adjusted based on the interference intensity within the factory, achieving a balance between pairing success rate and equipment pairing efficiency. This solves the problem of significantly reduced pairing success rate in traditional technologies with fixed pairing parameters, especially under strong interference or long distances. Furthermore, because many metal devices exist in the factory, distance analysis between devices may be inaccurate due to obstruction from these devices. Therefore, by setting an actual obstruction coefficient to correct the distance between devices, the accuracy of the distance influence coefficient is improved. This further enhances the matching degree between the interference influence coefficient and the actual interference conditions in the factory. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the process flow of the IoT-based intelligent pairing method for wireless devices according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: Figure 1 As shown, the present invention provides a technical solution, a method for intelligent pairing of wireless devices based on the Internet of Things, the method comprising the following steps: S1. Set up an interference analysis mechanism to collect wireless interference signals in the factory and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals; S2. Set up a distance interference analysis mechanism to detect the actual distance between the pairing initiator and the pairing response device, and analyze the distance influence coefficient based on the actual distance. S3. Set a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the obtained comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. S4. Set a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

[0017] In step S1: The interference signal analysis mechanism collects wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency acquisition device, and uses machine learning algorithms to analyze and extract the collected interference signals to obtain the type of interference signal; and obtains the strength of a single interference signal and the strength of a comprehensive interference signal.

[0018] The types of interference signals include electromagnetic interference signals, multipath interference signals, and co-frequency interference signals. The individual interference intensities of different interference signals are analyzed in real time. The interference intensity of electromagnetic interference signals is denoted as W1, the interference intensity of multipath interference signals as W2, and the interference intensity of co-frequency interference signals as W3. The collected mixed interference signals are processed using signal power spectral density analysis to obtain the comprehensive interference intensity M. The superposition type of interference signals is analyzed based on the interference intensity. The analysis results are as follows: Where S represents the actual interference superposition coefficient; S0 represents the set reference interference superposition coefficient; In step S2: The distance interference analysis mechanism is used to detect the distance between the pairing device and the device to be paired, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, use the metal obstruction coefficient to correct the distance between the devices to obtain the actual distance, and analyze the distance influence coefficient based on the actual distance.

[0019] Collect the distance d between the pairing device and the device to be paired, and correct the device distance according to the thickness of the obstructing metal to obtain the actual distance d′ between the devices; analyze the distance influence coefficient based on the actual distance between the devices, and calculate the distance influence coefficient according to the following formula: Where Q represents the distance influence coefficient, d min Indicates the upper limit of the monitoring distance, d max Indicates the lower limit of distance detection; To detect whether there is metal obstruction between the detection devices, if so, measure the thickness of the obstructing metal, record the thickness as L, and analyze the actual obstruction coefficient. ;in, Indicates the actual shading coefficient. This represents the set baseline shading coefficient, and L0 represents the set base metal shading thickness. Based on the metal shading analysis results, it is determined whether to correct the distance between devices. The analysis results are as follows: If there is no metal obstruction, no correction is made for the equipment distance, and the actual distance between the equipment is: d′=d; If there is obstruction between devices, the distance between the devices is corrected. The actual distance between the devices after correction is: d′=d×θ.

[0020] In step S3: The pairing parameter adjustment mechanism is used to analyze the interference influence coefficient by combining the comprehensive interference intensity, interference superposition coefficient and distance influence coefficient, and adjust the basic timeout threshold and automatic matching number of a single pairing according to the interference influence coefficient.

[0021] During the device pairing process, the interference impact coefficient is analyzed based on the obtained comprehensive interference intensity, interference superposition coefficient, and distance influence coefficient. The interference impact coefficient is calculated using the following formula: Where M0 represents the set comprehensive interference baseline threshold; This indicates the weight that influences the overall interference intensity. This indicates that the set interference superposition coefficient affects the weight; This indicates the influence weight of the distance influence coefficient. Set the basic timeout threshold T0 for a single pairing and the basic threshold K0 for the number of automatic matches; adjust the basic timeout threshold and the number of automatic matches for a single pairing based on the calculated interference impact coefficient. The adjusted single match duration threshold is as follows: The adjusted automatic matching threshold is: ; If a pairing attempt fails after the duration of a single pairing attempt reaches the adjusted threshold, a new pairing attempt will be restarted until the pairing is successful. If the number of pairing attempts reaches the automatic pairing attempt threshold and the pairing still fails, automatic pairing will stop, and staff will be prompted to perform manual pairing.

[0022] In step S4: The weight adjustment mechanism is used to compare and analyze the comprehensive interference intensity with the comprehensive interference intensity threshold, and to determine whether to adjust the weight of the comprehensive interference intensity based on the analysis results of the comprehensive interference intensity.

[0023] The actual acquired comprehensive interference intensity is compared with the set comprehensive interference intensity threshold. Based on the comparison analysis results, it is determined whether to adjust the influence weights. The analysis results are as follows: If M≤M0, then the weight of the overall interference intensity will not be adjusted; If M > M0, then the weighting of the overall interference intensity is adjusted, and the weighting of the interference superposition coefficient and the distance influence coefficient are adjusted simultaneously; the adjusted interference intensity influence weighting is: The adjusted interference superposition coefficient has the following impact weight: The adjusted influence weight of the distance influence coefficient is: .

[0024] The IoT-based intelligent pairing system for wireless devices includes an interference signal analysis module, a distance impact analysis module, a pairing parameter adjustment module, and a weight adjustment module. The interference signal analysis module is used to set up an interference analysis mechanism to collect wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency acquisition device, analyze the extracted interference signal types, and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals. The distance impact analysis module is used to set up a distance interference analysis mechanism to detect the actual distance between the pairing initiating device and the pairing responding device, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, and analyze the distance impact coefficient based on the actual distance. The pairing parameter adjustment module sets a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the acquired comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. The weight adjustment module is used to set up a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

[0025] Example 1: In step S1: the types of interference signals include electromagnetic interference signals, multipath interference signals, and co-frequency interference signals; the individual interference intensity of different interference signals is analyzed in real time, and the interference intensity of the electromagnetic interference signal is recorded as W1, the interference intensity of the multipath interference signal as W2, and the interference intensity of the co-frequency interference signal as W3; the collected mixed interference signals are processed using the signal power spectral density analysis method to obtain the comprehensive interference intensity M; and the superposition type of interference signals is analyzed based on the interference intensity. The analysis results are as follows: Where S represents the actual interference superposition coefficient; S0 represents the set reference interference superposition coefficient; The interference intensity of the collected electromagnetic interference signal is W1=245dBm, the interference intensity of the multipath interference signal is W2=136dBm, and the interference intensity of the co-frequency interference signal is W3=25dBm. The analysis yields M=190.3dBm. S0=0.8 and S=0.37 are set.

[0026] In step S2: The distance d between the pairing device and the device to be paired is collected, and the device distance is corrected according to the thickness of the obstructing metal to obtain the actual distance d′ between the devices; the distance influence coefficient is analyzed based on the actual distance between the devices, and the distance influence coefficient is calculated according to the following formula: Where Q represents the distance influence coefficient, d min Indicates the upper limit of the monitoring distance, d max Indicates the lower limit of distance detection; To detect whether there is metal obstruction between the detection devices, if so, measure the thickness of the obstructing metal, record the thickness as L, and analyze the actual obstruction coefficient. ;in, Indicates the actual shading coefficient. This represents the set baseline shading coefficient, and L0 represents the set base metal shading thickness. Based on the metal shading analysis results, it is determined whether to correct the distance between devices. The analysis results are as follows: If there is no metal obstruction, no correction is made for the equipment distance, and the actual distance between the equipment is: d′=d; If there is obstruction between devices, the distance between the devices is corrected, and the actual distance between the devices after correction is: d′=d×θ; Data collection parameters: d = 20m; L = 3cm, L0 = 3cm; θ0 = 0.6; d min =0.1m, d max =100m; Q=0.12.

[0027] In step S3: During the device pairing process, the interference impact coefficient is analyzed based on the acquired comprehensive interference intensity, interference superposition coefficient, and distance influence coefficient. The interference impact coefficient is calculated according to the following formula: Where M0 represents the set comprehensive interference baseline threshold; This indicates the weight that influences the overall interference intensity. This indicates that the set interference superposition coefficient affects the weight; This indicates the influence weight of the distance influence coefficient. Set the basic timeout threshold T0 for a single pairing and the basic threshold K0 for the number of automatic matches; adjust the basic timeout threshold and the number of automatic matches for a single pairing based on the calculated interference impact coefficient. The adjusted single match duration threshold is as follows: The adjusted automatic matching threshold is: ; If a pairing attempt fails after the duration of a single pairing attempt reaches the adjusted threshold, a new pairing attempt will be restarted until the pairing is successful. If the number of pairing attempts reaches the automatic pairing attempt threshold and the pairing still fails, automatic pairing will stop, and staff will be prompted to perform manual pairing.

[0028] In step S4: The actual acquired comprehensive interference intensity is compared with the set comprehensive interference intensity threshold. Based on the comparison analysis results, it is determined whether to adjust the influence weight. The analysis results are as follows: If M≤M0, then the weight of the overall interference intensity will not be adjusted; If M > M0, then the weighting of the overall interference intensity is adjusted, and the weighting of the interference superposition coefficient and the distance influence coefficient are adjusted simultaneously; the adjusted interference intensity influence weighting is: The adjusted interference superposition coefficient has the following impact weight: The adjusted influence weight of the distance influence coefficient is: ; M0=150dBm; f1=0.4; f2=0.3; f3=0.3; T0=10s, K0=5; f 11 =0.437; f 22 =0.328; f 33 =0.235; φ=1.7; T=17s; K=9; During the device pairing process, if pairing fails after 17s, a new pairing process will be restarted until pairing is successful. If pairing fails after 9 attempts, automatic pairing will stop and the operator will be prompted to perform manual pairing.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for intelligent pairing of wireless devices based on the Internet of Things, characterized in that: The method includes the following steps: S1. Set up an interference analysis mechanism to collect wireless interference signals in the factory and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals; S2. Set up a distance interference analysis mechanism to detect the actual distance between the pairing initiator and the pairing response device, and analyze the distance influence coefficient based on the actual distance. S3. Set a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the obtained comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. S4. Set a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

2. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 1, characterized in that: In step S1: the interference signal analysis mechanism collects wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency acquisition device, and uses machine learning algorithms to analyze and extract the collected interference signals to obtain the type of interference signal; and obtains the strength of a single interference signal and the strength of a comprehensive interference signal.

3. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 2, characterized in that: The types of interference signals include electromagnetic interference signals, multipath interference signals, and co-frequency interference signals; the individual interference intensity of different interference signals is analyzed in real time, and the interference intensity of electromagnetic interference signals is recorded as W1, the interference intensity of multipath interference signals is recorded as W2, and the interference intensity of co-frequency interference signals is recorded as W3; and the collected mixed interference signals are processed using the signal power spectral density analysis method to obtain the comprehensive interference intensity M. Set a baseline interference superposition coefficient S0; adjust the baseline interference superposition coefficient using the ratio of the overall interference intensity to the sum of the three individual interference intensities to obtain the actual interference superposition coefficient S.

4. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 1, characterized in that: In step S2: the distance interference analysis mechanism is used to detect the distance between the pairing device and the device to be paired, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, use the metal obstruction coefficient to correct the distance between the devices to obtain the actual distance, and analyze the distance influence coefficient based on the actual distance.

5. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 4, characterized in that: Collect the distance d between the pairing device and the device to be paired, and correct the device distance according to the thickness of the obstructing metal to obtain the actual distance d′ between the devices; analyze the distance influence coefficient based on the actual distance between the devices, and calculate the distance influence coefficient according to the following formula: Where Q represents the distance influence coefficient, d min Indicates the upper limit of the monitoring distance, d max Indicates the lower limit of distance detection; To detect whether there is metal obstruction between the detection devices, if so, measure the thickness L of the obstructing metal; set a baseline obstruction coefficient. The basic shading metal thickness threshold L0 is set; the actual shading coefficient θ is obtained by analyzing the actual collected shading metal thickness and the basic shading metal thickness threshold; and the distance between devices is corrected based on the metal shading analysis results. The analysis results are as follows: If there is no metal obstruction, no correction is made for the equipment distance, and the actual distance between the equipment is: d′=d; If there is obstruction between devices, the distance between the devices is corrected. The actual distance between the devices after correction is: d′=d×θ.

6. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 1, characterized in that: In step S3: the pairing parameter adjustment mechanism is used to analyze the interference influence coefficient by combining the comprehensive interference intensity, interference superposition coefficient and distance influence coefficient, and adjust the basic timeout threshold and automatic matching number of a single pairing according to the interference influence coefficient.

7. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 6, characterized in that: During the device pairing process, the interference impact coefficient is analyzed based on the obtained comprehensive interference intensity, interference superposition coefficient, and distance influence coefficient. The interference impact coefficient is calculated using the following formula: Where M0 represents the set comprehensive interference baseline threshold; This indicates the weight that influences the overall interference intensity. This indicates that the set interference superposition coefficient affects the weight; This indicates the influence weight of the distance influence coefficient. Set the basic timeout threshold T0 for a single pairing and the basic threshold K0 for the number of automatic matches; adjust the basic timeout threshold and the number of automatic matches for a single pairing based on the calculated interference impact coefficient. The adjusted single match duration threshold is as follows: The adjusted automatic matching threshold is: ; If a pairing attempt fails after the duration of a single pairing attempt reaches the adjusted threshold, a new pairing attempt will be restarted until the pairing is successful. If the number of pairing attempts reaches the automatic pairing attempt threshold and the pairing still fails, automatic pairing will stop, and staff will be prompted to perform manual pairing.

8. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 1, characterized in that: In step S4: the weight adjustment mechanism is used to compare and analyze the comprehensive interference intensity with the comprehensive interference intensity threshold, and to determine whether to adjust the weight of the comprehensive interference intensity based on the analysis results of the comprehensive interference intensity.

9. The intelligent pairing method for wireless devices based on the Internet of Things according to claim 8, characterized in that: The actual acquired comprehensive interference intensity is compared with the set comprehensive interference intensity threshold. Based on the comparison analysis results, it is determined whether to adjust the influence weights. The analysis results are as follows: If M≤M0, then the weight of the overall interference intensity will not be adjusted; If M > M0, then the weighting of the overall interference intensity is adjusted, and the weighting of the interference superposition coefficient and the distance influence coefficient are adjusted simultaneously; the adjusted interference intensity influence weighting is: The adjusted interference superposition coefficient has the following impact weight: The adjusted influence weight of the distance influence coefficient is: .

10. A wireless device intelligent pairing system based on the Internet of Things, applied to the wireless device intelligent pairing method based on the Internet of Things as described in any one of claims 1-9, characterized in that: The system includes an interference signal analysis module, a distance impact analysis module, a pairing parameter adjustment module, and a weight adjustment module; The interference signal analysis module is used to set up an interference analysis mechanism to collect wireless interference signals in the factory environment in real time through a high-sensitivity radio frequency collector, analyze the extracted interference signal types, and analyze the interference intensity of different interference types and the comprehensive interference intensity of mixed interference signals. The distance impact analysis module is used to set up a distance interference analysis mechanism to detect the actual distance between the pairing initiating device and the pairing response device, and to detect in real time whether there are metal obstructions between the devices, analyze the metal obstruction coefficient in real time, and analyze the distance impact coefficient based on the actual distance. The pairing parameter adjustment module sets up a pairing parameter adjustment mechanism to dynamically adjust the optimal timeout threshold and automatic matching number threshold in the current scenario by combining the acquired comprehensive interference intensity, interference superposition coefficient and distance influence coefficient. The weight adjustment module is used to set up a weight adjustment mechanism to determine whether to adjust the weights affecting the overall interference intensity based on the analysis results of the overall interference intensity level.

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