Relay adjusting method and device for wireless networking and elevator Internet of Things system

Abstract

The invention provides a wireless networking relay adjusting method and device and an elevator Internet of Things system, and the method comprises the steps: carrying out the cooperative processing of a low-frequency signal in an elevator shaft and a high-frequency signal in an elevator car through joint channel estimation, and obtaining the fusion feature of a communication signal between the elevator car and the elevator shaft; establishing a dual-band communication link between the elevator shaft and the elevator car by using the fusion feature, and further determining a link budget margin of each relay node in the dual-band communication link; a joint estimation value of the signal coverage intensity of the elevator car in the next signal blind area is determined according to the signal attenuation characteristics and the multipath interference characteristics of all the Internet of Things areas; and when the joint estimation value of the signal coverage intensity is lower than a preset signal threshold, performing pre-adjustment on the relay capability of high-frequency short-distance communication in the dual-band communication link of the next signal blind area based on each link budget margin. On the basis of the scheme, dual-frequency-band cooperative adjustment of the communication signals in the signal blind area of the elevator Internet of Things system can be achieved.

Description

Technical Field

[0001] This application relates to the field of Internet of Things communication technologies. More specifically, this application relates to a relay adjustment method, device, and elevator Internet of Things system for wireless networking. Background Art

[0002] With the acceleration of the urbanization process and the continuous increase in the elevator inventory, the traditional elevator management mode is difficult to meet the high requirements of modern cities for elevator safety and operation efficiency. The elevator Internet of Things came into being. The elevator Internet of Things deploys a variety of sensors at key parts of the elevator to collect the operation status data of the elevator in real time. The operation status data of the elevator is transmitted to the cloud platform via a wireless or wired network, and is deeply processed using big data analysis and artificial intelligence algorithms to achieve functions such as fault warning, remote monitoring, and intelligent maintenance.

[0003] Traditional technologies adopt a passive fault recovery mode, which can only start the reconnection process after a communication interruption occurs. This passive fault recovery mode has a significant response delay and cannot meet the strict requirements of elevator safety monitoring for real-time performance. Especially in key scenarios such as emergency braking and fault alarm, it may cause safety hazards. The existing systems in Tonghua City adopt fixed-power transmission and static routing strategies, resulting in the inability to adapt to the dynamic channel changes brought about by the movement of the elevator, causing severe fluctuations in the signal at the edge of the blind area. At the same time, during non-blind area periods, it operates at a high power continuously, generating a large amount of ineffective energy consumption. Therefore, how to achieve dual-band collaborative adjustment of communication signals in the signal blind area of the elevator Internet of Things system has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a relay adjustment method, device, and elevator Internet of Things system for wireless networking, which can achieve dual-band collaborative adjustment of communication signals in the signal blind area of the elevator Internet of Things system.

[0005] In a first aspect, this application provides a relay adjustment method for wireless networking, which is used for relay adjustment of wireless networking in an elevator Internet of Things system. A low-frequency backbone network is deployed inside the elevator shaft in advance, and high-frequency short-distance communication is adopted inside the elevator car. The method includes: Collecting the low-frequency signals of the low-frequency backbone network in the elevator shaft during elevator operation, and synchronously collecting the high-frequency signals transmitted through the high-frequency short-distance communication module in the elevator car; Carrying out collaborative processing on the low-frequency signal and the high-frequency signal through joint channel estimation to obtain the fusion characteristics of the communication signals between the elevator car and the elevator shaft in the elevator Internet of Things, using the fusion characteristics of the communication signals to establish a dual-band communication link between the elevator shaft and the elevator car, and further determining the link budget margin of each relay node in the dual-band communication link; Predict the next signal blind area that the elevator car will enter during elevator operation based on the historical operating state of the elevator car, and then determine the combined estimated value of the signal coverage strength of the elevator car in the next signal blind area based on the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft; When the combined estimated value of the signal coverage strength is lower than a preset signal threshold, pre-adjust the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margins of each link.

[0006] In some embodiments, the specific steps of jointly processing the low-frequency signal and the high-frequency signal through joint channel estimation to obtain the fusion characteristics of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things include: Demodulate and extract the path loss exponent in the elevator shaft from the low-frequency signal; Parse and obtain the channel impulse response of the elevator car from the high-frequency signal; Jointly estimate the path loss exponent and the channel impulse response as the fusion characteristics of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation.

[0007] In some embodiments, the specific steps of establishing a dual-band communication link between the elevator shaft and the elevator car using the fusion characteristics of the communication signal include: Extract the control instructions of the low-frequency backbone network in the elevator shaft and the transmission instructions of the high-frequency short-range communication module in the elevator car from the fusion characteristics of the communication signal; Establish a dual-band communication link between the elevator shaft and the elevator car through the control instructions and the transmission instructions.

[0008] In some embodiments, the specific steps of determining the link budget margins of each relay node in the dual-band communication link include: For each relay node in the dual-band communication link, measure the round-trip delay, maximum available transmit power, and receive sensitivity of the relay node; Determine the equivalent isotropic radiated power of the relay node through the maximum available transmit power and the receive sensitivity; Determine the link budget margin of the relay node according to the equivalent isotropic radiated power and the round-trip delay, and then obtain the link budget margins of each relay node in the dual-band communication link.

[0009] In some embodiments, the specific steps of predicting the next signal blind area that the elevator car will enter during elevator operation based on the historical operating state of the elevator car include: Collect the historical operating state of the elevator car, and at the same time obtain the signal strength of each Internet of Things area in the elevator shaft; Filter out all signal blind spots from within the elevator hoistway based on the respective signal strengths; Predict the entry area of the elevator car within a specified future time period based on the historical operating state; Perform a time-series comparison between the entry area and each signal blind spot to obtain the next signal blind spot that the elevator car enters during elevator operation.

[0010] In some embodiments, determining a joint estimated value of the signal coverage strength of the elevator car in the next signal blind spot based on the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area within the elevator hoistway specifically includes: Regard all Internet of Things areas within the next signal blind spot as signal areas; Determine the coverage strength values of each signal area based on the signal attenuation characteristics and multipath interference characteristics of each signal area; Determine the joint estimated value of the signal coverage strength of the elevator car in the next signal blind spot based on all the coverage strength values.

[0011] In some embodiments, pre-adjusting the relay capacity of high-frequency short-range communication in the dual-band communication link of the next signal blind spot based on the respective link budget margins specifically includes: Obtain all relay nodes in the dual-band communication link of the next signal blind spot; Perform stage optimization on the relay capacity of high-frequency short-range communication in the dual-band communication link of the next signal blind spot based on the link budget margins of each relay node.

[0012] In a second aspect, the present application provides a relay adjustment device for wireless networking, including: An acquisition module, configured to acquire low-frequency signals of the low-frequency backbone network within the elevator hoistway during elevator operation, and synchronously acquire high-frequency signals transmitted through the high-frequency short-range communication module within the elevator car; A processing module, configured to perform collaborative processing on the low-frequency signal and the high-frequency signal through joint channel estimation to obtain the fusion characteristics of the communication signal between the elevator car and the elevator hoistway in the elevator Internet of Things, use the fusion characteristics of the communication signal to establish a dual-band communication link between the elevator hoistway and the elevator car, and further determine the link budget margins of each relay node in the dual-band communication link; The processing module is further configured to predict the next signal blind spot that the elevator car enters during elevator operation based on the historical operating state of the elevator car, and further determine the joint estimated value of the signal coverage strength of the elevator car in the next signal blind spot based on the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area within the elevator hoistway; An execution module, configured to, when a combined estimated value of the signal coverage intensity is lower than a preset signal threshold, perform pre-adjustment on the relay capability of high-frequency short-range communication in a dual-band communication link of the next signal blind area based on respective link budget margins.

[0013] In a third aspect, the present application provides an elevator Internet of Things system, including the above-mentioned relay adjustment device for wireless networking.

[0014] In a fourth aspect, the present application provides a computer device, including a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned relay adjustment method for wireless networking.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In a relay adjustment method, device and elevator Internet of Things system for wireless networking provided by the present application, during the operation of an elevator, a low-frequency signal of a low-frequency backbone network in an elevator shaft is collected, and a high-frequency signal transmitted through a high-frequency short-range communication module in an elevator car is synchronously collected; the low-frequency signal and the high-frequency signal are cooperatively processed through joint channel estimation to obtain a fusion feature of a communication signal between the elevator car and the elevator shaft in the elevator Internet of Things, and a dual-band communication link between the elevator shaft and the elevator car is established using the fusion feature of the communication signal, and then the link budget margin of each relay node in the dual-band communication link is determined; based on the historical operating state of the elevator car, the next signal blind area that the elevator car will enter during the operation of the elevator is predicted, and then a combined estimated value of the signal coverage intensity of the elevator car in the next signal blind area is determined through the signal attenuation feature and the multipath interference feature of each Internet of Things area in the elevator shaft; when the combined estimated value of the signal coverage intensity is lower than a preset signal threshold, pre-adjustment is performed on the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area based on respective link budget margins.

[0016] It can be seen that in this application, when the combined estimated value of the signal coverage intensity is lower than the preset signal threshold, the relay capability of the high-frequency short-distance communication in the dual-band communication link for the next signal blind area is pre-adjusted based on the link budget margin of each link. First, by determining the link budget margin, the actual communication efficiency of each relay node in the dual-band environment can be obtained. When a signal blind area is predicted, the system can allocate communication resources based on the link budget margin of each link, preferentially enabling nodes with outstanding high-frequency band coverage capabilities to enhance the signal in the target area, and at the same time selecting nodes with stable low-frequency band transmissions to ensure the transmission of key instructions, enabling the pre-configuration of the optimal communication path before the blind area is formed, significantly improving the network adaptability in complex environments, and ensuring the continuity and reliability of communication services during the operation of the elevator. Then, by determining the combined estimated value of the signal coverage intensity, the evolution trend of the communication quality in the future blind area can be accurately predicted, thereby realizing the forward-looking optimization of network parameters. The proactive optimization mode based on prediction fundamentally changes the time lag defect of the traditional passive response mechanism, greatly shortening the duration of communication interruption, and providing better service guarantees for key services such as elevator safety monitoring and emergency communication. In summary, based on the above solutions, dual-band collaborative adjustment of communication signals in the signal blind area of the elevator IoT system can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 is an exemplary flowchart of a relay adjustment method for wireless networking according to some embodiments of the present application; Figure 2 is an application scenario diagram of an elevator IoT according to some embodiments of the present application; Figure 3 is a schematic flowchart of implementing pre-adjustment according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a relay adjustment device for wireless networking according to some embodiments of the present application; Figure 5 is a schematic structural diagram of a computer device for implementing a relay adjustment method for wireless networking according to some embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to better understand the technical solutions of the present application, the following will describe the technical solutions of the present application in detail in combination with the accompanying drawings of the specification and specific embodiments.

[0020] Reference Figure 1 , which is an exemplary flowchart of a relay adjustment method for wireless networking according to some embodiments of the present application. The relay adjustment method for wireless networking mainly includes the following steps: In step 101, during the operation of the elevator, low-frequency signals of the low-frequency backbone network in the elevator shaft are collected, and high-frequency signals transmitted through the high-frequency short-range communication module in the elevator car are synchronously collected.

[0021] It should be noted that in the present application, the low-frequency signal refers to an electromagnetic wave that mainly propagates by diffraction in the elevator shaft, and this low-frequency signal is used to maintain the basic connection; the high-frequency signal refers to an electromagnetic wave that mainly propagates by direct path inside the elevator car, and this high-frequency signal is used for high-speed data transmission; synchronous collection means that through hardware timestamp and software clock calibration, the time alignment accuracy of the dual-band data is ensured to be within 1 millisecond.

[0022] In specific implementation, first, at the infrastructure layer, low-power wide-area network nodes operating in the 470 MHz band are deployed at intervals of 20 meters on the side wall of the elevator shaft to form a low-frequency backbone network with strong penetration. At the same time, a compact terminal device supporting millimeter-wave communication is installed on the top of the elevator car to form a dual-band coverage system; second, at the signal processing layer, the central controller performs time alignment and spatial mapping on the position fingerprint data in the low-frequency band and the channel sounding reference signal in the high-frequency band to construct a three-dimensional signal propagation model; finally, at the data collection layer, the elevator shaft nodes are used to periodically send beacon frames containing position identifiers, and the elevator car communication terminal synchronously collects the real-time status data reported by the sensors in the elevator car through the 60 GHz band as high-frequency signals by scanning the received field strength and time difference of arrival.

[0023] In some embodiments, reference Figure 2 As described, this figure is an application scenario diagram of an elevator Internet of Things according to some embodiments of the present application. This figure shows an application scenario of an elevator Internet of Things system. The elevator Internet of Things system collects the operating status and environmental information of the elevator in real time through devices such as audio and video collection devices and additional sensors installed inside the elevator. This information is wirelessly communicated with the enterprise elevator Internet of Things platform through a collection and transmission device and via a protocol conversion device; the platform can remotely monitor the operating conditions of the elevator, realizing functions such as fault prediction, maintenance management, and safety monitoring; the control device is responsible for the basic operation of the elevator, while the display device provides information for passengers; the entire elevator Internet of Things system is connected through the Internet to ensure the real-time transmission and processing of data, improving the intelligent level and safety management efficiency of the elevator, and providing a safer and more convenient elevator riding experience for users.

[0024] In step 102, the low-frequency signal and the high-frequency signal are co-processed through joint channel estimation to obtain the fusion features of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things. The dual-band communication link between the elevator shaft and the elevator car is established using the fusion features of the communication signal, and then the link budget margin of each relay node in the dual-band communication link is determined.

[0025] In some embodiments, the co-processing of the low-frequency signal and the high-frequency signal through joint channel estimation to obtain the fusion features of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things can be implemented by the following steps: Demodulate and extract the path loss exponent in the elevator shaft from the low-frequency signal; Parse and obtain the channel impulse response of the elevator car from the high-frequency signal; Jointly estimate the path loss exponent and the channel impulse response as the fusion features of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation.

[0026] In specific implementation, first, the demodulation and extraction of the path loss exponent in the elevator shaft from the low-frequency signal can be implemented in the following manner, that is: in the low-frequency signal processing stage, by receiving the known reference signal transmitted by the fixed node in the elevator shaft, measuring the received power values at different position points in the elevator shaft, and using the least squares method to fit the curve slope of the signal strength changing with distance, so as to take the mean value of all curve slopes as the path loss exponent in the elevator shaft; then, the parsing and obtaining of the channel impulse response of the elevator car from the high-frequency signal can be implemented in the following manner, that is: in the high-frequency signal parsing stage, using the training sequence between the transceiver devices in the elevator car for correlation detection, capturing the arrival time and amplitude information of the multipath signal through a sliding window, and constructing a channel impulse response including the information of the direct path and the reflected path; finally, the joint estimation of the path loss exponent and the channel impulse response as the fusion features of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation can be implemented in the following manner, that is: initialize a joint channel estimation model based on a convolutional neural network, take the path loss exponent as the large-scale fading factor of the joint channel estimation model, take the channel impulse response as the small-scale fading feature of the joint channel estimation model, use the joint channel estimation model to perform weighted fusion on the path loss exponent and the channel impulse response, and take the result of the weighted fusion of the joint channel estimation model as the fusion features of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things.

[0027] It should be noted that in this application, the fused feature representation is a composite channel descriptor that combines spatial wide-area coverage accuracy and local detail resolution capabilities; the path loss exponent is a quantization parameter used to describe the attenuation degree per unit distance when the signal propagates in the elevator shaft space; the channel impulse response is a time-domain model that characterizes the electromagnetic wave propagation path characteristics in the enclosed space of the elevator car; the joint channel estimation model is a deep learning-driven multi-dimensional channel feature fusion system that constructs a dual-path feature extraction architecture through a convolutional neural network: the upper branch of the joint channel estimation model processes the path loss exponent to capture wide-area propagation characteristics, and the lower branch analyzes the channel impulse response to extract local multipath details. Through the feature map fusion layer, spatio-temporal domain adaptive weighting of the large-scale fading factor and small-scale fading characteristics is achieved. The fused feature output by the joint channel estimation model simultaneously contains the coverage ability information of the low-frequency network and the delay characteristics of the high-frequency link, forming a three-dimensional channel representation that can accurately reflect the complex electromagnetic environment of the elevator.

[0028] In some embodiments, establishing a dual-band communication link between the elevator shaft and the elevator car using the fused features of the communication signal can be achieved by the following steps: Extract the control instructions of the low-frequency backbone network in the elevator shaft and the transmission instructions of the high-frequency short-range communication module in the elevator car from the fused features of the communication signal; Establish a dual-band communication link between the elevator shaft and the elevator car through the control instructions and the transmission instructions.

[0029] It should be noted that in this application, the dual-band communication link is a heterogeneous network connection architecture that can simultaneously meet the dual requirements of wide coverage and high bandwidth in the elevator scenario. Specifically, in implementation, first, to extract the control instructions of the low-frequency backbone network in the elevator shaft and the transmission instructions of the high-frequency short-range communication module in the elevator car from the fusion characteristics of the communication signals, the following method can be used, that is: in the instruction extraction stage, the central controller deconstructs and analyzes the fusion characteristics. Through the preset protocol parsing rules, the control instructions containing network management information such as node wake-up timing and power adjustment parameters are separated from the low-frequency components, and at the same time, the transmission instructions involving transmission parameters such as modulation mode and bandwidth allocation are parsed from the high-frequency components. Among them, the control instructions refer to the set of operation commands that coordinate the collaborative work of the backbone network nodes in the elevator shaft, and the transmission instructions refer to the set of parameter configurations that standardize the communication behavior of the terminal devices in the elevator car. Then, to establish the dual-band communication link between the elevator shaft and the elevator car through the control instructions and the transmission instructions, the following method can be used, that is: in the link establishment stage, the elevator Internet of Things system dynamically adjusts the transmission power and working cycle of the network nodes on the elevator shaft side according to the control instructions to achieve wide-area coverage optimization. At the same time, according to the transmission instructions, the waveform parameters and resource block allocation of the communication module on the elevator car side are accurately configured to complete local connection optimization. The dual-band communication resources are orthogonally arranged in the time-frequency two-dimensional plane through the time synchronization mechanism to form a complementary and collaborative dual-channel data transmission system as the dual-band communication link between the elevator shaft and the elevator car.

[0030] In some embodiments, determining the link budget margin of each relay node in the dual-band communication link can be achieved by the following steps: For each relay node in the dual-band communication link, measure the round-trip delay, maximum available transmit power, and receive sensitivity of the relay node; Determine the equivalent isotropic radiated power of the relay node through the maximum available transmit power and the receive sensitivity; Determine the link budget margin of the relay node according to the equivalent isotropic radiated power and the round-trip delay, and then obtain the link budget margin of each relay node in the dual-band communication link.

[0031] In specific implementation, first, for each relay node in the dual-band communication link, measuring the round-trip delay, maximum available transmit power, and receive sensitivity of the relay node can be achieved in the following manner, that is: for each relay node in the dual-band communication link, by periodically sending detection signals and receiving response packets, accurately record the accumulated delay value of the signal transmission and processing between nodes as the round-trip delay. At the same time, obtain the maximum output power value of the node under the condition of meeting the bit error rate requirement as the maximum available transmit power through power scan testing, and determine the lowest signal strength that the node can reliably demodulate as the receive sensitivity; then, determining the equivalent isotropic radiated power of the relay node through the maximum available transmit power and the receive sensitivity can be achieved in the following manner, that is: add the maximum available transmit power to the node antenna gain, and then subtract the feeder loss to finally obtain the equivalent isotropic radiated power representing the spatial radiation ability of the relay node; finally, determining the link budget margin of the relay node according to the equivalent isotropic radiated power and the round-trip delay, and further obtaining the link budget margin of each relay node in the dual-band communication link can be achieved in the following manner, that is: initialize a performance evaluation model with weighted evaluation, use the equivalent isotropic radiated power as the weight factor of the relay node in this performance evaluation model, use the round-trip delay as the performance index of the relay node in this performance evaluation model, and use this performance evaluation model to evaluate the forwarding ability of the relay node, so as to use the evaluation result of this performance evaluation model as the link budget margin of the relay node. Through the above method, the link budget margin of each relay node in the dual-band communication link can be obtained.

[0032] It should be noted that in this application, the link budget margin is a comprehensive evaluation index reflecting the data transmission efficiency of the relay node in a specific network topology. The higher the value of the link budget margin, the better the forwarding performance of the node in the dual-band communication link; the round-trip delay refers to the total time consumption of the signal from transmission to reception; the maximum available transmit power refers to the maximum signal transmission intensity that the radio frequency front end of the relay node can provide within the compliance range; the receive sensitivity refers to the minimum input power required for the receiver to correctly demodulate the signal; the equivalent isotropic radiated power refers to the effective radiation intensity of the relay node under the condition of an ideal omnidirectional antenna; the performance evaluation model is a node capability quantization tool based on the weighted scoring method. Through mathematical modeling, the radio frequency performance (equivalent isotropic radiated power) and the delay characteristic (round-trip delay) are normalized and weighted and fused. The equivalent isotropic radiated power is used as a positive weight factor to reflect the signal coverage ability of the node, and the round-trip delay is used as a negative performance index to characterize the transmission efficiency. The two generate a comprehensive evaluation value from 0 to 100 through a linear weighted function. This comprehensive evaluation value directly represents the actual forwarding efficiency of the node in a complex elevator environment, where the higher the score, the more suitable the node is as a relay device to be preferentially called.

[0033] In step 103, based on the historical operating state of the elevator car, the next signal blind area that the elevator car will enter during elevator operation is predicted, and then the joint estimated value of the signal coverage intensity of the elevator car in the next signal blind area is determined through the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft.

[0034] In some embodiments, predicting the next signal blind area that the elevator car will enter during elevator operation based on the historical operating state of the elevator car can be implemented by the following steps: Collect the historical operating state of the elevator car, and at the same time obtain the signal strength of each Internet of Things area in the elevator shaft; Screen out all signal blind areas from the elevator shaft through each signal strength; Predict the entry area of the elevator car within a specified future time period based on the historical operating state; Compare the entry area and each signal blind area in time sequence to obtain the next signal blind area that the elevator car will enter during elevator operation.

[0035] It should be noted that in this application, the next signal blind area refers to the area where the communication quality will decline that the elevator car will encounter during recent operation; the historical operating state refers to the set of speed, acceleration, and position change characteristics of the elevator car in the time dimension; the signal strength refers to the radio signal power value received at a specific spatial position; the signal blind area refers to the physical space range where a reliable communication connection cannot be guaranteed; the entry area refers to the spatial range covered by the movement trajectory of the elevator car within the prediction time period.

[0036] In specific implementation, first, collect the historical operation status of the elevator car, and at the same time, the signal strength of each Internet of Things area in the elevator shaft can be obtained by the following method, that is: record the historical operation trajectory through the acceleration sensor and position encoder built in the elevator car as the historical operation status of the elevator car. For each Internet of Things area in the elevator shaft, use the Internet of Things nodes distributed in the elevator shaft to periodically report the set of signal strength measurement values of each Internet of Things area as the signal strength of the Internet of Things area. Through the above method, the signal strength of each Internet of Things area in the elevator shaft can be obtained; second, all signal blind spots can be screened out from the elevator shaft through each signal strength by the following method, that is: regard the Internet of Things area where the signal strength measurement value is lower than the preset blind spot threshold for a continuous preset number of times (default is 3 times) in the signal strength as the signal blind spot, and all signal blind spots in the elevator shaft can be obtained; then, based on the historical operation status, predict the area that the elevator car will enter within a specified future time period by the following method, that is: use the time series analysis method to model the historical operation status of the elevator car to calculate the floor interval that the elevator car may reach within a specified future time period (default is 30 seconds), and thus regard the floor interval as the area that the elevator car will enter within the specified future time period; finally, perform a time series comparison between the entering area and each signal blind spot to obtain the next signal blind spot that the elevator car enters during elevator operation by the following method, that is, obtain the signal blind spot included in the entering area from all signal blind spots as the entering blind spot, arrange the entering blind spots in chronological order, and regard the first entering blind spot after arrangement as the next signal blind spot that the elevator car enters during elevator operation.

[0037] In some embodiments, the joint estimation value of the signal coverage strength of the elevator car in the next signal blind spot can be determined by the following steps through the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft: Regard all Internet of Things areas in the next signal blind spot as signal areas; Determine the coverage strength value of each signal area through the signal attenuation characteristics and multipath interference characteristics of each signal area; Determine the joint estimation value of the signal coverage strength of the elevator car in the next signal blind spot through all the coverage strength values.

[0038] In specific implementation, first, all Internet of Things (IoT) areas in the next signal blind area are regarded as signal areas. Then, the coverage intensity values of each signal area can be determined according to the signal attenuation characteristics and multipath interference characteristics of each signal area, which can be achieved in the following way: for each signal area, collect the historical monitoring data of the signal area, calculate the attenuation rate of the signal intensity changing with distance as the signal attenuation characteristic, and count the intensity ratio of the reflected wave to the direct wave in the multipath components as the multipath interference characteristic. Then, use a weighted algorithm to fuse the signal attenuation characteristic and the multipath interference characteristic into the coverage intensity value of the signal area. In this way, the coverage intensity value of each signal area can be obtained. Finally, the joint estimation value of the signal coverage intensity of the elevator car in the next signal blind area can be determined by all the coverage intensity values, which can be achieved in the following way: take the mean value of all the coverage intensity values as the joint estimation value of the signal coverage intensity of the elevator car in the next signal blind area.

[0039] It should be noted that in this application, the joint estimation value of the signal coverage intensity refers to the pre-estimated value of the communication conditions faced by the elevator car when moving in the blind area; the signal area refers to the smallest spatial evaluation unit with complete signal acquisition capabilities; the coverage intensity value is a quantitative index reflecting the quality of communication in a specific area.

[0040] In step 104, when the joint estimation value of the signal coverage intensity is lower than the preset signal threshold, pre-adjust the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margins of each link.

[0041] It should be noted that in this application, the signal threshold represents the minimum quality threshold required to maintain reliable communication. This signal threshold is a quantitative index determined by comprehensive experimental measurement and theoretical calculation according to the communication protocol requirements of the elevator IoT system. When the joint estimation value of the signal coverage intensity is lower than the preset signal threshold, it means that the communication reliability fails, the bit error rate will exceed the protocol tolerance of the communication protocol in the elevator IoT system, which may lead to service quality degradation, an increase in the real-time service transmission delay, and an increase in the packet loss rate of elevator status monitoring data.

[0042] In some embodiments, pre-adjust the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margins of each link. Refer to Figure 3 As described, this figure is a schematic flowchart of pre-adjustment in some embodiments of this application. The pre-adjustment in this embodiment can be achieved by the following steps: In step 1041, obtain all relay nodes in the dual-band communication link of the next signal blind area. In step 1042, perform stage optimization on the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margins of each relay node.

[0043] In specific implementation, first, to obtain all relay nodes in the next signal blind area in the dual-band communication link, the following method can be adopted, that is: according to the three-dimensional coordinates of the next signal blind area and in combination with the pre-stored elevator shaft node deployment map, filter out relay nodes that simultaneously meet the following conditions: located within the influence range of the blind area, supporting dual-band communication, and the current load being lower than the threshold, then all relay nodes in the next signal blind area in the dual-band communication link can be obtained; then, to perform stage optimization on the relay capacity of the high-frequency short-distance communication in the dual-band communication link of the next signal blind area based on the link budget margin of each relay node, the following method can be adopted, that is: form the execution body for optimizing the dual-band communication link with all relay nodes. In the first stage, for the relay node closest to the elevator car, increase the link budget margin of the relay node to the rated maximum value. In the second stage, adjust the beamforming direction of adjacent nodes to align the main lobe of the relay node with the center of the blind area. In the third stage, optimize the time slot allocation scheme at the media access control layer, prioritize ensuring high-frequency communication resources, and perform a progressive dynamic adjustment process on network parameters according to real-time communication requirements. The hierarchical and progressive optimization mechanism not only ensures the communication quality in key areas but also avoids excessive consumption of network resources, achieving the best balance between communication reliability and system energy efficiency in the elevator movement scenario.

[0044] In addition, on the other hand of this application, in some embodiments, this application provides an Internet of Things system, which includes a relay adjustment device for wireless networking. Refer to Figure 4 , this figure is a schematic structural diagram of the relay adjustment device for wireless networking shown according to some embodiments of this application. The relay adjustment device for wireless networking includes: a collection module 201, a processing module 202, and an execution module 203, which are described as follows: Collection module 201. In this application, the collection module 201 is mainly used to collect low-frequency signals of the low-frequency backbone network in the elevator shaft during elevator operation, and synchronously collect high-frequency signals transmitted through the high-frequency short-distance communication module in the elevator car; Processing module 202. In this application, the processing module 202 is used to perform collaborative processing on the low-frequency signal and the high-frequency signal through joint channel estimation to obtain the fusion characteristics of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things, use the fusion characteristics of the communication signal to establish a dual-band communication link between the elevator shaft and the elevator car, and further determine the link budget margin of each relay node in the dual-band communication link; It should be noted that the processing module 202 is further used to predict the next signal blind area that the elevator car will enter during elevator operation based on the historical operation state of the elevator car, and further determine the joint estimated value of the signal coverage intensity of the elevator car in the next signal blind area through the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft; Execution module 203. In the present application, the execution module 203 is mainly used to pre-adjust the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margins when the combined estimated value of the signal coverage strength is lower than a preset signal threshold.

[0045] The above text has introduced in detail the examples of the relay adjustment method, device, and elevator Internet of Things system for wireless networking provided by the embodiments of the present application. It can be understood that, in order to implement the above functions, the corresponding device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving the hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0046] In some embodiments, the present application further provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned relay adjustment method for wireless networking.

[0047] In some embodiments, refer to Figure 5 , the dotted line in this figure indicates that the unit or module is optional. This figure is a schematic structural diagram of a computer device for implementing the relay adjustment method for wireless networking provided by the embodiments of the present application. The relay adjustment method for wireless networking described in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device includes at least one processor 301, a memory 302, and at least one communication unit 305. The computer device can be a terminal device, a server, or a chip.

[0048] The processor 301 can be a general-purpose processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU). The CPU can be used to control the computer device, execute software programs, and process the data of software programs. The computer device can also include a communication unit 305 for implementing the input (reception) and output (transmission) of signals.

[0049] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip. Alternatively, the communication unit 305 may be the communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0050] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be the transceiver of the terminal device or the server. Alternatively, the communication unit 305 may be the transceiver circuit of the terminal device or the server.

[0051] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be run by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiments according to the instructions 303. Optionally, data (such as a target audit model) may also be stored in the memory 302. Optionally, the processor 301 may also read the data stored in the memory 302. The data may be stored at the same storage address as the program 304, or may be stored at a different storage address from the program 304.

[0052] The processor 301 and the memory 302 may be provided separately or integrated together. For example, they may be integrated on a system on chip (SOC) of a terminal device.

[0053] It should be understood that the steps of the above method embodiments can be completed by the logic circuit in hardware form or the instructions in software form in the processor 301. The processor 301 may be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices. For example, discrete gates, transistor logic devices, or discrete hardware components.

[0054] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] For example, in some embodiments, the present application further provides a computer-readable storage medium storing instructions or code that, when run on a computer, cause the computer to implement the above-described relay adjustment method for wireless networking when executed.

[0056] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A wireless networking relay adjustment method is used for wireless networking relay adjustment in an elevator Internet of Things system. A low-frequency backbone network is deployed in advance in the elevator shaft, and high-frequency short-distance communication is used inside the elevator car. The method is characterized in that: The method comprises the following steps: During the operation of the elevator, the low-frequency signal of the low-frequency backbone network in the elevator shaft is collected, and the high-frequency signal transmitted by the high-frequency short-distance communication module in the elevator car is simultaneously collected; The low-frequency signal and the high-frequency signal are collaboratively processed by joint channel estimation to obtain fusion characteristics of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things, and a dual-band communication link between the elevator shaft and the elevator car is established using the fusion characteristics of the communication signal, thereby determining the link budget margin of each relay node in the dual-band communication link; Based on the historical operation status of the elevator car, the next signal blind area that the elevator car will enter during the elevator operation is predicted, and then the joint estimated value of the signal coverage strength of the elevator car in the next signal blind area is determined through the signal attenuation characteristics and multipath interference characteristics of each IoT area in the elevator shaft; When the joint estimated value of the signal coverage strength is lower than a preset signal threshold, the relay capacity of the high frequency short distance communication in the dual-band communication link of the next signal blind spot is pre-adjusted based on the budget margin of each link.

2. The method according to claim 1, characterized in that The low-frequency signal and the high-frequency signal are collaboratively processed by joint channel estimation to obtain the fusion characteristics of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things, which specifically include: Demodulating and extracting a path loss index in the elevator shaft from the low-frequency signal; Analyze and obtain the channel impulse response of the elevator car from the high frequency signal; The path loss index and the channel impulse response are jointly estimated through joint channel estimation as a fusion feature of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things.

3. The method according to claim 1, characterized in that Using the fusion feature of the communication signal to establish a dual-band communication link between the elevator shaft and the elevator car specifically includes: Extracting control instructions of a low-frequency backbone network in the elevator shaft and transmission instructions of a high-frequency short-distance communication module in the elevator car from the fusion features of the communication signal; A dual-band communication link between the elevator shaft and the elevator car is established through the control instruction and the transmission instruction.

4. The method according to claim 1, characterized in that Determining the link budget margin of each relay node in the dual-band communication link specifically includes: For each relay node in the dual-band communication link, measuring the round-trip delay, maximum available transmit power and receiving sensitivity of the relay node; Determine the equivalent isotropic radiated power of the relay node by using the maximum available transmit power and the receive sensitivity; The link budget margin of the relay node is determined according to the equivalent isotropic radiated power and the round-trip delay, and then the link budget margin of each relay node in the dual-band communication link is obtained.

5. The method according to claim 1, characterized in that The next signal blind zone that the elevator car will enter during elevator operation is predicted based on the historical operation status of the elevator car, specifically including: Collect the historical operating status of the elevator car and obtain the signal strength of each IoT area in the elevator shaft; Filter out all signal blind spots in the elevator shaft through various signal strengths; predicting an entry area of ​​the elevator car within a specified future time period based on the historical operating status; The entry area is compared with each signal blind area in time sequence to obtain the next signal blind area that the elevator car enters during elevator operation.

6. The method according to claim 1, characterized in that The joint estimated value of the signal coverage strength of the elevator car in the next signal blind area is determined by the signal attenuation characteristics and multipath interference characteristics of each IoT area in the elevator shaft, specifically including: Take all IoT areas in the next signal blind zone as signal areas; Determine the coverage strength value of each signal area through the signal attenuation characteristics and multipath interference characteristics of each signal area; A joint estimated value of the signal coverage strength of the elevator car in the next signal blind area is determined by all the coverage strength values.

7. The method according to claim 1, characterized in that Pre-adjusting the relay capability of high frequency short-distance communication in the dual-band communication link in the next signal blind area based on the budget margin of each link specifically includes: Obtain all relay nodes in the next signal blind area in the dual-band communication link; Based on the link budget margin of each relay node, the relay capacity of high-frequency short-distance communication in the dual-band communication link in the next signal blind area is optimized in stages.

8. A relay adjustment device for wireless networking, characterized in that: include: The acquisition module is used to collect low-frequency signals from the low-frequency backbone network in the elevator shaft during elevator operation, and synchronously collect high-frequency signals transmitted by the high-frequency short-distance communication module in the elevator car; A processing module, configured to collaboratively process the low-frequency signal and the high-frequency signal through joint channel estimation, obtain a fusion feature of a communication signal between an elevator car and an elevator shaft in an elevator Internet of Things, establish a dual-band communication link between the elevator shaft and the elevator car using the fusion feature of the communication signal, and further determine a link budget margin of each relay node in the dual-band communication link; The processing module is also used to predict the next signal blind area that the elevator car enters during elevator operation based on the historical operation status of the elevator car, and then determine the joint estimated value of the signal coverage strength of the elevator car in the next signal blind area through the signal attenuation characteristics and multipath interference characteristics of each IoT area in the elevator shaft; The execution module is used to pre-adjust the relay capacity of high-frequency short-distance communication in the dual-band communication link of the next signal blind spot based on the budget margin of each link when the joint estimated value of the signal coverage strength is lower than the preset signal threshold.

9. An elevator Internet of Things system, characterized in that: It includes the relay adjustment device for wireless networking as claimed in claim 8.

10. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the relay adjustment method for wireless networking described in any one of claims 1 to 7.

Images