A wireless networking relay adjustment method and device and elevator internet of things system

Abstract

The application provides a relay adjustment method and device for wireless networking and an elevator Internet of Things system. Low-frequency signals in an elevator shaft and high-frequency signals in an elevator car are cooperatively processed through joint channel estimation to obtain fusion features of communication signals between the elevator car and the elevator shaft. A dual-band communication link between the elevator shaft and the elevator car is established using the fusion features, and the link budget margins of each relay node in the dual-band communication link are determined. The joint estimation value of signal coverage strength of the elevator car in the next signal blind area is determined through the signal attenuation features and multipath interference features of each Internet of Things area. When the joint estimation value of signal coverage strength is lower than a preset signal threshold, the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margins. The above scheme can achieve dual-band cooperative adjustment of communication signals in the signal blind area of the elevator Internet of Things system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things communication, and more particularly, to a relay adjustment method and device for wireless networking and an elevator Internet of Things system. BACKGROUND

[0002] With the acceleration of urbanization and the continuous rise of elevator ownership, the traditional elevator management mode cannot meet the high requirements of modern cities on elevator safety and operation efficiency. The elevator Internet of Things emerges as the times require. The elevator Internet of Things deploys various sensors at key parts of the elevator to collect real-time operation state data of the elevator. The operation state data of the elevator is transmitted to the cloud platform via wireless or wired network, and is deeply processed by using big data analysis and artificial intelligence algorithms to realize functions such as fault warning, remote monitoring, intelligent maintenance, etc.

[0003] The traditional technology adopts a passive fault recovery mode, which can only start the reconnection process after the communication interruption occurs. This passive fault recovery mode has significant response delay and cannot meet the stringent real-time requirements of elevator safety monitoring, especially in critical scenarios such as emergency braking and fault alarm, which may cause safety hazards. The existing system in Tonghua City uses a fixed power transmission and static routing strategy, which cannot adapt to the dynamic channel changes caused by elevator movement, resulting in dramatic fluctuations in edge signals in the blind area. At the same time, it runs at a high power continuously during the non-blind area period, resulting in a large amount of invalid energy consumption. Therefore, how to realize the dual-band collaborative adjustment of communication signals in the signal blind area of the elevator Internet of Things system has become a difficult problem in the industry. SUMMARY

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

[0005] In a first aspect, the present application provides a relay adjustment method for wireless networking, which is used for relay adjustment in the elevator Internet of Things system. A low-frequency backbone network is deployed inside the elevator shaft, and a high-frequency short-range communication is used inside the elevator car. The method comprises:

[0006] Collecting low-frequency signals of the low-frequency backbone network in the elevator shaft during elevator operation, and synchronously collecting high-frequency signals transmitted by the high-frequency short-range communication module in the elevator car;

[0007] Performing collaborative processing on the low-frequency signals and the high-frequency signals through joint channel estimation to obtain fusion features of communication signals between the elevator car and the elevator shaft in the elevator Internet of Things, using the fusion features of the communication signals to establish a dual-band communication link between the elevator shaft and the elevator car, and then determining the link budget margin of each relay node in the dual-band communication link;

[0008] predicting a next signal blind area into which the elevator car enters in the elevator operation based on a historical operating state of the elevator car, and further determining a joint estimation value of signal coverage strength of the elevator car in the next signal blind area through signal attenuation characteristics and multipath interference characteristics of each Internet of Things region in the elevator shaft;

[0009] when the joint estimation value of the signal coverage strength is lower than a preset signal threshold, pre-adjusting a relay capability of high-frequency short-range communication in a dual-band communication link of the next signal blind area based on each link budget margin.

[0010] In some embodiments, 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, specifically including:

[0011] demodulating and extracting a path loss index in the elevator shaft from the low-frequency signal;

[0012] obtaining a channel impulse response of the elevator car from the high-frequency signal;

[0013] jointly estimating the path loss index and the channel impulse response as the fusion feature of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation.

[0014] In some embodiments, the dual-band communication link between the elevator shaft and the elevator car is established using the fusion feature of the communication signal, specifically including:

[0015] extracting a control instruction of a low-frequency backbone network in the elevator shaft and a transmission instruction of a high-frequency short-range communication module in the elevator car from the fusion feature of the communication signal;

[0016] establishing the dual-band communication link between the elevator shaft and the elevator car through the control instruction and the transmission instruction.

[0017] In some embodiments, determining the link budget margin of each relay node in the dual-band communication link specifically includes:

[0018] measuring a round-trip delay, a maximum available transmit power and a receiving sensitivity of the relay node for each relay node in the dual-band communication link;

[0019] determining an equivalent isotropically radiated power of the relay node through the maximum available transmit power and the receiving sensitivity;

[0020] determining the link budget margin of the relay node according to the equivalent isotropically radiated power and the round-trip delay, and further obtaining the link budget margin of each relay node in the dual-band communication link.

[0021] In some embodiments, predicting the next signal blind area into which the elevator car enters in the elevator operation based on the historical running state of the elevator car specifically comprises:

[0022] Collecting the historical running state of the elevator car, and simultaneously acquiring signal strengths of each Internet of Things area in the elevator shaft;

[0023] Screening all signal blind areas from the elevator shaft through each signal strength;

[0024] Predicting an entering area of the elevator car in a future specified time period based on the historical running state;

[0025] Comparing the entering area and each signal blind area in time sequence to obtain the next signal blind area into which the elevator car enters in the elevator operation.

[0026] In some embodiments, determining a joint estimation value of signal coverage strength of the elevator car in the next signal blind area through signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft specifically comprises:

[0027] Taking all Internet of Things areas in the next signal blind area as signal areas;

[0028] Determining coverage strength values of each signal area through signal attenuation characteristics and multipath interference characteristics of each signal area;

[0029] Determining the joint estimation value of signal coverage strength of the elevator car in the next signal blind area through all coverage strength values.

[0030] In some embodiments, pre-adjusting the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area based on each link budget margin specifically comprises:

[0031] Acquiring all relay nodes of the next signal blind area in the dual-band communication link;

[0032] Staging optimizing the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margin of each relay node.

[0033] In a second aspect, the application provides a relay adjustment device for wireless networking, comprising:

[0034] The collection module is configured to collect a low-frequency signal of a low-frequency backbone network in the elevator shaft in the elevator operation, and simultaneously collect a high-frequency signal transmitted by a high-frequency short-range communication module in the elevator car;

[0035] The processing module is configured to perform cooperative processing on the low-frequency signal and the high-frequency signal by 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, establish a dual-band communication link between the elevator shaft and the elevator car by 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.

[0036] The processing module is further configured to predict a next signal blind area entered by the elevator car in the elevator operation based on a historical running state of the elevator car, and further determine a joint estimation value of signal coverage strength of the elevator car in the next signal blind area by using signal attenuation features and multipath interference features of each Internet of Things region in the elevator shaft.

[0037] The execution module is configured to perform pre-adjustment on a relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margin when the joint estimation value of the signal coverage strength is lower than a preset signal threshold.

[0038] In a third aspect, the present application provides an elevator Internet of Things system comprising the relay adjustment device for wireless networking.

[0039] In a fourth aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computer device executes the relay adjustment method for wireless networking.

[0040] The technical scheme provided by the embodiments of the present application has the following beneficial effects:

[0041] In the relay adjustment method, device and elevator Internet of Things system for wireless networking provided by the present application, a low-frequency signal of a low-frequency backbone network in an elevator shaft is collected in the elevator operation, and a high-frequency signal transmitted by 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 by 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. A dual-band communication link between the elevator shaft and the elevator car is established by using the fusion feature of the communication signal, and further a link budget margin of each relay node in the dual-band communication link is determined. A next signal blind area entered by the elevator car in the elevator operation is predicted based on a historical running state of the elevator car, and further a joint estimation value of signal coverage strength of the elevator car in the next signal blind area is determined by using signal attenuation features and multipath interference features of each Internet of Things region in the elevator shaft. When the joint estimation value of the signal coverage strength is lower than a preset signal threshold, a relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margin.

[0042] Therefore, in the present application, when the joint estimation value of signal coverage strength is lower than the preset signal threshold, the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margin. First, the link budget margin is determined to obtain the actual communication efficiency of each relay node in the dual-band environment. When the signal blind area is predicted, the system can allocate communication resources based on the link budget margin to preferentially enable nodes with high-frequency coverage capability to enhance the signal in the target area, while selecting nodes with stable low-frequency transmission to ensure key command transmission. This can pre-configure the optimal communication path before the blind area is formed, significantly improve the network adaptability in complex environments, and ensure the continuity and reliability of communication services during elevator operation. Then, the joint estimation value of signal coverage strength can accurately predict the evolution trend of communication quality in the future blind area, thereby realizing proactive network parameter optimization. The predicted proactive optimization mode fundamentally changes the time lag defect of the traditional passive response mechanism, significantly shortens the communication interruption duration, and provides better service guarantee for key businesses such as elevator safety monitoring and emergency communication. In summary, based on the above scheme, the dual-band cooperative adjustment of communication signals in the signal blind area of the elevator Internet of Things system can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0043] 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 needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0044] Figure 1 is an exemplary flowchart of a relay adjustment method for wireless networking according to some embodiments of the present application;

[0045] Figure 2 is an application scenario diagram of an elevator Internet of Things according to some embodiments of the present application;

[0046] Figure 3 is a flowchart of pre-adjustment according to some embodiments of the present application;

[0047] Figure 4 is a structural schematic diagram of a relay adjustment device for wireless networking according to some embodiments of the present application;

[0048] Figure 5 is a structural schematic diagram of a computer device for implementing a relay adjustment method for wireless networking according to some embodiments of the present application. DETAILED DESCRIPTION

[0049] For better understanding of the technical solutions of the present application, the technical solutions of the present application will be described in detail below in combination with the drawings of the specification and specific embodiments.

[0050] Referring to Figure 1 The figure is an exemplary flow chart of a wireless networking relay adjustment method according to some embodiments of the present application, which mainly includes the following steps:

[0051] In step 101, low-frequency signals of a low-frequency backbone network in an elevator shaft are collected in elevator operation, and high-frequency signals transmitted by a high-frequency short-range communication module in an elevator car are collected synchronously.

[0052] It should be noted that in the present application, the low-frequency signal refers to an electromagnetic wave mainly propagating by diffraction in the elevator shaft, which is used to maintain the basic connection; the high-frequency signal refers to an electromagnetic wave mainly propagating by direct path in the elevator car, which is used for high-speed data transmission; synchronous collection refers to ensuring the time alignment accuracy of the dual-band data within 1 millisecond through hardware timestamp and software clock calibration.

[0053] In specific implementation, first, at the infrastructure layer, low-power wide-area network nodes working at 470MHz frequency band are deployed at intervals of 20 meters between the elevator shaft sidewalls to form a low-frequency backbone network with strong penetration, and a compact terminal device supporting millimeter wave communication is installed on the top of the elevator car to constitute a dual-band coverage system; second, at the signal processing layer, the central controller performs time alignment and spatial mapping of the position fingerprint data of the low-frequency band and the channel sounding reference signal of the high-frequency band to build a three-dimensional signal propagation model; finally, at the data collection layer, the elevator shaft nodes periodically send beacon frames containing position identifiers, and the elevator car communication terminal scans the received field strength and time difference of arrival to synchronously collect real-time status data reported by sensors in the elevator car as high-frequency signals through the 60GHz frequency band.

[0054] In some embodiments, referring to Figure 2The application relates to an elevator Internet of Things system, which comprises an audio and video acquisition device, a sensor and the like installed in an elevator, a transmission device, a protocol conversion device, an enterprise elevator Internet of Things platform, a control device and a display device.

[0055] In step 102, 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 an elevator car and an elevator shaft in the elevator Internet of Things, a dual-band communication link between the elevator shaft and the elevator car is established by using the fusion feature of the communication signal, and a link budget margin of each relay node in the dual-band communication link is determined.

[0056] In some embodiments, 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 an elevator car and an elevator shaft in the elevator Internet of Things can be realized by the following steps:

[0057] A path loss index in the elevator shaft is demodulated and extracted from the low-frequency signal;

[0058] A channel impulse response of the elevator car is obtained by analyzing the high-frequency signal;

[0059] The path loss index and the channel impulse response are jointly estimated as the fusion feature of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation.

[0060] In a specific implementation, first, the path loss exponent in the elevator shaft can be extracted from the low-frequency signal by the following method: in the low-frequency signal processing stage, the received power values of different position points in the elevator shaft are measured by receiving the known reference signal transmitted by the fixed nodes in the elevator shaft, and the curve slope of the signal strength changing with the distance is fitted by the least square method, so that the average of all curve slopes is taken as the path loss exponent in the elevator shaft; then, the channel impulse response of the elevator car can be obtained from the high-frequency signal by the following method: in the high-frequency signal analysis stage, the training sequence between the transceiver devices in the elevator car is used for correlation detection, the time of arrival and amplitude information of the multipath signal are captured by a sliding window, and the channel impulse response containing the direct path and reflected path information is constructed; finally, the path loss exponent and the channel impulse response can be jointly estimated as the fusion feature of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things by the following method: a joint channel estimation model based on a convolutional neural network is initialized, the path loss exponent is taken as the large-scale fading factor of the joint channel estimation model, the channel impulse response is taken as the small-scale fading feature of the joint channel estimation model, the path loss exponent and the channel impulse response are weighted and fused using the joint channel estimation model, and the result of the weighted and fused joint channel estimation model is taken as the fusion feature of the communication signal between the elevator car and the elevator shaft in the elevator Internet of Things.

[0061] It should be noted that in this application, the fusion feature represents a composite channel descriptor with spatial wide-area coverage accuracy and local detail resolution capability; the path loss exponent is a quantitative parameter used to describe the unit distance attenuation degree of the signal in the elevator shaft space; the channel impulse response is a time-domain model representing the electromagnetic wave propagation path characteristics in the closed space of the elevator car; the joint channel estimation model is a deep learning driven multi-dimensional channel feature fusion system, which constructs a double-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 the wide-area propagation characteristics, the lower branch analyzes the channel impulse response to extract local multipath details, and the spatial domain adaptive weighting of the large-scale fading factor and the small-scale fading feature is realized through a feature map fusion layer. The fusion feature output by the joint channel estimation model contains the coverage capability information of the low-frequency network and the time delay characteristics of the high-frequency link, forming a three-dimensional channel representation that can accurately reflect the complex electromagnetic environment of the elevator.

[0062] In some embodiments, the establishment of a dual-band communication link between the elevator shaft and the elevator car using the fusion feature of the communication signal can be achieved by the following steps:

[0063] 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 are extracted from the fusion feature of the communication signal;

[0064] establish a dual-band communication link between the elevator shaft and the elevator car through the control instruction and the transmission instruction.

[0065] 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. In specific implementation, first, the control instruction of the low-frequency backbone network in the elevator shaft and the transmission instruction of the high-frequency short-range communication module in the elevator car can be extracted from the fusion characteristics of the communication signal, that is, in the instruction extraction stage, the central controller deconstructs and analyzes the fusion characteristics, separates the control instruction containing network management information such as node wake-up timing and power adjustment parameters from the low-frequency component through the preset protocol analysis rule, and analyzes the transmission instruction involving transmission parameters such as modulation mode and bandwidth allocation from the high-frequency component, wherein the control instruction refers to the operation command set for coordinating the cooperative work of the backbone network nodes in the elevator shaft, and the transmission instruction refers to the parameter configuration set for regulating the communication behavior of the terminal devices in the elevator car. Then, the dual-band communication link between the elevator shaft and the elevator car can be established through the control instruction and the transmission instruction, 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 instruction, realizes wide-area coverage optimization, and at the same time, accurately configures the waveform parameters and resource block allocation of the communication module on the elevator car side according to the transmission instruction, completes local connection optimization, and through the time synchronization mechanism, orthogonally arranges the dual-band communication resources in the time-frequency two-dimensional plane to form a complementary cooperative dual-channel data transmission system as the dual-band communication link between the elevator shaft and the elevator car.

[0066] 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:

[0067] For each relay node in the dual-band communication link, measuring the round-trip delay, maximum available transmission power, and receiving sensitivity of the relay node;

[0068] Determining the equivalent isotropically radiated power of the relay node through the maximum available transmission power and the receiving sensitivity;

[0069] Determining the link budget margin of the relay node according to the equivalent isotropically radiated power and the round-trip delay, and thereby obtaining the link budget margin of each relay node in the dual-band communication link.

[0070] In a specific implementation, first, the round-trip delay, the maximum available transmit power and the receiving sensitivity of each relay node in the dual-band communication link are measured in the following manner: for each relay node in the dual-band communication link, a probe signal is periodically sent and a response packet is received, the time delay accumulated value of signal transmission and processing between nodes is accurately recorded as the round-trip delay, the maximum output power value of the node under the requirement of bit error rate is obtained through power scan test as the maximum available transmit power, and the minimum signal strength that the node can reliably demodulate is determined as the receiving sensitivity; then, the equivalent isotropically radiated power of the relay node is determined by the maximum available transmit power and the receiving sensitivity in the following manner: the maximum available transmit power is added to the antenna gain of the node, and then the feeder loss is subtracted, and finally the equivalent isotropically radiated power representing the spatial radiation capability of the relay node is obtained; finally, the link budget margin of the relay node is determined according to the equivalent isotropically 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 in the following manner: a weighted performance evaluation model is initialized, the equivalent isotropically radiated power is taken as the weight factor of the relay node in the performance evaluation model, and the round-trip delay is taken as the performance index of the relay node in the performance evaluation model, the performance evaluation model is used to evaluate the forwarding capability of the relay node, and the evaluation result of the performance evaluation model is taken as the link budget margin of the relay node, and thus the link budget margin of each relay node in the dual-band communication link is obtained.

[0071] It should be noted that in the present application, the link budget margin is a comprehensive evaluation index reflecting the data transmission efficiency of the relay node in a specific network topology, and the higher the value of the link budget margin represents 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 whole process from signal sending to receiving; the maximum available transmit power refers to the maximum signal transmission strength that the radio frequency front end of the relay node can provide within the compliance range; the receiving sensitivity refers to the minimum input power required for the receiver to correctly demodulate the signal; the equivalent isotropically radiated power refers to the effective radiation strength of the relay node under the condition of ideal omnidirectional antenna; the performance evaluation model is a node capability quantification tool based on weighted scoring method, which normalizes and weights the radio frequency performance (equivalent isotropically radiated power) and the delay characteristic (round-trip delay) through mathematical modeling, the equivalent isotropically radiated power is taken as a positive weight factor to reflect the signal coverage capability of the node, the round-trip delay is taken as a negative performance index to represent the transmission efficiency, and the two are generated into a comprehensive evaluation value of 0-100 points through a linear weighting function, which directly represents the actual forwarding efficiency of the node in a complex elevator environment, and the higher the score represents the more suitable the node is as a relay device for priority calling.

[0072] In step 103, the next signal blind area that the elevator car will enter in the elevator operation is predicted based on the historical running state of the elevator car, and then the joint estimation 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.

[0073] In some embodiments, the prediction of the next signal blind area that the elevator car will enter in the elevator operation based on the historical running state of the elevator car can be implemented by the following steps:

[0074] Collecting the historical running state of the elevator car and obtaining the signal intensity of each Internet of Things area in the elevator shaft;

[0075] Screening all signal blind areas from the elevator shaft through each signal intensity;

[0076] Predicting the entering area of the elevator car in the future specified time period based on the historical running state;

[0077] Comparing the entering area and each signal blind area in time sequence to obtain the next signal blind area that the elevator car will enter in the elevator operation.

[0078] It should be noted that in the present application, the next signal blind area refers to the communication quality degradation area that the elevator car will encounter in the near future running process; the historical running state refers to the collection of speed, acceleration and position change characteristics of the elevator car in the time dimension; the signal intensity refers to the received radio signal power value at a specific spatial position; the signal blind area refers to the physical space range that cannot guarantee reliable communication connection; and the entering area refers to the spatial range covered by the motion trajectory of the elevator car in the predicted time period.

[0079] In a specific implementation, first, the historical running state of the elevator car is collected, and the signal strength of each Internet of Things area in the elevator shaft can be obtained in the following manner: the historical running trajectory is recorded by the acceleration sensor and the position encoder built in the elevator car as the historical running state of the elevator car, and for each Internet of Things area in the elevator shaft, the set of signal strength measurement values of each Internet of Things area is periodically reported by the Internet of Things nodes distributed in the elevator shaft as the signal strength of the Internet of Things area, and thus the signal strength of each Internet of Things area in the elevator shaft can be obtained; second, all signal blind areas in the elevator shaft can be selected from the elevator shaft by each signal strength in the following manner: the Internet of Things area with a signal strength measurement value lower than a preset blind area threshold for a continuous preset number of times (default is 3 times) is regarded as a signal blind area, and thus all signal blind areas in the elevator shaft can be obtained; then, the entering area of the elevator car in a future specified time period can be predicted based on the historical running state in the following manner: the historical running state of the elevator car is modeled by using a time series analysis method to calculate the floor interval that the elevator car can reach in a future specified time period (default is 30 seconds), and thus the floor interval is regarded as the entering area of the elevator car in the future specified time period; finally, the next signal blind area entered by the elevator car during elevator operation can be obtained by time sequence comparison between the entering area and each signal blind area in the following manner: the signal blind area contained in the entering area is obtained from all signal blind areas as an entering blind area, the entering blind areas are arranged in chronological order, and the first entering blind area after the arrangement is regarded as the next signal blind area entered by the elevator car during elevator operation.

[0080] In some embodiments, the joint estimation value of the signal coverage strength of the elevator car in the next signal blind area can be determined by the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft in the following steps:

[0081] All Internet of Things areas in the next signal blind area are regarded as signal areas;

[0082] The coverage strength value of each signal area is determined by the signal attenuation characteristics and multipath interference characteristics of each signal area;

[0083] The joint estimation value of the signal coverage strength of the elevator car in the next signal blind area is determined by all coverage strength values.

[0084] In a specific implementation, first, all Internet of Things areas in the next signal blind area are taken as signal areas; then, the coverage strength value of each signal area is determined by the signal attenuation characteristics and multipath interference characteristics of each signal area, which can be implemented in the following manner: for each signal area, historical monitoring data of the signal area is collected, the attenuation rate of signal strength with distance is calculated as the signal attenuation characteristic, and the intensity ratio of reflected wave to direct wave in the multipath component is counted as the multipath interference characteristic, and the signal attenuation characteristic and the multipath interference characteristic are fused into the coverage strength value of the signal area by using a weighted algorithm, so as to obtain the coverage strength value of each signal area; finally, the joint estimation value of signal coverage strength of the elevator car in the next signal blind area is determined by all the coverage strength values, which can be implemented in the following manner: the mean value of all the coverage strength values is taken as the joint estimation value of signal coverage strength of the elevator car in the next signal blind area.

[0085] It should be noted that in the present application, the joint estimation value of signal coverage strength refers to the estimated value of 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 capability; and the coverage strength value is a quantitative index reflecting the quality of communication in a specific area.

[0086] In step 104, when the joint estimation value of signal coverage strength is lower than the preset signal threshold, the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margin of each link.

[0087] It should be noted that in the present application, the signal threshold represents the minimum quality threshold required to maintain reliable communication, which is a quantitative index determined by comprehensive experimental measurement and theoretical calculation according to the communication protocol requirements of the elevator Internet of Things system. When the joint estimation value of signal coverage strength is lower than the preset signal threshold, the communication reliability fails, the bit error rate exceeds the protocol tolerance of the communication protocol in the elevator Internet of Things system, which may lead to degradation of service quality, increase of real-time service transmission delay, and increase of elevator state monitoring data packet loss rate.

[0088] In some embodiments, the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margin of each link, which is described with reference to Figure 3 The figure is a flowchart for implementing pre-adjustment in some embodiments of the present application. In the present embodiment, pre-adjustment can be implemented in the following steps:

[0089] In step 1041, all relay nodes of the next signal blind area in the dual-band communication link are obtained;

[0090] In step 1042, the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area is stage-optimized based on the link budget margin of each relay node.

[0091] In a specific implementation, first, all relay nodes in the dual-band communication link of the next signal blind area can be obtained in the following manner: according to the three-dimensional coordinates of the next signal blind area, in combination with the pre-stored elevator shaft node deployment map, relay nodes that meet the following conditions are filtered out: located within the influence range of the blind area, support dual-band communication, and the current load is lower than a threshold, and then all relay nodes in the dual-band communication link of the next signal blind area are obtained; then, the relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area can be stage-optimized based on the link budget margin of each relay node in the following manner: all relay nodes constitute the execution subject of the dual-band communication link optimization, in the first stage, the link budget margin of the relay node closest to the elevator car is improved to the rated maximum value, in the second stage, the beamforming direction of the adjacent node is adjusted so that the main lobe of the relay node is aligned with the center of the blind area, and in the third stage, the time slot allocation scheme is optimized in the medium access control layer, the high-frequency communication resources are preferentially guaranteed, and the gradual dynamic adjustment process of the network parameters according to the real-time communication demand, the hierarchical and progressive optimization mechanism not only guarantees the communication quality of the key area, but also avoids the excessive consumption of network resources, and realizes the best balance between communication reliability and system energy efficiency in the elevator moving scenario.

[0092] In addition, another aspect of the present application, in some embodiments, the present application provides an Internet of Things system, which comprises a wireless networking relay adjustment device, referring to Figure 4 The figure is a structural schematic diagram of a wireless networking relay adjustment device according to some embodiments of the present application, which comprises a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0093] The collection module 201 is mainly used for collecting low-frequency signals of the low-frequency backbone network in the elevator shaft during the operation of the elevator, and synchronously collecting high-frequency signals transmitted by the high-frequency short-range communication module in the elevator car in the present application;

[0094] The processing module 202 is used for cooperatively processing the low-frequency signals and the high-frequency signals through joint channel estimation to obtain the fusion features of the communication signals between the elevator car and the elevator shaft in the elevator Internet of Things, establishing a dual-band communication link between the elevator shaft and the elevator car using the fusion features of the communication signals, and then determining the link budget margin of each relay node in the dual-band communication link in the present application;

[0095] It should be noted that the processing module 202 is also used to predict the next signal blind zone that the elevator car will enter during elevator operation based on the historical operating status of the elevator car, and then determine the joint estimate of the signal coverage strength of the elevator car in the next signal blind zone through the signal attenuation characteristics and multipath interference characteristics of each Internet of Things area in the elevator shaft.

[0096] The execution module 203 in this application is mainly used to pre-adjust the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind zone 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.

[0097] The foregoing has detailed examples of wireless networking relay adjustment methods, devices, and elevator IoT systems provided in the embodiments of this application. It is understood that the corresponding devices, in order to achieve the above functions, include hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described wireless networking relay adjustment method.

[0099] In some embodiments, reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device implementing a relay adjustment method for wireless networking according to an embodiment of this application. The wireless networking relay adjustment method described in the above embodiments can be achieved through… Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0100] The processor 301 can be a general processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU), which can be used to control a computer device, execute a software program, and process data of the software program. The computer device can further include a communication unit 305 to implement input (reception) and output (transmission) of signals.

[0101] For example, the computer device can be a chip, and the communication unit 305 can be an input and / or output circuit of the chip, or the communication unit 305 can be a communication interface of the chip, and the chip can be a component of a terminal device or a network device or other device.

[0102] For another example, the computer device can be a terminal device or a server, and the communication unit 305 can be a transceiver of the terminal device or the server, or the communication unit 305 can be a transceiver circuit of the terminal device or the server.

[0103] The computer device can include one or more memories 302, which store a program 304 that can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 can further store data (such as a target audit model). Optionally, the processor 301 can further read the data stored in the memory 302, and the data can be stored in the same storage address as the program 304, or the data can be stored in a different storage address from the program 304.

[0104] The processor 301 and the memory 302 can be separately arranged or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0105] It should be understood that each step of the above method embodiments can be completed by a logic circuit in the form of hardware or instructions in the form of software in the processor 301. The processor 301 can 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 or transistor logic devices, or discrete hardware components.

[0106] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code.

[0107] For example, in some embodiments, the present application also provides a computer readable storage medium having instructions or codes stored therein, which, when executed on a computer, cause the computer to perform the above-mentioned method for adjusting a relay in a wireless networking.

[0108] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, the appended claims are intended to encompass within their scope all such variations and modifications as are included within the scope of the application.

[0109] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for wireless networking relay adjustment, for wireless networking relay adjustment in an elevator Internet of Things system, a low-frequency backbone network is deployed in advance in an elevator shaft, and a high-frequency short-range communication is used in an elevator car, characterized in that, The method comprises the following steps: Collecting low-frequency signals of a low-frequency backbone network in an elevator shaft during elevator operation, and synchronously collecting high-frequency signals transmitted by a high-frequency short-range communication module in an elevator car; Cooperatively processing the low-frequency signals and the high-frequency signals through joint channel estimation to obtain fusion features of communication signals between the elevator car and the elevator shaft in an elevator Internet of Things, using the fusion features of the communication signals to establish a dual-band communication link between the elevator shaft and the elevator car, and further determining a link budget margin of each relay node in the dual-band communication link; Based on the historical operating state of the elevator car, predicting a next signal blind area entered by the elevator car during elevator operation, and further determining a joint estimation value of signal coverage strength of the elevator car in the next signal blind area through signal attenuation features and multipath interference features of each Internet of Things area in the elevator shaft; When the joint estimation value of the signal coverage strength is lower than a preset signal threshold, pre-adjusting the relay capability of high-frequency short-range communication in the dual-band communication link of the next signal blind area based on the link budget margin of each relay node.

2. The method of claim 1, wherein, Cooperatively processing the low-frequency signals and the high-frequency signals through joint channel estimation to obtain fusion features of communication signals between the elevator car and the elevator shaft in an elevator Internet of Things specifically comprises: Demodulating and extracting a path loss index in the elevator shaft from the low-frequency signals; Analyzing and obtaining a channel impulse response of the elevator car from the high-frequency signals; Jointly estimating the path loss index and the channel impulse response as the fusion features of the communication signals between the elevator car and the elevator shaft in the elevator Internet of Things through joint channel estimation.

3. The method of claim 1, wherein, Using the fusion features of the communication signals to establish a dual-band communication link between the elevator shaft and the elevator car specifically comprises: Extracting control instructions of the low-frequency backbone network in the elevator shaft and transmission instructions of the high-frequency short-range communication module in the elevator car from the fusion features of the communication signals; Establishing the dual-band communication link between the elevator shaft and the elevator car through the control instructions and the transmission instructions.

4. The method of claim 1, wherein, Determining the link budget margin of each relay node in the dual-band communication link specifically comprises: For each relay node in the dual-band communication link, measuring a round-trip delay, a maximum available transmission power and a receiving sensitivity of the relay node; Determining an equivalent isotropically radiated power of the relay node through the maximum available transmission power and the receiving sensitivity; Determining a link budget margin of the relay node according to the equivalent isotropically radiated power and the round-trip delay, and further obtaining the link budget margin of each relay node in the dual-band communication link.

5. The method of claim 1, wherein, Based on the historical operating state of the elevator car, predicting a next signal blind area entered by the elevator car during elevator operation specifically comprises: Collecting the historical operating state of the elevator car, and simultaneously obtaining signal strengths of each Internet of Things area in the elevator shaft; Filtering all signal blind areas from the elevator shaft through each signal strength; Based on the historical operating state, predicting an entering area of the elevator car in a future specified time period; Comparing the entering area and each signal blind area in time sequence to obtain the next signal blind area entered by the elevator car during elevator operation.

6. The method of claim 1, wherein, The joint estimation value of the signal coverage strength of the elevator car in the next signal blind area is determined by the signal attenuation characteristics and the multipath interference characteristics of each Internet of Things area in the elevator shaft, and specifically includes: All Internet of Things areas in the next signal blind area are regarded as signal areas; The coverage strength values of each signal area are determined by the signal attenuation characteristics and the multipath interference characteristics of each signal area; The joint estimation 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 of claim 1, wherein, The relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area is pre-adjusted based on the link budget margin of each link, and specifically includes: All relay nodes in the dual-band communication link of the next signal blind area are obtained; The relay capability of the high-frequency short-range communication in the dual-band communication link of the next signal blind area is optimized based on the link budget margin of each relay node.

8. A relay conditioning device for wireless networking, characterized by It includes: The acquisition module is used to acquire the low-frequency signal of the low-frequency backbone network in the elevator shaft during the operation of the elevator, and synchronously acquire the high-frequency signal transmitted by the high-frequency short-range communication module in the elevator car; The processing module is used to cooperatively process the low-frequency signal and the high-frequency signal 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, establish a dual-band communication link between the elevator shaft and the elevator car using the fusion characteristics of the communication signal, and then determine the 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 entered by the elevator car during the operation of the elevator based on the historical running state of the elevator car, and then determine the joint estimation value of the signal coverage strength of the elevator car in the next signal blind area by the signal attenuation characteristics and the multipath interference characteristics of each Internet of Things area in the elevator shaft; The execution module is 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 margin of each link when the joint estimation value of the signal coverage strength is lower than a preset signal threshold.

9. An elevator Internet of Things system, characterized by The relay adjustment device of the wireless networking of claim 8 is included.

10. A computer device, comprising: The computer device includes a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device executes the relay adjustment method of the wireless networking of any one of claims 1 to 7. The computer device includes a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device executes the relay adjustment method of the wireless networking of any one of claims 1 to 7.

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