A Method and System, Device, and Readable Storage Medium for Wireless Intelligent Electrical Equipment Networking and Cooperative Control

Abstract

The present application relates to a method and system, device, and readable storage medium for networking and cooperative control of wireless intelligent electrical devices. The method constructs a heterogeneous wireless communication network and real-time detects implicit channel conflicts caused by the superposition of multi-protocol signals. By combining the global interference characteristics of the device node topology map with a reinforcement learning algorithm, the channel backoff window is dynamically optimized, and the time slot allocation strategy is optimized through a dynamic optimization algorithm. Finally, the cooperative control of multi-protocol device groups in high-density scenarios is achieved, effectively solving the channel competition conflict problem caused by the inability of traditional methods to sense cross-protocol signals, significantly improving the communication reliability and the real-time performance of device cooperative control. At the same time, the device operating state is automatically adjusted according to the scenario requirements, achieving the technical effect of optimizing the overall system efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication networks, and in particular relates to a method and system, device, and readable storage medium for networking and cooperative control of wireless intelligent electrical devices. Background Art

[0002] In the field of networking and cooperative control of wireless intelligent electrical devices, with the rapid development of industrial automation and Internet of Things technologies, a large number of heterogeneous devices (such as Wi-Fi sensors, UWB positioning tags, and ZigBee actuators) are deployed in high-density scenarios such as factories. These devices usually adopt different communication protocols and need to complete real-time data interaction and control instruction transmission within a shared frequency band.

[0003] However, the existing technologies have significant defects in a multi-protocol coexistence environment: due to the differences in physical layer signal characteristics of different protocols (for example, the short-time pulses of UWB and the continuous carriers of Wi-Fi), devices cannot accurately perceive each other's implicit channel occupancy behavior, resulting in the disordered superposition of cross-protocol signals in the time-frequency domain and generating a large number of unpredictable conflict events. Traditional conflict avoidance mechanisms (such as CSMA / CA) are designed only for a single protocol, and their fixed backoff window and static time slot allocation strategies are difficult to adapt to a dynamic interference environment, causing a sharp drop in channel utilization rate and a sharp increase in retransmission rate, ultimately resulting in the transmission delay of cooperative control instructions exceeding the industrial real-time requirements. In addition, the existing methods lack the ability to globally analyze the spatial distribution of devices and the interference coupling relationship, and parameter adjustment often relies only on local information, making it difficult to achieve cooperative optimization across device groups.

[0004] The above problems seriously restrict the reliability and operating efficiency of intelligent electrical systems in high-density scenarios. Therefore, there is an urgent need for a new networking control method that can integrate multi-dimensional environmental perception, dynamic parameter optimization, and cross-protocol cooperative scheduling. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a method and system, device, and readable storage medium for networking and cooperative control of wireless intelligent electrical devices.

[0006] In a first aspect, the present application provides a method for networking and cooperative control of wireless intelligent electrical devices, including:

[0007] S1: Connect electrical devices with different protocols to the same network to generate a device node topology diagram;

[0008] S2: Detect implicit channel competition conflict events caused by the superposition of UWB pulse signals and Wi-Fi signals based on the device node topology diagram and cross-protocol physical layer signal characteristics;

[0009] S3: According to the implicit channel competition conflict event and the device node topology map, optimize the channel backoff window through a reinforcement learning algorithm and optimize the time slot allocation strategy through a dynamic optimization algorithm to generate optimized communication parameters;

[0010] S4: Based on the optimized communication parameters and the node distribution of the device node topology map, send control instructions to the target electrical device.

[0011] In a second aspect, the present application also provides a wireless intelligent electrical device networking and collaborative control system, including:

[0012] A device access module, configured to connect electrical devices with different protocols to the same network to generate a device node topology map;

[0013] A conflict detection module, configured to detect an implicit channel competition conflict event caused by the superposition of UWB pulse signals and Wi-Fi signals based on the device node topology map and the cross-protocol physical layer signal characteristics;

[0014] A communication parameter optimization module, configured to optimize the channel backoff window through a reinforcement learning algorithm and optimize the time slot allocation strategy through a dynamic optimization algorithm according to the implicit channel competition conflict event and the device node topology map to generate optimized communication parameters;

[0015] A control instruction sending module, configured to send control instructions to the target electrical device based on the optimized communication parameters and the node distribution of the device node topology map.

[0016] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements a wireless intelligent electrical device networking and collaborative control method as in the first aspect.

[0017] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements a wireless intelligent electrical device networking and collaborative control method as in the first aspect.

[0018] The above-mentioned wireless intelligent electrical device networking and cooperative control method, system, device, and readable storage medium construct a heterogeneous wireless communication network and detect implicit channel conflicts caused by multi-protocol signal superposition in real time. By combining the global interference characteristics of the device node topology map and the reinforcement learning algorithm to dynamically optimize the channel backoff window and optimizing the time slot allocation strategy through the dynamic optimization algorithm, the cooperative control of multi-protocol device groups in high-density scenarios is finally realized, effectively solving the channel competition conflict problem caused by the inability to perceive cross-protocol signals in traditional methods, significantly improving the communication reliability and the real-time performance of device cooperative control. At the same time, the device operating state is automatically adjusted according to the scenario requirements, achieving the technical effect of optimizing the overall efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 FIG. is a schematic flow chart of a wireless intelligent electrical device networking and cooperative control method provided by the present invention;

[0021] Figure 2 FIG. is a schematic structural diagram of a wireless intelligent electrical device networking and cooperative control system provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the objectives, technical solutions, and advantages of the present application more clearly understood, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] Referring to Figure 1 , which shows a schematic flow chart of a wireless intelligent electrical device networking and cooperative control method provided by the present application. The method includes the following steps:

[0024] S1: Connect electrical devices with different protocols to the same network to generate a device node topology map.

[0025] Specifically, to achieve the access of devices with different protocols, a protocol conversion gateway can be used. For example, devices with the ZigBee protocol can be connected to the Ethernet through a ZigBee-to-Wi-Fi gateway, or devices with the Bluetooth protocol can be connected to the network through a Bluetooth-to-Wi-Fi gateway. The above gateways can identify the data packets of devices with different protocols and convert them into a common format that can be transmitted in the same network, such as Wi-Fi or Ethernet data packets.

[0026] After the device is connected to the network, a device node topology diagram is generated. The purpose of this step is to clarify the positions, connection relationships, and communication paths of various devices in the network. The device node topology diagram can be generated through a network scanning tool. The network scanning tool can automatically discover the devices in the network and draw a network topology structure diagram based on information such as the IP addresses and MAC addresses of the devices. For example, by using specialized network topology discovery software, it can send probe data packets to each device in the network, collect the response information of the devices, and then generate an intuitive topology diagram based on the above information.

[0027] S2: Based on the device node topology diagram and the cross-protocol physical layer signal characteristics, detect the implicit channel competition conflict events caused by the superposition of UWB pulse signals and Wi-Fi signals.

[0028] Specifically, UWB (Ultra Wideband) pulse signals have the characteristics of high-precision positioning and low power consumption, while Wi-Fi signals have a wide coverage range and a high data transmission rate. When the above two signals are transmitted on the same frequency band, signal superposition may occur, resulting in implicit channel competition conflicts.

[0029] To detect such conflict events, a signal monitoring device can be used. This device can monitor physical layer parameters such as signal strength, frequency, and phase in the network in real time. For example, by using a spectrum analyzer, it can scan and analyze the signals in a specific frequency band and capture the superposition of UWB pulse signals and Wi-Fi signals. When it detects an abnormal increase in signal strength or a distortion in the signal waveform, it can be determined that there may be an implicit channel competition conflict event.

[0030] In addition, the information in the device node topology diagram can be combined to further determine the specific location of the conflict and the devices involved. For example, if it is found in the topology diagram that UWB and Wi-Fi signal transmissions exist simultaneously on the communication path between several devices, and the signal monitoring device detects signal conflict characteristics in this area, it can be determined that there is an implicit channel competition conflict between these devices.

[0031] S3: According to the implicit channel competition conflict events and the device node topology diagram, optimize the channel backoff window through a reinforcement learning algorithm and optimize the time slot allocation strategy through a dynamic optimization algorithm to generate optimized communication parameters.

[0032] Specifically, the reinforcement learning algorithm is a machine learning method based on trial-and-error learning and reward mechanism. In the present invention, the devices in the network can be regarded as agents, and the agents select actions according to the current network state (including channel conflict situation, device load, etc.), that is, adjust the channel backoff window and time slot allocation strategy. Then, corresponding rewards or punishments are given according to indicators such as the success rate and delay of device communication, so that the agents continuously learn and optimize their action selections. For example, when a device detects a channel conflict, the agent will select a larger channel backoff window according to the reinforcement learning algorithm to avoid conflicts from occurring again. Over time, the device will continuously adjust its strategy according to the feedback reward signal and finally find an optimal channel backoff scheme in the current network environment.

[0033] The dynamic optimization algorithm can dynamically adjust the time slot allocation according to the real-time state of the network and the requirements of the devices. For example, a dynamic time slot allocation strategy based on genetic algorithm is adopted, and the time slot allocation scheme is continuously optimized by simulating the process of biological evolution. The genetic algorithm gradually screens out better schemes from the initial time slot allocation scheme through operations such as selection, crossover, and mutation. During the optimization process, the device node topology map provides information about the network structure, providing the relative positions and communication relationships between each device and other devices, so as to allocate time slots more reasonably. For example, the devices located on the critical path of the network may be given higher priorities and obtain more resources in time slot allocation to ensure the communication fluency of the entire network.

[0034] S4: Send control instructions to the target electrical device based on the optimized communication parameters and the node distribution of the device node topology map.

[0035] Specifically, when sending control instructions, consider the node distribution situation in the device node topology map. For example, if the target device is located in a subnet of the network and there are multiple relay nodes in this subnet, then the optimal transmission path can be selected to send the control instructions. According to the optimized communication parameters, determine the time slot and channel parameters of each transmission node to ensure that the control instructions will not conflict with the communication of other devices during the transmission process.

[0036] In addition, the control instructions can be sorted by priority according to the priority of the devices and the urgency of the tasks. The instructions with high priorities can preferentially occupy the communication resources to ensure that important devices can receive the control commands in time and make responses. For example, in a smart home system, the control instructions of security devices can have higher priorities to ensure the timely guarantee of home security.

[0037] The above-mentioned method for networking and cooperative control of wireless intelligent electrical devices constructs a heterogeneous wireless communication network and real-time detects implicit channel conflicts caused by the superposition of multi-protocol signals. By combining the global interference characteristics of the device node topology map with the reinforcement learning algorithm, it dynamically optimizes the channel backoff window and time slot allocation strategy, and finally realizes the cooperative control of multi-protocol device groups in high-density scenarios, effectively solving the channel competition conflict problem caused by the inability to perceive cross-protocol signals in traditional methods, significantly improving the communication reliability and the real-time performance of device cooperative control. At the same time, it automatically adjusts the device operating state according to the scenario requirements, achieving the technical effect of optimizing the overall system efficiency.

[0038] In an alternative embodiment, S1 includes the following steps:

[0039] S11: Broadcast a network access beacon frame to the electrical device; wherein, the beacon frame includes multi-protocol band identifiers and corresponding band parameters, and the band parameters include the center frequency of the band defined in the beacon frame and the corresponding transmit power.

[0040] Specifically, to connect electrical devices with different protocols to the same network, a network access beacon frame can be broadcast to the electrical device. The beacon frame is a special network data packet that contains multi-protocol band identifiers and corresponding band parameters. The band parameters can further include the center frequency of the band defined in the beacon frame and the corresponding transmit power.

[0041] The function of the beacon frame is to broadcast the access information of the network to the devices in the network, enabling the devices to select a suitable communication band and access the network according to the access information. The multi-protocol band identifier is used to distinguish different communication protocols, such as ZigBee, Bluetooth, Wi-Fi, etc. The center frequency of the band determines at which specific frequency the device communicates, while the transmit power determines the coverage range and intensity of the device signal.

[0042] S12: Based on the multi-protocol band identifier, select a communication band that matches the protocol type of the electrical device, and obtain the device ID, protocol type, and location coordinates of the electrical device through the communication band.

[0043] Specifically, based on the received multi-protocol band identifier, the device selects a communication band that matches its own protocol type. For example, if the device is of the ZigBee protocol, the device will select the band corresponding to the ZigBee protocol. When a suitable communication band is selected, the device can obtain information such as its own device ID, protocol type, and location coordinates through this band. The device ID is the unique identifier of the device in the network, used to distinguish different devices; the protocol type specifies the communication protocol followed by the device; and the location coordinates provide the location information of the device in physical space.

[0044] S13: Calculate the connection weights between electrical devices based on the location coordinates and frequency band parameters.

[0045] Specifically, calculate the connection weights between electrical devices according to the obtained location coordinates and frequency band parameters. The connection weight is an indicator that measures the communication quality and efficiency between devices, and it can comprehensively consider factors such as the distance between devices, signal strength, and interference conditions. Specifically, the closer the distance between devices, the stronger the signal strength, and the less interference, the higher the connection weight. Conversely, if the distance between devices is far, the signal strength is weak, or there is more interference, the connection weight will be low. Various algorithms can be used to calculate the connection weight, such as the weighted algorithm based on signal strength, the comprehensive algorithm considering distance and interference, etc.

[0046] S14: Construct a device node topology graph based on the device ID, protocol type, and connection weight.

[0047] Specifically, construct a device node topology graph according to the device ID, protocol type, and calculated connection weight. The device node topology graph graphically shows the connection relationships and distributions of various devices in the network.

[0048] In the topology graph, each device node is marked with its device ID and protocol type, and the connection lines between nodes are marked and distinguished according to the magnitude of the connection weight. The connection lines between device nodes with high connection weights can be represented by thick lines or special colors to highlight their strong connection relationships; while the connection lines between device nodes with low connection weights can be represented by thin lines or ordinary colors.

[0049] In an optional embodiment, S13 includes the following steps:

[0050] S131: Calculate the node spacing of electrical devices according to the location coordinates.

[0051] Specifically, calculate the spacing between device nodes according to the location coordinates of the electrical devices. The calculation formula for the node spacing can be the Euclidean distance formula:

[0052] ;

[0053] where and are the location coordinates of device nodes i and j respectively.

[0054] S132: Based on the free space path loss model, calculate the path loss according to the node spacing and the center frequency of the frequency band; the calculation formula for the path loss is:

[0055] ;

[0056] where Device node for an electrical device To the device node Node spacing; Indicates that at the center frequency of the frequency band is And the node spacing is When, the device node To the device node Path loss between.

[0057] Specifically, path loss refers to the attenuation of a signal during propagation due to factors such as distance and frequency, and it is one of the important factors affecting communication quality. The free space path loss model is a commonly used method for calculating path loss, and its formula is ; Wherein, Is the node spacing from device node i to device node j, in meters; Is the center frequency of the frequency band, in hertz; Indicates that at the center frequency of the frequency band is And the node spacing is When, the device node To the device node Path loss between, in decibels (dB). Through this formula, the attenuation degree of the signal at a specific frequency and distance can be calculated.

[0058] S133: Calculate the interference intensity between each device node at the center frequency of the frequency band according to the path loss and the transmit power; the formula for calculating the interference intensity is:

[0059] ;

[0060] Wherein, Represents the transmit power at the center frequency of the frequency band ; Represents at the center frequency of the frequency band On, the device node And the device node Interference intensity between.

[0061] Specifically, the interference intensity reflects the degree of mutual interference between devices, and it is of great significance for determining the communication quality and connection weight between devices. The formula for calculating the interference intensity is ; Wherein, Represents the transmit power at the center frequency of the frequency band , In decibel-milliwatts (dBm); Represents at the center frequency of the frequency band On, the interference intensity between device node i and device node j, in decibel-milliwatts (dBm). This formula obtains the interference intensity between devices at a specific frequency by subtracting the path loss from the transmit power.

[0062] S134: Calculate the connection weights of each electrical device according to the interference intensity between each device node; the calculation formula for the connection weight is:

[0063] ;

[0064] where, is the connection weight between device node and device node , is the set of available frequency bands defined in the beacon frame.

[0065] Specifically, the connection weight is an index that comprehensively reflects the communication quality and interference degree between devices, and it plays a key role in constructing the device node topology graph and optimizing communication parameters. The calculation formula for the connection weight is: ; where, is the connection weight between device node i and device node j; F is the set of available frequency bands defined in the beacon frame. This formula sums up the interference intensities between device nodes on each frequency band and calculates the connection weight by taking the reciprocal. The larger the connection weight, the better the communication quality and the smaller the interference between devices; conversely, the smaller the connection weight, the worse the communication quality and the larger the interference between devices.

[0066] In an optional embodiment, S2 includes the following steps:

[0067] S21: Filter out the device node pairs with interference intensity greater than the preset intensity threshold according to the connection weights of the device node topology graph.

[0068] Specifically, filter out the device node pairs with interference intensity greater than the preset intensity threshold according to the connection weights in the device node topology graph. The connection weight reflects the communication quality and interference degree between devices. By analyzing this weight, the device combinations that may have channel competition conflicts can be quickly located.

[0069] In practical applications, the setting of the preset intensity threshold can be determined according to the specific network environment and device performance. For example, in a smart home system, if the interference intensity between two device nodes exceeds a certain threshold, it may affect their normal communication and thus become a potential conflict source. By filtering the device node pairs, the detection range can be narrowed down to the area most likely to have conflicts, improving the detection efficiency.

[0070] S22: Identify the overlapping area of the UWB pulse signal and the Wi-Fi signal by collecting the time-domain signal waveforms of the device node pairs.

[0071] Specifically, for the selected device node pairs, by collecting their time-domain signal waveforms, the overlapping regions of UWB pulse signals and Wi-Fi signals are identified. The purpose of this step is to determine in which time periods and frequency bands the two signals coexist and may overlap, thus leading to channel competition conflicts.

[0072] Professional signal monitoring devices such as oscilloscopes or spectrum analyzers can be used to collect time-domain signal waveforms. This device can capture and record the waveform characteristics of the signal in real time, including information such as the amplitude, frequency, and phase of the signal. Through the analysis of the waveform, the overlapping parts of UWB pulse signals and Wi-Fi signals can be accurately identified. For example, on the waveform diagram, the overlapping region may show phenomena such as abnormal increase in signal amplitude or waveform distortion, and this feature can help determine the specific location and scope of the overlap.

[0073] S23: Calculate the conflict probability of the overlapping region. If the conflict probability is greater than the preset probability threshold, it is determined as an implicit channel competition conflict event.

[0074] Specifically, the conflict probability refers to the possibility of communication conflicts caused by the two signals simultaneously occupying the channel in the overlapping region. If the calculated conflict probability is greater than the preset probability threshold, it is determined as an implicit channel competition conflict event.

[0075] The calculation of the conflict probability can be based on statistical methods, and is obtained by analyzing historical data and real-time monitoring data. For example, the ratio of the number of signal conflicts occurring in the overlapping region to the total number of monitoring times can be statistically calculated as an estimated value of the conflict probability. The setting of the preset probability threshold can also be determined according to the actual application scenario and the requirements for system performance. When the conflict probability exceeds this threshold, it indicates that the channel competition conflict has posed a threat to the normal operation of the system, and corresponding optimization measures can be taken.

[0076] In an alternative embodiment, S22 includes the following steps:

[0077] S221: Perform wavelet transform on the time-domain signal waveform to extract the time-frequency ridge line features of UWB pulses.

[0078] Specifically, perform wavelet transform on the collected time-domain signal waveform to extract the time-frequency ridge line features of UWB pulses. Wavelet transform is an effective time-frequency analysis method that can decompose the signal into different scales and positions, thereby capturing the local features of the signal.

[0079] UWB pulse signals are characterized by being short and high-frequency, and appear as sharp pulses in the time-frequency domain. Through wavelet transform, the pulse features can be extracted from the complex signal waveform. The time-frequency ridge line feature refers to the line where the signal energy is concentratedly distributed in the time-frequency plane, which can reflect the main features and change trends of UWB pulse signals.

[0080] S222: Detect the Wi-Fi signal through cyclic prefix correlation and calculate the correlation coefficient; the correlation coefficient represents the autocorrelation of the time-domain signal waveform at different positions.

[0081] Specifically, detect the Wi-Fi signal through cyclic prefix correlation and calculate the correlation coefficient. The cyclic prefix is an important part of the Wi-Fi signal frame structure, which is used to combat multipath effects and maintain signal orthogonality.

[0082] The correlation coefficient represents the autocorrelation of the time-domain signal waveform at different positions. By calculating the correlation coefficient between the signal and the ideal cyclic prefix template, it can be determined whether there is a Wi-Fi signal in the signal. If the correlation coefficient is high, it indicates that there is a part in the signal that matches the cyclic prefix, thus the existence of the Wi-Fi signal can be determined.

[0083] S223: When the time-frequency ridge line feature is greater than the first threshold and the correlation coefficient is greater than the second threshold, it is determined that the UWB pulse signal and the Wi-Fi signal are superimposed and in conflict.

[0084] Specifically, when the extracted time-frequency ridge line feature is greater than the first threshold and the correlation coefficient is greater than the second threshold, it is determined that the UWB pulse signal and the Wi-Fi signal are superimposed and in conflict. The settings of the first threshold and the second threshold can be determined according to the actual signal environment and device performance.

[0085] The time-frequency ridge line feature being greater than the first threshold means the presence of the UWB pulse signal is obvious, while the correlation coefficient being greater than the second threshold indicates the presence of the Wi-Fi signal is also relatively certain. The combination of the two can effectively avoid misjudgment and ensure that it is determined as a superimposed conflict only when both signals exist simultaneously and have sufficient strength.

[0086] S224: Take the area of the superimposed conflict as the superimposed area.

[0087] Specifically, take the area determined as the superimposed conflict as the superimposed area. This area is the part where the UWB pulse signal and the Wi-Fi signal simultaneously occupy the channel, and it is also the key area where implicit channel competition conflicts may occur.

[0088] In an alternative embodiment, S3 includes the following steps:

[0089] S31: Calculate the conflict level of each channel according to the conflict probability of the implicit channel competition conflict event, the interference intensity of the device node topology map, and the connection weight; the calculation formula for the conflict level is:

[0090] ;

[0091] Where, is the conflict level, representing the conflict degree of the channel where the device node is located; represents other device nodes within the communication range of the device node that can interfere with each other , is the conflict probability.

[0092] Specifically, according to the conflict probability of the implicit channel competition conflict event, the interference intensity and connection weight of the device node topology graph, calculate the conflict level of each channel. The conflict level is a comprehensive index to measure the channel conflict degree, which considers the probability of conflict occurrence and the interference and connection relationship between devices. The calculation formula of the conflict level is . Among them, is the conflict level of the channel where device node i is located; is the conflict probability; is the connection weight between device nodes i and j; is the interference intensity of device nodes i and j at the center frequency of the frequency band; represents other device nodes j within the communication range of device node i that can interfere with each other.

[0093] This formula combines the conflict probability with the connection weight and interference intensity between device nodes, comprehensively reflecting the conflict degree of the channel. The higher the conflict level, the more serious the channel conflict, and more active optimization measures can be taken.

[0094] S32: Use the Q-Learning algorithm to dynamically adjust the backoff window according to the conflict level; among them, the state of the Q-Learning algorithm is the conflict level, and the action of the Q-Learning algorithm is the adjustment operation of the backoff window. The reward function of the Q-Learning algorithm is: ;

[0095] Among them, is the total number of implicit channel competition conflict events, is the total number of data transmissions, is the number of packet retransmissions triggered by the implicit channel competition conflict event, is the weight factor.

[0096] Specifically, use the Q-Learning algorithm to dynamically adjust the backoff window according to the calculated conflict level. The Q-Learning algorithm is a reinforcement learning algorithm that selects the optimal action by learning the value function (Q function) of the state-action pair.

[0097] In this embodiment, the state of the Q-Learning algorithm is the conflict level, and the action is the adjustment operation of the backoff window. The reward function is defined as . Among them, is the total number of implicit channel competition conflict events; is the total number of data transmissions; is the number of packet retransmissions triggered by implicit channel competition conflict events; is the weight factor.

[0098] The design of this reward function aims to encourage reducing the number of conflicts and retransmissions. When the number of conflicts and retransmissions is small, the reward value is high, thus prompting the algorithm to select a backoff window adjustment strategy that can reduce conflicts.

[0099] S33: Dynamically allocate the time slot occupancy factor according to the distribution density of device nodes in the device node topology graph and the spatial distribution of implicit channel competition conflict events; the dynamic allocation formula of the time slot occupancy factor is:

[0100] ;

[0101] Among them, is the time slot occupancy factor, is the distribution density of device nodes in area in the device node topology graph, is the conflict event frequency in area , is the number of time slots allocated to area , is the total number of time slots in the communication cycle.

[0102] Specifically, dynamically allocate the time slot occupancy factor according to the distribution density of device nodes in the device node topology graph and the spatial distribution of implicit channel competition conflict events. The time slot occupancy factor determines the proportion of time slots that each device node can occupy in the communication cycle, which is crucial for balancing the communication load and reducing conflicts. The dynamic allocation formula of the time slot occupancy factor is . Among them, is the time slot occupancy factor; is the distribution density of device nodes in area k of the device node topology graph; is the conflict event frequency in area k; is the number of time slots allocated to area k; is the total number of time slots in the communication cycle.

[0103] This formula comprehensively considers the distribution density of device nodes and the frequency of conflict events, and reasonably allocates time slot resources to different regions. Regions with high distribution density and high conflict frequency will obtain more time slot resources to meet their communication needs and reduce conflicts.

[0104] S34: Use the backoff window and time slot occupancy factor as the optimized communication parameters.

[0105] Specifically, use the optimized backoff window and time slot occupancy factor as the optimized communication parameters. These parameters will be used to guide the behavior of devices during communication, including how to select the backoff window and how to allocate time slot resources.

[0106] In an alternative embodiment, S4 includes the following steps:

[0107] S41: According to the positions of the device nodes in the device node topology diagram, divide the electrical devices into multiple control clusters, and each control cluster includes a central node and associated sub-nodes.

[0108] Specifically, divide the electrical devices into multiple control clusters according to the positions of the device nodes in the device node topology diagram. Each control cluster includes a central node and multiple associated sub-nodes. This clustering strategy aims to improve the transmission efficiency and execution efficiency of control instructions.

[0109] When dividing the control clusters, factors such as the physical positions of the device nodes, communication ranges, and device types can be considered. For example, in a smart home system, the devices can be divided into different control clusters according to the layout of the rooms, and the devices in each room form a control cluster, with one device as the central node and other devices as sub-nodes. The central node is responsible for receiving and forwarding control instructions, and the sub-nodes perform corresponding operations according to the instructions of the central node.

[0110] S42: Based on the optimized communication parameters, send control instructions to the central nodes, and the control instructions include the power adjustment thresholds of the sub-nodes.

[0111] Specifically, based on the optimized communication parameters, send control instructions to each central node. The control instructions contain key information such as the power adjustment thresholds of the sub-nodes. When sending the control instructions, the optimization results of the communication parameters can be considered, such as the channel backoff window and time slot allocation strategy. This parameter determines the transmission timing and resource allocation of the control instructions, and avoids conflicts with the communication of other devices. For example, if a central node of a certain control cluster is allocated larger time slot resources, then it can receive control instructions earlier in the communication cycle, thus improving the timeliness of control.

[0112] S43: Based on the control instructions, control the associated sub-nodes to switch to the corresponding working modes.

[0113] Specifically, the central node controls the associated child nodes to switch to the corresponding working modes according to the received control instructions. The switching of the working modes can include various operations such as power adjustment, function enabling or disabling. For example, the control instructions may require some child nodes to reduce power consumption to save energy; or require some child nodes to enter the high-power mode to improve their performance.

[0114] After receiving the control instructions forwarded by the central node, the child nodes will make corresponding adjustments according to the parameters in the instructions. For example, if the power adjustment threshold is set to a specific value, the child nodes will adjust their working power below or above the threshold, depending on the requirements of the control instructions. This switching of the working modes enables the entire system to dynamically adjust the working states of the devices according to the actual needs, achieving the goal of collaborative control.

[0115] The above-mentioned method for networking and collaborative control of wireless intelligent electrical devices constructs a heterogeneous wireless communication network integrating multi-protocol devices, real-time detects the implicit channel competition conflicts caused by the superposition of different physical layer signals, combines the global interference characteristics of the device node topology map and the spatio-temporal distribution of conflict events, uses the reinforcement learning algorithm to dynamically optimize the backoff window parameters and time slot allocation strategy, and collaboratively adjusts the device operation modes based on the node density and conflict risk, effectively solving the problem of unbalanced channel resource competition caused by the inability to sense cross-protocol signals in high-density scenarios, significantly reducing the conflict probability and retransmission overhead. At the same time, through time-frequency-space multi-dimensional collaborative control, it realizes the on-demand dynamic scheduling of the device group, improving the overall energy efficiency and communication reliability of the system while ensuring industrial-level real-time performance.

[0116] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0117] Based on the same inventive concept, an embodiment of the present application also provides a system for implementing the above-mentioned wireless intelligent electrical device networking and cooperative control method. The implementation solutions provided by this system for solving problems are similar to those described in the above method. Therefore, the specific limitations in one or more embodiments of the wireless intelligent electrical device networking and cooperative control system provided below can refer to the limitations on a wireless intelligent electrical device networking and cooperative control method in the above text, and will not be repeated here.

[0118] In an exemplary embodiment, as Figure 2 shown, a wireless intelligent electrical device networking and cooperative control system 20 is provided, including:

[0119] A device access module 21, configured to access electrical devices with different protocols to the same network and generate a device node topology map.

[0120] A conflict detection module 22, configured to detect implicit channel competition conflict events caused by the superposition of UWB pulse signals and Wi-Fi signals based on the device node topology map and cross-protocol physical layer signal characteristics.

[0121] A communication parameter optimization module 23, configured to optimize the channel backoff window and time slot allocation strategy through a reinforcement learning algorithm according to the implicit channel competition conflict events and the device node topology map, and generate optimized communication parameters.

[0122] A control instruction issuing module 24, configured to issue control instructions to target electrical devices based on the optimized communication parameters and the node distribution of the device node topology map.

[0123] Optionally, the device access module 21 includes:

[0124] A broadcast access unit 211, configured to broadcast a network access beacon frame to electrical devices; wherein, the beacon frame includes multi-protocol frequency band identifiers and corresponding frequency band parameters, and the frequency band parameters include the center frequency of the frequency band defined in the beacon frame and the corresponding transmission power.

[0125] A frequency band selection unit 212, configured to select a communication frequency band matching the protocol type of the electrical device based on the multi-protocol frequency band identifier, and obtain the device ID, protocol type, and location coordinates of the electrical device through the communication frequency band.

[0126] A weight calculation unit 213, configured to calculate the connection weights between electrical devices according to the location coordinates and frequency band parameters.

[0127] A topology construction unit 214, configured to construct a device node topology map according to the device ID, protocol type, and connection weights.

[0128] Optionally, the weight calculation unit 213 includes:

[0129] The node spacing calculation subunit 2131 is used to calculate the node spacing of electrical equipment according to the position coordinates.

[0130] The path loss calculation subunit 2132 is used to calculate the path loss based on the free space path loss model according to the node spacing and the center frequency of the frequency band; the calculation formula for the path loss is:

[0131] ;

[0132] where, is the node spacing from the device node to the device node ; represents that at the center frequency of the frequency band is and the node spacing is the path loss between the device node and the device node .

[0133] The interference intensity calculation subunit 2133 is used to calculate the interference intensity between each device node at the center frequency of the frequency band according to the path loss and the transmission power; the calculation formula for the interference intensity is:

[0134] ;

[0135] where, represents the transmission power of the center frequency of the frequency band ; represents that at the center frequency of the frequency band the interference intensity between the device node and the device node .

[0136] The connection weight determination subunit 2134 is used to calculate the connection weight of each electrical equipment according to the interference intensity between each device node; the calculation formula for the connection weight is:

[0137] ;

[0138] where, is the connection weight between the device node and the device node , is the set of available frequency bands defined in the beacon frame.

[0139] Optionally, the conflict detection module 22 includes:

[0140] The node pair screening unit 221 is configured to screen device node pairs with interference intensity greater than a preset intensity threshold according to the connection weights of the device node topology graph.

[0141] The signal acquisition unit 222 is configured to identify the overlapping region of the UWB pulse signal and the Wi-Fi signal by acquiring the time-domain signal waveforms of the device node pairs.

[0142] The conflict determination unit 223 is configured to calculate the conflict probability of the overlapping region. If the conflict probability is greater than a preset probability threshold, it is determined as an implicit channel competition conflict event.

[0143] Optionally, the signal acquisition unit 222 includes:

[0144] The feature extraction subunit 2221 is configured to perform wavelet transform on the time-domain signal waveforms to extract the time-frequency ridge line features of the UWB pulses.

[0145] The correlation detection subunit 2222 is configured to detect the Wi-Fi signal through cyclic prefix correlation and calculate the correlation coefficient; the correlation coefficient represents the autocorrelation of the time-domain signal waveforms at different positions.

[0146] The conflict judgment subunit 2223 is configured to determine that there is an overlap conflict between the UWB pulse signal and the Wi-Fi signal when the time-frequency ridge line features are greater than a first threshold and the correlation coefficient is greater than a second threshold.

[0147] The region marking subunit 2224 is configured to use the overlapping conflict region as the overlapping region.

[0148] Optionally, the communication parameter optimization module 23 includes:

[0149] The conflict level calculation unit 231 is configured to calculate the conflict level of each channel according to the conflict probability of the implicit channel competition conflict event, the interference intensity and the connection weights of the device node topology graph; the calculation formula of the conflict level is:

[0150] ;

[0151] Wherein, is the conflict level, indicating the conflict degree of the channel where the device node is located; represents other device nodes that can interfere with each other within the communication range with the device node , is the conflict probability.

[0152] The backoff window adjustment unit 232 is configured to dynamically adjust the backoff window according to the conflict level by using the Q-Learning algorithm; wherein, the state of the Q-Learning algorithm is the conflict level, and the action of the Q-Learning algorithm is the adjustment operation of the backoff window, and the reward function of the Q-Learning algorithm is:

[0153] ;

[0154] where is the total number of implicit channel competition conflict events, is the total number of data transmissions, is the number of packet retransmissions triggered by implicit channel competition conflict events, is the weight factor.

[0155] The time slot occupancy factor allocation unit 233 is used to dynamically allocate the time slot occupancy factor according to the distribution density of device nodes in the device node topology map and the spatial distribution of implicit channel competition conflict events; the dynamic allocation formula of the time slot occupancy factor is:

[0156] ;

[0157] where is the time slot occupancy factor, is the distribution density of device nodes in area in the device node topology map, is the area is the conflict event frequency, is the number of time slots allocated to area and is the total number of time slots in the communication cycle.

[0158] The communication parameter generation unit 234 is used to use the backoff window and the time slot occupancy factor as the optimized communication parameters.

[0159] Optionally, the control instruction issuing module 24 includes:

[0160] The device division unit 241 is used to divide electrical devices into multiple control clusters according to the positions of device nodes in the device node topology map, and each control cluster includes a central node and associated sub-nodes.

[0161] The instruction issuing unit 242 is used to issue control instructions to the central node based on the optimized communication parameters, and the control instructions include the power adjustment threshold of the sub-nodes.

[0162] The mode control unit 243 is used to control the associated sub-nodes to switch to the corresponding working mode based on the control instructions.

[0163] Embodiments of the present application also provide a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the foregoing method embodiments are implemented.

[0164] Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0165] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial descriptions of the method embodiments. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0166] The above embodiments only represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the application embodiments. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A method for networking and collaborative control of wireless intelligent electrical devices, characterized in that, The method includes: S1: Connect electrical devices with different protocols to the same network to generate a device node topology diagram; S2: Based on the device node topology diagram and cross - protocol physical layer signal characteristics, detect implicit channel competition conflict events caused by the superposition of UWB pulse signals and Wi - Fi signals; S3: According to the implicit channel competition conflict events and the device node topology diagram, optimize the channel back - off window through a reinforcement learning algorithm and optimize the time - slot allocation strategy through a dynamic optimization algorithm to generate optimized communication parameters; S4: Based on the optimized communication parameters and the node distribution of the device node topology diagram, send control instructions to the target electrical device; Among them, S3 includes: S31: Calculate the conflict level of each channel according to the conflict probability in the superposition area of UWB pulse signals and Wi - Fi signals in the implicit channel competition conflict event, the interference intensity and connection weight between each device node in the device node topology diagram; the calculation formula of the conflict level is: ； Among them, is the conflict level, indicating the conflict degree of the channel where the device node is located; represents other device nodes that can interfere with each other within the communication range of the device node , is the conflict probability; is the connection weight between the device node and the device node ; represents the interference intensity between the device node and the device node at the center frequency of the frequency band; S32: Use the Q-Learning algorithm to dynamically adjust the backoff window according to the conflict level; wherein, the state of the Q-Learning algorithm is the conflict level, and the action of the Q-Learning algorithm is the adjustment operation of the backoff window, and the reward function of the Q-Learning algorithm is as follows: ； Wherein, is the total number of the implicit channel competition conflict events, is the total number of data transmissions, is the number of data packet retransmissions triggered by the implicit channel competition conflict events, is a weight factor; S33: Dynamically allocate the time - slot occupancy factor according to the distribution density of device nodes in the device node topology diagram and the spatial distribution of the implicit channel competition conflict event; the dynamic allocation formula of the time - slot occupancy factor is: ； wherein, is the time slot occupancy factor, is the distribution density of device nodes in area in the device node topology diagram of the device, is the conflict event frequency in area , is the number of time slots allocated to area , is the total number of time slots in the communication cycle; S34: Take the back - off window and the time - slot occupancy factor as the optimized communication parameters.

2. The method according to claim 1, wherein S1 includes: S11: Broadcast a network access beacon frame to the electrical device; where the beacon frame includes multi - protocol band identifiers and corresponding band parameters, and the band parameters include the central frequency of the band defined in the beacon frame and the corresponding transmission power; S12: Based on the multi - protocol band identifier, select a communication band matching the protocol type of the electrical device, and obtain the device ID, protocol type, and location coordinates of the electrical device through the communication band; S13: Calculate the connection weight between each electrical device according to the location coordinates and the band parameters; S14: Construct the device node topology diagram according to the device ID, the protocol type, and the connection weight.

3. The method according to claim 2, wherein S13 includes: S131: Calculate the node spacing of the electrical device according to the location coordinates; S132: Based on the free - space path - loss model, calculate the path loss according to the node spacing and the central frequency of the band; the calculation formula of the path loss is: ； Among them, is the device node of the electrical device to the device node of the node spacing; indicates that at the center frequency of the frequency band is and the node spacing is when, the device node to the device node the path loss between; S133: Calculate the interference intensity between each device node at the central frequency of the band according to the path loss and the transmission power; the calculation formula of the interference intensity is: ； Among them, represents the center frequency of the frequency band of the transmission power; represents at the center frequency of the frequency band on the device node and the device node of the interference intensity between; S134: Calculate the connection weight of each electrical device according to the interference intensity between each device node; the calculation formula of the connection weight is: ； Among them, is the device node and the connection weight between the device nodes is the set of available frequency bands defined in the beacon frame.

4. The method according to claim 3, characterized in that S2 includes: S21: Screen device node pairs with interference intensity greater than a preset intensity threshold according to the connection weight of the device node topology diagram; S22: Identify the superposition area of UWB pulse signals and Wi - Fi signals by collecting the time - domain signal waveforms of the device node pairs; S23: Calculate the conflict probability of the overlapping region. If the conflict probability is greater than the preset probability threshold, it is determined as an implicit channel competition conflict event.

5. The method according to claim 4, wherein The S22 includes: S221: Perform wavelet transform on the time-domain signal waveform to extract the time-frequency ridge line features of UWB pulses. S222: Detect Wi-Fi signals through cyclic prefix correlation and calculate the correlation coefficient. The correlation coefficient represents the autocorrelation of the time-domain signal waveform at different positions. S223: When the time-frequency ridge line features are greater than the first threshold and the correlation coefficient is greater than the second threshold, it is determined that there is an overlap conflict between the UWB pulse signal and the Wi-Fi signal. S224: Use the overlapping conflict region as the overlapping region.

6. The method according to any one of claims 1 to 5, characterized in that, The S4 includes: S41: Divide the electrical equipment into multiple control clusters according to the positions of the device nodes in the device node topology diagram. Each control cluster includes a central node and associated sub-nodes. S42: Based on the optimized communication parameters, send a control instruction to the central node. The control instruction includes the power adjustment threshold of the sub-node. S43: Based on the control instruction, control the associated sub-nodes to switch to the corresponding working mode.

7. A wireless intelligent electrical device networking and collaborative control system, characterized in that, The system includes: A device access module for accessing electrical equipment with different protocols to the same network and generating a device node topology diagram. A conflict detection module for detecting an implicit channel competition conflict event caused by the overlap of UWB pulse signals and Wi-Fi signals based on the device node topology diagram and cross-protocol physical layer signal characteristics. A communication parameter optimization module for optimizing the channel backoff window through a reinforcement learning algorithm and optimizing the time slot allocation strategy through a dynamic optimization algorithm according to the implicit channel competition conflict event and the device node topology diagram, and generating optimized communication parameters. A control instruction sending module for sending a control instruction to the target electrical equipment based on the optimized communication parameters and the node distribution of the device node topology diagram. Among them, the communication parameter optimization module includes: A conflict level calculation unit for calculating the conflict level of each channel according to the conflict probability of the overlapping region between the UWB pulse signal and the Wi-Fi signal in the implicit channel competition conflict event, the interference intensity and connection weight between each device node in the device node topology diagram. The calculation formula for the conflict level is: ； Among them, is the conflict level, indicating the conflict degree of the channel where the device node is located; represents other device nodes that can interfere with each other within the communication range of the device node ; , is the conflict probability; is the connection weight between the device node and the device node ; represents the interference intensity between the device node and the device node at the center frequency of the frequency band ; A backoff window adjustment unit, which is used to dynamically adjust the backoff window according to the conflict level by using the Q-Learning algorithm; wherein, the state of the Q-Learning algorithm is the conflict level, the action of the Q-Learning algorithm is the adjustment operation of the backoff window, and the reward function of the Q-Learning algorithm is: ； Wherein, is the total number of the implicit channel competition conflict events, is the total number of data transmissions, is the number of data packet retransmissions triggered by the implicit channel competition conflict events, is the weight factor; A time slot occupancy factor allocation unit for dynamically allocating the time slot occupancy factor according to the distribution density of the device nodes in the device node topology diagram and the spatial distribution of the implicit channel competition conflict event. The dynamic allocation formula for the time slot occupancy factor is: ； wherein, is the time slot occupancy factor, is the distribution density of device nodes in area in the device node topology diagram of the device, is the conflict event frequency in area ; is the number of time slots allocated to area ; is the total number of time slots in the communication cycle; A communication parameter generation unit for using the backoff window and the time slot occupancy factor as the optimized communication parameters.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 6.

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