Wireless intelligent electrical equipment networking and cooperative control method and system, equipment and readable storage medium

Abstract

The invention relates to a wireless intelligent electrical equipment networking and cooperative control method and system, equipment and a readable storage medium. According to the method, a heterogeneous wireless communication network is constructed, implicit channel conflicts caused by multi-protocol signal superposition are detected in real time, a channel backoff window is dynamically optimized in combination with global interference characteristics of a device node topological graph and a reinforcement learning algorithm, and a time slot allocation strategy is optimized through a dynamic optimization algorithm. Finally, cooperative control of a multi-protocol device group in a high-density scene is achieved, the problem of channel competition conflicts caused by the fact that cross-protocol signals cannot be sensed in a traditional method is effectively solved, communication reliability and real-time performance of device cooperative control are remarkably improved, meanwhile, the device operation state is automatically adjusted according to scene requirements, and the method is suitable for large-scale popularization and application. And the technical effect of optimizing the overall efficiency of the system is achieved.

Description

Technical Field

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

[0002] In the field of networking and collaborative control of wireless intelligent electrical equipment, with the rapid development of industrial automation and Internet of Things technology, 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 use different communication protocols and need to complete real-time data interaction and control command transmission in a shared frequency band.

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

[0004] The above problems seriously restrict the reliability and operation 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 collaborative scheduling. Summary of the invention

[0005] Based on this, it is necessary to provide a wireless intelligent electrical equipment networking and collaborative control method and system, equipment, and readable storage medium to address the above technical problems.

[0006] In a first aspect, the present application provides a wireless intelligent electrical device networking and collaborative control method, including: S1: Connect electrical devices with different protocols to the same network and generate a device node topology diagram; S2: Detect implicit channel contention 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; S3: Based on the implicit channel contention conflict events and the device node topology, the channel backoff window is optimized by the reinforcement learning algorithm and the time slot allocation strategy is optimized by the 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, control instructions are issued to the target electrical equipment.

[0007] In a second aspect, the present application also provides a wireless intelligent electrical equipment networking and collaborative control system, including: The device access module is used to connect electrical devices of different protocols to the same network and generate a device node topology diagram; The conflict detection module is used to detect implicit channel contention 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; A communication parameter optimization module is used to optimize the channel backoff window through a reinforcement learning algorithm and the time slot allocation strategy through a dynamic optimization algorithm according to implicit channel competition conflict events and device node topology diagrams, and generate optimized communication parameters; The control instruction issuing module is used to issue control instructions to the target electrical equipment based on the optimized communication parameters and the node distribution of the device node topology diagram.

[0008] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a wireless intelligent electrical device networking and collaborative control method as in the first aspect is implemented.

[0009] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for networking and collaborative control of wireless intelligent electrical devices as in the first aspect is implemented.

[0010] The above-mentioned wireless intelligent electrical equipment networking and collaborative control method and system, equipment, and readable storage medium, by constructing a heterogeneous wireless communication network and detecting implicit channel conflicts caused by the superposition of multi-protocol signals in real time, dynamically optimizing the channel backoff window by combining the global interference characteristics of the device node topology diagram with the reinforcement learning algorithm and optimizing the time slot allocation strategy through the dynamic optimization algorithm, ultimately achieves collaborative control of multi-protocol device groups in high-density scenarios, effectively solves the channel competition conflict problem caused by the inability to perceive cross-protocol signals in traditional methods, significantly improves communication reliability and the real-time performance of device collaborative control, and automatically adjusts the device operating status according to scenario requirements to achieve the technical effect of optimizing the overall efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A flowchart of a wireless intelligent electrical equipment networking and collaborative control method provided by the present invention; Figure 2 A structural schematic diagram of a wireless intelligent electrical equipment networking and collaborative control system provided by the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying 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.

[0014] refer to Figure 1 , which shows a flow chart of a wireless intelligent electrical device networking and collaborative control method provided by the present application, the method comprising the following steps: S1: Connect electrical devices with different protocols to the same network and generate a device node topology diagram.

[0015] Specifically, in order to achieve access to devices with different protocols, a protocol conversion gateway can be used. For example, a ZigBee protocol device can be connected to the Ethernet through a ZigBee to Wi-Fi gateway, or a Bluetooth protocol device can be connected to the network through a Bluetooth to Wi-Fi gateway. The above gateway can identify 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.

[0016] After the device is connected to the network, a device node topology map is generated. The purpose of this step is to clarify the location of each device in the network, the connection relationship and the communication path between them. Generating a device node topology map can be achieved through a network scanning tool. The network scanning tool can automatically discover devices in the network and draw a network topology diagram based on the device's IP address, MAC address and other information. For example, using a dedicated network topology discovery software, it can send a detection data packet to each device in the network, collect the device's response information, and then generate an intuitive topology map based on the above information.

[0017] S2: Based on the device node topology and cross-protocol physical layer signal characteristics, detect implicit channel contention conflict events caused by the superposition of UWB pulse signals and Wi-Fi signals.

[0018] 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 high data transmission rate. When the above two signals are transmitted on the same frequency band, signal superposition may occur, resulting in implicit channel contention conflicts.

[0019] To detect the conflict event, a signal monitoring device can be used. The device can monitor the physical layer parameters such as signal strength, frequency, and phase in the network in real time. For example, a spectrum analyzer can be used to scan and analyze signals in a specific frequency band to capture the superposition of UWB pulse signals and Wi-Fi signals. When an abnormal increase in signal strength or a distortion in the signal waveform is detected, it can be determined that an implicit channel contention conflict event may exist.

[0020] In addition, the specific location of the conflict and the devices involved can be further determined by combining the information in the device node topology map. For example, if it is found in the topology map 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 an implicit channel contention conflict occurs between these devices.

[0021] S3: According to the implicit channel contention conflict events and the device node topology map, the channel backoff window is optimized through the reinforcement learning algorithm and the time slot allocation strategy is optimized through the dynamic optimization algorithm to generate optimized communication parameters.

[0022] 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 intelligent agents, which select actions according to the current network status (including channel conflict conditions, device load, etc.), that is, adjust the channel backoff window and time slot allocation strategy. Then, corresponding rewards or penalties are given according to indicators such as the success rate and delay of device communication, so that the intelligent agent can continuously learn and optimize its action selection. For example, when a device detects a channel conflict, the intelligent agent will select a larger channel backoff window according to the reinforcement learning algorithm to avoid conflicts again. Over time, the device will continuously adjust its strategy according to the feedback reward signal, and finally find an optimal channel backoff solution under the current network environment.

[0023] Dynamic optimization algorithms can dynamically adjust time slot allocation according to the real-time status of the network and the needs of the equipment. For example, a dynamic time slot allocation strategy based on a genetic algorithm is used to continuously optimize the time slot allocation plan by simulating the process of biological evolution. The genetic algorithm gradually selects better plans from the initial time slot allocation plan through operations such as selection, crossover and mutation. During the optimization process, the device node topology diagram provides information about the network structure, the relative position and communication relationship between each device and other devices, so as to allocate time slots more reasonably. For example, devices located on the critical path of the network may be given a higher priority and obtain more resources in time slot allocation to ensure the smooth communication of the entire network.

[0024] S4: Based on the optimized communication parameters and the node distribution of the device node topology diagram, control instructions are issued to the target electrical equipment.

[0025] Specifically, when issuing control instructions, the node distribution in the device node topology diagram is considered. For example, if the target device is located in a subnet of the network and there are multiple relay nodes in the subnet, the optimal transmission path can be selected to send the control instruction. Based on the optimized communication parameters, the time slot and channel parameters of each transmission node are determined to ensure that the control instruction will not conflict with the communication of other devices during the transmission process.

[0026] In addition, control instructions can be prioritized according to the priority of the device and the urgency of the task. High-priority instructions can take up communication resources first to ensure that important devices can receive control commands and respond in a timely manner. For example, in a smart home system, control instructions for security devices can have a higher priority to ensure that home safety is protected in a timely manner.

[0027] The above-mentioned wireless intelligent electrical equipment networking and collaborative control method constructs a heterogeneous wireless communication network and detects implicit channel conflicts caused by the superposition of multi-protocol signals in real time. It combines the global interference characteristics of the device node topology map with the reinforcement learning algorithm to dynamically optimize the channel backoff window and time slot allocation strategy, and finally realizes the collaborative control of multi-protocol device groups in high-density scenarios. It effectively solves the channel competition conflict problem caused by the inability to perceive cross-protocol signals in traditional methods, significantly improves communication reliability and the real-time performance of equipment collaborative control, and automatically adjusts the equipment operation status according to scenario requirements to achieve the technical effect of optimizing the overall efficiency of the system.

[0028] In an optional embodiment, S1 includes the following steps: S11: Accessing a beacon frame to a broadcast network of the electrical device; wherein the beacon frame includes a multi-protocol frequency band identifier and corresponding frequency band parameters, and the frequency band parameters include a frequency band center frequency defined in the beacon frame and a corresponding transmission power.

[0029] Specifically, in order to connect electrical devices of different protocols to the same network, a beacon frame can be connected to the broadcast network of the electrical device. The beacon frame is a special network data packet that contains a multi-protocol frequency band identifier and corresponding frequency band parameters. The frequency band parameters can further include the frequency band center frequency and corresponding transmission power defined in the beacon frame.

[0030] The function of the beacon frame is to broadcast network access information to devices in the network, so that the devices can select the appropriate communication frequency band and access the network according to the access information. Multi-protocol frequency band identification is used to distinguish different communication protocols, such as ZigBee, Bluetooth, Wi-Fi, etc. The center frequency of the frequency band determines the specific frequency on which the device communicates, while the transmission power determines the coverage and strength of the device signal.

[0031] S12: Based on the multi-protocol frequency band identification, a communication frequency band matching the protocol type of the electrical device is selected, and a device ID, a protocol type, and a location coordinate of the electrical device are obtained through the communication frequency band.

[0032] Specifically, based on the received multi-protocol frequency band identifier, the device selects a communication frequency band that matches its own protocol type. For example, if the device is a ZigBee protocol, the device will select the frequency band corresponding to the ZigBee protocol. When a suitable communication frequency band is selected, the device can obtain its own device ID, protocol type, and location coordinates through the frequency band. The device ID is a unique identifier of the device in the network, which is 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 the physical space.

[0033] S13: Calculate the connection weights between the electrical devices according to the location coordinates and the frequency band parameters.

[0034] Specifically, the connection weights between the electrical devices are calculated based on the acquired location coordinates and frequency band parameters. The connection weight is an indicator to measure the communication quality and efficiency between devices, which can comprehensively consider factors such as the distance between devices, signal strength, and interference. Specifically, the closer the distance between the devices, the stronger the signal strength, and the less interference, the higher the connection weight. Conversely, if the distance between the devices is far, the signal strength is weak, or there is more interference, the connection weight will be lower. A variety of algorithms can be used to calculate the connection weight, such as a weighted algorithm based on signal strength, a comprehensive algorithm that considers distance and interference, etc.

[0035] S14: Construct a device node topology map according to the device ID, protocol type, and connection weight.

[0036] Specifically, a device node topology map is constructed based on the device ID, protocol type, and calculated connection weight. The device node topology map graphically displays the connection relationship and distribution of each device in the network.

[0037] In the topology diagram, 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 size of the connection weight. Device nodes with high connection weights can be represented by thick lines or special colors to highlight the strong connection relationship between them; while device nodes with low connection weights can be represented by thin lines or ordinary colors.

[0038] In an optional embodiment, S13 includes the following steps: S131: Calculate the node spacing of the electrical equipment according to the position coordinates.

[0039] Specifically, the distance between device nodes is calculated based on the location coordinates of the electrical equipment. The calculation formula for the node distance can be the Euclidean distance formula: ; in, and are the location coordinates of device nodes i and j respectively.

[0040] S132: Based on the free space path loss model, the path loss is calculated according to the node spacing and the center frequency of the frequency band. The calculation formula for the path loss is: ; in, Device nodes for electrical equipment To the device node The node spacing; Indicates that the center frequency of the frequency band is The node spacing is When the device node To the device node The path loss between .

[0041] Specifically, path loss refers to the attenuation of a signal during propagation due to factors such as distance and frequency. 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. Its formula is: ;in, is the node distance from device node i to device node j, in meters; is the center frequency of the frequency band, in Hertz; Indicates that the center frequency of the frequency band is The node spacing is When the device node To the device node The path loss between the two is expressed in decibels (dB). This formula can be used to calculate the attenuation of the signal at a specific frequency and distance.

[0042] S133: Calculate the interference intensity between the device nodes at the center frequency of the frequency band according to the path loss and the transmission power; the calculation formula of the interference intensity is: ; in, Indicates the center frequency of the frequency band The transmission power; Indicates the center frequency of the frequency band On the device node With device node The intensity of interference between them.

[0043] Specifically, the interference intensity reflects the degree of mutual interference between devices, which is of great significance for determining the communication quality and connection weight between devices. The calculation formula for interference intensity is: ;in, Indicates the center frequency of the frequency band The transmission power is expressed in decibel milliwatts (dBm); Indicates the center frequency of the frequency band The interference intensity between device node i and device node j is expressed in decibel milliwatts (dBm). This formula subtracts the path loss from the transmit power to obtain the interference intensity between devices at a specific frequency.

[0044] S134: Calculate the connection weight of each electrical device according to the interference strength between each device node; the calculation formula of the connection weight is: ; in, For device nodes With device node The connection weights between It is the set of available frequency bands defined in the beacon frame.

[0045] Specifically, the connection weight is an indicator that comprehensively reflects the communication quality and interference level between devices. It plays a key role in building the device node topology map and optimizing communication parameters. The calculation formula of the connection weight is: ;in, 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 the interference strength between device nodes on each frequency band and calculates the connection weight by taking the inverse. The larger the connection weight, the better the communication quality between devices and the smaller the interference; conversely, the smaller the connection weight, the poorer the communication quality between devices and the greater the interference.

[0046] In an optional embodiment, S2 includes the following steps: S21: Filter device node pairs whose interference strength is greater than a preset strength threshold according to the connection weight of the device node topology graph.

[0047] Specifically, according to the connection weights in the device node topology graph, device node pairs with interference strength greater than a preset strength threshold are screened out. The connection weights reflect the communication quality and interference level between devices. By analyzing the weights, device combinations that may have channel contention conflicts can be quickly located.

[0048] In practical applications, the preset strength threshold can be set based on the specific network environment and device performance. For example, in a smart home system, if the interference strength between two device nodes exceeds a certain threshold, it may affect their normal communication and become a potential source of conflict. By screening device node pairs, the detection range can be narrowed to the area where conflicts are most likely to occur, thereby improving detection efficiency.

[0049] S22: Identify the overlapping area of ​​the UWB pulse signal and the Wi-Fi signal by collecting the time domain signal waveform of the device node pair.

[0050] Specifically, for the selected device node pairs, their time domain signal waveforms are collected to identify the overlapping areas of the UWB pulse signal and the Wi-Fi signal. The purpose of this step is to determine in which time periods and frequency bands the two signals exist simultaneously and may overlap, thus causing channel contention conflicts.

[0051] Professional signal monitoring equipment, such as an oscilloscope or spectrum analyzer, can be used to collect time domain signal waveforms. This equipment can capture and record the waveform characteristics of the signal in real time, including the amplitude, frequency, phase and other information of the signal. By analyzing the waveform, the superposition of the UWB pulse signal and the Wi-Fi signal can be accurately identified. For example, on the waveform graph, the superposition area may show abnormal increase in signal amplitude or waveform distortion. This feature can help determine the specific location and range of the superposition.

[0052] S23: Calculate the conflict probability of the overlapping area. If the conflict probability is greater than a preset probability threshold, it is determined to be an implicit channel contention conflict event.

[0053] Specifically, the conflict probability refers to the possibility that two signals occupy the channel at the same time in the superposition area, resulting in a communication conflict. If the calculated conflict probability is greater than a preset probability threshold, it is determined to be an implicit channel contention conflict event.

[0054] The calculation of the conflict probability can be based on statistical methods and can be obtained by analyzing historical data and real-time monitoring data. For example, the ratio of the number of signal conflicts in the superposition area to the total number of monitoring times can be counted as an estimate of the conflict probability. The setting of the preset probability threshold can also be determined based on the actual application scenario and the requirements for system performance. When the conflict probability exceeds the threshold, it means that the channel contention conflict has posed a threat to the normal operation of the system, and corresponding optimization measures can be taken.

[0055] In an optional embodiment, S22 includes the following steps: S221: Perform wavelet transform on the time domain signal waveform to extract the time-frequency ridge features of the UWB pulse.

[0056] Specifically, the collected time domain signal waveform is subjected to wavelet transform to extract the time-frequency ridge features of the UWB pulse. 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.

[0057] UWB pulse signals are short-lived and high-frequency, and appear as sharp pulses in the time-frequency domain. Through wavelet transform, pulse features can be extracted from complex signal waveforms. The time-frequency ridge feature refers to the line where the signal energy is concentrated on the time-frequency plane, which can reflect the main characteristics and changing trends of UWB pulse signals.

[0058] 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.

[0059] Specifically, the Wi-Fi signal is detected through cyclic prefix correlation and the correlation coefficient is calculated. 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.

[0060] 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 is possible to determine whether there is a Wi-Fi signal in the signal. If the correlation coefficient is high, it means that there is a part in the signal that matches the cyclic prefix, so the presence of a Wi-Fi signal can be determined.

[0061] S223: When the time-frequency ridge 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 conflicting.

[0062] Specifically, when the extracted time-frequency ridge 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 conflicting. The first threshold and the second threshold can be set according to the actual signal environment and device performance.

[0063] When the time-frequency ridge feature is greater than the first threshold, it means that the UWB pulse signal is more obvious, and when the correlation coefficient is greater than the second threshold, it means that the Wi-Fi signal is more certain. The combination of the two can effectively avoid misjudgment and ensure that only when the two signals exist at the same time and the strength is sufficient, it is judged as a superposition conflict.

[0064] S224: The area with the overlay conflict is taken as the overlay area.

[0065] Specifically, the area determined to be the superposition conflict is used as the superposition area. This area is the part where the UWB pulse signal and the Wi-Fi signal occupy the channel at the same time, and is also a key area where implicit channel contention conflict may occur.

[0066] In an optional embodiment, S3 includes the following steps: S31: Calculate the conflict level of each channel according to the conflict probability of the implicit channel contention conflict event, the interference strength of the device node topology map and the connection weight; the calculation formula of the conflict level is: ; in, is the conflict level, indicating the device node The degree of conflict on the channel; Representation and device node Other device nodes that can interfere with each other within the communication range , is the conflict probability.

[0067] Specifically, the conflict level of each channel is calculated based on the conflict probability of implicit channel contention conflict events, the interference strength of the device node topology map, and the connection weight. The conflict level is a comprehensive indicator to measure the degree of channel conflict, which takes into account the probability of conflict occurrence and the interference and connection relationship between devices. The calculation formula for the conflict level is: .in, is the conflict level of the channel where the device node i is located; is the conflict probability; is the connection weight between device nodes i and j; is the center frequency of device nodes i and j in the frequency band The interference intensity on Represents other device nodes j that can interfere with device node i within the communication range.

[0068] This formula combines the conflict probability with the connection weight and interference strength between device nodes to comprehensively reflect the degree of channel conflict. The higher the conflict level, the more serious the channel conflict is, and more active optimization measures can be taken.

[0069] S32: Using the Q-Learning algorithm, dynamically adjust the backoff window according to the conflict level; wherein the state of the Q-Learning algorithm is the conflict level, the action of Q-Learning algorithm To adjust the backoff window, the reward function of the Q-Learning algorithm is: ; in, is the total number of implicit channel contention conflict events, is the total number of data transmissions, is the number of packet retransmissions triggered by implicit channel contention events, is the weight factor.

[0070] Specifically, the Q-Learning algorithm is used 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.

[0071] In this embodiment, the state of the Q-Learning algorithm For conflict level, action is the adjustment operation of the backoff window. The reward function is defined as .in, is the total number of implicit channel contention conflict events; is the total number of data transmissions; is the number of packet retransmissions triggered by implicit channel contention conflict events; is the weight factor.

[0072] The reward function is designed to encourage the reduction of conflicts and retransmissions. When the number of conflicts and retransmissions is small, the reward value is high, which encourages the algorithm to choose a backoff window adjustment strategy that can reduce conflicts.

[0073] S33: dynamically allocate time slot proportion factors according to the distribution density of device nodes in the device node topology diagram and the spatial distribution of implicit channel competition conflict events; the dynamic allocation formula of the time slot proportion factors is: ; in, is the time slot ratio factor, The area in the device node topology diagram The distribution density of device nodes, For Region The frequency of conflict events, Assigned to the area The number of time slots, is the total number of time slots in the communication cycle.

[0074] Specifically, the time slot ratio factor is dynamically allocated according to the distribution density of device nodes in the device node topology diagram and the spatial distribution of implicit channel competition conflict events. The time slot ratio factor determines the proportion of time slots that each device node can occupy in the communication cycle, which is crucial for balancing communication load and reducing conflicts. The dynamic allocation formula of the time slot ratio factor is: .in, is the time slot proportion factor; is the distribution density of device nodes in area k in the device node topology graph; is the frequency of conflict events in region k; is the number of time slots allocated to region k; is the total number of time slots in the communication cycle.

[0075] This formula comprehensively considers the distribution density of device nodes and the frequency of conflict events, and reasonably allocates time slot resources to different areas. Areas with high distribution density and high conflict frequency will obtain more time slot resources to meet their communication needs and reduce conflicts.

[0076] S34: taking the backoff window and the time slot ratio factor as optimized communication parameters.

[0077] Specifically, the optimized backoff window and time slot ratio factor are used as optimized communication parameters. These parameters will be used to guide the behavior of the device during the communication process, including how to select the backoff window and how to allocate time slot resources.

[0078] In an optional embodiment, S4 includes the following steps: S41: Divide the electrical equipment into a plurality of control clusters according to the positions of the device nodes in the device node topology diagram, each control cluster including a central node and associated sub-nodes.

[0079] Specifically, the electrical equipment is divided into multiple control clusters according to the position of the device nodes in the device node topology diagram. Each control cluster contains a central node and multiple associated sub-nodes. This clustering strategy aims to improve the transmission efficiency and execution efficiency of control instructions.

[0080] When dividing control clusters, factors such as the physical location of device nodes, communication range, and device type can be considered. For example, in a smart home system, devices can be divided into different control clusters based on the layout of the room. The devices in each room form a control cluster, with one device as the central node and the other devices as sub-nodes. The central node is responsible for receiving and forwarding control instructions, and the sub-nodes perform corresponding operations based on the instructions of the central node.

[0081] S42: Based on the optimized communication parameters, a control instruction is issued to the central node, where the control instruction includes a power adjustment threshold of the sub-node.

[0082] Specifically, based on the optimized communication parameters, control instructions are issued to each central node. The control instructions contain key information such as the power adjustment threshold of the subnode. When issuing control instructions, the optimization results of communication parameters, such as channel backoff window and time slot allocation strategy, can be considered. This parameter determines the transmission timing and resource allocation of control instructions to avoid conflicts with communications with other devices. For example, if the central node of a control cluster is allocated a larger time slot resource, it can receive control instructions earlier in the communication cycle, thereby improving the timeliness of control.

[0083] S43: Based on the control instruction, control the associated sub-node to switch to the corresponding working mode.

[0084] Specifically, the central node controls its associated sub-nodes to switch to the corresponding working mode according to the received control instruction. The switching of working modes may include various operations such as power adjustment, function enabling or disabling, etc. For example, the control instruction may require some sub-nodes to reduce power consumption to save energy; or require some sub-nodes to enter high power mode to improve their performance.

[0085] After receiving the control command forwarded by the central node, the sub-node will make corresponding adjustments according to the parameters in the command. For example, if the power adjustment threshold is set to a specific value, the sub-node will adjust its working power to below or above the threshold, depending on the requirements of the control command. This switching of working modes enables the entire system to dynamically adjust the working status of the equipment according to actual needs and achieve the goal of collaborative control.

[0086] The above-mentioned wireless intelligent electrical equipment networking and collaborative control method constructs a heterogeneous wireless communication network that integrates multi-protocol devices, detects implicit channel competition conflicts caused by the superposition of different physical layer signals in real time, combines the global interference characteristics of the device node topology diagram and the spatiotemporal distribution of conflict events, and uses a reinforcement learning algorithm to dynamically optimize the backoff window parameters and time slot allocation strategy. It also collaboratively adjusts the device operation mode based on node density and conflict risk, effectively solving the channel resource competition imbalance problem caused by the inability to perceive cross-protocol signals in high-density scenarios, significantly reducing the conflict probability and retransmission overhead, and realizing on-demand dynamic scheduling of device groups through multi-dimensional collaborative control of time, frequency and space, thereby improving the overall energy efficiency and communication reliability of the system while ensuring industrial-grade real-time performance.

[0087] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, 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 can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0088] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the wireless intelligent electrical device networking and collaborative control method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more wireless intelligent electrical device networking and collaborative control system embodiments provided below can refer to the above limitations on a wireless intelligent electrical device networking and collaborative control method, which will not be repeated here.

[0089] In an exemplary embodiment, Figure 2 As shown, a wireless intelligent electrical equipment networking and collaborative control system 20 is provided, including: The device access module 21 is used to access electrical devices of different protocols to the same network and generate a device node topology diagram.

[0090] The conflict detection module 22 is used to detect implicit channel contention 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.

[0091] The communication parameter optimization module 23 is used to optimize the channel backoff window and time slot allocation strategy through a reinforcement learning algorithm according to implicit channel competition conflict events and device node topology diagrams, and generate optimized communication parameters.

[0092] The control instruction issuing module 24 is used to issue control instructions to the target electrical device based on the optimized communication parameters and the node distribution of the device node topology diagram.

[0093] Optionally, the device access module 21 includes: The broadcast access unit 211 is used to access the beacon frame to the broadcast network of the electrical device; wherein the beacon frame includes a multi-protocol frequency band identifier and corresponding frequency band parameters, and the frequency band parameters include the frequency band center frequency defined in the beacon frame and the corresponding transmission power.

[0094] The frequency band selection unit 212 is used to select a communication frequency band that matches 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.

[0095] The weight calculation unit 213 is used to calculate the connection weights between the electrical devices according to the location coordinates and the frequency band parameters.

[0096] The topology construction unit 214 is used to construct a device node topology map according to the device ID, protocol type and connection weight.

[0097] Optionally, the weight calculation unit 213 includes: The node spacing calculation subunit 2131 is used to calculate the node spacing of the electrical equipment according to the position coordinates.

[0098] 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 of the path loss is: ; in, Device nodes for electrical equipment To the device node The node spacing; Indicates that the center frequency of the frequency band is The node spacing is When the device node To the device node The path loss between .

[0099] The interference strength calculation subunit 2133 is used to calculate the interference strength between each device node at the center frequency of the frequency band according to the path loss and the transmission power; the calculation formula of the interference strength is: ; in, Indicates the center frequency of the frequency band The transmission power; Indicates the center frequency of the frequency band On the device node With device node The intensity of interference between them.

[0100] The connection weight determination subunit 2134 is used to calculate the connection weight of each electrical device according to the interference strength between each device node; the calculation formula of the connection weight is: ; in, For device nodes With device node The connection weights between It is the set of available frequency bands defined in the beacon frame.

[0101] Optionally, the conflict detection module 22 includes: The node pair screening unit 221 is used to screen device node pairs whose interference strength is greater than a preset strength threshold according to the connection weights of the device node topology graph.

[0102] The signal acquisition unit 222 is used to identify the overlapping area of ​​the UWB pulse signal and the Wi-Fi signal by acquiring the time domain signal waveform of the device node pair.

[0103] The conflict determination unit 223 is used to calculate the conflict probability of the superposition area, and if the conflict probability is greater than a preset probability threshold, it is determined to be an implicit channel contention conflict event.

[0104] Optionally, the signal acquisition unit 222 includes: The feature extraction subunit 2221 is used to perform wavelet transform on the time domain signal waveform to extract the time-frequency ridge features of the UWB pulse.

[0105] The correlation detection subunit 2222 is used 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 waveform at different positions.

[0106] The conflict judgment subunit 2223 is used to judge that the UWB pulse signal and the Wi-Fi signal are superimposed and conflicting when the time-frequency ridge feature is greater than the first threshold and the correlation coefficient is greater than the second threshold.

[0107] The area marking subunit 2224 is used to set the area with superposition conflict as the superposition area.

[0108] Optionally, the communication parameter optimization module 23 includes: The conflict level calculation unit 231 is used to calculate the conflict level of each channel according to the conflict probability of the implicit channel contention conflict event, the interference strength and the connection weight of the device node topology map; the calculation formula of the conflict level is: ; in, Is the conflict level, indicating the device node The degree of conflict on the channel; Representation and device node Other device nodes that can interfere with each other within the communication range , is the probability of conflict.

[0109] The backoff window adjustment unit 232 is used to dynamically adjust the backoff window according to the conflict level using the Q-Learning algorithm; wherein the state of the Q-Learning algorithm is the conflict level, the action of Q-Learning algorithm To adjust the backoff window, the reward function of the Q-Learning algorithm is: ; in, is the total number of implicit channel contention conflict events, is the total number of data transmissions, is the number of packet retransmissions triggered by implicit channel contention events, is the weight factor.

[0110] The time slot proportion factor allocation unit 233 is used to dynamically allocate the time slot proportion 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 events; the dynamic allocation formula of the time slot proportion factor is: ; in, is the time slot ratio factor, The area in the device node topology diagram The distribution density of device nodes, For Region The frequency of conflict events, Assigned to the area The number of time slots, is the total number of time slots in the communication cycle.

[0111] The communication parameter generating unit 234 is configured to use the backoff window and the time slot occupation factor as optimized communication parameters.

[0112] Optionally, the control instruction issuing module 24 includes: The device division unit 241 is used to divide the electrical devices into a plurality of control clusters according to the positions of the device nodes in the device node topology diagram, each control cluster including a central node and associated sub-nodes.

[0113] The instruction issuing unit 242 is used to issue a control instruction to the central node based on the optimized communication parameters, where the control instruction includes a power adjustment threshold of the sub-node.

[0114] The mode control unit 243 is used to control the associated sub-node to switch to the corresponding working mode based on the control instruction.

[0115] An embodiment of the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0116] The embodiments of the present application further 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 above-mentioned method embodiments are implemented.

[0117] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0118] The above-mentioned embodiments only express several implementation methods of the embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the embodiments of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the embodiments of the present application, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A wireless intelligent electrical equipment networking and collaborative control method, characterized in that: The method comprises: S1: Connect electrical devices with different protocols to the same network and generate a device node topology diagram; S2: Detecting implicit channel contention conflict events caused by superposition of UWB pulse signals and Wi-Fi signals based on the device node topology map and cross-protocol physical layer signal characteristics; S3: According to the implicit channel competition conflict event and the device node topology map, the channel backoff window is optimized by a reinforcement learning algorithm and the time slot allocation strategy is optimized by 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, a control instruction is issued to the target electrical device.

2. The method according to claim 1, characterized in that The S1 includes: S11: Accessing a beacon frame to the broadcast network of the electrical device; wherein the beacon frame includes a multi-protocol frequency band identifier and corresponding frequency band parameters, and the frequency band parameters include a frequency band center frequency defined in the beacon frame and a corresponding transmission power; S12: Based on the multi-protocol frequency band identifier, select a communication frequency 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 frequency band; S13: Calculating connection weights between the electrical devices according to the location coordinates and the frequency band parameters; S14: constructing the device node topology map according to the device ID, the protocol type and the connection weight.

3. The method according to claim 2, characterized in that The S13 includes: S131: Calculating the node spacing of the electrical equipment according to the position coordinates; S132: 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 of the path loss is: ； in, A device node for the electrical device To the device node The node spacing; Indicates that the center frequency of the frequency band is The node spacing is When the device node To the device node The path loss between S133: Calculate the interference intensity between the device nodes at the center frequency of the frequency band according to the path loss and the transmission power; the calculation formula of the interference intensity is: ； in, Indicates the center frequency of the frequency band The transmission power; Indicates the center frequency of the frequency band On the device node With device node The intensity of interference between S134: Calculate the connection weight of each of the electrical devices according to the interference strength between the device nodes; the calculation formula of the connection weight is: ； in, For device nodes With device node The connection weights between It is a set of available frequency bands defined in the beacon frame.

4. The method according to claim 3, characterized in that The S2 includes: S21: screening device node pairs whose interference strength is greater than a preset strength threshold according to the connection weight of the device node topology map; S22: Identify the overlapping area of ​​the UWB pulse signal and the Wi-Fi signal by collecting the time domain signal waveform of the device node pair; S23: Calculate the conflict probability of the superposition area, and if the conflict probability is greater than a preset probability threshold, determine it as an implicit channel contention conflict event.

5. The method according to claim 4, characterized in that The S22 includes: S221: performing wavelet transform on the time domain signal waveform to extract the time-frequency ridge features of the UWB pulse; S222: Detect the Wi-Fi signal through cyclic prefix correlation and calculate a correlation coefficient; the correlation coefficient represents the autocorrelation of the time domain signal waveform at different positions; S223: When the time-frequency ridge feature is greater than a first threshold and the correlation coefficient is greater than a second threshold, it is determined that the UWB pulse signal and the Wi-Fi signal are superimposed and conflicting; S224: Use the area with the superposition conflict as the superposition area.

6. The method according to claim 4, characterized in that The S3 includes: S31: Calculate the conflict level of each channel according to the conflict probability of the implicit channel contention conflict event, the interference intensity of the device node topology map and the connection weight; the calculation formula of the conflict level is: ； in, is the conflict level, indicating the device node The degree of conflict on the channel; Representation and device node Other device nodes that can interfere with each other within the communication range , is the conflict probability; S32: Using the Q-Learning algorithm, dynamically adjust the backoff window according to the conflict level; wherein the state of the Q-Learning algorithm is the conflict level, the action of the Q-Learning algorithm For the adjustment operation of the backoff window, the reward function of the Q-Learning algorithm is: ； in, is the total number of implicit channel contention conflict events, is the total number of data transmissions, is the number of data packet retransmissions triggered by the implicit channel contention conflict event, is the weight factor; S33: dynamically allocating a time slot proportion 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 of the time slot proportion factor is: ； in, is the time slot proportion factor, The area in the device node topology diagram The distribution density of device nodes, For Region The frequency of conflict events, Assign to area The number of time slots, is the total number of time slots in the communication cycle; S34: Using the backoff window and the time slot ratio factor as the optimized communication parameters.

7. The method according to any one of claims 1 to 6, characterized in that The S4 includes: S41: dividing the electrical equipment into a plurality of control clusters according to the positions of the device nodes in the device node topology graph, each of the control clusters comprising a central node and associated sub-nodes; S42: Based on the optimized communication parameters, a control instruction is issued to the central node, where the control instruction includes a power adjustment threshold of a subnode; S43: Based on the control instruction, control the associated sub-node to switch to a corresponding working mode.

8. A wireless intelligent electrical equipment networking and collaborative control system, characterized in that: The system comprises: The device access module is used to connect electrical devices of different protocols to the same network and generate a device node topology diagram; A conflict detection module, used to detect implicit channel contention conflict events caused by superposition of UWB pulse signals and Wi-Fi signals based on the device node topology map and cross-protocol physical layer signal characteristics; A communication parameter optimization module, configured to optimize the channel backoff window by a reinforcement learning algorithm and optimize the time slot allocation strategy by a dynamic optimization algorithm according to the implicit channel competition conflict event and the device node topology map, and generate optimized communication parameters; The control instruction issuing module is used to issue control instructions to the target electrical equipment based on the optimized communication parameters and the node distribution of the device node topology diagram.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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