Curtain wall LED large-scale data wireless transmission and synchronization method

Abstract

The invention discloses a curtain wall LED large-scale data wireless transmission and synchronization method, and the method enables a data package to be displayed on a curtain wall through the steps of networking, ID distribution, synchronous time delay automatic compensation, data receiving, data forwarding and data display. A dynamic prediction model is constructed by combining environmental parameters including temperature, humidity, node density and electromagnetic interference, the system can adapt to complex environmental changes, it is ensured that the system can stably operate under various conditions, and the dynamic prediction of the system can be realized through dynamic time delay prediction, intelligent compensation and a distributed negotiation mechanism in the face of complex environmental influences. The curtain wall LED display system realizes efficient transmission and accurate synchronization of large-scale data, guarantees real-time performance and consistency of curtain wall LED display, remarkably improves display effect and stability, adapts to complex environments, is high in anti-interference capability and low in maintenance cost, supports efficient cooperation of large-scale nodes, and has important market and application prospects.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a curtain wall LED large-scale data wireless transmission and synchronization method. Background Art

[0002] Currently, data transmission and synchronization for curtain wall LEDs is achieved through wired networks (TCP / UDP, RS485, etc.). This technical solution is extremely troublesome in construction and maintenance. Furthermore, wired networks are extremely susceptible to aging in outdoor environments such as wind, rain, and high temperatures, seriously shortening their service life and dramatically increasing maintenance costs. Therefore, developing a large-scale wireless data transmission and synchronization technology for curtain wall LEDs can significantly improve performance indicators in terms of construction, maintenance, and lifespan.

[0003] However, wireless transmission is affected by the surrounding environment, bandwidth, and other factors, and its delay will increase compared to wired transmission. Existing technologies generally compensate for delays by measuring actual delays, which will result in a certain lag, thus causing freezes and other issues during curtain wall playback. Summary of the Invention

[0004] Based on the deficiency of the prior art in which compensation by actually measuring time delay has hysteresis, the present invention provides a curtain wall LED large-scale data wireless transmission and synchronization method.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: A curtain wall LED large-scale data wireless transmission and synchronization method includes the following steps: S1, networking: the host's data distribution unit activates the node networking unit, receives network access requests from each node networking unit, and groups the network access to the same network segment; S2, ID allocation: The node networking unit of the data distribution unit allocates IDs to the slave nodes entering the network segment; S3, Synchronous Delay Automatic Compensation: 1. Collect environmental perception data and construct a multi-dimensional delay prediction model through dynamic weight allocation. 2. Negotiate delays between adjacent nodes to form distributed compensation parameters to build a decentralized delay negotiation mechanism. 3. Dynamically switch channels and set interference avoidance strategies to dynamically adjust compensation parameters. 4. Implement edge computing-assisted compensation through local compensation calculation offloading and incremental compensation parameter updates. 5. Store delay parameters on-chain and verify and backtrack through the blockchain through an exception backtracking mechanism. S4, data reception: receiving UDP broadcast data from the data distribution unit, extracting and displaying valid data from each node according to the assigned ID; S5, data forwarding: Each node receives the distributed data and directly forwards it, avoiding the accumulation of data filtering and processing delays; S6, data display: After the node extracts valid data, it displays and outputs it according to the synchronization command of the data distribution unit and the delay parameters of each node.

[0006] As a preferred method, a first multi-dimensional delay prediction model is established: , where T is temperature, H is humidity, N is node density, E is electromagnetic interference, and each parameter is preceded by a weight coefficient. When calculating the weight coefficient, the actual delay, temperature, humidity, node density, and electromagnetic interference E are measured first and a data set is constructed. Each row of data contains the input variables T, H, N, E and the actual delay , The least squares method is used to obtain α, β, γ, and δ.

[0007] As a preference, a second multi-dimensional delay prediction model is established: The predicted delay is verified by multiple sets of measured delay data and compared with the original multi-dimensional delay prediction model: Compare the two and choose the model that is closer to the measured value.

[0008] As a preference, for the second multi-dimensional delay prediction model, the intervals are divided according to the thresholds of the environmental parameters, and the segmentation coefficient is defined as: α = , using the sliding window to dynamically update the coefficients: ,in is the learning rate, The gradient of the mean square error with respect to the weight coefficient α is introduced. The graph attention network GAT is used to dynamically allocate parameter weights according to the node neighborhood relationship: ,for Attention weight of node i, Q: query matrix, is the key matrix, V is the value matrix, and d is the feature dimension.

[0009] As a preferred method, the time series features are embedded and the lag term of the historical delay is added, and the formula of the second multi-dimensional delay prediction model is expanded to: , using LSTM or ARIMA models to capture temporal dependencies, based on the distance between nodes Define the spatial attenuation factor: = , where γ is the spatial attenuation factor, is the basic spatial factor, is the distance between node i and node j, is the distance scaling parameter.

[0010] As a preferred method, an interference suppression factor is introduced into the electromagnetic interference compensation term: , dynamically reduce parameter sensitivity under high electromagnetic interference, and filter the delay data of abnormal nodes through distributed consensus algorithm: .

[0011] Preferably, the node broadcasts local delay parameters to devices within a 3-hop range. The local delay parameters include predicted values, and an improved Byzantine fault tolerance algorithm is used to select a reliable delay benchmark value. Each node generates a compensation matrix based on the negotiation result: , broadcast the compensation parameters to the group members through the LoRa physical channel.

[0012] As a preference, detect the current channel bit error rate, when the bit error rate is greater than When , trigger multi-channel scanning and select the optimal channel combination: Score= , BW is the bandwidth, is the signal-to-noise ratio, For delay, after the new channel is enabled, the compensation value is corrected according to the formula: , is the actual physical distance between the transmitter and the receiver in the old channel, It is the actual physical distance between the transmitter and receiver in the new channel.

[0013] As a priority, nodes that need compensation are selected based on their current load and historical delay data. Weighted random sampling is used to prioritize nodes that have a greater impact on system performance. The edge computing node ECN is assigned to handle the compensation calculation tasks for 20% of the nodes in the processing group. The ant colony optimization algorithm is used to allocate computing resources. In each iteration, only the neighborhood of the current optimal node is searched. The edge computing node ECN generates incremental compensation packets every 5ms: , and broadcast to associated nodes through TDMA time slots; edge computing nodes (ECN) are used to perform local parameter updates: = , , and perform 8-bit fixed-point quantization on the model parameters: =Round( ), regularly statistical parameter distribution, and update and , and trim the parameters: , and then quantify.

[0014] As a preference, the Tangle structure is used to write key compensation parameters into a lightweight blockchain, and dual verification nodes are set up for data consistency verification. When the synchronization error exceeds ±50μs, the data of the last 10 blocks are called for root cause analysis to trigger a dynamic weight coefficient reset instruction.

[0015] Compared with the existing technology, the advantages of the present invention are as follows: This application uses a multi-dimensional delay prediction model and combines environmental parameters such as temperature, humidity, node density, and electromagnetic interference to build a dynamic prediction model, which can adapt to complex environmental changes and ensure that the system can operate stably under various conditions. In the face of complex environmental influences, through dynamic delay prediction, intelligent compensation and distributed negotiation mechanism, it realizes efficient transmission and precise synchronization of large-scale data, ensures the real-time and consistency of curtain wall LED display, significantly improves the display effect and stability of the curtain wall LED display system, adapts to complex environments, has strong anti-interference ability and low maintenance cost, and supports efficient collaboration of large-scale nodes, and has important market and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be described in further detail below with reference to the accompanying drawings and preferred embodiments. However, those skilled in the art will appreciate that these drawings are drawn only for the purpose of explaining the preferred embodiments and should not be construed as limiting the scope of the present invention. Furthermore, unless otherwise specified, the drawings are merely schematic representations of the composition or structure of the depicted objects and may contain exaggerated representations. Furthermore, the drawings are not necessarily drawn to scale.

[0017] Figure 1 This is a block diagram of the networking and ID allocation process; Figure 2 This is a block diagram of the principle of the automatic compensation process for synchronization delay; Figure 3 This is a principle block diagram of the data receiving and forwarding process; Figure 4 A block diagram of the data display process; DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Those skilled in the art will appreciate that these descriptions are merely illustrative and exemplary and should not be construed as limiting the scope of protection of the present invention.

[0019] A curtain wall LED large-scale data wireless transmission and synchronization method includes the following steps: S1, networking: the host's data distribution unit activates the node networking unit, receives network access requests from each node networking unit, and groups the network access to the same network segment; S2, ID allocation: The node networking unit of the data distribution unit allocates IDs to the slave nodes entering the network segment; S3, Synchronous Delay Automatic Compensation: 1. Collect environmental perception data and construct a multi-dimensional delay prediction model through dynamic weight allocation. 2. Negotiate delays between adjacent nodes to form distributed compensation parameters to build a decentralized delay negotiation mechanism. 3. Dynamically switch channels and set interference avoidance strategies to dynamically adjust compensation parameters. 4. Implement edge computing-assisted compensation through local compensation calculation offloading and incremental compensation parameter updates. 5. Store delay parameters on-chain and verify and backtrack through the blockchain through an exception backtracking mechanism. S4, data reception: receiving UDP broadcast data from the data distribution unit, extracting and displaying valid data from each node according to the assigned ID; S5, data forwarding: Each node receives the distributed data and directly forwards it, avoiding the accumulation of data filtering and processing delays; S6, data display: After the node extracts valid data, it displays and outputs it according to the synchronization command of the data distribution unit and the delay parameters of each node.

[0020] As a preferred method, a first multi-dimensional delay prediction model is established: , where T is temperature, H is humidity, N is node density, E is electromagnetic interference, and each parameter is preceded by a weight coefficient. When calculating the weight coefficient, the actual delay, temperature, humidity, node density, and electromagnetic interference E are measured first and a data set is constructed. Each row of data contains the input variables T, H, N, E and the actual delay , The least squares method is used to obtain α, β, γ, and δ.

[0021] As a preference, a second multi-dimensional delay prediction model is established: The predicted delay is verified by multiple sets of measured delay data and compared with the original multi-dimensional delay prediction model: Compare the two and choose the model that is closer to the measured value.

[0022] As a preference, for the second multi-dimensional delay prediction model, the intervals are divided according to the thresholds of the environmental parameters, and the segmentation coefficient is defined as: α = , using the sliding window to dynamically update the coefficients: ,in is the learning rate, The gradient of the mean square error with respect to the weight coefficient α is introduced. The graph attention network GAT is used to dynamically allocate parameter weights according to the node neighborhood relationship: ,for Attention weight of node i, Q: query matrix, is the key matrix, V is the value matrix, and d is the feature dimension.

[0023] As a preferred method, the time series features are embedded and the lag term of the historical delay is added, and the formula of the second multi-dimensional delay prediction model is expanded to: , using LSTM or ARIMA models to capture temporal dependencies, based on the distance between nodes Define the spatial attenuation factor: = , where γ is the spatial attenuation factor, is the basic spatial factor, is the distance between node i and node j, is the distance scaling parameter. Time series features and lag terms are introduced, combined with LSTM or ARIMA models to capture temporal dependencies. A spatial attenuation factor is defined based on inter-node distances. This integrates temporal and spatial factors to improve the timeliness and spatial consistency of the prediction model.

[0024] As a preferred method, an interference suppression factor is introduced into the electromagnetic interference compensation term: , dynamically reduce parameter sensitivity under high electromagnetic interference, and filter the delay data of abnormal nodes through distributed consensus algorithm: This solution introduces an interference suppression factor to dynamically reduce the impact of electromagnetic interference, and uses a distributed consensus algorithm to filter abnormal node data, enhance the ability to resist electromagnetic interference, ensure data reliability, and adapt to complex electromagnetic environments. is the compensated interference suppression factor, which is used to dynamically reduce the parameter sensitivity under high electromagnetic interference. is the initial interference suppression factor, which is a basic value used to start calculating the interference suppression factor after compensation. The maximum electromagnetic interference intensity is a preset threshold, indicating the maximum electromagnetic interference intensity that the system can withstand. The current electromagnetic interference intensity is a real-time monitoring parameter that reflects the degree of electromagnetic interference in the current environment. It is the predicted delay value, which is the delay data filtered by the distributed consensus algorithm and used for subsequent compensation calculations. It is a Byzantine fault-tolerant median algorithm, which is used to filter out reliable delay data by using the median method in the presence of abnormal nodes. The predicted delay value for each node. Each node has its own predicted delay value, which is screened by this algorithm.

[0025] Preferably, the node broadcasts local delay parameters to devices within a 3-hop range. The local delay parameters include predicted values, and an improved Byzantine fault tolerance algorithm is used to select a reliable delay benchmark value. Each node generates a compensation matrix based on the negotiation result: , and broadcasts the compensation parameters to group members via the LoRa physical channel. Nodes broadcast local delay parameters and use an improved Byzantine fault-tolerant algorithm to filter trusted reference values, generate and broadcast the compensation matrix, enhancing network fault tolerance and ensuring data consistency and reliability, making it suitable for distributed systems. is the compensation matrix, which contains the parameters used to compensate for the delay error. Each node generates the matrix based on the negotiation result. n is the number of nodes. is the predicted delay value of node i, is the measured delay value of the i-th node, which is the delay data actually measured and used together with the predicted delay value to generate the compensation matrix.

[0026] As a preference, detect the current channel bit error rate, when the bit error rate is greater than When , trigger multi-channel scanning and select the optimal channel combination: Score= , BW is the bandwidth, is the signal-to-noise ratio, For delay, after the new channel is enabled, the compensation value is corrected according to the formula: , is the actual physical distance between the transmitter and the receiver in the old channel, The actual physical distance between the transmitter and receiver in the new channel. This triggers multi-channel scanning based on the channel bit error rate, selects the optimal channel combination, and dynamically adjusts compensation values to adapt to channel changes, improving channel utilization and communication stability while ensuring high-bandwidth and low-latency transmission requirements.

[0027] As a priority, nodes that need compensation are selected based on their current load and historical delay data. Weighted random sampling is used to prioritize nodes that have a greater impact on system performance. The edge computing node ECN is assigned to handle the compensation calculation tasks for 20% of the nodes in the processing group. The ant colony optimization algorithm is used to allocate computing resources. In each iteration, only the neighborhood of the current optimal node is searched. The edge computing node ECN generates incremental compensation packets every 5ms: , and broadcast to associated nodes through TDMA time slots; edge computing nodes (ECN) are used to perform local parameter updates: = , , and perform 8-bit fixed-point quantization on the model parameters: =Round( ), regularly statistical parameter distribution, and update and , and trim the parameters: , and then quantize it. Compensation nodes are selected based on node load and historical data, and computing resources are allocated using weighted random sampling and ant colony optimization algorithms. Parameters are updated and quantified through edge computing. This makes resource allocation efficient, edge computing improves real-time performance, and parameter quantization reduces computational complexity, making it suitable for large-scale node scenarios.

[0028] As a preferred approach, key compensation parameters are written into a lightweight blockchain using the Tangle structure, dual verification nodes are set up for data consistency verification, and when the synchronization error exceeds ±50μs, the data of the last 10 blocks are called for root cause analysis to trigger a dynamic weight coefficient reset instruction. Key compensation parameters are written into a lightweight blockchain using the Tangle structure, dual verification nodes are set up for data consistency verification, and synchronization error analysis and dynamic weight coefficient reset are supported, ensuring data security and consistency, supporting efficient root cause analysis, and improving system reliability and maintainability.

[0029] The actual delay to be used is calculated by recording the timestamp when the data packet is sent and the timestamp when it is received. The difference between the two is the delay.

[0030] The above describes the large-scale wireless data transmission and synchronization method for curtain wall LEDs provided by the present invention. Specific examples are used to illustrate the principles and implementations of the present invention. The above examples are intended only to facilitate understanding of the present invention and its core concepts. It should be noted that those skilled in the art will readily appreciate that various improvements and modifications to the present invention can be made without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the claims.

Claims

1. A curtain wall LED large-scale data wireless transmission and synchronization method, characterized in that: Comprises the following steps: S1, networking: the host data distribution unit activates the node networking unit, receives network access requests from each node networking unit, the same network segment network grouping; S2, ID allocation: The node networking unit of the data distribution unit allocates IDs to the slave nodes entering the network segment; S3, Synchronous Delay Automatic Compensation:

1. Collect environmental perception data and construct a multi-dimensional delay prediction model through dynamic weight allocation.

2. Negotiate delays between adjacent nodes to form distributed compensation parameters to build a decentralized delay negotiation mechanism.

3. Dynamically switch channels and set interference avoidance strategies to dynamically adjust compensation parameters.

4. Implement edge computing-assisted compensation through local compensation calculation offloading and incremental compensation parameter updates.

5. Store delay parameters on-chain and verify and backtrack through the blockchain through an exception backtracking mechanism. S4, data reception: receiving UDP broadcast data from the data distribution unit, extracting and displaying valid data from each node according to the assigned ID; S5, data forwarding: Each node receives the distributed data and directly forwards it, avoiding the accumulation of data filtering and processing delays; S6, data display: After the node extracts valid data, it displays and outputs it according to the synchronization command of the data distribution unit and the delay parameters of each node.

2. A curtain wall LED large-scale data wireless transmission and synchronization method according to claim 1, characterized in that: Establish the first multi-dimensional delay prediction model: , where T is temperature, H is humidity, N is node density, E is electromagnetic interference, and each parameter is preceded by a weight coefficient. When calculating the weight coefficient, the actual delay, temperature, humidity, node density, and electromagnetic interference E are measured first and a data set is constructed. Each row of data contains the input variables T, H, N, E and the actual delay , The least squares method is used to obtain α, β, γ, and δ.

3. A curtain wall LED large-scale data wireless transmission and synchronization method according to claim 2, characterized in that: Establish the second multi-dimensional delay prediction model: The predicted delay is verified by multiple sets of measured delay data and compared with the original multi-dimensional delay prediction model: Compare the two and choose the model that is closer to the measured value.

4. A curtain wall LED large-scale data wireless transmission and synchronization method according to claim 3, characterized in that: For the second multi-dimensional delay prediction model, the interval is divided according to the threshold of the environmental parameters, and the segmentation coefficient is defined: α = , using the sliding window to dynamically update the coefficients: ,in is the learning rate, The gradient of the mean square error with respect to the weight coefficient α is introduced. The graph attention network GAT is used to dynamically allocate parameter weights according to the node neighborhood relationship: ,for Attention weight of node i, Q: query matrix, is the key matrix, V is the value matrix, and d is the feature dimension.

5. A curtain wall LED large-scale data wireless transmission and synchronization method according to claim 4, characterized in that: The time series features are embedded and the lag term of the historical delay is added, and the formula of the second multi-dimensional delay prediction model is expanded to: , using LSTM or ARIMA models to capture temporal dependencies, based on the distance between nodes Define the spatial attenuation factor: = , where γ is the spatial attenuation factor, is the basic spatial factor, is the distance between node i and node j, is the distance scaling parameter.

6. A curtain wall LED large-scale data wireless transmission and synchronization method according to claim 5, characterized in that: In order to counteract the electromagnetic interference compensation term, an interference suppression factor is introduced: , dynamically reduce parameter sensitivity under high electromagnetic interference, and filter the delay data of abnormal nodes through distributed consensus algorithm: .

7. The method for large-scale wireless data transmission and synchronization of curtain wall LEDs according to claim 1, characterized in that: The node broadcasts the local delay parameter to the devices within the adjacent 3-hop range. The local delay parameter includes the predicted value and uses the improved Byzantine fault tolerance algorithm to select the reliable delay benchmark value. Each node generates a compensation matrix based on the negotiation results: , broadcast the compensation parameters to the group members through the LoRa physical channel.

8. The curtain wall LED large-scale data wireless transmission and synchronization method according to claim 1, characterized in that: Detect the current channel bit error rate. When the bit error rate is greater than When , trigger multi-channel scanning and select the optimal channel combination: Score= , BW is the bandwidth, is the signal-to-noise ratio, For delay, after the new channel is enabled, the compensation value is corrected according to the formula: , The actual physical distance between the transmitter and receiver in the old channel, It is the actual physical distance between the transmitter and receiver in the new channel.

9. The curtain wall LED large-scale data wireless transmission and synchronization method according to claim 1, characterized in that: Nodes that require compensation are selected based on their current load and historical latency data. Weighted random sampling is used to prioritize nodes with the greatest impact on system performance. The edge computing node ECN is assigned to handle the compensation computation tasks for 20% of the nodes in the processing group. An ant colony optimization algorithm is used to allocate computing resources. In each iteration, only the neighborhood of the current optimal node is searched. The edge computing node ECN generates incremental compensation packets every 5ms. , and broadcast to associated nodes through TDMA time slots; edge computing nodes (ECN) are used to perform local parameter updates: = , , and perform 8-bit fixed-point quantization on the model parameters: =Round( ), regularly statistical parameter distribution, and update and , and trim the parameters: , and then quantify.

10. The curtain wall LED large-scale data wireless transmission and synchronization method according to claim 1, characterized in that: The key compensation parameters are written into the lightweight blockchain using the Tangle structure, and dual verification nodes are set up for data consistency verification. When the synchronization error exceeds ±50μs, the data of the last 10 blocks are called for root cause analysis to trigger the dynamic weight coefficient reset instruction.

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