Wind power plant data remote transmission system

Abstract

The invention discloses a wind power plant data remote transmission system, and particularly relates to the field of data remote transmission. Comprising a data initialization module, a data priority dynamic division module, an adaptive compression strategy regulation and control module, a dynamic routing optimization module, a multi-node transmission synchronization control module, a data integrity verification and retransmission module and a transmission security encryption module. The data initialization module is used for collecting multi-dimensional data and establishing a three-dimensional coordinate system for positioning; the data priority dynamic division module is used for calculating priority coefficients based on data types and dynamically sorting transmission queues according to a coefficient descending order; according to the method, the compression algorithm and parameters are dynamically selected through the compression adjustment factor, the compression strategy adaptive to the real-time channel state is obtained, the problem of insufficient environmental adaptability is solved, the data transmission efficiency in a complex environment is improved, delay control is reduced, and the advantage of adapting to the dynamic environmental change of the wind power plant is achieved.

Description

Technical Field

[0001] This invention relates to the field of data remote transmission technology, and more specifically, to a wind farm data remote transmission system. Background Technology

[0002] In wind farm operation and maintenance systems, remote data transmission is the core link connecting wind turbine terminals and monitoring centers, and its stability directly determines the effectiveness of remote diagnostics, load scheduling, and fault early warning. Existing technologies mainly rely on wireless communication networks, industrial Ethernet, or microwave relays to achieve bidirectional transmission of wind turbine operating parameters, environmental data, and control commands, forming the foundational link in the wind farm's perception, transmission, and decision-making closed loop.

[0003] While existing transmission technologies can meet routine operation and maintenance needs, they have significant limitations in complex scenarios: First, they lack environmental adaptability. Strong winds causing tower vibrations can lead to antenna phase shifts, resulting in high signal modulation error rates. Periodic electromagnetic radiation from rotating blades can interfere with wireless channels, causing significant data transmission jitter. Second, resource scheduling is rigid. Existing solutions employ fixed bandwidth allocation strategies. When a single wind turbine experiences a sudden failure, fault data competes for channel space with regular operation data, easily causing high delays in the transmission of critical information. Third, multi-node coordination is poor. When hundreds or thousands of wind turbines transmit concurrently within a wind farm, the lack of a dynamic time slot allocation mechanism leads to a high probability of data collisions between adjacent nodes, resulting in high packet loss rates and severely impacting data integrity.

[0004] To address the aforementioned issues, there is an urgent need for a remote data transmission system for wind farms. This system would deploy multi-dimensional sensing terminals and intelligent scheduling modules to collect environmental interference, equipment status, and data characteristic parameters in real time. It would then construct a full-link control mechanism that includes dynamic compression, routing optimization, time slot synchronization, and security encryption. This would enable stable, efficient, and secure data transmission in complex environments, solving the problems of weak environmental interference resistance, inefficient resource scheduling, and insufficient coordination in existing technologies. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a wind farm data remote transmission system, which solves the problems mentioned in the background art through the following solution.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a wind farm data remote transmission system, comprising: Data initialization module: Collects multidimensional data and establishes a three-dimensional coordinate system for positioning; Data priority dynamic partitioning module: Calculates priority coefficients based on data types and dynamically sorts the transmission queues in descending order of the coefficients; Adaptive compression strategy control module: Calculates compression adjustment factor based on the multidimensional data, dynamically selects compression algorithm and parameters, and matches channel capacity; Dynamic routing optimization module: Based on the coordinates of transmission nodes and real-time status parameters, a multi-path evaluation model is constructed to achieve dynamic selection of the optimal route; Multi-node transmission synchronization control module: Calculates time slot adjustment coefficients based on concurrent transmission status, dynamically allocates time slots, and adjusts the transmission time difference between adjacent nodes to avoid channel conflicts; Data integrity verification and retransmission module: Calculates the data integrity index and triggers retransmission control based on the value; Transmission security encryption module: Generates dynamic keys based on node identity and real-time parameters to ensure the confidentiality of data transmission.

[0007] Preferably, the multidimensional data includes environmental interference data, equipment status data, and data feature data; the environmental interference data specifically includes: real-time wind speed V at the wind turbine tower collected by a wind speed sensor; electromagnetic interference intensity E at the center frequency of the channel collected by an electromagnetic interference detector; and ambient temperature T at the transmission node collected by a temperature and humidity sensor; the equipment status data specifically includes: signal strength S and signal modulation error rate M collected by a signal analyzer at the receiving end; and the data feature data specifically includes: the byte length L of the data to be transmitted extracted by the data classification module, the generated timestamp t, and the urgency level identifier F, where F=0 / 1, and 1 indicates urgency.

[0008] Preferably, the origin O(0, 0, 0) is taken as the center of the wind farm's booster station, the positive x-axis is along the prevailing wind direction of the wind farm, the y-axis is perpendicular to the x-axis in the horizontal plane, and the z-axis is perpendicular upward from the ground. The coordinates of each transmission node are marked as follows: It also calibrates the coordinate offset every hour using the BeiDou positioning module.

[0009] Preferably, the data type includes emergency control command type. Fault early warning type Real-time running type and historical statistics The priority coefficient ,in This indicates the maximum allowed delay time for the corresponding data type. This represents the correction coefficient for the corresponding data type; the sorting is done in descending order of the P value to ensure that high-priority data occupies the channel first.

[0010] Preferably, the compression adjustment factor ,in Indicates the real-time channel rate. Indicates the channel's rated rate; the dynamic selection compression algorithm and parameters include: when K At 0.8, a lossless compression algorithm is used, with a dictionary size of 64MB, 8 compression levels, and a fixed compression ratio of 1:1.3; at 0.4... When K < 0.8, lossy compression based on wavelet transform is adopted, and the compression ratio is [missing information]. And the data is sharded, with shard size... When K < 0.4, enable data dimensionality reduction and compression. For each class of data, one out of every five sampling points is retained. The data only transmits the mean and peak values, and the compression ratio is dynamically adjusted. .

[0011] Preferably, the multi-path evaluation model calculates a quality index for candidate routes. ,in This represents the average signal strength across all nodes in the routing system. Indicates the average transmission rate. This represents the average modulation error rate. Indicates the total route distance. This indicates the maximum transmission distance of the wind farm; the dynamic selection is as follows: when there is a route with Q≥0.7, the path with the largest Q value is selected; when all routes are 0.3≤Q<0.7, a 2-hop relay path is selected; when Q<0.3, temporary bandwidth expansion is triggered and backup routes are enabled.

[0012] Preferably, the time slot adjustment coefficient ,in Indicates the time slot difference between adjacent nodes; the dynamic allocation of time slots: when A value greater than 0.6 indicates that the time slot difference is too small, resulting in a high risk of conflict. The control center sends an adjustment command to the lagging node in the time slot to adjust the time slot difference. Increase to 0.08-0.1s; when 0.2≤ A value ≤0.6 indicates a moderate time slot difference: maintain the current time slot and only record... Used for periodic optimization; when When the value is less than 0.2, it indicates that the time slot difference is too large and the bandwidth is wasted. Compress the time slot interval of low priority data to 0.03-0.05s to improve channel utilization.

[0013] Preferably, the data integrity index ,in Indicates the number of bytes correctly received. Indicates the total number of bytes sent. Indicates the number of error packets detected. This represents the total number of packets sent. Indicates the time difference of out-of-order data packets. The threshold for allowing out-of-order delivery is indicated; the retransmission control is as follows: when I ≥ 0.98: the data is considered complete and no retransmission is needed; when 0.9 ≤ I < 0.98: only erroneous data packets are retransmitted, and the number of retransmissions is limited. When I < 0.9: Trigger full frame retransmission and synchronously enable redundant coding until I ≥ 0.95.

[0014] Preferably, the dynamic key ,in Represents a hash function. Indicates the current timestamp. This indicates rounding down; the key is updated every 10 seconds, and the receiving end decrypts synchronously using the same algorithm to ensure that unauthorized nodes cannot crack it.

[0015] The technical effects and advantages of this invention are as follows: 1. This invention dynamically selects compression algorithms and parameters by adjusting compression factors to obtain a compression strategy that adapts to real-time channel conditions, solving the problem of insufficient environmental adaptability, improving data transmission efficiency in complex environments, reducing latency control, and achieving the benefits of adapting to the dynamic environmental changes of wind farms. 2. This invention extracts urgency identification feature parameters through a data classification module, combines them with priority coefficients to achieve dynamic sorting of the transmission queue, and selects the optimal transmission path based on the routing quality index, thus obtaining a scheduling mechanism that prioritizes the use of resources for critical data. This solves the problem of rigid resource scheduling and improves the response speed of critical instructions. 3. This invention dynamically allocates transmission time slots for multiple nodes by synchronously adjusting coefficients, achieves accurate retransmission by combining integrity index, and ensures transmission security based on dynamic keys. It obtains a multi-node collaborative transmission mechanism with low collision, high integrity, and strong encryption, which solves the problem of poor multi-node collaboration, reduces the collision probability and packet loss rate when typhoon generators transmit concurrently, improves the data integrity compliance rate, increases the difficulty of key cracking, and reduces the loss caused by downtime due to transmission failure. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] As attached Figure 1The wind farm data remote transmission system shown includes a data initialization module, a data priority dynamic allocation module, an adaptive compression strategy control module, a dynamic routing optimization module, a multi-node transmission synchronization control module, a data integrity verification and retransmission module, and a transmission security encryption module.

[0019] The data initialization module: collects multi-dimensional data and establishes a three-dimensional coordinate system for positioning; In this embodiment, it is specifically noted that: the multidimensional data includes environmental interference data, equipment status data, and data feature data; the environmental interference data specifically includes: real-time wind speed V at the wind turbine tower collected by a wind speed sensor; electromagnetic interference intensity E at the channel center frequency collected by an electromagnetic interference detector; and ambient temperature T at the transmission node collected by a temperature and humidity sensor; the equipment status data specifically includes: signal strength S and signal modulation error rate M collected by a signal analyzer; the data feature data specifically includes: the byte length L of the data to be transmitted extracted by the data classification module, the generated timestamp t, and the urgency level identifier F, where F=0 / 1, 1 indicates urgency; with the center of the wind farm booster station as the origin O(0,0,0), the positive x-axis direction along the prevailing wind direction of the wind farm, the y-axis perpendicular to the x-axis in the horizontal plane, and the z-axis vertically upward from the ground, the coordinates of each transmission node are as follows: The coordinate offset is calibrated hourly using the BeiDou positioning system. Among the environmental parameters, wind speed (V) directly affects the vibration amplitude of the wind turbine tower, leading to transmission antenna displacement; high-frequency sampling is needed to capture instantaneous changes. Electromagnetic interference intensity is related to the rotation frequency of the wind turbine blades, and its intensity directly interferes with the signal-to-noise ratio of the wireless channel; wideband detection is required to cover the main interference frequency bands. Ambient temperature affects the operating efficiency of the transmission equipment and requires accurate data acquisition. Among the equipment status parameters, signal strength and modulation error rate are core indicators for measuring channel quality. Buffer size and write rate reflect the equipment's processing capacity, preventing data loss due to buffer overflow. Transmit power needs to be monitored in real time to balance transmission distance and energy consumption. The three-dimensional coordinate system, with the booster station as the origin, combined with the prevailing wind direction and altitude, conforms to the layout characteristics of wind farms where wind turbines are arranged along the prevailing wind direction and the booster station serves as the data aggregation center, facilitating subsequent route distance calculations and spatial topology analysis. BeiDou positioning calibration can offset coordinate offsets caused by wind turbine foundation settlement, ensuring the long-term validity of spatial parameters.

[0020] The data priority dynamic partitioning module calculates priority coefficients based on data types and dynamically sorts the transmission queues in descending order of the coefficients. In this embodiment, it should be specifically noted that the data type includes emergency control command type. Fault early warning type Real-time running type and historical statistics The priority coefficient ,in This indicates the maximum allowed delay time for the corresponding data type. This represents the correction coefficient for the corresponding data type; the sorting is done in descending order of the P value to ensure that high-priority data occupies the channel first.

[0021] The adaptive compression strategy control module calculates the compression adjustment factor based on the multidimensional data, dynamically selects the compression algorithm and parameters, and matches the channel capacity. In this embodiment, it should be specifically noted that the compression adjustment factor ,in Indicates the real-time channel rate. Indicates the channel's rated rate; the dynamic selection compression algorithm and parameters include: when K At 0.8, a lossless compression algorithm is used, with a dictionary size of 64MB, 8 compression levels, and a fixed compression ratio of 1:1.3; at 0.4... When K < 0.8, lossy compression based on wavelet transform is adopted, and the compression ratio is [missing information]. And the data is sharded, with shard size... When K < 0.4, enable data dimensionality reduction and compression. For each class of data, one out of every five sampling points is retained. The data only transmits the mean and peak values, and the compression ratio is dynamically adjusted. Data compression needs to be dynamically matched with channel conditions: when environmental interference is strong or channel rate is low, strong compression is needed to reduce data volume; when channel conditions are good, data integrity should be prioritized. In the formula for calculating the compression adjustment factor K: [1-(V / 25)²] reflects the higher the wind speed, the stronger the compression requirement; [1-(E / 120)²] reflects the attenuation of channel capacity by electromagnetic interference; directly related to bandwidth adequacy; [1+(S+90) / 30] strengthens the impact of signal strength on compression. The compression strategy is graded according to the cost-benefit principle: when K≥0.8, channel conditions are good, and lossless compression, although computationally expensive, avoids data distortion; when 0.4≤K<0.8, wavelet lossy compression is used to reduce data volume with an accuracy loss of <5%; when K<0.4, dimensionality reduction compression is enabled, ensuring core information transmission by sacrificing non-critical data details.

[0022] The dynamic routing optimization module: Based on the coordinates of the transmission nodes and real-time status parameters, it constructs a multi-path evaluation model to achieve dynamic selection of the optimal route; In this embodiment, it should be specifically noted that the multi-path evaluation model calculates a quality index for candidate routes. ,in This represents the average signal strength across all nodes in the routing system. Indicates the average transmission rate. This represents the average modulation error rate. Indicates the total route distance. This indicates the maximum transmission distance of the wind farm; the dynamic selection is as follows: when there is a route with Q≥0.7, the path with the largest Q value is selected; when all routes are 0.3≤Q<0.7, a 2-hop relay path is selected; when Q<0.3, temporary bandwidth expansion is triggered and backup routes are enabled.

[0023] The multi-node transmission synchronization control module calculates the time slot adjustment coefficient based on the concurrent transmission status, dynamically allocates time slots, and adjusts the transmission time difference between adjacent nodes to avoid channel conflicts. In this embodiment, it should be specifically noted that the time slot adjustment coefficient ,in Indicates the time slot difference between adjacent nodes; the dynamic allocation of time slots: when A value greater than 0.6 indicates that the time slot difference is too small, resulting in a high risk of conflict. The control center sends an adjustment command to the lagging node in the time slot to adjust the time slot difference. Increase to 0.08-0.1s; when 0.2≤ A value ≤0.6 indicates a moderate time slot difference: maintain the current time slot and only record... Used for periodic optimization; when When the value is less than 0.2, it indicates that the time slot difference is too large and the bandwidth is wasted. Compress the time slot interval of low priority data to 0.03-0.05s to improve channel utilization.

[0024] The data integrity verification and retransmission module calculates the data integrity index and triggers retransmission control based on the value. In this embodiment, it is specifically necessary to explain that the data integrity index ,in Indicates the number of bytes correctly received. Indicates the total number of bytes sent. Indicates the number of error packets detected. This represents the total number of packets sent. Indicates the time difference of out-of-order data packets. The threshold for allowing out-of-order delivery is indicated; the retransmission control is as follows: when I ≥ 0.98: the data is considered complete and no retransmission is needed; when 0.9 ≤ I < 0.98: only erroneous data packets are retransmitted, and the number of retransmissions is limited. When I < 0.9: Trigger full frame retransmission and synchronously enable redundant coding until I ≥ 0.95.

[0025] The transmission security encryption module generates a dynamic key based on node identity and real-time parameters to ensure the confidentiality of data transmission.

[0026] In this embodiment, it is specifically necessary to explain that the dynamic key ,in Represents a hash function. Indicates the current timestamp. This indicates rounding down; the key is updated every 10 seconds, and the receiving end decrypts synchronously using the same algorithm to ensure that unauthorized nodes cannot crack it.

[0027] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind farm data remote transmission system, characterized in that, include: Data initialization module: Collects multidimensional data and establishes a three-dimensional coordinate system for positioning; Data priority dynamic partitioning module: Calculates priority coefficients based on data types and dynamically sorts the transmission queues in descending order of the coefficients; Adaptive compression strategy control module: Calculates compression adjustment factor based on the multidimensional data, dynamically selects compression algorithm and parameters, and matches channel capacity; Dynamic routing optimization module: Based on the coordinates of transmission nodes and real-time status parameters, a multi-path evaluation model is constructed to achieve dynamic selection of the optimal route; Multi-node transmission synchronization control module: Calculates time slot adjustment coefficients based on concurrent transmission status, dynamically allocates time slots, and adjusts the transmission time difference between adjacent nodes to avoid channel conflicts; Data integrity verification and retransmission module: Calculates the data integrity index and triggers retransmission control based on the value; Transmission security encryption module: Generates dynamic keys based on node identity and real-time parameters to ensure the confidentiality of data transmission.

2. The wind farm data remote transmission system according to claim 1, characterized in that: The multidimensional data includes environmental interference data, equipment status data, and data feature data; The environmental interference data specifically includes: real-time wind speed V at the wind turbine tower collected by a wind speed sensor; electromagnetic interference intensity E at the center frequency of the channel collected by an electromagnetic interference detector; and ambient temperature T at the transmission node collected by a temperature and humidity sensor. The equipment status data specifically includes: signal strength S and signal modulation error rate M collected by a signal analyzer at the receiving end. The data feature data specifically includes: the byte length L of the data to be transmitted extracted by the data classification module, the generated timestamp t, and the urgency level identifier F, where F=0 / 1, and 1 indicates urgency.

3. The wind farm data remote transmission system according to claim 1, characterized in that: The three-dimensional coordinate system is defined as follows: The origin O(0, 0, 0) is the center of the wind farm's booster station; the positive x-axis is along the prevailing wind direction; the y-axis is perpendicular to the x-axis in the horizontal plane; and the z-axis is perpendicular to the ground. The coordinates of each transmission node are marked as follows: It also calibrates the coordinate offset every hour using the BeiDou positioning system.

4. The wind farm data remote transmission system according to claim 1, characterized in that: The data types include emergency control command type. Fault early warning type Real-time running type and historical statistics The priority coefficient ,in This indicates the maximum allowed delay time for the corresponding data type. This represents the correction coefficient for the corresponding data type; the sorting is done in descending order of the P value to ensure that high-priority data occupies the channel first.

5. A wind farm data remote transmission system according to claim 2, characterized in that: The compression adjustment factor ,in Indicates the real-time channel rate. Indicates the channel's rated rate; The dynamic selection compression algorithm and parameters include: when K At 0.8, a lossless compression algorithm is used, with a dictionary size of 64MB, 8 compression levels, and a fixed compression ratio of 1:1.3; at 0.4... When K < 0.8, lossy compression based on wavelet transform is adopted, and the compression ratio is [missing information]. And the data is sharded, with shard size... When K < 0.4, enable data dimensionality reduction and compression. For each class of data, one out of every five sampling points is retained. The data only transmits the mean and peak values, and the compression ratio is dynamically adjusted. .

6. The wind farm data remote transmission system according to claim 1, characterized in that: The multi-path evaluation model calculates a quality index for candidate routes. ,in This represents the average signal strength across all nodes in the routing system. Indicates the average transmission rate. This represents the average modulation error rate. Indicates the total route distance. This indicates the maximum transmission distance of the wind farm; the dynamic selection is as follows: when there is a route with Q≥0.7, the path with the largest Q value is selected; when all routes are 0.3≤Q<0.7, a 2-hop relay path is selected; when Q<0.3, temporary bandwidth expansion is triggered and backup routes are enabled.

7. A wind farm data remote transmission system according to claim 1, characterized in that: The time slot adjustment coefficient ,in Indicates the time slot difference between adjacent nodes; the dynamic allocation of time slots: when A value greater than 0.6 indicates that the time slot difference is too small, resulting in a high risk of conflict. The control center sends an adjustment command to the lagging node in the time slot to adjust the time slot difference. Increased to 0.08-0.1s; When 0.2≤ A value ≤0.6 indicates a moderate time slot difference: maintain the current time slot and only record... Used for periodic optimization; when When the value is less than 0.2, it indicates that the time slot difference is too large and the bandwidth is wasted. Compress the time slot interval of low priority data to 0.03-0.05s to improve channel utilization.

8. A wind farm data remote transmission system according to claim 1, characterized in that: The data integrity index ,in Indicates the number of bytes correctly received. Indicates the total number of bytes sent. Indicates the number of error packets detected. This represents the total number of packets sent. Indicates the time difference of out-of-order data packets. The threshold for allowing out-of-order delivery is indicated; the retransmission control is as follows: when I ≥ 0.98: the data is considered complete and no retransmission is needed; when 0.9 ≤ I < 0.98: only erroneous data packets are retransmitted, and the number of retransmissions is limited. When I < 0.9: Trigger full frame retransmission and synchronously enable redundant coding until I ≥ 0.

95.

9. A wind farm data remote transmission system according to claim 1, characterized in that: The dynamic key ,in Represents a hash function. Indicates the current timestamp. This indicates rounding down; the key is updated every 10 seconds, and the receiving end decrypts synchronously using the same algorithm to ensure that unauthorized nodes cannot crack it.

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