New energy cluster weak network adaptive data acquisition method, device, equipment and medium

Abstract

The application provides a new energy cluster weak network adaptive data acquisition method, device, equipment and medium, comprising: real-time monitoring network quality parameters through a dual-mode gateway deployed at a new energy station side, and dividing network states according to preset threshold values; dynamically adjusting data acquisition frequency based on the current network state; enabling corresponding data storage modes according to the network state, adopting a dual mode of real-time transmission and local backup in network fluctuation or weak network, switching to full local cache when the network is disconnected, and executing a preset local device control logic; after the network is restored, according to the received data time stamp fed back by the centralized control center, the cached data is transmitted to the centralized control center according to the priority for data consistency verification, and after the verification is passed, the dual-mode gateway is notified to clean up the confirmed cached data, so as to solve the problems of reliable acquisition of new energy cluster data in a complex network environment, safe operation of equipment, and ensuring data complete synchronization after the network is restored.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation monitoring technology, and in particular to a method, device, equipment and medium for adaptive data acquisition in weak network conditions of new energy power clusters. Background Technology

[0002] New energy power generation is characterized by strong volatility and dispersed distribution. Currently, centralized control centers are commonly used to remotely monitor wind power, photovoltaic, and energy storage stations across different regions. Existing new energy cluster data acquisition systems typically employ a combination of fixed-frequency sampling and real-time uploading. That is, each station's equipment collects operating parameters at preset fixed time intervals and transmits them to the centralized control center in real time via the network to achieve remote monitoring and control of the equipment's operating status.

[0003] However, most new energy power stations are located in remote areas with weak network infrastructure, generally suffering from unstable signals, limited bandwidth, and frequent outages. In this network environment, the fixed-frequency sampling + real-time upload mode exposes the following technical defects: First, network fluctuations lead to a high data packet loss rate, preventing the control center from obtaining complete equipment operation data and affecting accurate assessment of equipment status; second, the fixed sampling frequency does not consider network capacity, maintaining high-frequency transmission even in weak networks, further exacerbating network congestion and data loss; third, during network outages, the equipment loses connection with the control center, resulting in direct data loss, and the equipment lacks local autonomous operation protection logic, posing security risks; fourth, after network recovery, there is a lack of intelligent retransmission mechanisms, making it impossible to fully synchronize historical data, affecting the accuracy of the control center's scheduling decisions.

[0004] Therefore, there is an urgent need for an adaptive data acquisition method for new energy clusters in weak network environments to solve the technical problems of reliable data acquisition, safe operation of equipment, and complete data synchronization after network recovery in complex network environments such as weak networks and network outages. Summary of the Invention

[0005] To overcome the problems existing in related technologies, this disclosure provides a method, device, equipment and medium for adaptive data acquisition of new energy clusters in weak network conditions, so as to solve the technical problems in related technologies of achieving reliable data acquisition of new energy clusters, ensuring safe operation of equipment and ensuring complete data synchronization after network recovery in complex network environments such as weak network and network outage.

[0006] This specification provides one or more embodiments of a new energy cluster weak network adaptive data acquisition method, including the following steps: The dual-mode gateway deployed on the side of the new energy power station monitors network quality parameters in real time and classifies the network status into stable, fluctuating, weak network and outage according to preset thresholds. The dual-mode gateway dynamically adjusts the data collection frequency based on the current network status, wherein the worse the network status, the lower the sampling frequency; The dual-mode gateway enables the corresponding data storage mode according to the network status. Specifically, it adopts a dual mode of real-time transmission and local backup when the network fluctuates or is weak, and switches to full local caching when the network is disconnected, and executes preset local device control logic. After the network is restored, the dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamp of the received data fed back by the central control center, it retransmits the cached data to the central control center according to priority, performs data consistency verification, and notifies the dual-mode gateway to clear the confirmed cached data after the verification passes.

[0007] Preferably, the step of monitoring network quality parameters in real time through a dual-mode gateway deployed on the new energy power station side, and classifying the network status into stable, fluctuating, weak, and disconnected states according to preset thresholds, specifically includes the following steps: The dual-mode gateway collects network signal strength, bandwidth, latency, and packet loss rate in real time. The network status is classified according to a preset packet loss rate threshold, and the network status is periodically fed back to the central control center; The connectivity with the central control center is monitored via a heartbeat connection. If no heartbeat response is received for three consecutive times, the network is considered to be disconnected.

[0008] Preferably, the dual-mode gateway dynamically adjusts the data collection frequency based on the current network status, specifically including the following steps: An AI model is constructed that correlates the device operating status with the network status. The AI ​​model takes the real-time operating parameters of the new energy device, the current network status, and the device type as inputs, and outputs the data sampling frequency for different device types. The AI ​​model continuously learns the operating patterns of new energy equipment and network fluctuation characteristics based on historical data, dynamically optimizes the threshold of sampling frequency, and updates model parameters regularly.

[0009] Preferably, the worse the network condition, the lower the sampling frequency, specifically including the following steps: Key parameters should have their sampling frequency reduced under weak network conditions; In weak network conditions, the sampling frequency can be further reduced or sampling can be paused.

[0010] Preferably, the step of switching to full local caching and executing preset local device control logic when the network is disconnected specifically includes the following steps: The local cache supports 72 hours of full data storage, and the cached data is stored in a structured manner according to timestamp, parameter type, and device number. The preset local device control logic automatically triggers safety protection actions based on the device type and real-time operating parameters. These safety protection actions include automatic load reduction when the wind turbine exceeds its speed, triggering heat dissipation control when the photovoltaic inverter overheats, and maintaining the energy storage system at the SOC safety threshold.

[0011] Preferably, the step of retransmitting cached data to the central control center according to priority, performing data consistency verification, and notifying the dual-mode gateway to clear the confirmed cached data after the verification passes specifically includes the following steps: Prioritizing urgent data and deferred to ordinary data, cached data is retransmitted using a segmented transmission method, and the upload bandwidth is dynamically controlled to not exceed a preset proportion of the current network bandwidth. The urgent data includes fault alarm data and data exceeding the security threshold. The central control center identifies abnormal data retransmissions by comparing timestamps and verifying data correlation, and triggers secondary retransmissions for the identified abnormal data. After the retransmission is completed, the dual-mode gateway automatically clears the cached data that has been confirmed to be received and retains the data from the most recent 24 hours as a backup.

[0012] Preferably, the method further includes the following steps: Different levels of alarm notifications are triggered based on network status, and the results of sampling strategy adjustments, cache usage, and retransmission progress are fed back to the central control center in real time.

[0013] This specification provides one or more embodiments of a new energy cluster weak network adaptive data acquisition device, including: The network monitoring module is used to monitor network quality parameters in real time through a dual-mode gateway deployed on the side of the new energy power station, and classify the network status according to a preset threshold. The network status includes stable, fluctuating, weak network, and network outage. The sampling frequency optimization module is used to dynamically adjust the data sampling frequency based on the current network status. The worse the network status, the lower the sampling frequency. The hierarchical caching module is used by the dual-mode gateway to enable the corresponding data storage mode according to the network status. Specifically, it adopts a dual mode of real-time transmission and local backup when the network fluctuates or is weak, and switches to full local caching when the network is disconnected, and executes preset local device control logic. The intelligent retransmission module is used to retransmit cached data to the central control center according to the timestamp of the received data fed back by the central control center after the network is restored, and to perform data consistency verification. After the verification is passed, the dual-mode gateway is notified to clear the confirmed cached data.

[0014] This specification provides one or more embodiments of a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described adaptive data acquisition method for weak network conditions in new energy clusters.

[0015] This specification provides one or more embodiments of a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described adaptive data acquisition method for weak network conditions in new energy clusters.

[0016] This disclosure provides a method, device, equipment, and medium for adaptive data acquisition in weak network conditions for new energy clusters. Its advantages lie in that it uses a dual-mode gateway deployed at the new energy power station to monitor network quality parameters in real time and classifies the network status into stable, fluctuating, weak, and disconnected states based on preset thresholds. This achieves precise quantitative assessment of the communication environment, providing real-time and reliable decision-making basis for subsequent adaptive strategy adjustments and avoiding resource waste caused by blind data acquisition and transmission in traditional solutions. The dual-mode gateway dynamically adjusts the data acquisition frequency based on the current network status; the worse the network status, the lower the sampling frequency, reducing the amount of data generated under weak network conditions from the source, alleviating transmission pressure, and ensuring the relative integrity of core operational data. The dual-mode gateway adjusts the data acquisition frequency based on the network status. The network status enables the corresponding data storage mode. During network fluctuations or weak networks, a dual-mode approach of real-time transmission and local backup is adopted. In the event of a network outage, it switches to full local caching and executes preset local device control logic to ensure no data loss and no device loss of control during the outage, laying the foundation for subsequent retransmission and data security. After the network recovers, the dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamps of the received data returned by the central control center, it retransmits the cached data to the central control center according to priority and performs data consistency verification. After successful verification, it notifies the dual-mode gateway to clear the confirmed cached data, achieving complete synchronization of historical data, avoiding the impact of retransmission on real-time transmission, and ensuring the data reliability of the central control center's scheduling decisions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a new energy cluster weak network adaptive data acquisition method provided in one or more embodiments of this specification; Figure 2 A schematic diagram of the structure of a new energy cluster weak network adaptive data acquisition device provided for one or more embodiments of this specification; Figure 3 This is a schematic diagram of the structure of a computer device provided for one or more embodiments of this specification. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this invention.

[0020] The present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0021] Method Implementation Examples According to embodiments of the present invention, a method for adaptive data acquisition in weak network conditions of new energy clusters is provided, such as... Figure 1 The diagram shown is a flowchart illustrating the adaptive data acquisition method for weak network conditions in a new energy cluster provided in this embodiment. The adaptive data acquisition method for weak network conditions in a new energy cluster according to this embodiment includes the following steps: S110 monitors network quality parameters in real time through a 5G / NB-IoT dual-mode gateway deployed on the side of new energy power stations, and classifies the network status into stable, fluctuating, weak network and outage according to preset thresholds.

[0022] The S120 dual-mode gateway dynamically adjusts the data collection frequency based on the current network status. The worse the network status, the lower the sampling frequency.

[0023] The S130 dual-mode gateway enables the corresponding data storage mode according to the network status. In the event of network fluctuations or weak network conditions, it adopts a dual mode of real-time transmission and local backup. When the network is disconnected, it switches to full local caching and executes the preset local device control logic.

[0024] S140. After the network is restored, the dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamp of the received data fed back by the central control center, the cached data is retransmitted to the central control center according to priority, and data consistency is verified. After the verification is passed, the dual-mode gateway is notified to clear the confirmed cached data.

[0025] The method provided in this embodiment monitors network quality parameters in real time through a dual-mode gateway deployed on the new energy power station side. Based on preset thresholds, it categorizes network status into stable, fluctuating, weak, and out-of-network states, achieving precise quantitative assessment of the communication environment. This provides real-time and reliable decision-making basis for subsequent adaptive strategy adjustments, avoiding resource waste caused by blind data collection and transmission in traditional solutions. The dual-mode gateway dynamically adjusts the data collection frequency based on the current network status; the worse the network status, the lower the sampling frequency, reducing data generation under weak network conditions from the source, alleviating transmission pressure, and ensuring the relative integrity of core operational data. The dual-mode gateway activates the corresponding data storage mode according to the network status. In the event of network fluctuations or weak networks, a dual-mode system of real-time transmission and local backup is employed. During network outages, the system switches to full local caching and executes preset local device control logic to ensure that data is not lost and devices remain under control during outages, laying the foundation for subsequent retransmission and data security. After network recovery, the dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamps of the received data returned by the central control center, the cached data is retransmitted to the central control center according to priority, and data consistency is verified. After the verification passes, the dual-mode gateway is notified to clear the confirmed cached data, achieving complete synchronization of historical data, avoiding the impact of retransmission on real-time transmission, and ensuring the data reliability of the central control center's scheduling decisions.

[0026] In one embodiment, a dual-mode gateway deployed at the new energy power station monitors network quality parameters in real time and classifies the network status into stable, fluctuating, weak, and disconnected states based on preset thresholds. This includes the following steps: The dual-mode gateway collects four core parameters in real time: network signal strength, bandwidth, latency, and packet loss rate.

[0027] The network status is classified according to the preset packet loss rate threshold. Among them, a packet loss rate of <5% is considered stable, 5%-20% is considered fluctuating, 20%-50% is considered weak, and 100% is considered network outage. The evaluation results of the network status are fed back to the central control center periodically, such as every 30 seconds.

[0028] The dual-mode gateway monitors connectivity with the central control center via a heartbeat connection. The heartbeat interval is 30 seconds, and if no heartbeat response is received for three consecutive times, it is determined that the network is disconnected.

[0029] The method provided in this embodiment collects multi-dimensional parameters such as network signal strength, bandwidth, latency, and packet loss rate in real time through a dual-mode gateway. Based on preset thresholds, it classifies the network status into four levels: stable, fluctuating, weak, and out of service, and periodically feeds this information back to the central control center. Simultaneously, it accurately determines the out-of-service status through heartbeat monitoring. This achieves refined perception and quantitative assessment of the communication environment, providing a real-time and reliable decision-making basis for subsequent adaptive sampling, storage, and retransmission strategies. It fundamentally avoids the resource waste and data loss caused by blind sampling and transmission in traditional solutions.

[0030] In one embodiment, the dual-mode gateway dynamically adjusts the data collection frequency based on the current network status, specifically including the following steps: An AI model is constructed to correlate equipment operating status with network status, enabling intelligent control of data sampling strategies for new energy equipment. The AI ​​model uses real-time operating parameters of the new energy equipment, current network status, and equipment type as multi-dimensional input features. Real-time operating parameters include, but are not limited to, key status variables such as voltage, current, power, temperature, speed, fault codes, and operating time. Network status includes communication quality indicators such as network signal strength, latency, packet loss rate, bandwidth usage, and connection stability. Equipment type is used to distinguish terminal equipment from different new energy power plants, functional nodes, and data collection priorities, including but not limited to wind turbines, photovoltaic inverters, and energy storage converters. Based on the above input information, the model infers and outputs the optimal data sampling frequency adapted to the current scenario for different equipment types. It dynamically allocates differentiated collection cycles and data upload strategies for different equipment types, operating conditions, and network environments, reducing invalid data transmission and resource consumption while ensuring observable and traceable operating status. Specifically, the worse the network condition, the lower the sampling frequency. When the network is stable, key parameters (such as power and fault alarm signals) are sampled every 1 second, and ordinary parameters (such as ambient temperature and humidity) are sampled every 5 seconds. When the network fluctuates, key parameters are sampled every 1 second, and ordinary parameters are sampled every 10 seconds. When the network is weak, key parameters are sampled every 2 seconds, and ordinary parameters are sampled every 20 seconds. Unnecessary redundant data sampling is suspended.

[0031] The AI ​​model continuously learns the operating patterns of new energy equipment and network fluctuation characteristics based on historical data, dynamically optimizes the threshold of sampling frequency, and updates model parameters regularly to improve strategy adaptability.

[0032] The method provided in this embodiment integrates device operating status and network status using an AI model to dynamically output differentiated sampling frequencies and continuously learns and optimizes based on historical data. Compared with traditional fixed-frequency sampling, it achieves intelligent adaptation of sampling strategies to the network environment, significantly reducing the generation of invalid data in weak network conditions while ensuring the integrity of data from critical devices, alleviating transmission pressure from the source, and improving overall acquisition efficiency.

[0033] In one embodiment, when the network is disconnected, switching to full local caching and executing preset local device control logic specifically includes the following steps: The dual-mode gateway has a built-in SSD local cache with a capacity that supports 72 hours of full data storage, including key parameters, general parameters, and device status information. The cached data is stored in a structured manner by timestamp, parameter type, and device number, which facilitates quick retrieval and retransmission later.

[0034] The preset local device control logic automatically triggers safety protection actions based on the device type and real-time operating parameters. These safety protection actions include automatic load reduction when the wind turbine exceeds its speed, triggering heat dissipation control when the photovoltaic inverter overheats, and maintaining the energy storage system at the SOC safety threshold to avoid safety hazards caused by unattended equipment.

[0035] The method provided in this embodiment achieves dual protection of zero data loss and safe operation of equipment during network outages by combining local caching and autonomous control: 72-hour full caching ensures complete retention of historical data, and structured storage facilitates rapid retrieval and retransmission later; at the same time, preset control logic for different equipment such as wind turbines, photovoltaics, and energy storage automatically triggers safety protection actions when the network is down, preventing the equipment from being out of control due to loss of connection, and significantly improving the safety of on-site operation.

[0036] In one embodiment, cached data is retransmitted to the central control center according to priority, and data consistency verification is performed. After the verification passes, the dual-mode gateway is notified to clear the confirmed cached data. Specifically, the following steps are included: Prioritizing urgent data and deferred to ordinary data, cached data is retransmitted in segments, and the upload bandwidth is dynamically controlled to not exceed a preset proportion of the current network bandwidth, not exceeding 60% of the current network bandwidth. Urgent data includes fault alarm data and data exceeding the safety threshold, in order to avoid congestion again.

[0037] After receiving the supplementary data, the central control center identifies abnormal supplementary data by comparing timestamps and verifying data correlation, such as matching power change trends and judging the rationality of temperature thresholds. It then triggers a second supplementary transmission for the identified abnormal data to ensure data integrity and accuracy.

[0038] After the retransmission is completed, the dual-mode gateway automatically clears the cached data that has been confirmed to be received and retains the data from the most recent 24 hours as a backup.

[0039] The method provided in this embodiment achieves efficient and accurate synchronization of historical data after network recovery through intelligent retransmission and consistency verification mechanisms: priority scheduling of urgent data ensures timely delivery of critical information, and fragmented transmission and bandwidth control avoid secondary congestion; dual verification of timestamp comparison and correlation verification ensures data integrity and accuracy, and abnormal data triggers secondary retransmission; after retransmission is completed, confirmed data is automatically cleaned up and the most recent backup is retained to achieve cyclical utilization of storage space.

[0040] In one embodiment, the following steps are also included: Different levels of alarm notifications are triggered based on network status, and the results of sampling strategy adjustments, cache usage, and retransmission progress are fed back to the central control center in real time, enabling the central control center to monitor the data acquisition status in real time. Specifically, the central control center system will issue a pop-up alarm during weak network conditions, send an SMS notification to maintenance personnel within 1 hour of network outage, and issue a telephone reminder and push a summary of the local operating status of the equipment during the outage period if the network outage exceeds 4 hours.

[0041] The method provided in this embodiment enables timely early warning and differentiated handling of network anomalies through hierarchical alarms. At the same time, it feeds back the sampling strategy, cache status and retransmission progress to the central control center in real time, enhancing the transparent monitoring of the on-site data collection status and improving the efficiency of operation and maintenance response and the controllability of the system.

[0042] Device Examples According to embodiments of the present invention, a new energy cluster weak network adaptive data acquisition device is provided, such as... Figure 2 The diagram shown is a structural schematic of the adaptive data acquisition device for weak network conditions in a new energy cluster provided in this embodiment. The adaptive data acquisition device for weak network conditions in a new energy cluster according to this embodiment includes: The network monitoring module 21 is used to monitor network quality parameters in real time through a dual-mode gateway deployed on the side of the new energy power station, and classify the network status according to preset thresholds. The network status includes stable, fluctuating, weak network and network outage.

[0043] The sampling frequency optimization module 22 is used to dynamically adjust the data sampling frequency based on the current network status. The worse the network status, the lower the sampling frequency.

[0044] The hierarchical caching module 23 is used by the dual-mode gateway to enable the corresponding data storage mode according to the network status. In the case of network fluctuations or weak network, it adopts a dual mode of real-time transmission and local backup. When the network is disconnected, it switches to full local caching and executes the preset local device control logic.

[0045] The intelligent retransmission module 24 is used to retransmit the cached data to the central control center according to the timestamp of the received data fed back by the central control center after the network is restored, and to perform data consistency verification. After the verification is passed, the dual-mode gateway is notified to clear the confirmed cached data.

[0046] The device provided in this embodiment uses a dual-mode gateway deployed on the side of the new energy power station via a network monitoring module 21 to monitor network quality parameters in real time. Based on preset thresholds, it categorizes network status into stable, fluctuating, weak, and disconnected states, achieving precise quantitative assessment of the communication environment. This provides real-time and reliable decision-making basis for subsequent adaptive strategy adjustments, avoiding resource waste caused by blind data collection and transmission in traditional solutions. The data collection frequency optimization module 22 dynamically adjusts the data collection frequency of the dual-mode gateway based on the current network status. The worse the network status, the lower the sampling frequency, reducing data generation under weak network conditions from the source, alleviating transmission pressure, and ensuring the relative integrity of core operational data. The hierarchical caching module 23 is used by the dual-mode gateway to activate caches according to network status. The system employs a corresponding data storage mode, utilizing a dual-mode approach of real-time transmission and local backup during network fluctuations or weak network conditions. In the event of a network outage, it switches to full local caching and executes preset local device control logic to ensure no data loss or device malfunction during the outage, laying the foundation for subsequent data retransmission and data security. The intelligent retransmission module 24 is used after network recovery. The dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamps of the received data returned by the central control center, it retransmits the cached data to the central control center according to priority, performs data consistency verification, and notifies the dual-mode gateway to clear the confirmed cached data after successful verification. This achieves complete synchronization of historical data, preventing retransmission from affecting real-time transmission and ensuring the data reliability of the central control center's scheduling decisions.

[0047] The embodiments of the present invention are device embodiments corresponding to the above method embodiments. The specific operations of each module processing step can be understood with reference to the description of the method embodiments, and will not be repeated here.

[0048] like Figure 3 As shown, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the adaptive data acquisition method for weak network of new energy clusters in the above embodiments, or when the computer program is executed by a processor, it implements the adaptive data acquisition method for weak network of new energy clusters in the above embodiments.

[0049] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0050] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention are known to those skilled in the art.

Claims

1. A method for adaptive data acquisition in weak network conditions for new energy clusters, characterized in that, Includes the following steps: The dual-mode gateway deployed on the side of the new energy power station monitors network quality parameters in real time and classifies the network status into stable, fluctuating, weak network and outage according to preset thresholds. The dual-mode gateway dynamically adjusts the data collection frequency based on the current network status, wherein the worse the network status, the lower the sampling frequency; The dual-mode gateway enables the corresponding data storage mode according to the network status. Specifically, it adopts a dual mode of real-time transmission and local backup when the network fluctuates or is weak, and switches to full local caching when the network is disconnected, and executes preset local device control logic. After the network is restored, the dual-mode gateway initiates a retransmission request to the central control center. Based on the timestamp of the received data fed back by the central control center, it retransmits the cached data to the central control center according to priority, performs data consistency verification, and notifies the dual-mode gateway to clear the confirmed cached data after the verification passes.

2. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, The process involves real-time monitoring of network quality parameters using a dual-mode gateway deployed at the new energy power station, and classifying the network status into stable, fluctuating, weak, and disconnected states based on preset thresholds. This includes the following steps: The dual-mode gateway collects network signal strength, bandwidth, latency, and packet loss rate in real time. The network status is classified according to a preset packet loss rate threshold, and the network status is periodically fed back to the central control center; The connectivity with the central control center is monitored via a heartbeat connection. If no heartbeat response is received for three consecutive times, the network is considered to be disconnected.

3. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, The dual-mode gateway dynamically adjusts the data collection frequency based on the current network status, specifically including the following steps: An AI model is constructed that correlates the device operating status with the network status. The AI ​​model takes the real-time operating parameters of the new energy device, the current network status, and the device type as inputs, and outputs the data sampling frequency for different device types. The AI ​​model continuously learns the operating patterns of new energy equipment and network fluctuation characteristics based on historical data, dynamically optimizes the threshold of sampling frequency, and updates model parameters regularly.

4. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, The worse the network condition, the lower the sampling frequency, specifically including the following steps: Key parameters should have their sampling frequency reduced under weak network conditions; In weak network conditions, the sampling frequency can be further reduced or sampling can be paused.

5. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, The step of switching to full local caching and executing preset local device control logic when the network is disconnected specifically includes the following steps: The local cache supports 72 hours of full data storage, and the cached data is stored in a structured manner according to timestamp, parameter type, and device number. The preset local device control logic automatically triggers safety protection actions based on the device type and real-time operating parameters. These safety protection actions include automatic load reduction when the wind turbine exceeds its speed, triggering heat dissipation control when the photovoltaic inverter overheats, and maintaining the energy storage system at the SOC safety threshold.

6. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, The process of retransmitting cached data to the central control center according to priority, performing data consistency verification, and notifying the dual-mode gateway to clear the confirmed cached data after the verification passes includes the following steps: Prioritizing urgent data and deferred to ordinary data, cached data is retransmitted using a segmented transmission method, and the upload bandwidth is dynamically controlled to not exceed a preset proportion of the current network bandwidth. The urgent data includes fault alarm data and data exceeding the security threshold. The central control center identifies abnormal data retransmissions by comparing timestamps and verifying data correlation, and triggers secondary retransmissions for the identified abnormal data. After the retransmission is completed, the dual-mode gateway automatically clears the cached data that has been confirmed to be received and retains the data from the most recent 24 hours as a backup.

7. The adaptive data acquisition method for weak network conditions in new energy clusters as described in claim 1, characterized in that, It also includes the following steps: Different levels of alarm notifications are triggered based on network status, and the results of sampling strategy adjustments, cache usage, and retransmission progress are fed back to the central control center in real time.

8. A new energy cluster weak network adaptive data acquisition device, characterized in that, include: The network monitoring module is used to monitor network quality parameters in real time through a dual-mode gateway deployed on the side of the new energy power station, and classify the network status according to a preset threshold. The network status includes stable, fluctuating, weak network, and network outage. The sampling frequency optimization module is used to dynamically adjust the data sampling frequency based on the current network status. The worse the network status, the lower the sampling frequency. The hierarchical caching module is used by the dual-mode gateway to enable the corresponding data storage mode according to the network status. Specifically, it adopts a dual mode of real-time transmission and local backup when the network fluctuates or is weak, and switches to full local caching when the network is disconnected, and executes preset local device control logic. The intelligent retransmission module is used to retransmit cached data to the central control center according to the timestamp of the received data fed back by the central control center after the network is restored, and to perform data consistency verification. After the verification is passed, the dual-mode gateway is notified to clear the confirmed cached data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the adaptive data acquisition method for weak network of new energy clusters as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive data acquisition method for weak network of new energy clusters as described in any one of claims 1 to 7.

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