Cloud edge cooperative control method for power system, gateway, platform and medium

Abstract

The invention provides a power system-oriented cloud-side cooperative control method, a gateway, a platform and a medium, and aims to realize a cloud-side cooperative security optimization control strategy method and solve the problem that time marks of data acquired by various edge devices are inconsistent. The edge gateway is applied to the cloud edge cooperative control system, and the system further comprises a cloud platform and an edge device. The method comprises the following steps: when real-time equipment data of edge equipment is received, adding a gateway time mark corresponding to an edge gateway into the real-time equipment data; sending the real-time equipment data to a cloud platform, and receiving a control strategy generated by the cloud platform according to the real-time data; the control strategy at least comprises one or more of energy storage charging and discharging information, power generation information and power grid flow distribution information; and performing security and validity verification on the control strategy, and if the control strategy passes the security and validity verification, sending the control strategy to the edge device to complete cooperative control of the edge device.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a cloud-edge collaborative control method, device, gateway, and medium for power systems. Background Art

[0002] In power systems, cloud platforms and edge devices exchange data and commands via wireless communication. Due to the latency inherent in wireless communication, when the cloud platform issues control commands, the commands calculated and issued just a moment ago may no longer be applicable to the current device state, reducing the overall security of the cloud-edge collaborative system. For source-grid-load-storage systems, close coordination between all components is required to ensure efficient energy utilization and safe system operation. If there is significant latency in communication between the cloud platform and edge devices, the system may not respond promptly to load changes, making it impossible to adjust the power generation of the power source, the charging and discharging power of the energy storage device, and the power flow distribution of the power grid, thus compromising the security of the entire system.

[0003] Furthermore, inconsistent time scales for data collected by edge devices can complicate data analysis and decision-making for cloud platforms. Cloud platforms must analyze data collected by different devices based on accurate chronological order to understand the dynamic operation of the system. While this issue can be alleviated by establishing unified time scale standards and building effective processing mechanisms, cloud-edge collaborative control presents even more complex challenges. Different types of edge devices have varying capabilities and functions. The key challenge is how to integrate security controls to enable the cloud platform and edge devices to collaboratively complete complex computations and avoid potential security risks. Summary of the Invention

[0004] This application provides a cloud-edge collaborative control method, gateway, platform and medium for power systems, aiming to implement a cloud-edge collaborative security optimization control strategy method, while solving the problem of large communication delays between the cloud platform and edge devices, which may cause the system to respond untimely to load changes and be unable to adjust the power generation power of the power supply, the charging and discharging power of the energy storage equipment, and the power flow distribution of the power grid in a timely manner, thereby affecting the security of the entire system.

[0005] In a first aspect, the present application provides a cloud-edge collaborative control method for a power system, which is applied to an edge gateway of a cloud-edge collaborative control system. The cloud-edge collaborative control system includes a cloud platform and an edge device. The cloud platform is wirelessly connected to the edge gateway, and the edge gateway is wired to the edge device. The method includes:

[0006] When receiving the real-time device data of the edge device, adding the gateway timestamp corresponding to the edge gateway to the real-time device data;

[0007] Sending the real-time device data to the cloud platform and receiving a control strategy generated by the cloud platform based on the real-time data; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and grid flow distribution information;

[0008] The preliminary control information is subjected to security and validity checks. If the control strategy passes the security and validity checks, the control strategy is sent to the edge device to complete the collaborative control of the edge device.

[0009] This approach forms an efficient cloud-edge collaborative architecture by wirelessly connecting the cloud platform to the edge gateway and wiredly connecting the edge gateway to the edge device. This approach rapidly transmits real-time device data received from the edge device to the cloud platform, generates control strategies on the cloud platform, and promptly transmits this information back to the edge device. Even in the presence of significant communication delays between the cloud platform and the edge device, the timely processing and forwarding of data by the edge gateway significantly improves the overall system's response security, enabling the system to more securely respond to load changes.

[0010] The method also incorporates steps to verify the security and effectiveness of the control strategy. Only when the current control information passes the verification mechanism of the edge gateway will it be sent to the edge device for execution. This mechanism not only prevents the execution of unsafe control instructions caused by network errors, data loss, or malicious attacks, but also ensures that all control instructions sent to the edge device comply with the safety standards and operating logic of the power system, effectively improving the safety and reliability of the system.

[0011] The collaborative work between the edge gateway and the cloud platform enables the cloud platform to generate control strategies based on more accurate and updated real-time data. This information covers aspects such as energy storage charging and discharging management, power generation adjustment, and grid power flow optimization. This means that the power system can more efficiently adjust itself based on current actual needs and conditions, which not only helps improve the operating efficiency of the entire power system, but also effectively responds to emergencies and ensures the stability and quality of power supply.

[0012] By achieving efficient coordinated control between the cloud platform and edge devices, unnecessary power consumption can be reduced. For example, by preemptively increasing power generation or overcharging energy storage devices when there is no load increase, this can help power companies save operating costs. Furthermore, good system coordination can reduce the need for human intervention, lowering the workload and associated labor costs of operations and maintenance personnel.

[0013] This approach is not only applicable to existing power systems but can also be continuously optimized and upgraded as technology evolves and new edge devices are introduced. For example, as new edge computing technologies emerge, this approach can be adapted to new hardware through software or algorithm updates, continuing to play its role in cloud-edge collaborative control and increasing the system's flexibility and adaptability.

[0014] In summary, this cloud-edge collaborative control method provides an efficient, safe, reliable and flexible control solution for the power system, which helps to improve the overall performance and service quality of the power system, and also provides strong technical support for the digital transformation of the power industry.

[0015] In some embodiments, before adding the gateway time stamp corresponding to the edge gateway to the real-time device data, it also includes: when receiving the communication protocol synchronization message sent by the cloud platform, calibrating the gateway time stamp according to the communication protocol synchronization message; wherein, the cloud platform sends the communication protocol synchronization message at a preset time interval.

[0016] In a second aspect, the present application provides a cloud-edge collaborative control method for a power system, which is applied to a cloud platform of a cloud-edge collaborative control system, wherein the cloud-edge collaborative control system includes an edge gateway and an edge device, wherein the cloud platform is wirelessly connected to the edge gateway, and the edge gateway is wired to the edge device; the method includes:

[0017] Upon receiving the real-time device data of the edge device sent by the edge gateway, obtaining the historical device data corresponding to the edge device;

[0018] Parsing the real-time device data and the historical device data to generate device trend information of the edge device; the device trend information includes at least one or more of load change trend, device performance change trend, energy storage capacity change trend, and grid power change trend;

[0019] Generate a control strategy based on the device trend information; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and power flow distribution information;

[0020] The control strategy is sent to the edge gateway so that the edge gateway completes the cloud-edge collaborative control of the edge device according to the control strategy.

[0021] Wireless connections between the cloud platform and edge gateways, as well as wired connections between edge gateways and edge devices, enable rapid acquisition of real-time device data and the rapid issuance of control strategies. The addition of edge computing to the system allows edge devices to perform partial prejudgments and processing while the cloud platform parses and generates control information. This significantly reduces response delays caused by communication latency and improves the ability to respond promptly to load changes.

[0022] By comprehensively analyzing real-time and historical data, this method can more accurately predict and understand the current state and future development trends of the power system, providing a scientific basis for formulating reasonable control strategies. This not only effectively responds to emergencies and avoids safety accidents caused by unresponsiveness, but also ensures the stable operation of the power system through reasonable power supply adjustment, energy storage device charge and discharge control, and grid power flow management, thereby improving the safety performance of the entire power system.

[0023] By generating device trend information (such as load, performance, capacity, and power trends), the cloud platform can more closely understand the operating conditions and changing patterns of each edge device, providing precise guidance for adjustments in power generation, energy storage management, and grid currents. This helps achieve optimal allocation of power resources, reduces unnecessary energy loss, and improves overall system efficiency and economic benefits.

[0024] With faster power system response and improved operational safety, power companies' service quality and user experience will also be significantly improved. For example, when electricity demand in a certain area suddenly increases, the system can quickly adjust power generation plans and optimize transmission routes to ensure that users in that area can obtain the required power in a timely manner. Or, when it is predicted that a user's load is about to reach the grid's capacity limit, capacity expansion can be prepared in advance to avoid affecting normal power supply.

[0025] By effectively managing and controlling all aspects of the power system, this method helps to better integrate renewable energy resources, promote the use of clean energy, and reduce the consumption of traditional energy, thereby contributing to the realization of environmental protection and sustainable development goals.

[0026] In summary, this cloud-edge collaborative control method not only solves the problems caused by communication delays, but also improves the working efficiency, safety and service level of the power system, which is of great significance to promoting the intelligent transformation and sustainable development of the power industry.

[0027] In a third aspect, an embodiment of the present application provides an edge gateway comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement a cloud-edge collaborative control method for a power system as provided in any embodiment of the first aspect of the present application when executing the computer program.

[0028] In a fourth aspect, an embodiment of the present application provides a cloud platform comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement a cloud-edge collaborative control method for a power system as provided in any embodiment of the second aspect of the present application when executing the computer program.

[0029] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements a cloud-edge collaborative control method for a power system as provided in any embodiment of the present application.

[0030] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 This is a schematic block diagram of the structure of a cloud-edge collaborative control system provided by an embodiment of the present application;

[0033] Figure 2 This is a schematic flowchart of the steps of the first cloud-edge collaborative control method for power systems provided by one embodiment of the present application;

[0034] Figure 3 This is a schematic flowchart of the steps of the second cloud-edge collaborative control method for power systems provided by one embodiment of the present application;

[0035] Figure 4 This is a schematic block diagram of the structure of a first cloud-edge collaborative control device for a power system provided by an embodiment of the present application;

[0036] Figure 5 This is a schematic block diagram of the structure of a second cloud-edge collaborative control device for a power system provided by an embodiment of the present application;

[0037] Figure 6This is a schematic block diagram of the structure of an edge gateway provided by an embodiment of the present application;

[0038] Figure 7 This is a schematic block diagram of the structure of a cloud platform provided in one embodiment of the present application.

[0039] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0042] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0043] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0044] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0045] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0046] In power systems, cloud platforms and edge devices exchange data and commands via wireless communication. Due to the latency inherent in wireless communication, when the cloud platform issues control commands, the commands calculated and issued just a moment ago may no longer be applicable to the current device state, reducing the overall security of the cloud-edge collaborative system. For source-grid-load-storage systems, close coordination between all components is required to ensure efficient energy utilization and safe system operation. If there is significant latency in communication between the cloud platform and edge devices, the system may not respond promptly to load changes, making it impossible to adjust the power generation of the power source, the charging and discharging power of the energy storage device, and the power flow distribution of the power grid, thus compromising the security of the entire system.

[0047] Furthermore, inconsistent time scales for data collected by edge devices can complicate data analysis and decision-making for cloud platforms. Cloud platforms must analyze data collected by different devices based on accurate chronological order to understand the dynamic operation of the system. While this issue can be alleviated by establishing unified time scale standards and building effective processing mechanisms, cloud-edge collaborative control presents even more complex challenges. Different types of edge devices have varying capabilities and functions. The key challenge is how to integrate security controls to enable the cloud platform and edge devices to collaboratively complete complex computations and avoid potential security risks.

[0048] Therefore, there is an urgent need for a cloud-edge collaborative control method for power systems to solve at least one of the above problems.

[0049] To solve the above problems, this application provides a cloud-edge collaborative control method for power systems, which is used to Figure 1 The edge gateway of the cloud-edge collaborative control system for the power system shown in the figure, the cloud-edge collaborative control system includes a cloud platform and an edge device, the cloud platform is wirelessly connected to the edge gateway, and the edge gateway is wiredly connected to the edge device.

[0050] like Figure 2 As shown, the cloud-edge collaborative control method for power systems provided includes steps S101 to S103. The details are as follows:

[0051] Step S101: When receiving real-time device data from an edge device, add a gateway timestamp corresponding to the edge gateway to the real-time device data.

[0052] Specifically, in this step, the edge gateway is responsible for receiving real-time data uploaded from edge devices. When using wired transmission, its high reliability significantly reduces the impact of communication delays on gateway data reception, ensuring that data reaches the edge gateway stably and promptly. Upon receiving the data, the edge gateway immediately performs a key operation: combining the device data with the time at which the data was acquired (i.e., the gateway time stamp).

[0053] In practical applications, if all edge devices have time synchronization capabilities, the time synchronization of the entire system will be greatly improved. For example, each edge device and edge gateway is equipped with a high-precision time synchronization module. In this case, the edge gateway records the time of data reception, accurately accounting for both device acquisition time and data transmission time. Leveraging the stability of wired transmission and edge time synchronization, the edge gateway can record data time promptly and accurately, laying a solid foundation for subsequent time series data processing and analysis on the cloud platform.

[0054] Suppose an inverter (an edge device) collects current power quality data at 2:30:00 PM and reliably transmits this data to an edge gateway via a wired connection. Due to the high efficiency and stability of wired connections, transmission latency is almost negligible. The edge gateway receives this information synchronously at 2:30:00 PM. At this point, the edge gateway adds a timestamp of 14:30:00 (the gateway time stamp) to the raw data, ensuring that the data received by the cloud platform contains accurate time information.

[0055] The above steps effectively address the issue of inconsistent data timestamps caused by the diversity of edge devices, ensuring that data received by the cloud platform is correctly time-stamped and improving data processing accuracy. Adding gateway timestamps to data increases the reliability of data during transmission, making it easier to verify the time validity of the data during subsequent data analysis.

[0056] Step S102. Send real-time device data to the cloud platform and receive a control strategy generated by the cloud platform based on the real-time data; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and grid flow distribution information.

[0057] Specifically, the edge gateway transmits real-time device data, time-stamped with the gateway, to the cloud platform via a wireless network. The cloud platform analyzes this data and generates preliminary control instructions. These control instructions may involve various parts of the power system, such as adjusting the charge and discharge status of energy storage devices, regulating the power generation of generator sets, and optimizing the power flow distribution of the power grid. Importantly, these control instructions are generated based on the cloud platform's comprehensive analysis of global information, aiming to achieve optimal operation of the power system.

[0058] Suppose the cloud platform receives real-time device data with gateway timestamps and discovers that the grid load is high during a certain period, while the predicted renewable energy generation is insufficient to meet demand. Based on this, the cloud platform generates preliminary control instructions, instructing a certain energy storage station to begin discharging and simultaneously increasing the power generation of some non-renewable energy generators. Alternatively, the cloud platform detects that the current in certain areas of the grid exceeds the normal range. To prevent overload, the cloud platform may issue control instructions to adjust the grid power distribution, instructing adjustments to the power transmission strategy of the relevant transmission lines.

[0059] Through global data analysis on the cloud platform, we can more accurately predict and respond to changes in power system demand, achieving overall optimization. Even when faced with complex power system operating conditions, we can quickly generate local adjustment control instructions, helping to respond to abnormal situations in a timely manner.

[0060] Step S103: Perform security and validity checks on the control strategy. If the control strategy passes the security and validity checks, the control strategy is sent to the edge device to complete the collaborative control of the edge device.

[0061] Specifically, after receiving the control policy from the cloud platform, the edge gateway performs two verifications on this information: safety and validity. Safety verification ensures that the control instructions will not damage the power system or equipment, while validity verification ensures that the control instructions are practical under the current system operating conditions. Only after passing these two verifications can the edge gateway forward the control instructions to the corresponding edge device, instructing it to perform the specific control operation.

[0062] Security verification example: If the cloud platform issues a command for a full discharge of a certain energy storage station, and the edge gateway detects that the current remaining power of the energy storage station is very low and insufficient to support the full discharge operation, it will consider this command to be a security risk and will not forward it.

[0063] Example of validity verification: Suppose the cloud platform instructs a generator to increase its power, but the edge gateway detects that the generator is already operating at full capacity. Further increases in power are not only ineffective but may also cause a failure. In this case, the edge gateway will not forward this control information.

[0064] Safety verification prevents the execution of control instructions that could endanger system or equipment safety, further ensuring the safe operation of the power system. Validity verification ensures that only control instructions that are truly valid and feasible under the current system state are forwarded, avoiding invalid operations and improving system reliability. Avoiding the execution of invalid or harmful control instructions reduces unnecessary consumption of power system resources.

[0065] In summary, the above method effectively solves the problems caused by inconsistent data time stamps and network delays in cloud-edge collaborative control by adding time stamps at the edge gateway and implementing strict security and validity checks. It is of great significance to improving the operational safety and efficiency of the power system.

[0066] In some embodiments, before adding the gateway time stamp corresponding to the edge gateway to the real-time device data, it also includes: when receiving the communication protocol synchronization message sent by the cloud platform, calibrating the gateway time stamp according to the communication protocol synchronization message; wherein, the cloud platform sends the communication protocol synchronization message at a preset time interval.

[0067] Before adding the edge gateway's corresponding gateway time stamp to real-time device data, the following steps are also included: Receiving a communication protocol calibration message: The edge gateway receives the communication protocol calibration message sent by the cloud platform. Calibrating the gateway time stamp: The edge gateway's internal time stamp is calibrated based on the time information in the communication protocol calibration message. Preset interval: The cloud platform sends communication protocol calibration messages at preset intervals (e.g., every minute or every hour) to ensure the continued accuracy of the gateway time stamp.

[0068] Regularly calibrating the gateway time stamp ensures more accurate timestamps on data recorded by the edge gateway, reducing time stamp errors caused by clock drift. Regular time stamp calibration helps maintain time synchronization between the cloud platform and the edge gateway, ensuring data consistency during transmission and processing. Accurate time stamps help the cloud platform more precisely analyze dynamic changes in the power system, thereby generating more reliable control instructions.

[0069] Assume that the cloud platform sends a communication protocol calibration message every minute. At 2:30:00 PM, the cloud platform sends a calibration message. Upon receiving it, the edge gateway adjusts its internal time stamp to 14:30:00. When the edge device sends real-time data at 2:30:05 PM, the edge gateway immediately records the received data as 14:30:05 and adds this timestamp to the data. This ensures that the data remains accurately time-stamped, even with network delays.

[0070] In some embodiments, the security and effectiveness verification of the control strategy includes: obtaining the security constraints corresponding to the cloud-edge collaborative control system; the safety constraints include at least one or more of the upper and lower limits of power, upper and lower limits of energy storage charging, upper and lower limits of equipment operating temperature, voltage fluctuation range conditions, and frequency stability range conditions; and completing the security and effectiveness verification of the control strategy according to the safety constraints.

[0071] When checking the safety and effectiveness of the control strategy, the following steps are included: Obtaining safety constraints: The edge gateway obtains safety constraints from the cloud-edge collaborative control system. These safety constraints include but are not limited to: Power upper and lower limit conditions: Ensure that the power output / input of the device is within a safe range. Energy storage charging upper and lower limit conditions: Ensure that the charging / discharging power and power of the energy storage device are within a safe range. Equipment operating temperature upper and lower limit conditions: Ensure that the operating temperature of the equipment is within a safe range to prevent overheating or overcooling. Voltage fluctuation range conditions: Ensure that the grid voltage fluctuation is within the allowable range to prevent equipment damage. Frequency stability range conditions: Ensure that the grid frequency is within a stable range to ensure the normal operation of the power system. Complete verification according to safety constraints: The edge gateway verifies the control strategy one by one based on the obtained safety constraints to ensure that each control instruction meets the safety and effectiveness requirements.

[0072] By defining safety constraints, we ensure that every instruction in the control strategy will not cause safety hazards to the equipment or system. The verification process ensures the validity and feasibility of control instructions, reduces invalid operations, and improves system reliability. This prevents equipment failures or system anomalies caused by instructions outside the safety range, thereby reducing maintenance costs and downtime.

[0073] Suppose the control strategy includes a command for the energy storage device to discharge at 1000kW. The edge gateway obtains the upper and lower charging limits of the energy storage device, which are 0-800kW. During the verification mechanism, the edge gateway finds that the 1000kW command exceeds the safety range of the energy storage device. It then runs the local safety policy to calculate a safety value based on the current system state and sends it to the energy storage device.

[0074] In some embodiments, the edge gateway stores a local security policy corresponding to the edge device; if the control policy fails the security and validity check, it also includes: obtaining abnormal data in the control policy that fails the security and validity check and the abnormal data type corresponding to the abnormal data; obtaining local security data corresponding to the abnormal data type in the local security policy; updating the abnormal data according to the local security data to complete the update of the control policy; sending the updated control policy to the edge device to complete the collaborative control of the edge device.

[0075] If the control policy fails security and validity checks, the edge gateway takes the following steps: Acquire exception data and type: The edge gateway acquires the exception data and its corresponding exception data type from the control policy that failed verification. Acquire local security data: The edge gateway searches its stored local security policy for the local security data corresponding to the exception data type. Update exception data: The exception data in the control policy is updated based on the local security data. Send updated control information: The updated control policy is sent to the edge device to complete collaborative control of the edge device.

[0076] Even if the control policy fails verification, the edge gateway can quickly adjust control instructions based on local security policies, ensuring the system resumes normal operation within a short period of time. The existence of local security policies reduces reliance on the cloud platform and mitigates the risk of control failure due to network issues. By adjusting local policies, the system can better adapt to emergencies and complex environments, improving its adaptive and self-healing capabilities.

[0077] Suppose the control policy includes an instruction to increase the generator's power by 500 kW. However, the edge gateway detects that the generator is already operating at full capacity, at 1000 kW. After verification, the instruction fails the safety check. The edge gateway then searches its local safety policy for the generator's power limit and finds that it is 1000 kW. Therefore, the edge gateway adjusts the power increase value in the control instruction to 0 kW and sends the updated instruction to the generator, ensuring it remains within the safety range.

[0078] Please refer to Figure 3 , this application also provides a cloud-edge collaborative control method for power systems, the method is used for Figure 1 The cloud platform for the cloud-edge collaborative control system for the power system shown in the figure includes an edge gateway and an edge device. The cloud platform is wirelessly connected to the edge gateway, and the edge gateway is wired to the edge device. The cloud platform can be deployed on a single server or a server cluster.

[0079] like Figure 3 As shown, the provided cloud-edge collaborative control method for power systems includes steps S201 to S204. The details are as follows:

[0080] Step S201: upon receiving the real-time device data of the edge device sent by the edge gateway, obtaining the historical device data corresponding to the edge device.

[0081] Specifically, when the cloud platform receives the latest detection data (such as real-time indicators such as voltage, current, and temperature) from the edge device through the edge gateway, it needs to obtain the edge device's past data from its stored historical data. This historical data may include the values ​​of various indicators in the normal operation of the device as well as data in the fault state.

[0082] For example, a smart meter in a substation reports that the current electricity consumption is 500 kWh. Upon receiving this real-time data, the cloud platform extracts the meter's electricity consumption records every 15 minutes for the past 24 hours from the database.

[0083] By comparing real-time data with historical data, we can more accurately identify trends in equipment status, thereby improving the accuracy of decision-making. At the same time, this can also help quickly detect abnormal equipment conditions and prevent potential problems.

[0084] Step S202. Analyze the real-time device data and the historical device data to generate device trend information of the edge device; the device trend information includes at least one or more of load change trend, device performance change trend, energy storage capacity change trend and grid power change trend.

[0085] Specifically, the algorithm analyzes the real-time data and historical data collected in step S201 to analyze the trends of device operating status changes. These trends may involve, but are not limited to, device load, performance, storage capacity, and power output.

[0086] Based on the data compared in step S201, the cloud platform may find that the substation's electricity consumption has been increasing over the past six hours and is expected to continue this trend over the next two hours. This may also involve evaluating the performance of substation equipment, such as whether the equipment's performance has degraded under high temperatures.

[0087] The generated trend information provides strong support for subsequent control decisions. For example, understanding the load growth trend can help to pre-emptively deploy backup power generation resources to avoid power shortages during peak demand periods.

[0088] Step S203. Generate a control strategy based on the load change trend, performance change trend, capacity change trend and power change trend; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information and power flow distribution information.

[0089] Specifically, based on the trend information analyzed in step S202, the cloud platform formulates and generates preliminary control commands or adjustment suggestions. These commands may cover specific operations such as scheduling energy storage device charging and discharging, adjusting power output, and optimizing grid load distribution.

[0090] If the load at a substation shows an upward trend and is predicted to continue into the next day, the cloud platform might generate instructions to start charging some energy storage devices in the area and increase the power generation of backup generators. It might also adjust the flow of power on some lines in the grid to optimize power transmission efficiency.

[0091] By formulating and issuing control commands in advance, the power system can adapt more quickly to changes in the external environment or changes in internal demand, reducing the risk of untimely control due to wireless communication delays and improving the system's flexibility and response speed.

[0092] Step S204: Send the control strategy to the edge gateway, so that the edge gateway completes the cloud-edge collaborative control of the edge device according to the control strategy.

[0093] Specifically, the cloud platform sends the control strategy developed in step S203 to the edge gateway via the wireless network. After receiving this information, the edge gateway converts it into specific operating instructions suitable for the edge device and transmits it to each device via the wired network to achieve precise device control.

[0094] For example, the cloud platform sends control commands to the edge gateway to adjust the charging and discharging of energy storage devices, increase power generation, and optimize power flow distribution. The edge gateway then implements these advanced control strategies into meter reading adjustments, generator startup signals, and transformer and circuit breaker operation commands. Ultimately, by executing these commands, it effectively manages all components of the power grid. This mechanism ensures rapid response to power system control even in the face of network delays, effectively reducing the risk of miscontrol caused by delays and enhancing system security and stability. Furthermore, the participation of the edge gateway shares the workload of the cloud platform, alleviating pressure on the cloud and improving the efficiency of the entire system.

[0095] In summary, this cloud-edge collaborative control approach for power systems significantly improves the operational efficiency and safety of power systems by rationally processing data interactions between cloud platforms and edge devices, accurately predicting future trends, and issuing control commands in a timely and effective manner. This collaborative control approach is particularly well-suited for power systems that require highly dynamic adjustments and rapid responses.

[0096] In some embodiments, the parsing of the real-time device data and the historical device data to generate device trend information of the edge device includes: obtaining a data time stamp corresponding to the real-time device data; obtaining historical contemporaneous data corresponding to the edge device in the historical device data according to the data time stamp; and parsing the real-time device data and the historical contemporaneous data to generate device trend information of the edge device.

[0097] Parsing the real-time device data and the historical device data to generate device trend information for the edge device, the specific steps include: obtaining the data timestamp corresponding to the real-time device data: the cloud platform first obtains the timestamp (data timestamp) of the real-time data sent by the edge device to ensure that the data collection time can be accurately determined. Obtaining the historical data corresponding to the edge device in the historical device data according to the data timestamp: the cloud platform extracts data from the historical data in the same time period as the real-time data (for example, the same time on the same date) based on the obtained data timestamp, so as to perform historical comparison. Parsing the real-time device data and the historical data to generate device trend information for the edge device: by comparing the real-time data and the historical data, the cloud platform generates device trend information, including load change trend, performance change trend, capacity change trend and power change trend, etc.

[0098] By comparing data timescales with historical data from the same period, the cloud platform can more accurately identify the actual operating status of equipment, avoiding misjudgments due to time differences. Based on historical data from the same period, the cloud platform can build more accurate forecasting models, improving the accuracy of future trend predictions. With more precise trend information, the cloud platform can make decisions and issue control instructions more quickly, thereby improving the responsiveness and efficiency of the entire system. This reduces the additional data processing required due to inconsistent data timescales, lowering the system's operational costs and complexity.

[0099] Exemplarily, parsing the real-time device data and the historical data for the same period to generate device trend information for the edge device includes: parsing the real-time device data to obtain real-time charge and discharge data, real-time power generation data, and real-time weather data corresponding to the real-time device data; parsing the historical data for the same period to obtain historical charge and discharge data, historical power generation data, and historical weather data corresponding to the real-time device data;

[0100] The load change trend is obtained based on the real-time equipment data and the historical data for the same period; the performance change trend and the capacity change trend are obtained based on the real-time charge and discharge data and the historical charge and discharge data; the power change trend is obtained based on the real-time power generation data, real-time weather data, historical power generation data and historical weather data.

[0101] Parse the real-time device data and the historical data for the same period to generate device trend information for the edge device. The specific steps include: Parse the real-time device data: The cloud platform parses the real-time device data to extract real-time charge and discharge data, real-time power generation data, and real-time weather data. Parse the historical data for the same period: The cloud platform parses the historical data for the same period to extract historical charge and discharge data, historical power generation data, and historical weather data for the same time period as the real-time device data. Obtain the load change trend: Analyze the load change trend by comparing the real-time load data with the historical load data for the same period. Obtain the performance change trend and capacity change trend: Analyze the performance and capacity change trend of the energy storage device by comparing the real-time charge and discharge data with the historical charge and discharge data. Obtain the power change trend: Analyze the power change trend of the power generation equipment by comparing the real-time power generation data, real-time weather data with the historical power generation data and historical weather data.

[0102] By simultaneously analyzing real-time data and historical data from the same period, the cloud platform can gain a more comprehensive understanding of the equipment's operating status and improve the accuracy of trend analysis. Based on this more detailed trend information, the cloud platform can develop more refined control strategies. For example, if a sustained load increase is detected, backup power generation resources can be deployed in advance to optimize grid load distribution. If the performance or capacity of energy storage equipment shows a downward trend, timely maintenance or adjustment of charging and discharging strategies can be performed to extend the equipment's lifespan. By factoring in weather factors, the output power of power generation equipment can be more accurately predicted, optimizing power management and improving system reliability and efficiency. Under varying environmental conditions (such as weather changes), by comparing historical and real-time data, the system can better adapt to external environmental changes and ensure stable operation. Accurate trend analysis can reduce operational errors caused by inconsistent data and improve system safety and reliability.

[0103] The technical content of the above-mentioned embodiments and examples primarily improves the cloud platform's ability to parse edge device data and the accuracy of trend analysis by introducing data time stamps and comparing historical data from the same period. This not only optimizes the data processing process and reduces the risk of misjudgment and misoperation, but also improves the system's response speed and overall performance, ultimately enhancing the safety and efficiency of the power system.

[0104] Now let's take a complete example to illustrate Figures 1 to 3 The corresponding cloud-edge collaborative control method for power systems: A data time-scale synchronization method is proposed to address the situation where edge devices produced by different manufacturers may use different time-scale settings, or even some devices may not provide time-scale information at all. This method is applicable when the cloud platform and edge gateway are connected wirelessly, and the edge gateway and devices are connected by wired wires.

[0105] First, the cloud platform proactively sends a message to the edge gateway, requesting that it support protocol-based time synchronization. Upon receiving this instruction, the edge gateway immediately performs its own time synchronization to ensure accurate time, performing this operation at regular intervals based on the communication protocol used. Subsequently, since the edge gateway and device utilize wired connections, such as Ethernet, serial, and fiber optic, the latency of wired connections is minimal and negligible. Therefore, when a device uploads data to the edge gateway, the edge gateway timestamps the data based on the time it receives it from the edge device. After completing the time-stamping operation, the edge gateway uploads this accurately time-stamped data to the cloud platform, ensuring that the data received by the cloud platform has accurate and consistent time stamps, providing a reliable time basis for subsequent data processing and analysis.

[0106] The power system is constantly changing, and even momentary changes can pose safety risks. While cloud-based control can improve system efficiency, network latency means that control commands issued at a given moment may not reach the device until their state has changed, leading to safety risks. This method aims to address the safety risks associated with network latency and better utilize cloud-based optimized control capabilities.

[0107] First, leveraging the cloud platform's powerful computing power, the data analysis and strategy optimization function is deployed and run on the cloud platform. The cloud platform uses real-time and historical data to conduct in-depth analysis and optimization of the control strategy, generating a more optimal control strategy. After determining the control strategy, the cloud platform transmits it to the edge gateway. The edge gateway receives the control strategy from the cloud platform and, after verification, issues control instructions to the relevant devices, thereby achieving optimized operation and efficient management of the entire energy storage system.

[0108] When real-time data with accurate time stamps is transmitted to the cloud platform, it will be stored in a dedicated data warehouse. At the same time, the corresponding historical data will be retrieved from the historical database. This historical data covers information such as the operating status of the power system and equipment performance over different time periods. The cloud platform then uses advanced data processing algorithms and analytical models to conduct a comprehensive analysis of real-time and historical data. For example, by comparing current real-time data with historical data from the same period, load trends can be analyzed; based on the historical charge and discharge data and current real-time status data of the energy storage equipment, the performance and remaining capacity of the energy storage system can be evaluated. For power generation data, historical weather data and real-time meteorological information are combined to predict the power generation capacity for a period of time in the future.

[0109] Based on this analysis, the cloud platform makes optimized decisions and formulates reasonable control strategies, such as the energy storage system's charge and discharge plan for Day 1, optimizing power generation plans, and adjusting the power flow distribution of the power grid. These control strategies aim to achieve efficient energy utilization, stable system operation, and reduced operating costs.

[0110] Finally, the cloud platform sends the generated control strategy to the edge device in the form of a file. After receiving the strategy file, the edge gateway parses and runs it. Then, after ensuring the security and effectiveness of the strategy through a verification mechanism, it sends specific instructions to each device to achieve precise control of the power system and safe operation of the system.

[0111] After the edge gateway executes the control policy issued by the cloud platform, it activates a security verification mechanism. This mechanism uses strict security constraints as a prerequisite to conduct a comprehensive and detailed verification of the running policy.

[0112] The system compares and analyzes each policy parameter and operating mode against pre-set security constraints. If the policy passes verification, it complies with security standards, and control instructions are automatically issued to ensure smooth policy execution. If the policy fails verification, the local security control policy is applied. Based on the relevant rules and algorithms of the local policy, a safety value is recalculated and a control instruction generated based on this safety value is issued to ensure safe system operation. This security verification mechanism effectively reduces potential risks and ensures efficient, secure, and reliable policy execution.

[0113] Enter the safety constraint phase of the verification mechanism. During this phase, the system compares and analyzes each parameter and operating mode in the policy against the strict, preset safety constraints. These safety constraints cover multiple aspects, including the safety range of device parameters and system operating state restrictions. Examples include upper and lower power limits, upper and lower limits for energy storage charging, upper and lower limits for device operating temperature, voltage fluctuation range, and frequency stability range. If the policy successfully passes the safety constraint verification, it means it meets safety standards. At this point, the verification mechanism automatically issues control instructions, which are transmitted to each edge device via the edge gateway to ensure smooth execution of the policy. The issuance of control instructions prompts edge devices to adjust their operating states according to the policy requirements, achieving precise control of the power system.

[0114] However, if the strategy fails to pass the safety constraint test, the system will start the local safety strategy. The local safety strategy is customized according to the characteristics of the implemented power system. For example: In a certain power system, the transformer bears the power of photovoltaic, energy storage and mains power. Since there is an upper limit on the power it can withstand, the local safety strategy can be set so that the sum of photovoltaic power, energy storage power and mains power is less than or equal to the rated safe operating power of the transformer. If the three setting values ​​in the strategy do not meet the safety constraints, the value that does not meet the safety constraints can be calculated by the local safety strategy according to the current system status to send a new power control value, so that the system remains in a safe state of operation. This calculation process fully considers various factors of the current power system, including but not limited to the operating status of the equipment, load demand, energy supply, etc.

[0115] Finally, based on the security value recalculated based on local policy-related rules and algorithms, control instructions are generated and sent to edge devices to adjust the operating status of the devices.

[0116] Through such a verification mechanism process, potential risks can be effectively reduced, ensuring that the implementation of strategies is both efficient, safe and reliable, and providing a solid guarantee for the stable operation of the power system.

[0117] This embodiment further offers the following beneficial effects: First, it addresses the issue of time-stamping device data collection. All edge devices collect and upload data using this method, adding time stamps to the data, ensuring consistency in the data received by the cloud platform. This provides a reliable basis for the cloud platform to accurately analyze device operating status and formulate appropriate control strategies, thereby achieving optimal energy allocation.

[0118] Secondly, to address network latency, optimization of the cloud-edge communication mechanism effectively reduces the potential safety risks associated with command transmission delays. This ensures that critical commands, such as energy storage system charging and discharging instructions and grid power flow adjustment instructions, are accurately delivered to edge devices, avoiding the risk of uncontrolled device operation and significantly improving system stability. This enables the system to better respond to changes in power demand and ensures the safe and stable operation of the power system.

[0119] Finally, this invention employs multiple security measures. First, based on strict security constraints, policies are thoroughly and meticulously verified. Once verified, control instructions are automatically issued to ensure smooth policy execution. Second, for minimum assurance, local security control strategies are employed to recalculate safety values ​​and generate control instructions, ensuring safe system operation and effectively mitigating potential risks.

[0120] See also Figure 4 , Figure 4This is a schematic diagram of the structure of the first cooperative control device 300 for the power system provided in the embodiment of the present application. The first cooperative control device 300 for the power system is used to perform Figure 2 The steps of the cloud-edge collaborative control method for the power system shown in the corresponding embodiment.

[0121] like Figure 4 As shown, the first collaborative control device 300 includes:

[0122] The data receiving unit 301 is configured to add a gateway timestamp corresponding to the edge gateway to the real-time device data when receiving the real-time device data from the edge device.

[0123] The data sending unit 302 is used to send the real-time device data to the cloud platform and receive the control strategy generated by the cloud platform based on the real-time data; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information and grid flow distribution information.

[0124] The collaborative control unit 303 is used to perform security and validity verification on the preliminary control information. If the control strategy passes the security and validity verification, the control strategy is sent to the edge device to complete the collaborative control of the edge device.

[0125] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the first collaborative control device 300 and each module described above can refer to Figure 2 The corresponding processes in the cloud-edge collaborative control method embodiment for the power system described in the corresponding embodiment will not be repeated here.

[0126] See also Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of the second collaborative control device 400 cloud platform for the power system provided in the embodiment of the present application. The second collaborative control device 400 cloud platform is used to execute Figure 3 The steps of the cloud-edge collaborative control method for the power system shown in the corresponding embodiment.

[0127] like Figure 4 As shown, the second collaborative control device 400 includes:

[0128] The history acquisition unit 401 is configured to acquire historical device data corresponding to the edge device upon receiving the real-time device data of the edge device sent by the edge gateway.

[0129] The data parsing unit 402 is used to parse the real-time device data and the historical device data to generate device trend information of the edge device; the device trend information includes at least one or more of the load change trend, device performance change trend, energy storage capacity change trend and grid power change trend.

[0130] The preliminary generation unit 403 is used to generate a control strategy based on the device trend information; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information and power flow distribution information.

[0131] The control completion unit 404 is used to send the control strategy to the edge gateway so that the edge gateway completes the cloud-edge collaborative control of the edge device according to the control strategy.

[0132] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the second collaborative control device 400 and each module described above can refer to the above Figure 3 The corresponding processes in the cloud-edge collaborative control method embodiment for the power system described in the corresponding embodiment will not be repeated here.

[0133] Figure 2 The corresponding cloud-edge collaborative control method for power systems can be implemented in the form of a computer program. Figure 4 Run on the device shown.

[0134] See also Figure 6 , Figure 6 The schematic block diagram of the edge gateway provided in the embodiment of the present application is shown in FIG. The edge gateway includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0135] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to execute Figure 2 Corresponding to any cloud-edge collaborative control method for power systems.

[0136] The processor is used to provide computing and control capabilities to support the operation of the entire edge gateway.

[0137] The internal memory provides an environment for the operation of computer programs in non-volatile storage media. When the computer program is executed by the processor, the processor can execute any cloud-edge collaborative control method for the power system.

[0138] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific edge gateway may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0139] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0140] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0141] When receiving the real-time device data of the edge device, adding the gateway timestamp corresponding to the edge gateway to the real-time device data;

[0142] Sending the real-time device data to the cloud platform and receiving a control strategy generated by the cloud platform based on the real-time data; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and grid flow distribution information;

[0143] The preliminary control information is subjected to security and validity checks. If the control strategy passes the security and validity checks, the control strategy is sent to the edge device to complete the collaborative control of the edge device.

[0144] Figure 3 The corresponding cloud-edge collaborative control method for power systems can be implemented in the form of a computer program. Figure 5 Run on the device shown.

[0145] See also Figure 7 , Figure 7 1 is a schematic block diagram of the structure of a cloud platform provided in an embodiment of the present application. The cloud platform includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0146] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to execute Figure 3 Corresponding to any cloud-edge collaborative control method for power systems.

[0147] The processor is used to provide computing and control capabilities to support the operation of the entire cloud platform.

[0148] The internal memory provides an environment for the operation of computer programs in non-volatile storage media. When the computer program is executed by the processor, the processor can execute any cloud-edge collaborative control method for the power system.

[0149] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific cloud platform may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0150] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0151] In one embodiment, the processor is configured to execute a computer program stored in the memory to implement the following steps:

[0152] Upon receiving the real-time device data of the edge device sent by the edge gateway, obtaining the historical device data corresponding to the edge device;

[0153] Parsing the real-time device data and the historical device data to generate device trend information of the edge device; the device trend information includes at least one or more of load change trend, device performance change trend, energy storage capacity change trend, and grid power change trend;

[0154] Generate a control strategy based on the device trend information; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and power flow distribution information;

[0155] The control strategy is sent to the edge gateway so that the edge gateway completes the cloud-edge collaborative control of the edge device according to the control strategy.

[0156] The present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the Figure 2 or Figure 3 The steps of the cloud-edge collaborative control method for the power system provided by any corresponding embodiment.

[0157] The computer-readable storage medium may be an internal storage unit of the edge gateway described in the aforementioned embodiment, such as a hard disk or memory of the edge gateway. The computer-readable storage medium may also be an external storage device of the edge gateway, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the edge gateway.

[0158] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific structure of the ultra-thin high-temperature colored glaze glass with high flatness described above can refer to the corresponding process in the embodiments of the ultra-thin high-temperature colored glaze glass processing method with high flatness described in the above embodiments, and will not be repeated here.

[0159] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A cloud-edge collaborative control method for power systems, characterized in that: An edge gateway applied to a cloud-edge collaborative control system, the cloud-edge collaborative control system comprising a cloud platform and an edge device, the cloud platform being wirelessly connected to the edge gateway, and the edge gateway being wiredly connected to the edge device; the method comprising: When receiving the real-time device data of the edge device, adding the gateway timestamp corresponding to the edge gateway to the real-time device data; Sending the real-time device data to the cloud platform and receiving a control strategy generated by the cloud platform based on the real-time data; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and grid flow distribution information; The preliminary control information is subjected to security and validity checks. If the control strategy passes the security and validity checks, the control strategy is sent to the edge device to complete the collaborative control of the edge device.

2. The method according to claim 1, characterized in that Before adding the gateway time stamp corresponding to the edge gateway to the real-time device data, the method further includes: Upon receiving the communication protocol calibration message sent by the cloud platform, calibrating the gateway time scale according to the communication protocol calibration message; The cloud platform sends the communication protocol calibration message at a preset time interval.

3. The method according to claim 1, characterized in that The safety and effectiveness verification of the control strategy includes: Obtain security constraints corresponding to the cloud-edge collaborative control system; the security constraints include at least one or more of the following conditions: upper and lower limits of grid power, upper and lower limits of energy storage charging and discharging power, upper and lower limits of energy storage SOC, and upper and lower limits of equipment operating temperature; The safety and effectiveness verification of the control strategy is completed according to the safety constraints.

4. The method according to claim 1, wherein The edge gateway stores a local security policy corresponding to the edge device; If the control strategy fails to pass the safety and effectiveness verification, the method further includes: Acquiring abnormal data in the control strategy that fails the safety and effectiveness verification; Calculating local security data in the local security policy based on the current system state corresponding to the cloud-edge collaborative control system; Updating the abnormal data according to the local security data to complete the update of the control strategy; The updated control strategy is sent to the edge device to complete the collaborative control of the edge device.

5. A cloud-edge collaborative control method for power systems, characterized in that: A cloud platform applied to a cloud-edge collaborative control system, the cloud-edge collaborative control system including an edge gateway and an edge device, the cloud platform being wirelessly connected to the edge gateway, and the edge gateway being wiredly connected to the edge device; the method comprising: Upon receiving the real-time device data of the edge device sent by the edge gateway, obtaining the historical device data corresponding to the edge device; Parsing the real-time device data and the historical device data to generate device trend information of the edge device; the device trend information includes at least one or more of load change trend, device performance change trend, energy storage capacity change trend, and grid power change trend; Generate a control strategy based on the device trend information; the control strategy includes at least one or more of energy storage charging and discharging information, grid power supply information, power generation information, and power flow distribution information; The control strategy is sent to the edge gateway so that the edge gateway completes the cloud-edge collaborative control of the edge device according to the control strategy.

6. The method according to claim 5, characterized in that The parsing of the real-time device data and the historical device data to generate device trend information of the edge device includes: Obtaining a data time stamp corresponding to the real-time device data; Obtaining historical contemporaneous data corresponding to the edge device from the historical device data according to the data time stamp; The real-time device data and the historical data for the same period are analyzed to generate device trend information of the edge device.

7. The method according to claim 6, characterized in that The analyzing the real-time device data and the historical data for the same period to generate device trend information of the edge device includes: Parse the real-time device data to obtain real-time energy storage charging and discharging data, real-time power generation data, and real-time weather data corresponding to the edge device; Analyze the historical data for the same period to obtain historical energy storage charging and discharging data, historical power generation data, and historical weather data corresponding to the edge device; Obtaining the load change trend according to the real-time equipment data and historical data for the same period; Obtaining the energy storage performance change trend and the energy storage capacity change trend based on the real-time energy storage charge and discharge data and the historical energy storage charge and discharge data; The power variation trend is obtained based on the real-time power generation data, real-time weather data, historical power generation data and historical weather data, and is used to predict the power generation within a preset time period.

8. An edge gateway, characterized in that: The edge gateway includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 1 to 4 when executing the computer program.

9. A cloud platform, characterized in that: The edge gateway includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 5 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 4, or the method according to any one of claims 5 to 7.

Images