Cloud-edge collaborative park integrated energy system control strategy

Abstract

The invention provides a cloud-side collaborative park integrated energy system control strategy, and relates to the technical field of integrated energy system control. The control strategy comprises the steps that the side end controller determines the uncertainty level of the corresponding energy equipment based on the operation data of the corresponding energy equipment, and if the uncertainty level is larger than or equal to a preset threshold value, the operation data is used as target operation data to be uploaded to the cloud energy management system according to the uncertainty level; if the uncertainty level is smaller than a preset threshold value, an initial control strategy corresponding to the energy equipment is determined according to the operation data, and the initial control strategy is uploaded to a cloud energy management system according to the uncertainty level; and the cloud energy management system determines an optimization strategy according to the target operation data and / or the initial control strategy. According to the invention, part of data processing and calculation tasks can be transferred to the edge end controller, the cloud end calculation pressure is reduced, and the real-time performance and the collaboration among the energy devices in the park integrated energy system are considered at the same time.

Description

A cloud-edge collaborative control strategy for integrated energy systems in industrial parks Technical Field

[0001] This invention relates to the field of integrated energy control technology, and in particular to a cloud-edge collaborative control strategy for a park integrated energy system. Background Technology

[0002] With the increasing frequency of economic activities and the continuous growth of energy demand in industrial parks, traditional energy management strategies are no longer sufficient to meet the current needs for efficient, safe, and sustainable energy supply. Especially against the backdrop of continuous advancements in smart grids, renewable energy integration, and the Internet of Things (IoT) technologies, the complexity and management difficulty of integrated energy systems in industrial parks have significantly increased. Addressing the challenges of numerous terminal devices, large data volumes, and poor timeliness of cloud computing models in low-carbon industrial park energy systems with multiple energy sources, edge computing offers advantages in data protection and transmission efficiency, improving real-time data response efficiency and thus enhancing the overall system's computational efficiency. Therefore, exploring a novel control strategy for integrated energy systems in industrial parks that integrates the advantages of cloud computing and edge computing is particularly important.

[0003] Cloud-edge collaborative technology, a distributed computing architecture that has emerged in recent years, achieves low latency and high efficiency in data processing by rationally distributing computing tasks between the cloud and the edge. In integrated energy systems for industrial parks, cloud-edge collaborative technology can fully leverage the powerful data processing capabilities of cloud computing and the real-time response advantages of edge computing to enable rapid collection, analysis, and optimized scheduling of energy data. It shifts the computing load from a cloud computing model that concentrates the computing load in the cloud to an edge computing model that distributes the computing load to the edge, reducing the computational pressure on park regulation and supporting stable park operation. However, current cloud-edge coordinated control strategies for integrated energy systems in industrial parks are still relatively limited and require further improvement. Summary of the Invention

[0004] This invention provides a cloud-edge collaborative control strategy for a park integrated energy system, in order to further improve the cloud-edge coordinated control strategy for a park integrated energy system.

[0005] In a first aspect, embodiments of the present invention provide a cloud-edge collaborative control strategy for a park integrated energy system, applied to a cloud-based energy management system and multiple edge controllers. The cloud-based energy management system connects to the multiple edge controllers, and the edge controllers connect to energy devices in the park integrated energy system. The control strategy includes: the edge controller determining the uncertainty level of the corresponding energy device based on its operating data; if the uncertainty level is greater than or equal to a preset threshold, uploading the operating data as target operating data to the cloud-based energy management system according to the uncertainty level; if the uncertainty level is less than the preset threshold, determining an initial control strategy for the corresponding energy device based on the operating data, and uploading the initial control strategy to the cloud-based energy management system according to the uncertainty level; and the cloud-based energy management system determining an optimization strategy based on the target operating data and / or the initial control strategy.

[0006] Secondly, embodiments of the present invention provide a cloud-edge collaborative control device for a park integrated energy system, applied in a cloud-based energy management system and multiple edge controllers. The cloud-based energy management system connects to the multiple edge controllers, and the edge controllers connect to energy devices in the park integrated energy system. The device includes: an edge control module, used to enable the edge controller to determine the uncertainty level of the corresponding energy device based on the operating data of the corresponding energy device; if the uncertainty level is greater than or equal to a preset threshold, the edge controller uploads the operating data as target operating data to the cloud-based energy management system according to the uncertainty level; if the uncertainty level is less than the preset threshold, the edge controller determines an initial control strategy for the corresponding energy device based on the operating data, and uploads the initial control strategy to the cloud-based energy management system according to the uncertainty level; and a cloud control module, used to enable the cloud-based energy management system to determine an optimization strategy based on the target operating data and / or the initial control strategy.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the control strategy as described in the first aspect or any possible implementation of the first aspect.

[0008] In this embodiment of the invention, the edge controller determines the uncertainty level of the corresponding energy device based on its operating data. If the uncertainty level is greater than or equal to a preset threshold, the operating data is uploaded to the cloud-based energy management system as target operating data according to the uncertainty level. If the uncertainty level is less than the preset threshold, an initial control strategy for the corresponding energy device is determined based on the operating data, and the initial control strategy is uploaded to the cloud-based energy management system according to the uncertainty level. The cloud-based energy management system then determines an optimization strategy based on the target operating data and / or the initial control strategy. This approach, by dividing uncertainty levels, transfers some data processing and computation tasks to the edge controller, reducing the computational pressure on the cloud while simultaneously ensuring real-time performance and the coordination between various energy devices in the integrated energy system of the park. Attached Figure Description

[0009] Figure 1 is an application scenario diagram of the cloud-edge collaborative park integrated energy system control strategy provided in an embodiment of the present invention; Figure 2 is an implementation flowchart of the cloud-edge collaborative park integrated energy system control strategy provided in an embodiment of the present invention; Figure 3 is a structural schematic diagram of the cloud-edge collaborative park integrated energy system control device provided in an embodiment of the present invention; Figure 4 is a schematic diagram of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0010] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0011] Figure 1 illustrates an application scenario of the cloud-edge collaborative integrated energy system control strategy for a park provided in this embodiment of the invention. As shown in Figure 1, the cloud-edge collaborative integrated energy system control strategy for a park provided in this embodiment of the invention can be applied to a cloud-based energy management system and multiple edge controllers. The cloud-based energy management system is responsible for the energy management of the entire park integrated energy system. The cloud-based energy management system connects to multiple edge controllers. Each edge controller is connected to a specific energy device, enabling effective management of each energy device within the park's integrated energy system. For example, as shown in Figure 1, the cloud-based energy management system can connect to 13 edge controllers. These 13 edge controllers are connected to specific energy devices, including upstream power grid transformers, photovoltaic systems, energy storage systems, heat pumps, gas turbines, gas boilers, thermal storage tanks, cold storage tanks, absorption chillers, electric chillers, electrical loads, thermal loads, and cooling loads, to achieve effective management of each energy device within the park's integrated energy system.

[0012] The edge controller can be installed near the equipment and connected directly to it via network cable. It collects data from various energy devices within the park's integrated energy system in real time and transmits it to the cloud as needed. Simultaneously, it receives and sends equipment control commands from the cloud-based energy management system.

[0013] The cloud-based energy management system can connect to each edge controller via network cable to monitor the operating data of each device in the park in real time, calculate and issue equipment control commands through energy consumption strategies, and improve energy utilization.

[0014] Referring to Figure 2, which shows the implementation flowchart of the cloud-edge collaborative park integrated energy system control strategy provided by the embodiment of the present invention, detailed as follows: Step 201, the edge controller determines the uncertainty level of the corresponding energy device based on the operating data of the corresponding energy device. If the uncertainty level is greater than or equal to a preset threshold, the operating data is uploaded to the cloud energy management system as the target operating data according to the uncertainty level. If the uncertainty level is less than the preset threshold, the initial control strategy of the corresponding energy device is determined based on the operating data, and the initial control strategy is uploaded to the cloud energy management system according to the uncertainty level.

[0015] In this embodiment, the operational data acquired by each edge controller can be from... A set of curves composed of various operating parameters .

[0016] After obtaining the operational data, the first step is data cleaning, i.e., verification. For each curve, the system searches for missing data, abrupt changes, and other anomalies. If the percentage of missing or anomalies in a curve reaches a certain threshold, the curve will be considered invalid and removed, and the data for that curve will be reacquired. If the percentage of missing or anomalies in a curve does not meet the criteria for deletion, the anomalies can be corrected.

[0017] For example, suppose the first a curve Real-time data collection For outlier data, a smoothing correction formula can be used to correct it, resulting in corrected data values. for: ;in, Representative from collected data The step size for forward data collection. Representative from collected data The step size for backward data collection.

[0018] After data cleaning, data normalization can be performed. For example, the max-min normalization method can be used to process the data, eliminating the influence of units and increasing reliability.

[0019] Based on this, this embodiment considers the complexity of the park's integrated energy system, which may include various new energy sources and adjustable load devices. Therefore, it assesses the uncertainty level of each energy device based on the collected operational data. If the uncertainty level of an energy device is high, it indicates that the device's current operation is highly volatile. Therefore, the operational data of that energy device is prioritized for uploading to the cloud-based energy management system for processing. If the uncertainty level of an energy device is low, it indicates that the device's current operation is less volatile. In this case, preliminary processing can be performed at the edge controller to obtain its initial control strategy, thereby reducing some of the data processing pressure on the cloud-based energy management system. The initial control strategy is then uploaded to the cloud-based energy management system, allowing it to incorporate the situation of less volatile energy devices during optimization processing. This balances the real-time nature of data processing with the synergy between various energy devices in the park's integrated energy system.

[0020] In one embodiment, determining the uncertainty level of a corresponding energy device based on its operating data includes: determining the length of a sliding window based on the difference between the historical operating data of the corresponding energy device and the predicted value of the historical operating data.

[0021] Based on the length of the sliding window and historical operating data, determine the average, maximum, and minimum operating data values ​​of the corresponding energy equipment.

[0022] The uncertainty level of the corresponding energy equipment is determined based on the average, maximum, and minimum operating data and the operating data of the corresponding energy equipment.

[0023] In one embodiment, determining the length of the sliding window based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy device includes: determining the mean of the difference based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy device.

[0024] If the mean difference is less than or equal to the preset mean difference, the product of the preset length of the sliding window and the contraction coefficient is used as the length of the sliding window.

[0025] If the average difference is greater than the preset average difference, the product of the preset length of the sliding window and the expansion coefficient is used as the length of the sliding window.

[0026] In this embodiment, by setting a sliding window, the uncertainty level of the corresponding energy equipment can be more reasonably assessed by combining the current status of the energy equipment under the current situation, eliminating the influence of accidental factors and improving reliability.

[0027] When the mean difference is less than or equal to the preset mean difference, it indicates that the current state of the energy equipment is relatively stable. Therefore, only a small amount of historical operating data is needed to assess the uncertainty of the current operating data. Thus, the product of the preset length of the sliding window and the contraction coefficient can be used as the length of the sliding window.

[0028] When the average difference is greater than the preset average difference, it indicates that the current state stability of the energy equipment is insufficient. More historical operating data is needed to accurately assess the uncertainty of the current operating data. Therefore, the product of the preset length of the sliding window and the expansion coefficient can be used as the length of the sliding window.

[0029] The preset length, contraction coefficient, and expansion coefficient of the sliding window can be set according to the actual situation of the specific energy equipment, and this embodiment does not limit this.

[0030] In this embodiment, after determining the length of the sliding window, the uncertainty level of the corresponding energy equipment can be more accurately assessed based on its mean, maximum, and minimum values ​​in the historical operating data within the length range of the sliding window.

[0031] In one embodiment, determining the uncertainty level of a corresponding energy device based on the average operating data, the maximum operating data, the minimum operating data, and the operating data of the corresponding energy device includes: calculating the difference between the maximum operating data and the operating data of the corresponding energy device, and calculating the difference between the minimum operating data and the operating data of the corresponding energy device, and determining the maximum of the two differences as the maximum difference.

[0032] The difference between the mean of the operating data and the operating data of the corresponding energy equipment is calculated and denoted as the target difference.

[0033] The uncertainty level of the corresponding energy equipment is determined based on the target difference and the maximum difference.

[0034] In this embodiment, the maximum fluctuation of the operating data can be measured by determining the maximum difference, and the average fluctuation of the operating data can be measured by determining the target difference. Then, the uncertainty level of the corresponding energy equipment is measured by combining the maximum fluctuation and the average fluctuation of the operating data.

[0035] For example, the uncertainty level of a corresponding energy device can be determined based on the mean or weighted mean of the target difference and the maximum difference. Alternatively, the uncertainty level can be determined based on the ratio of the target difference to the maximum difference, or the difference between the target difference and the maximum difference. The specific method used to determine the uncertainty level can be selected based on actual needs.

[0036] In one embodiment, uploading operational data as target operational data to the cloud-based energy management system based on the uncertainty level includes: determining the weighting factor corresponding to the operational data based on the uncertainty level.

[0037] The transmission time slots allocated to the running data are determined based on the weighting factors corresponding to the running data.

[0038] The operational data is uploaded to the cloud-based energy management system as the target operational data based on the transmission time slots allocated for the operational data.

[0039] In this embodiment, considering that the higher the uncertainty level of the energy equipment, the more priority should be given to transmitting its corresponding operating data to the cloud energy management system, the weight factor corresponding to the operating data can be determined according to the uncertainty level, more transmission time slots can be allocated to the operating data according to the weight factor corresponding to the operating data, and then the operating data can be uploaded to the cloud energy management system as the target operating data according to the transmission time slots allocated to the operating data.

[0040] In one embodiment, determining the weighting factor corresponding to the operational data based on the uncertainty level includes: calculating the ratio of the uncertainty level to the highest uncertainty level.

[0041] The weighting factor corresponding to the running data is determined based on the ratio.

[0042] In this embodiment, the highest uncertainty level can be the highest uncertainty level set by the system, or it can be the highest uncertainty level among the uncertainty levels corresponding to each energy device after obtaining the uncertainty level. Then, by calculating the ratio of the uncertainty level to the highest uncertainty level, the uncertainty of the current energy device can be quantified.

[0043] Based on this, the weighting factor corresponding to the operating data can be determined directly from the ratio. Alternatively, to obtain the weighting factor more accurately, the importance of energy equipment can be considered, and the weighting factor corresponding to the operating data can be determined by combining the ratio and the importance of the corresponding energy equipment.

[0044] In one embodiment, determining the transmission time slot allocated to the running data based on the weighting factor corresponding to the running data includes: based on... Determine the transmission time slots allocated for running data.

[0045] in, For the first The time slots allocated for the transmission of operational data from each energy device. For the first Weighting factors corresponding to the operating data of each energy device For the first The amount of data transmitted for the operation data of each energy device For channel capacity, This is the duration of the transmission frame.

[0046] In this embodiment, the transmission time slots allocated to the running data are determined based on the weight factor, transmission data volume, channel capacity, and transmission frame duration corresponding to the running data. This allows for the priority allocation of more transmission time slots to running data with higher weights, thereby ensuring the priority transmission of running data with higher uncertainty levels.

[0047] Step 202: The cloud-based energy management system determines the optimization strategy based on the target operating data and / or the initial control strategy.

[0048] For example, determining an optimization strategy based on target operating data and / or initial control strategy includes: optimizing based on target operating data and / or initial control strategy with the goal of minimizing the total cost of the park's integrated energy system, and determining the optimization strategy.

[0049] For example, the objective function can be: In the formula: The total cost of the park's integrated energy system; Costs of purchasing and selling electricity from external power grids; Cost of purchasing natural gas; For equipment operation and maintenance costs; Costs related to energy storage operation and losses; The cost of penalizing the abandonment of wind and solar power; This refers to the cost of carbon trading.

[0050] In addition, constraints can be set for the park's integrated energy system, which can mainly include power balance constraints, unit output, ramping constraints, equipment constraints, etc., and then optimization strategies can be solved based on the objective function and constraints.

[0051] This invention, in its embodiments, uses an edge controller to determine the uncertainty level of a corresponding energy device based on its operational data. If the uncertainty level is greater than or equal to a preset threshold, the operational data is uploaded to the cloud-based energy management system as target operational data. If the uncertainty level is less than the preset threshold, an initial control strategy for the corresponding energy device is determined based on the operational data, and this initial control strategy is uploaded to the cloud-based energy management system. The cloud-based energy management system then determines an optimization strategy based on the target operational data and / or the initial control strategy. This uncertainty level classification shifts some data processing and computational tasks to the edge controller, reducing cloud computing pressure while maintaining real-time performance and ensuring coordination between energy devices in the integrated energy system of the park.

[0052] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] The following are embodiments of the apparatus of the present invention. For details not described in detail, please refer to the corresponding control strategy embodiments described above.

[0054] Figure 3 shows a schematic diagram of the cloud-edge collaborative integrated energy system control device for a park provided in an embodiment of the present invention. This device is applied to a cloud energy management system and multiple edge controllers. The cloud energy management system is connected to multiple edge controllers, and the edge controllers are connected to energy devices in the integrated energy system of the park. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: As shown in Figure 3, the cloud-edge collaborative integrated energy system control device for a park includes: an edge control module 31, which is used to enable the edge controller to determine the uncertainty level of the corresponding energy device based on the operating data of the corresponding energy device. If the uncertainty level is greater than or equal to a preset threshold, the operating data is uploaded to the cloud energy management system as target operating data according to the uncertainty level. If the uncertainty level is less than the preset threshold, the initial control strategy of the corresponding energy device is determined based on the operating data, and the initial control strategy is uploaded to the cloud energy management system according to the uncertainty level.

[0055] The cloud control module 32 is used to enable the cloud energy management system to determine the optimization strategy based on the target operating data and / or the initial control strategy.

[0056] This invention, in its embodiments, uses an edge controller to determine the uncertainty level of a corresponding energy device based on its operational data. If the uncertainty level is greater than or equal to a preset threshold, the operational data is uploaded to the cloud-based energy management system as target operational data. If the uncertainty level is less than the preset threshold, an initial control strategy for the corresponding energy device is determined based on the operational data, and this initial control strategy is uploaded to the cloud-based energy management system. The cloud-based energy management system then determines an optimization strategy based on the target operational data and / or the initial control strategy. This uncertainty level classification shifts some data processing and computational tasks to the edge controller, reducing cloud computing pressure while maintaining real-time performance and ensuring coordination between energy devices in the integrated energy system of the park.

[0057] In one possible implementation, the edge control module 31 is specifically used to: determine the length of the sliding window based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy equipment.

[0058] Based on the length of the sliding window and historical operating data, determine the average, maximum, and minimum operating data values ​​of the corresponding energy equipment.

[0059] The uncertainty level of the corresponding energy equipment is determined based on the average, maximum, and minimum operating data and the operating data of the corresponding energy equipment.

[0060] In one possible implementation, the edge control module 31 is specifically used to: determine the average difference based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy equipment.

[0061] If the mean difference is less than or equal to the preset mean difference, the product of the preset length of the sliding window and the contraction coefficient is used as the length of the sliding window.

[0062] If the average difference is greater than the preset average difference, the product of the preset length of the sliding window and the expansion coefficient is used as the length of the sliding window.

[0063] In one possible implementation, the edge control module 31 is specifically used to: calculate the difference between the maximum value of the operating data and the operating data of the corresponding energy device, and calculate the difference between the minimum value of the operating data and the operating data of the corresponding energy device, and determine the maximum value of the two differences as the maximum difference.

[0064] The difference between the mean of the operating data and the operating data of the corresponding energy equipment is calculated and denoted as the target difference.

[0065] The uncertainty level of the corresponding energy equipment is determined based on the target difference and the maximum difference.

[0066] In one possible implementation, the edge control module 31 is specifically used to: determine the weighting factor corresponding to the running data based on the uncertainty level.

[0067] The transmission time slots allocated to the running data are determined based on the weighting factors corresponding to the running data.

[0068] The operational data is uploaded to the cloud-based energy management system as the target operational data based on the transmission time slots allocated for the operational data.

[0069] In one possible implementation, the edge control module 31 is specifically used to: calculate the ratio of the uncertainty level to the highest uncertainty level.

[0070] The weighting factor corresponding to the running data is determined based on the ratio.

[0071] In one possible implementation, the edge control module 31 is specifically used for: according to Determine the transmission time slots allocated for running data.

[0072] in, For the first The time slots allocated for the transmission of operational data from each energy device. For the first Weighting factors corresponding to the operating data of each energy device For the first The amount of data transmitted for the operation data of each energy device For channel capacity, This is the duration of the transmission frame.

[0073] In one possible implementation, the cloud control module 32 is specifically used to: optimize based on target operating data and / or initial control strategy, with the goal of minimizing the total cost of the park's integrated energy system, and determine the optimization strategy.

[0074] Figure 4 is a schematic diagram of an electronic device provided in an embodiment of the present invention. As shown in Figure 4, the electronic device 4 of this embodiment includes a processor 40 and a memory 41. The memory 41 stores a computer program 42. When the processor 40 executes the computer program 42, it implements the steps in the various control strategy embodiments described above. Alternatively, when the processor 40 executes the computer program 42, it implements the functions of each module / unit in the various device embodiments described above.

[0075] For example, computer program 42 may be divided into one or more modules / units, which are stored in memory 41 and executed by processor 40 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 42 in electronic device 4.

[0076] Electronic device 4 may include, but is not limited to, processor 40 and memory 41. Those skilled in the art will understand that FIG4 is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 4 may also include input / output devices, network access devices, buses, etc.

[0077] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0078] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0079] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A cloud-edge collaborative control strategy for a park integrated energy system, characterized in that, This system is applied to a cloud-based energy management system and multiple edge controllers. The cloud-based energy management system connects to the multiple edge controllers, and the edge controllers connect to energy devices in a park's integrated energy system. The control strategy includes: The edge controller determines the uncertainty level of the corresponding energy device based on its operating data; if the uncertainty level is greater than or equal to a preset threshold, it uploads the operating data as target operating data to the cloud-based energy management system based on the uncertainty level; if the uncertainty level is less than the preset threshold, it determines an initial control strategy for the corresponding energy device based on the operating data and uploads the initial control strategy to the cloud-based energy management system based on the uncertainty level; the cloud-based energy management system determines an optimization strategy based on the target operating data and / or the initial control strategy.

2. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 1, characterized in that, Determining the uncertainty level of a corresponding energy device based on its operating data includes: determining the length of a sliding window based on the difference between the historical operating data and the predicted value of the historical operating data; determining the mean, maximum, and minimum operating data values ​​of the corresponding energy device according to the length of the sliding window and the historical operating data; and determining the uncertainty level of the corresponding energy device based on the mean, maximum, and minimum operating data values ​​and the operating data of the corresponding energy device.

3. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 2, characterized in that, The length of the sliding window is determined based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy equipment. This includes: determining the mean difference based on the difference between the historical operating data and the predicted value of the historical operating data of the corresponding energy equipment; if the mean difference is less than or equal to a preset mean difference, the product of the preset length of the sliding window and the contraction coefficient is used as the length of the sliding window; if the mean difference is greater than the preset mean difference, the product of the preset length of the sliding window and the expansion coefficient is used as the length of the sliding window.

4. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 2, characterized in that, Determining the uncertainty level of a corresponding energy device based on the mean, maximum, and minimum operating data and the operating data of the corresponding energy device includes: calculating the difference between the maximum operating data and the operating data of the corresponding energy device, and calculating the difference between the minimum operating data and the operating data of the corresponding energy device, and determining the maximum of the two differences as the maximum difference; calculating the difference between the mean operating data and the operating data of the corresponding energy device, and recording it as the target difference; and determining the uncertainty level of the corresponding energy device based on the target difference and the maximum difference.

5. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 1, characterized in that, Uploading the operational data as target operational data to the cloud-based energy management system according to the uncertainty level includes: determining a weighting factor corresponding to the operational data based on the uncertainty level; determining a transmission time slot allocated to the operational data based on the weighting factor corresponding to the operational data; and uploading the operational data as target operational data to the cloud-based energy management system according to the transmission time slot allocated to the operational data.

6. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 5, characterized in that, Determining the weighting factor corresponding to the operational data based on the uncertainty level includes: calculating the ratio of the uncertainty level to the highest uncertainty level; and determining the weighting factor corresponding to the operational data based on the ratio.

7. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 5, characterized in that, Determining the transmission time slot allocated to the running data based on the weighting factor corresponding to the running data includes: based on The transmission time slots allocated to the running data are determined; wherein, For the first The time slots allocated for the transmission of operational data from each energy device. For the first Weighting factors corresponding to the operating data of each energy device For the first The amount of data transmitted for the operation data of each energy device For channel capacity, This is the duration of the transmission frame.

8. The cloud-edge collaborative integrated energy system control strategy for industrial parks according to claim 1, characterized in that, Determining an optimization strategy based on the target operating data and / or the initial control strategy includes: optimizing the overall cost of the park's integrated energy system based on the target operating data and / or the initial control strategy, with the goal of minimizing the total cost.

9. A cloud-edge collaborative integrated energy system control device for a park, characterized in that, This device is applied to a cloud-based energy management system and multiple edge controllers. The cloud-based energy management system connects to the multiple edge controllers, and the edge controllers connect to energy devices in a park's integrated energy system. The device includes: an edge control module, used to enable the edge controller to determine the uncertainty level of the corresponding energy device based on its operating data; if the uncertainty level is greater than or equal to a preset threshold, the edge controller uploads the operating data as target operating data to the cloud-based energy management system based on the uncertainty level; if the uncertainty level is less than the preset threshold, the edge controller determines an initial control strategy for the corresponding energy device based on the operating data and uploads the initial control strategy to the cloud-based energy management system based on the uncertainty level; and a cloud control module, used to enable the cloud-based energy management system to determine an optimization strategy based on the target operating data and / or the initial control strategy.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the control strategy as described in any one of claims 1 to 8.

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