Geographical time difference-based photovoltaic computing power cross-region scheduling method and system, electronic device and storage medium

Abstract

The application relates to a photovoltaic computing power cross-region scheduling method and system based on a geographical time difference, an electronic device and a storage medium. The method comprises the following steps: deploying photovoltaic computing power nodes in at least two different longitude geographical regions, wherein the longitude difference between the at least two different longitude geographical regions corresponds to a time difference of not less than 2 hours; collecting state data of each photovoltaic computing power node in real time, wherein the state data comprises photovoltaic power generation, computing power load information and geographical position information; obtaining local time corresponding to each photovoltaic computing power node according to the geographical position information, generating a sunshine state corresponding to each photovoltaic computing power node based on the local time, analyzing the sunshine state, the photovoltaic power generation and the computing power load information, and generating a computing power scheduling path; and executing the computing power scheduling path to complete cross-region computing power switching within a preset time. The application can utilize the time difference between the eastern and western regions, automatically call the daytime surplus computing power in the western region after sunset in the eastern region, and ensure that resources are not wasted.

Description

Technical Field

[0001] This invention relates to the field of computing power scheduling technology, and in particular to a method, system, electronic device and storage medium for cross-regional scheduling of photovoltaic computing power based on geographical time difference. Background Technology

[0002] Photovoltaic power generation, as a renewable and clean energy source, is a technology that directly generates electricity using solar energy.

[0003] In related technologies, computing devices can utilize photovoltaic power generation to perform tasks such as AI reasoning. However, photovoltaic power generation is affected by sunlight, generating electricity during the day and ceasing at night, resulting in a daily power generation curve with distinct peaks and troughs. Due to significant differences in sunset times across different longitudes, particularly between eastern and western regions, the solar resources of western regions cannot be effectively utilized by eastern regions, leading to resource waste. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, electronic device and storage medium for cross-regional scheduling of photovoltaic computing power based on geographical time difference. It can take advantage of the time difference between the east and west regions, and automatically call up the surplus computing power of the western region during the day after sunset in the eastern region to ensure that resources are not wasted.

[0005] The objective of this invention is achieved through the following technical solution: The first aspect of this application provides a method for cross-regional scheduling of photovoltaic computing power based on geographic time difference, comprising: deploying photovoltaic computing power nodes in at least two geographical regions with different longitudes, wherein the time difference corresponding to the longitude difference between the at least two geographical regions with different longitudes is not less than 2 hours; collecting status data of each photovoltaic computing power node in real time, the status data including photovoltaic power generation, computing power load information and geographical location information; obtaining the local time corresponding to each photovoltaic computing power node according to the geographical location information, generating the sunshine status corresponding to each photovoltaic computing power node based on the local time, parsing the sunshine status, the photovoltaic power generation and the computing power load information, and generating a computing power scheduling path; executing the computing power scheduling path to complete the cross-regional computing power switching within a preset time.

[0006] The step of parsing the sunshine status, photovoltaic power generation, and computing load information to generate a computing power scheduling path includes: obtaining the sunset time based on the sunshine status; constructing computing power supply and demand variables from the photovoltaic power generation and computing load information, and forming a dynamic scheduling matrix with the sunset time; and calculating the dynamic scheduling matrix using an algorithm to obtain the computing power scheduling path.

[0007] The method further includes generating a first-level load, a second-level load, and a third-level load based on the computing power load information.

[0008] A second aspect of this application provides a cross-regional scheduling system for photovoltaic computing power based on geographic time difference, comprising: a node deployment module for deploying photovoltaic computing power nodes in at least two geographic regions with different longitudes; a monitoring module for collecting real-time status data of each photovoltaic computing power node, the status data including photovoltaic power generation, computing load information, and geographic location information; a time difference scheduling algorithm module for obtaining the local time corresponding to each photovoltaic computing power node based on the geographic location information, generating the sunshine status corresponding to each photovoltaic computing power node based on the local time, parsing the sunshine status, the photovoltaic power generation, and the computing load information, and generating a computing power scheduling path; and a switching execution module for executing the computing power scheduling path and completing the cross-regional computing power switching within a preset time.

[0009] The time difference scheduling algorithm module is also used to obtain the sunset time based on the sunshine status; to form a computing power supply and demand variable by combining the photovoltaic power generation and the computing power load information, and to form a dynamic scheduling matrix with the sunset time; and to calculate the dynamic scheduling matrix using an algorithm to obtain the computing power scheduling path.

[0010] The system also includes a load classification management module, which is used to generate first-level load, second-level load and third-level load based on the computing power load information.

[0011] The system also includes an exception handling module, which is used to detect the operation of the node deployment module, the monitoring module, the time difference scheduling algorithm module, and the switching execution module.

[0012] The system also includes a revenue optimization module, which communicates with the node deployment module to calculate the computing power scheduling revenue of the node deployment module.

[0013] A third aspect of this application provides an electronic device, comprising: Processor; and A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0014] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0015] Compared with the prior art, the present invention has at least the following advantages: This application utilizes the natural law of geographical time difference between different regions. When one region is in the afternoon and another region is still in the afternoon, the surplus computing power of the region in the afternoon is automatically allocated to the region in the afternoon to make up for the computing power gap at night. This allows the surplus computing power in the afternoon to be allocated and the resources to be used effectively. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below.

[0017] Figure 1 This is a flowchart of a method for cross-regional scheduling of photovoltaic computing power based on geographical time difference in one embodiment of the present invention; Figure 2 This is a flowchart illustrating another embodiment of the cross-regional scheduling method for photovoltaic computing power based on geographical time difference, as described in one embodiment of the present invention. Figure 3 This is a flowchart illustrating another embodiment of the cross-regional scheduling method for photovoltaic computing power based on geographical time difference, as described in one embodiment of the present invention. Figure 4 This is a functional block diagram of a cross-regional photovoltaic computing power scheduling system based on geographic time difference in one embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0019] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0020] Unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0021] Computing equipment can generate electricity using photovoltaics, and then perform tasks such as AI reasoning. However, photovoltaic power generation is affected by sunlight, generating electricity during the day and stopping at night, resulting in a daily power generation curve with obvious peaks and troughs. Due to significant differences in sunset times across different longitudes, especially between the eastern and western regions, the solar resources of the western region cannot be effectively utilized by the eastern region, leading to resource waste.

[0022] To address the aforementioned issues, this application provides a method, system, electronic device, and storage medium for cross-regional scheduling of photovoltaic computing power based on geographical time difference. This method can utilize the time difference between eastern and western regions to automatically call upon surplus computing power from western regions during the daytime after sunset in eastern regions, ensuring that resources are not wasted.

[0023] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flowchart illustrating a cross-regional scheduling method for photovoltaic computing power based on geographical time difference, as shown in an embodiment of this application.

[0025] See Figure 1 A method for cross-regional scheduling of photovoltaic computing power based on geographical time difference, comprising: Step S101: Deploy photovoltaic computing nodes in at least two geographical regions with different longitudes, wherein the time difference corresponding to the longitude difference between at least two geographical regions with different longitudes is not less than 2 hours.

[0026] It should be noted that at least two photovoltaic computing nodes are deployed, and the time difference between these two regions is no less than 2 hours. For example, the first photovoltaic computing node is deployed in Qingyuan, Guangdong, serving as the eastern benchmark node (located at 113 degrees east longitude). The second photovoltaic computing node is deployed in Gansu, serving as the central transition node (located at 103 degrees east longitude). The third photovoltaic computing node is deployed in Kashgar, Xinjiang, serving as the western core node (located at 76 degrees east longitude). Sunset in Qingyuan, Guangdong, is about 40 minutes earlier than in Gansu, and sunset in Qingyuan, Guangdong, is about 2.5 hours earlier than in Xinjiang. The combined time difference of the three locations creates a nearly 6-hour window of sunlight relay.

[0027] Furthermore, the photovoltaic computing node includes a photovoltaic power generation device, computing equipment, a control unit, a DC-DC conversion unit, a combiner unit, and a data acquisition unit. The installed capacity of the photovoltaic power generation device is determined based on local solar resources and computing power requirements. The computing equipment includes AI servers, GPU clusters, etc. The control unit is responsible for data acquisition, instruction execution, and status reporting. The DC-DC conversion unit is used to directly convert photovoltaic DC power into the voltage adapted to the computing equipment, eliminating the need for an inverter. The combiner unit is used to combine the DC output of multiple photovoltaic modules. The data acquisition unit is used to collect operating parameters such as voltage, current, and temperature.

[0028] Step S102: Collect the status data of each photovoltaic computing node in real time. The status data includes photovoltaic power generation, computing load information and geographical location information.

[0029] It should be noted that the real-time acquisition frequency of this step is no less than 100Hz to ensure the timeliness of the data.

[0030] Step S103: Based on the geographical location information, obtain the local time corresponding to each photovoltaic computing power node, generate the sunshine status corresponding to each photovoltaic computing power node based on the local time, analyze the sunshine status, photovoltaic power generation and computing load information, and generate computing power scheduling path.

[0031] It should be noted that this step obtains the local time of each photovoltaic computing node based on geographical location information, thus determining the sunshine status. Finally, the optimal computing power scheduling path is dynamically calculated based on the sunshine status, photovoltaic power generation, and computing power load information. Further, during Guangdong's daytime hours (8:00-18:00): local computing power is prioritized, with surplus computing power output to Gansu and / or Xinjiang. During Guangdong's sunset hours (18:00-18:40): surplus computing power from Gansu during the day is utilized; during Guangdong's sunset hours (18:40-20:30): surplus computing power from Xinjiang during the day is utilized; during Xinjiang's sunset hours (20:30-8:00 the next day): local edge computing caching is activated or nodes in other time zones are utilized.

[0032] Step S104: Execute the computing power scheduling path and complete the cross-regional computing power switching within a preset time.

[0033] It should be noted that, in order to ensure that the switching is not delayed, the preset time is usually 50ms.

[0034] Figure 2 for Figure 1 A more detailed implementation method, a cross-regional scheduling method for photovoltaic computing power based on geographical time difference, includes: Step S201: Deploy photovoltaic computing nodes in at least two geographical regions with different longitudes, wherein the time difference corresponding to the longitude difference between at least two geographical regions with different longitudes is not less than 2 hours; This step can be described in step S101, and will not be repeated here.

[0035] Step S202: Collect the status data of each photovoltaic computing node in real time. The status data includes photovoltaic power generation, computing load information and geographical location information. This step can be described in step S102, and will not be repeated here.

[0036] Step S203: Based on the geographical location information, obtain the local time corresponding to each photovoltaic computing power node, generate the sunshine status corresponding to each photovoltaic computing power node based on the local time, and obtain the sunset time based on the sunshine status; construct the computing power supply and demand variables by combining the photovoltaic power generation and computing power load information, and form a dynamic scheduling matrix with the sunset time; use the algorithm to calculate the dynamic scheduling matrix to obtain the computing power scheduling path.

[0037] It should be noted that before generating a computing power scheduling path, the following conditions must be met: one of the photovoltaic computing power nodes must be within 4 hours after sunset, and its computing power gap must be greater than 10% of its core load requirement. The other photovoltaic computing power node must be in daytime and its surplus computing power must be no less than 1.2 times the computing power gap of the first photovoltaic computing power node.

[0038] It should also be noted that the above algorithm can be a greedy algorithm or a dynamic programming algorithm. Specifically, it also includes the following steps: Step S2031: Construct a time window constraint based on sunset time, and use photovoltaic power generation and computing load information to form computing power supply and demand variables.

[0039] It should be noted that the sunset time for the day is calculated based on the location's latitude and longitude and the date. Using sunset time as the key boundary constraint: when t < During periods when photovoltaic power generation is sufficient, the supply and demand variable for computing power is defined as net power generation capacity: In t≥ During this period, photovoltaic power generation approaches zero, at which time the computing power supply and demand variables are adjusted to... (That is, completely dependent on energy storage or the power grid). After normalization, dimensionless supply and demand factors are formed. Where Ppv(t) is the photovoltaic power generation at time t. Let S(t) be the computing power load demand at time t, and S(t) be the computing power supply and demand variable, representing the net capacity value after balancing photovoltaic power generation and load demand. The time index i (1 to N) and the computing power node index j (1 to M) are used as the two dimensions of the matrix, and the elements... This represents the scheduling cost or benefit at time slice i and node j. Where: , and is a weight coefficient, is the value of node j at time, which represents communication delay or energy consumption cost. Meanwhile, set the sunset time corresponding index k as a hard constraint, where k is the index corresponding to sunset: all column vectors with i<k can be preferentially allocated computing power directly supplied by photovoltaics; all column vectors are automatically switched to energy storage or grid power supply mode, and a penalty term is imposed on the scheduling path to suppress excessively late scheduling. Finally, an N×M-dimensional dynamic scheduling matrix D is obtained. Here is a penalty coefficient.

[0040] In this way, time, nodes and physical constraints are uniformly encoded into the matrix structure, so that the scheduling problem is transformed into a mathematical problem of finding the optimal path on a two-dimensional matrix. The introduction of the penalty term effectively prevents the impact of large-scale night computing power scheduling on the energy storage system and prolongs the service life of energy storage equipment.

[0041] Step S2032: Calculating the dynamic scheduling matrix by using a greedy algorithm or dynamic programming to obtain a computing power scheduling path.

[0042] It should be noted that, with the dynamic scheduling matrix DD as input, the optimal scheduling path is solved in one of the following two ways : Greedy algorithm: In each time slice i, select the node j with the maximum value in the current column as the scheduling target, and recursively proceed in sequence to form a locally optimal path. Dynamic programming algorithm: Define the state as the cumulative maximum benefit when reaching time i and the last scheduling node is j, and the state transition equation is:

[0043] where, i is a time index, j is a computing power node index, is the state value of dynamic programming, representing the cumulative maximum benefit when reaching the i-th time and the last scheduling node is j, is an element in the dynamic scheduling matrix, representing the direct benefit obtained by scheduling a task to node j at time i. refers to taking the maximum value among all possible nodes of the previous time . represents the node selected at the previous time. is a node switching cost coefficient, is a function representing the cost or overhead generated when switching from the node of the previous time to node j of the current time. Finally, the global optimal path is obtained through backtracking.

[0044] Furthermore, this method also includes generating first-level load, second-level load, and third-level load based on computing power load information.

[0045] It should be noted that the Tier 1 load consists of core businesses such as AI inference and real-time data processing, with a weighting coefficient of 0.6; the Tier 2 load consists of routine businesses such as data storage and batch processing, with a weighting coefficient of 0.3; and the Tier 3 load consists of non-critical computing and redundancy backup, with a weighting coefficient of 0.1. When photovoltaic power generation is sufficient, Tier 1 loads are prioritized, followed by Tier 2 and Tier 3 loads in sequence. When photovoltaic power generation is insufficient, Tier 1 loads are prioritized, and Tier 2 and Tier 3 loads are suspended or switched to other nodes. Cross-regional scheduling computing power is prioritized for allocation to Tier 1 loads.

[0046] Step S204: Execute the computing power scheduling path and complete the cross-regional computing power switching within a preset time.

[0047] This step can be described in step S104, and will not be repeated here.

[0048] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a cross-regional scheduling system for photovoltaic computing power based on geographical time difference, electronic equipment, and corresponding embodiments.

[0049] Figure 4 This is a functional block diagram of a cross-regional photovoltaic computing power scheduling system based on geographical time difference, as shown in an embodiment of this application.

[0050] See Figure 4 A cross-regional photovoltaic computing power scheduling system based on geographic time difference includes a node deployment module 100, a monitoring module 200, a time difference scheduling algorithm module 300, and a switching execution module 400. The node deployment module 100 is used to deploy photovoltaic computing power nodes in at least two geographic regions with different longitudes. The monitoring module 200 is used to collect the status data of each photovoltaic computing power node in real time, including photovoltaic power generation, computing power load information, and geographic location information. The time difference scheduling algorithm module 300 is used to obtain the local time corresponding to each photovoltaic computing power node according to the geographic location information, generate the sunshine status corresponding to each photovoltaic computing power node based on the local time, and parse the sunshine status, photovoltaic power generation, and computing power load information to generate a computing power scheduling path. The switching execution module 400 executes the computing power scheduling path and completes the cross-regional computing power switching within a preset time.

[0051] See Figure 4 In one embodiment, the time difference scheduling algorithm module 300 is further used to obtain the sunset time based on the sunshine status; to form a dynamic scheduling matrix by combining the photovoltaic power generation and computing load information into computing power supply and demand variables and the sunset time; and to calculate the dynamic scheduling matrix using an algorithm to obtain the computing power scheduling path.

[0052] See Figure 4 In one embodiment, the system further includes a load classification management module 500 for generating first-level load, second-level load and third-level load based on computing power load information.

[0053] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0054] See Figure 4 The system also includes an exception handling module 600, which is used to detect the operation of the node deployment module 100, the monitoring module 200, the time difference scheduling algorithm module 300, and the switching execution module 400.

[0055] It should be noted that: First, the anomaly handling module 600 monitors network connectivity and automatically switches to the backup transmission channel when a network interruption is detected. Second, the anomaly handling module 600 monitors the operating status of each photovoltaic computing node and immediately switches the load to the backup photovoltaic computing node when a photovoltaic computing node failure is detected. Third, the anomaly handling module 600 monitors photovoltaic power generation fluctuations and switches low-priority loads within milliseconds when a voltage drop is detected. Fourth, the anomaly handling module 600 performs timeout detection on scheduling commands and automatically resends and activates the backup scheduling path when a command is confirmed to have timed out.

[0056] See Figure 4 In one embodiment, the system further includes a revenue optimization module 700, which communicates with the node deployment module 100 to calculate the computing power scheduling revenue of the node deployment module 100.

[0057] It should be noted that the revenue optimization module 700 calculates the computing power scheduling revenue of each photovoltaic computing power node in real time, prioritizing the scheduling of computing power tasks with higher revenue while ensuring the core load. Furthermore, this module can dynamically adjust the scheduling strategy based on computing power market conditions.

[0058] To better illustrate the above system, for example: Time slot 1: 8:00~17:20 Node A (daytime): With sufficient solar power, local computing power is prioritized, and surplus computing power is output to nodes B and C; Node B (daytime): With sufficient solar power, local computing power is prioritized, and surplus computing power is output to Node C; Node C (daytime): Ample solar power, local computing power prioritized.

[0059] Time period two: 17:20~18:00 Node B is experiencing sunset, while Node A still has 40 minutes of daylight. Node B's surplus computing power is output to Node A; Node C continues throughout the day; Time period 3: 18:00~20:30 Node A is at sunset, Node B is at sunset, and Node C is still in daylight; Node C's daytime computing power is output to Node A; The primary load of node A is guaranteed by node C.

[0060] Time period four: 20:30 to 8:00 the next day None of the three locations have photovoltaic power supply, so they use edge computing to cache local computing power or call on photovoltaic computing power nodes in other time zones such as Europe and Central Asia.

[0061] Figure 5 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application.

[0062] See Figure 5 The electronic device 1000 includes a memory 1010 and a processor 1020.

[0063] The processor 1020 can be a central processing unit (CPU), or it can be an integrated circuit composed of other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be any conventional processor that can run the Linux kernel.

[0064] Memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 1020 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices employ mass storage devices (e.g., flash memory). In other embodiments, permanent storage devices may be removable storage devices. System memory may be read-write storage devices or volatile read-write storage devices, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory). In some embodiments, memory 1010 may include removable storage devices that are readable and / or writable, such as flash memory cards (e.g., SD cards, mini SD cards, Micro-SD cards, etc.). Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wire.

[0065] The memory 1010 stores executable code, which, when processed by the processor 1020, can cause the processor 1020 to execute part or all of the methods described above.

[0066] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0067] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0068] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different focuses; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0069] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for cross-regional scheduling of photovoltaic computing power based on geographical time difference, characterized in that, include: Photovoltaic computing nodes are deployed in at least two geographical regions with different longitudes, wherein the time difference between the longitude difference of at least two geographical regions with different longitudes is not less than 2 hours. Real-time collection of status data for each photovoltaic computing node, including photovoltaic power generation, computing load information, and geographical location information; Based on the geographical location information, the local time corresponding to each photovoltaic computing power node is obtained. Based on the local time, the sunshine status corresponding to each photovoltaic computing power node is generated. The sunshine status, photovoltaic power generation and computing power load information are parsed to generate a computing power scheduling path. The computing power scheduling path is executed to complete the cross-regional computing power switching within a preset time.

2. The method for cross-regional scheduling of photovoltaic computing power based on geographical time difference according to claim 1, characterized in that, The step of parsing the sunshine status, photovoltaic power generation, and computing load information to generate a computing power scheduling path includes: The sunset time is obtained based on the described sunlight conditions; The photovoltaic power generation and the computing power load information are used to form computing power supply and demand variables, which are then combined with the sunset time to form a dynamic scheduling matrix. The algorithm is used to calculate the dynamic scheduling matrix to obtain the computing power scheduling path.

3. The method for cross-regional scheduling of photovoltaic computing power based on geographical time difference according to claim 1, characterized in that, The method further includes: Based on the computing power load information, first-level load, second-level load and third-level load are generated.

4. A cross-regional scheduling system for photovoltaic computing power based on geographical time difference, characterized in that, include: A node deployment module is used to deploy photovoltaic computing nodes in at least two different longitude regions. The monitoring module is used to collect the status data of each photovoltaic computing node in real time. The status data includes photovoltaic power generation, computing load information and geographical location information. The time difference scheduling algorithm module is used to obtain the local time corresponding to each of the photovoltaic computing power nodes based on the geographical location information, generate the sunshine status corresponding to each of the photovoltaic computing power nodes based on the local time, parse the sunshine status, the photovoltaic power generation and the computing power load information, and generate a computing power scheduling path. The execution module is switched to execute the computing power scheduling path, and the cross-regional computing power switching is completed within a preset time.

5. The cross-regional scheduling system for photovoltaic computing power based on geographical time difference according to claim 4, characterized in that, The time difference scheduling algorithm module is also used to obtain the sunset time based on the sunshine status; The photovoltaic power generation and the computing power load information are used to form computing power supply and demand variables, which are then combined with the sunset time to form a dynamic scheduling matrix. The algorithm is used to calculate the dynamic scheduling matrix to obtain the computing power scheduling path.

6. The cross-regional scheduling system for photovoltaic computing power based on geographical time difference according to claim 4, characterized in that, The system also includes a load classification management module, which is used to generate first-level load, second-level load and third-level load based on the computing power load information.

7. The cross-regional scheduling system for photovoltaic computing power based on geographical time difference according to claim 4, characterized in that, The system also includes an exception handling module, which is used to detect the operation of the node deployment module, the monitoring module, the time difference scheduling algorithm module, and the switching execution module.

8. The cross-regional scheduling system for photovoltaic computing power based on geographical time difference according to claim 4, characterized in that, The system also includes a revenue optimization module, which communicates with the node deployment module to calculate the computing power scheduling revenue of the node deployment module.

9. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1-3.

10. A computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method as described in any one of claims 1-3.

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