Power grid dynamic partitioning system based on cloud service architecture

Abstract

The invention discloses a power grid dynamic partitioning system based on a cloud service architecture, and relates to the technical field of power grids, and the system comprises a data collection module, the output end of the data collection module is connected with a data verification module, and the data verification module comprises a file verification module, a storage formatting module and a data import module; the output end of the file verification module is connected with a storage formatting module and a data import module, the storage formatting module is connected with the data import module in parallel, the output end of the data acquisition module is connected with a data storage module, and the data verification module is connected with the data storage module in parallel. According to the method, the accuracy and integrity of the power grid data are ensured, the problem of loss in the data transmission process is effectively avoided, meanwhile, the function of transmitting and calculating the data at the same time is achieved, the data processing speed and efficiency are improved, power grid dynamic partition can be completed more quickly and accurately, and the management and control level of power grid operation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a power grid dynamic partitioning system based on a cloud service architecture. Background Art

[0002] The power grid dynamic zoning system is a technical system that divides the power grid into different areas in order to better manage and control the operation of the power grid.

[0003] The existing power grid dynamic partitioning system may experience data loss during data transmission, and when data is lost, it can only be discovered during calculation. At this time, the system needs to reprocess the data according to the previous collection and transmission steps, resulting in a waste of time. To this end, in response to the above defects, the present application proposes a power grid dynamic partitioning system based on a cloud service architecture. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a power grid dynamic partitioning system based on a cloud service architecture, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a power grid dynamic partitioning system based on a cloud service architecture, comprising: a data acquisition module, an output end of which is connected to a data verification module, and the data verification module comprises a file verification module, a storage formatting module and a data import module, the output end of the file verification module is connected to a storage formatting module and a data import module, and the storage formatting module and the data import module are connected in parallel, the output end of the data acquisition module is connected to a data storage module, and the data verification module and the data storage module are connected in parallel.

[0007] As a preferred solution of the power grid dynamic partitioning system based on the cloud service architecture described in the present invention, data collected by the power grid is verified, and the specific algorithm and process of the data verification module are as follows:

[0008] Randomly select a subsample S from the dataset, usually a random subset of the original sample;

[0009] Randomly select a feature j∈{1,2,…,m} from the feature set, where m is the total number of features;

[0010] Randomly select a split value c between the minimum and maximum values ​​of feature j;

[0011] The subsample S is divided into two parts according to the cut value c:

[0012] ·SL ={x ∈ S | x j < c} (left subtree)

[0013] ·S R ={x ∈ S | x j > c} (right subtree)

[0014] For S L and S R perform recursion until any of the following conditions is met: reaching the maximum depth h max The subsample has only one sample (completely isolated) or the subsample is empty;

[0015] For each sample x, calculate its path length h(x) in all trees, that is, the path length from the root node to the leaf node of this sample;

[0016] The anomaly score s(x) can be calculated from the path length h(x):

[0017]

[0018] where: n is the total number of samples, and c(n) is a constant representing the expected path length when the number of samples is n, and the calculation method is:

[0019]

[0020] where H(i) is the i-th harmonic number, and the calculation formula is:

[0021]

[0022] Divide the samples into normal and abnormal by setting a threshold θ;

[0023] If s(x) > θ, then the sample x is marked as abnormal;

[0024] If s(x) ≤ θ, then the sample x is marked as normal.

[0025] As a preferred solution of the power grid dynamic partitioning system based on the cloud service architecture described in the present invention, where: the output end of the data storage module is connected to a data transmission module, and the output end of the data transmission module is connected to a data cloud storage module;

[0026] The output end of the data cloud storage module is connected to a data analysis module, and the output end of the data analysis module is connected to a data refinement module;

[0027] The data transmission module is input-output connected with the data refinement module, and the data transmission module is in parallel with the data analysis module.

[0028] As a preferred solution of the power grid dynamic partitioning system based on cloud service architecture described in the present invention, wherein: the data refinement module and the data storage module are connected in parallel, and the data refinement module and the data cloud storage module are connected in parallel;

[0029] The output end of the data analysis module is connected to a dynamic partitioning module, and the dynamic partitioning module and the data refinement module are connected in parallel;

[0030] The data refinement module includes a data application marking module, a data classification module and a data differentiation module, and the output end of the data application marking module is connected to the data classification module, and the output end of the data classification module is connected to the data differentiation module.

[0031] As a preferred solution of the power grid dynamic partitioning system based on cloud service architecture described in the present invention, the output end of the dynamic partitioning module is connected to a decision-making module, and the output end of the decision-making module is connected to a visualization display module.

[0032] As a preferred solution of the power grid dynamic partitioning system based on cloud service architecture described in the present invention, wherein: the output end of the visualization display module is connected to the instruction execution module, and the output end of the instruction execution module is connected to the power grid equipment.

[0033] As a preferred solution of the power grid dynamic partitioning system based on cloud service architecture described in the present invention, wherein: the output end of the instruction execution module is connected to the power grid equipment operation module, and the power grid equipment operation module and the power grid equipment are connected in parallel.

[0034] To further solve the above technical problems, the present invention provides the following technical solutions: a method for dynamic partitioning of a power grid based on a cloud service architecture, comprising: collecting real-time data of the power grid through a data acquisition module, and outputting the collected data to a data verification module and a data storage module; after the file verification module in the data verification module verifies the data, the storage formatting module formats the data, and then the processed data is re-input into the data storage module through the data import module; the data in the data storage module is transmitted to a data cloud storage module, and then the data analysis module performs analysis based on the data in the data cloud storage module, and passes the analysis results to the dynamic partitioning module to complete the dynamic partitioning of the power grid.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the power grid dynamic partitioning system based on the cloud service architecture as described above when executing the computer program.

[0036] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the power grid dynamic partitioning system based on the cloud service architecture as described above are implemented.

[0037] The beneficial effects of the present invention are as follows: 1. The data acquisition module and data storage module of the present invention will transmit data to the file verification module to verify the data, ensuring that the data stored in the data storage module is consistent with the data collected by the data acquisition module, avoiding data loss during data transmission, affecting the subsequent judgment of the system, and avoiding the waste of time caused by discovering data loss during calculation.

[0038] 2. The file verification module of the present invention controls the storage formatting module to format the data in the data storage module, and then transmits the data verified by the file verification module back to the data storage module for storage through the data import module, thereby correcting the data and ensuring the accuracy of data transmission.

[0039] 3. The present invention classifies data through a data classification module, divides the classified data into multiple steps, divides the transmission order, and then differentiates the data through a data differentiation module, thereby transmitting the data to the data transmission module in the transmission order divided by the data classification module, so that the data can be transmitted in the order of use, which can achieve the effect of calculating while transmitting and speed up the data calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 It is the overall flow chart of the present invention;

[0042] Figure 2 This is a flow chart of the data verification module of the present invention;

[0043] Figure 3 This is a flow chart of the data refinement module of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a power grid dynamic partitioning system based on cloud service architecture.

[0047] See also Figure 1 The present invention provides a technical solution: comprising a data acquisition module, the output end of the data acquisition module is connected to a data verification module, and the data verification module comprises a file verification module, a storage formatting module and a data import module, the output end of the file verification module is connected to the storage formatting module and the data import module, and the storage formatting module and the data import module are connected in parallel, the output end of the data acquisition module is connected to a data storage module, and the data verification module and the data storage module are connected in parallel;

[0048] The data acquisition module and the data storage module will transmit the data to the file verification module to verify the data and ensure that the data stored in the data storage module is consistent with the data collected by the data acquisition module, so as to avoid data loss during data transmission and affect the subsequent judgment of the system, and also avoid the waste of time caused by discovering data loss during calculation. The file verification module will control the storage formatting module to format the data in the data storage module, and then transmit the data verified by the file verification module back to the data storage module for storage through the data import module, so as to correct the data and ensure the accuracy of data transmission.

[0049] Construct multiple random trees:

[0050] Randomly select samples from the dataset, usually between 1% and 100% of the original sample.

[0051] Randomly select features and then randomly select the split value to divide the data into two parts.

[0052] This process is continued recursively for each part until each sample is isolated or the preset tree height is reached.

[0053] Calculate the anomaly score: For each sample, calculate its isolation time in all trees (i.e., the length of the path to the leaf node). Use the isolation time to calculate the anomaly score: the shorter the path, the easier it is to be isolated, and the higher the score.

[0054] Set threshold for classification: Set a threshold based on the anomaly score to classify samples into normal and abnormal.

[0055] The data collected by the power grid is verified. The specific algorithm and process of the data verification module are as follows:

[0056] Randomly select a subsample S from the dataset, usually a random subset of the original sample;

[0057] Randomly select a feature j∈{1,2,…,m} from the feature set, where m is the total number of features;

[0058] Randomly select a split value c between the minimum and maximum values ​​of feature j;

[0059] The subsample S is divided into two parts according to the cut value c:

[0060] ·S L ={x∈S|xj<c}(left subtree)

[0061] ·S R ={x∈S|x j >c}(right subtree)

[0062] For S L and S R Recursively repeat until any of the following conditions is met: the maximum depth h is reached max The subsample has only one sample (completely isolated) and the subsample is empty;

[0063] For each sample x, calculate its path length h(x) in all trees, that is, the path length from the root node to the leaf node of the sample;

[0064] The anomaly score s(x) can be calculated from the path length h(x):

[0065]

[0066] Where: n is the total number of samples, c(n) is a constant representing the expected path length when the number of samples is n, calculated as:

[0067]

[0068] Where H(i) is the i-th harmonic number, and the calculation formula is:

[0069]

[0070] The samples are divided into normal and abnormal by setting the threshold θ;

[0071] If s(x)>θ, then sample x is marked as abnormal;

[0072] If s(x)≤θ, then the sample x is labeled as normal.

[0073] like Figure 1 As shown, the output end of the data storage module is connected to the data transmission module, and the output end of the data transmission module is connected to the data cloud storage module, the output end of the data cloud storage module is connected to the data analysis module, and the output end of the data analysis module is connected to the data refinement module;

[0074] The data transmission module will extract the data stored in the data storage module to transmit the data. The data refinement module will differentiate the data transmitted by the data transmission module so that the data transmission module can transmit the data in sequence. The data cloud storage module will store the data transmitted by the data transmission module, so as to facilitate the data analysis module to extract the data during data analysis.

[0075] like Figure 1 As shown, the data transmission module and the data refinement module are connected in input and output, and the data transmission module and the data analysis module are connected in parallel, the data refinement module and the data storage module are connected in parallel, and the data refinement module and the data cloud storage module are connected in parallel, the output end of the data analysis module is connected to the dynamic partitioning module, and the dynamic partitioning module and the data refinement module are connected in parallel;

[0076] The dynamic partitioning module performs partitioning operations according to the structure analyzed by the data analysis module and preset rules.

[0077] like Figure 3 As shown, the data refinement module includes a data application marking module, a data classification module and a data differentiation module, and the output end of the data application marking module is connected to the data classification module, and the output end of the data classification module is connected to the data differentiation module;

[0078] The data is classified through the data classification module, and the classified data is divided into multiple steps, and the transmission order is divided. Then, the data is differentiated through the data differentiation module, so that the data is transmitted to the data transmission module according to the transmission order divided by the data classification module. In this way, the data can be transmitted in the order of use, which can achieve the effect of calculating while transmitting and speed up the data calculation.

[0079] like Figure 1 As shown, the output end of the dynamic partitioning module is connected to the decision-making module, and the output end of the decision-making module is connected to the visualization module, the output end of the visualization module is connected to the instruction execution module, and the output end of the instruction execution module is connected to the power grid equipment, the output end of the instruction execution module is connected to the power grid equipment operation module, and the power grid equipment operation module and the power grid equipment are connected in parallel;

[0080] The partition-based structure is used to formulate corresponding control strategies and decision-making instructions through the decision-making module. The visualization module can display strategies in the form of graphics, etc., which is convenient for users to watch. At the same time, the data simulated by the power grid equipment operation module will also be displayed on the visualization module for the convenience of staff to observe. The instruction execution module can formulate the instructions generated by the decision-making module, so as to control the power grid equipment to work according to the instructions. The power grid equipment operation module can simulate the instructions generated by the decision-making module, so as to deduce what will happen if the instructions are followed, so as to facilitate the staff to check the feasibility of the instruction operation.

[0081] Working principle: First, the real-time data from each node of the power grid is collected through the data acquisition module, including current, voltage, power, switch status and other information, and then the collected data is transmitted to the data storage module for collection through the data acquisition module. At the same time, the data acquisition module and the data storage module will also transmit the data to the file verification module to verify the data to ensure that the data stored in the data storage module is consistent with the data collected by the data acquisition module, so as to avoid data loss during data transmission and affect the subsequent judgment of the system. If the data in the data storage module is consistent with the data collected by the data acquisition module, the system will continue to run. If the data in the data storage module is inconsistent with the data collected by the data acquisition module, the file verification module will control the storage formatting module to make the storage formatting module format the data in the data storage module, and then the data imported The module will re-transmit the data verified by the file verification module to the data storage module for storage to ensure the accuracy of the data in the data storage module;

[0082] When it is determined that the data in the data storage module is accurate, the data will be transmitted to the data transmission module. At this time, the data required by the data analysis module will be marked by the data application marking module, and sorted according to the analysis steps of the data analysis module. At the same time, the data transmission module will also transmit the data to the data application marking module. At this time, the data application marking module can directly mark the data introduced by the data transmission module, and then classify the data through the data classification module, divide the classified data into multiple steps, divide the transmission order, and then differentiate the data through the data differentiation module, so as to transmit the data to the data transmission module according to the transmission order divided by the data classification module, so that the data transmission The data in the data transmission module is gradually transmitted to the data cloud storage module for storage. At this time, the data analysis module can extract the data in the data cloud storage module before the data transmission module has completed the transmission. Because the data in the data cloud storage module is transmitted according to the steps required for the data analysis module to analyze, the data analysis module analyzes while the data transmission module transmits the data, so that the data transmission and data analysis of the device work synchronously, increasing the cloud computing speed of the device. At the same time, when there is little data in the data cloud storage module, the data analysis module will extract the data. This also facilitates the screening steps of the data analysis module when extracting data, reduces the difficulty of extraction, and avoids the waste of cloud computing computing power.

[0083] The data analysis module uses various algorithms and models to analyze and process the data to identify the operating status and characteristics of the power grid, and then performs partitioning operations through the dynamic partitioning module according to the structure and preset rules analyzed by the data analysis module. Then, based on the partition structure, the decision-making module formulates corresponding control strategies and decision-making instructions, and then displays the formulated strategies in the form of graphics on the visualization display module for users to view. At the same time, the visualization display module will also transmit the received data to the instruction execution module, and transmit the data to the power grid equipment operation module through the instruction execution module for simulation execution, and then display the simulation structure together on the visualization display module. After the staff has viewed it and found no problems, the instruction execution module will execute the power grid equipment control according to the strategy data.

[0084] In summary, the data acquisition module and data storage module of the present invention will transfer data to the file verification module, thereby verifying the data, ensuring that the data stored inside the data storage module is consistent with the data collected by the data acquisition module, avoiding data loss during data transmission, affecting the subsequent judgment of the system, and avoiding the time waste caused by discovering data loss during calculation. The file verification module of the present invention will control the storage formatting module to format the data in the data storage module, and then re-transmit the data verified by the file verification module to the data storage module for storage through the data import module, thereby correcting the data and ensuring the accuracy of data transmission. The present invention classifies data through the data classification module, divides the classified data into multiple steps, divides the transmission order, and then divides the data through the data differentiation module, so that the data is transmitted to the data transmission module according to the transmission order divided by the data classification module, so that the data can be transmitted in the order of use, and the effect of calculating while transmitting can be achieved, which can speed up the data calculation.

[0085] Embodiment 2 is an embodiment of the present invention, which provides a method for dynamic partitioning of a power grid based on a cloud service architecture, including:

[0086] S1: Collect real-time data of the power grid through the data acquisition module, and output the collected data to the data verification module and the data storage module;

[0087] S2: After the file verification module in the data verification module verifies the data, the storage formatting module formats the data, and then the data import module re-inputs the processed data into the data storage module;

[0088] S3: The data in the data storage module is transmitted to the data cloud storage module, and then the data analysis module performs analysis based on the data in the data cloud storage module, and passes the analysis results to the dynamic partitioning module to complete the dynamic partitioning of the power grid.

[0089] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0091] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0092] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power grid dynamic partitioning system based on cloud service architecture, characterized in that: include: The data acquisition module has an output end connected to a data verification module, and the data verification module includes a file verification module, a storage formatting module and a data import module. The output end of the file verification module is connected to the storage formatting module and the data import module, and the storage formatting module and the data import module are connected in parallel. The output end of the data acquisition module is connected to a data storage module, and the data verification module and the data storage module are connected in parallel.

2. The power grid dynamic partitioning system based on cloud service architecture according to claim 1, characterized in that: The data collected by the power grid is verified. The specific algorithm and process of the data verification module are as follows: Randomly select a subsample S from the dataset, usually a random subset of the original sample; Randomly select a feature j∈{1,2,…,m} from the feature set, where m is the total number of features; Randomly select a split value c between the minimum and maximum values ​​of feature j; The subsample S is divided into two parts according to the cut value c: ·S L ={x ∈ S | x j < c} (left subtree) ·S R ={x∈S|x j >c}(right subtree) For S L and S R Recursively repeat until any of the following conditions is met: the maximum depth h is reached max The subsample has only one sample (completely isolated) and the subsample is empty; For each sample x, calculate its path length h(x) in all trees, that is, the path length from the root node to the leaf node of the sample; The anomaly score s(x) can be calculated from the path length h(x): Where: n is the total number of samples, c(n) is a constant representing the expected path length when the number of samples is n, calculated as: Where H(i) is the i-th harmonic number, and the calculation formula is: The samples are divided into normal and abnormal by setting the threshold θ; If s(x)>θ, then sample x is marked as abnormal; If s(x)≤θ, then the sample x is labeled as normal.

3. The power grid dynamic partitioning system based on cloud service architecture as claimed in claim 2, characterized in that: The output end of the data storage module is connected to the data transmission module, and the output end of the data transmission module is connected to the data cloud storage module; The output end of the data cloud storage module is connected to a data analysis module, and the output end of the data analysis module is connected to a data refinement module; The data transmission module and the data refinement module are connected in input and output, and the data transmission module and the data analysis module are connected in parallel.

4. The power grid dynamic partitioning system based on cloud service architecture as claimed in claim 3, characterized in that: The data refinement module is connected in parallel with the data storage module, and the data refinement module is connected in parallel with the data cloud storage module; The output end of the data analysis module is connected to a dynamic partitioning module, and the dynamic partitioning module and the data refinement module are connected in parallel; The data refinement module includes a data application marking module, a data classification module and a data differentiation module, and the output end of the data application marking module is connected to the data classification module, and the output end of the data classification module is connected to the data differentiation module.

5. The power grid dynamic partitioning system based on cloud service architecture according to claim 4, characterized in that: The output end of the dynamic partitioning module is connected to the decision-making module, and the output end of the decision-making module is connected to the visualization display module.

6. The power grid dynamic partitioning system based on cloud service architecture according to claim 5, characterized in that: The output end of the visualization display module is connected to the instruction execution module, and the output end of the instruction execution module is connected to the power grid equipment.

7. The power grid dynamic partitioning system based on cloud service architecture according to claim 6, characterized in that: The output end of the instruction execution module is connected to a power grid equipment operation module, and the power grid equipment operation module and the power grid equipment are connected in parallel.

8. A method for dynamic partitioning of a power grid based on a cloud service architecture, based on the dynamic partitioning system for a power grid based on a cloud service architecture according to any one of claims 1 to 7, characterized in that: include, The real-time data of the power grid is collected through the data collection module, and the collected data is output to the data verification module and the data storage module; After the file verification module in the data verification module verifies the data, the storage formatting module formats the data, and then the data import module re-inputs the processed data into the data storage module; The data in the data storage module is transmitted to the data cloud storage module, and then the data analysis module performs analysis based on the data in the data cloud storage module, and passes the analysis results to the dynamic partitioning module to complete the dynamic partitioning of the power grid.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid dynamic partitioning system based on cloud service architecture described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid dynamic partitioning system based on cloud service architecture described in any one of claims 1 to 7 are implemented.

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