Power distribution network supply and demand reliability management method and terminal

Abstract

The application discloses a power distribution network supply and demand reliability management method and a terminal, establishes a historical power supply database, establishes a supply and demand comparison database according to the historical power supply database and a historical simulation database, and can obtain the historical supply and demand comparison situation of the power distribution network; total power supply percentage data is established according to the supply and demand comparison database, and a comparison difference value of the supply and demand comparison database is processed through a big data calculation mode; then, the supply and demand difference value of simulation power supply is reduced by combining the total power supply percentage data and the processing result of the comparison difference value, a power supply simulation database is established, and data in the power supply simulation database is encrypted and transmitted to a target power grid. In this way, the supply and demand excess difference value can be reduced, the reliability of the power distribution network can be effectively improved, and the operation and maintenance cost of the power distribution network is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network security management, and in particular to a distribution network supply and demand reliability management method and terminal. Background Art

[0002] Power reliability management is one of the responsibilities of power companies' safety management. It is necessary to effectively guarantee the reliability level of users' electricity use. At present, the requirements for distribution reliability are constantly increasing, and power grid companies need to continuously improve distribution reliability. Good distribution reliability can improve users' usage experience, reduce the probability of large-scale power outages, increase distribution network revenue, and avoid large-scale power accidents.

[0003] However, in existing technologies, due to the large differences in economic development, grid infrastructure and electricity consumption in different regions, the supply and demand relationship of the distribution network is in excess, which adds a lot of distribution network operation and maintenance costs when managing supply and demand reliability. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distribution network supply and demand reliability management method and terminal, which can narrow the supply and demand gap of the distribution network, improve the reliability of the distribution network, and effectively reduce the operation and maintenance costs of the distribution network equipment.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for managing the reliability of supply and demand of a distribution network comprises the following steps:

[0007] Establishing a historical power supply database, and establishing a supply and demand comparison database based on the historical power supply database and the historical simulation database;

[0008] Establishing total power supply percentage data based on the supply and demand comparison database;

[0009] Processing the comparison difference of the supply and demand comparison database by using a big data calculation method;

[0010] The supply-demand difference of the simulated power supply is reduced by combining the total power supply percentage data and the processing result of the comparison difference, a power supply simulation database is established, and the data in the power supply simulation database is encrypted and transmitted to the target power grid.

[0011] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0012] A distribution network supply and demand reliability management terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned distribution network supply and demand reliability management method is implemented.

[0013] The beneficial effects of the present invention are as follows: establishing a historical power supply database, establishing a supply and demand comparison database based on the historical power supply database and a historical simulation database, and obtaining the historical supply and demand comparison of the distribution network; establishing total power supply percentage data based on the supply and demand comparison database, and processing the comparison difference of the supply and demand comparison database through a big data calculation method; then combining the processing results of the total power supply percentage data and the comparison difference to reduce the supply and demand difference of the simulated power supply, establishing a power supply simulation database, and encrypting the data in the power supply simulation database and transmitting it to the target power grid. In this way, the difference between excess supply and demand can be reduced, effectively improving the reliability of the distribution network, and reducing the operation and maintenance costs of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a method for managing supply and demand reliability of a distribution network according to an embodiment of the present invention;

[0015] Figure 2 Schematic diagram of a distribution network supply and demand reliability management terminal according to an embodiment of the present invention;

[0016] Figure 3 A flowchart of the method steps for establishing a historical power supply database according to an embodiment of the present invention;

[0017] Description of labels:

[0018] 1. A distribution network supply and demand reliability management terminal; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0019] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0020] Please refer to Figure 1 , an embodiment of the present invention provides a distribution network supply and demand reliability management method, comprising the steps of:

[0021] Establishing a historical power supply database, and establishing a supply and demand comparison database based on the historical power supply database and the historical simulation database;

[0022] Establishing total power supply percentage data based on the supply and demand comparison database;

[0023] Processing the comparison difference of the supply and demand comparison database by using a big data calculation method;

[0024] The supply-demand difference of the simulated power supply is reduced by combining the total power supply percentage data and the processing result of the comparison difference, a power supply simulation database is established, and the data in the power supply simulation database is encrypted and transmitted to the target power grid.

[0025] From the above description, it can be seen that the beneficial effects of the present invention are: establishing a historical power supply database, establishing a supply and demand comparison database based on the historical power supply database and the historical simulation database, and being able to obtain the historical supply and demand comparison of the distribution network; establishing total power supply percentage data based on the supply and demand comparison database, and processing the comparison difference of the supply and demand comparison database through big data calculation; then combining the processing results of the total power supply percentage data and the comparison difference to reduce the supply and demand difference of the simulated power supply, establishing a power supply simulation database, and encrypting the data in the power supply simulation database and transmitting it to the target power grid. In this way, the difference between excess supply and demand can be reduced, the reliability of the distribution network can be effectively improved, and the operation and maintenance cost of the distribution network can be reduced.

[0026] Furthermore, the establishment of a historical power supply database includes:

[0027] The electricity usage of each household is collected at the power supply station, special electricity usage in the electricity usage is marked, and a historical power supply database is established. The special electricity usage is the electricity usage that exceeds the preset electricity usage range.

[0028] As can be seen from the above description, by collecting the electricity usage of each household from the power supply station and marking special electricity usage situations, a historical power supply database can be accurately established in this way.

[0029] Furthermore, total power supply percentage data is established based on the supply and demand comparison database, including:

[0030] The power supply data and power consumption data are obtained from the supply and demand comparison database, the power supply data are integrated and calculated to obtain the total power supply, the power consumption data are integrated and calculated to obtain the total power consumption, and the total power supply percentage data is obtained based on the ratio of the total power supply to the total power consumption.

[0031] From the above description, it can be seen that the total power supply and total power consumption are obtained by comparing the power supply data and power consumption data in the power supply comparison database, and then the total power supply percentage data is calculated. In this way, the total power supply percentage data can be accurately determined, and it is convenient to subsequently determine the power supply indicators of each power supply point based on this.

[0032] Furthermore, the processing of the comparison difference of the supply and demand comparison database by big data calculation includes:

[0033] The comparison difference of the supply and demand comparison database is split, and the data of the same time, the same area, the same temperature, the same rainfall, and the same daily average temperature are divided into independent tasks to obtain multiple subtasks;

[0034] A big data calculation method is used to calculate the first median of the power in each of the subtasks, the subtasks are merged, and a second median of the power is selected from multiple first medians to obtain a processing result.

[0035] Furthermore, combining the total power supply percentage data and the processing result of the comparison difference to reduce the supply and demand difference of the simulated power supply, and establishing a power supply simulation database, including:

[0036] Obtaining power supply indicators of each power supply location through the total power supply percentage data, allocating grid weights using a hierarchical analysis weighted method based on the power supply indicators, and simulating power distribution according to the grid weights;

[0037] When the simulated power supply is greater than the simulated power demand, the simulated power supply is decreased according to the processing result; when the simulated power supply is less than the simulated power demand, the simulated power supply is increased according to the processing result so that the simulated power supply and the simulated power demand are balanced, and multi-dimensional power supply allocation is performed according to the supply and demand relationship to obtain a power supply simulation database.

[0038] From the above description, it can be seen that through big data simulation analysis, a historical power supply database is created according to each time period and region, and the power consumption patterns and changes are accurately displayed based on the data, which facilitates the subsequent accurate budgeting of the current required power consumption based on big data and improves the accuracy of the budget. In addition, the historical power supply database is real and reliable data, which can more accurately and intuitively display the amplitude and range of power consumption changes, which is conducive to the accurate simulation of power distribution.

[0039] Furthermore, encrypting the data in the power supply simulation database and transmitting it to the target power grid includes:

[0040] Set encryption key and generate random key;

[0041] Using the encryption key to encrypt the data in the power supply simulation database for the first time to obtain a first ciphertext;

[0042] Using the random key to encrypt the data in the power supply simulation database for a second time to obtain a second ciphertext;

[0043] The first ciphertext and the second ciphertext are transmitted to a target power grid so that the target power grid decrypts the first ciphertext and the second ciphertext. If the decryption results of the first ciphertext and the second ciphertext are the same, the decryption results are correct data.

[0044] From the above description, it can be seen that data is encrypted using an encryption key and a random key respectively, and the secure encrypted transmission of data is guaranteed by checking whether the decryption results of the two ciphertexts are the same. In this way, dual key transmission can improve the stability and security of transmitted data.

[0045] Furthermore, the supply and demand comparison database includes the enterprise priority power supply part, the civilian stable power supply part, the commercial power supply part and the remaining power supply parts.

[0046] As can be seen from the above description, dividing the supply and demand comparison database into enterprise priority power supply, civilian stable power supply, commercial power supply, and remaining power supply allows for controllable budgeting of power distribution factors and improves the reliability of the distribution network. The enterprise priority power supply maintains stable power supply to enterprises, ensuring that the GDP of the region is not affected, achieving the goal of stable priority power supply and preventing the operation of enterprises from being prioritized. The civilian stable power supply ensures normal civilian electricity consumption, improving the satisfaction and happiness of residents in the region. The commercial power supply and remaining power supply are then followed, and are sufficient to maintain normal power supply ranges. Through accurate calculation and simulation of the historical power supply database, the supply and demand comparison database, and the big data system, it is possible to effectively calculate power supply data for each region and each part for the current quarter or month.

[0047] Please refer to Figure 2 Another embodiment of the present invention provides a distribution network supply and demand reliability management terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the above-mentioned distribution network supply and demand reliability management method.

[0048] The distribution network supply and demand reliability management method and terminal described above are suitable for solving the problem of excessive supply and demand in the distribution network due to large differences in economic development, grid infrastructure, and electricity consumption in different regions, which increases a large amount of distribution network operation and maintenance costs during supply and demand reliability management. The following is an explanation of the specific implementation method:

[0049] Example 1

[0050] Please refer to Figure 1 , a distribution network supply and demand reliability management method, comprising the steps of:

[0051] S1. Establish a historical power supply database, and establish a supply and demand comparison database based on the historical power supply database and the historical simulation database.

[0052] S11. Collect electricity usage information of each household at the power supply station, mark special electricity usage information among the electricity usage information, and establish a historical power supply database. The special electricity usage information is electricity usage information that exceeds a preset electricity usage range.

[0053] For details, please refer to Figure 3The user's electricity meter records the electricity consumption of each household, and transmits the meter data to the corresponding power supply department through the Internet of Things. The power supply department collects the data in a unified manner; special electricity consumption points in the user's electricity consumption data are marked; the collected data is transmitted to power grids at all levels; and an accurate historical power supply database is established based on the above collected data.

[0054] S12. Establish a supply and demand comparison database based on the historical power supply database and the historical simulation database, wherein the supply and demand comparison database includes the enterprise priority power supply part, the civilian stable power supply part, the commercial power supply part and the remaining power supply parts.

[0055] The historical simulation database is a historically created power supply simulation database.

[0056] S2. Establishing total power supply percentage data according to the supply and demand comparison database.

[0057] The power supply data and power consumption data are obtained from the supply and demand comparison database, the power supply data are integrated and calculated to obtain the total power supply, the power consumption data are integrated and calculated to obtain the total power consumption, and the total power supply percentage data is obtained based on the ratio of the total power supply to the total power consumption.

[0058] Specifically, when establishing the total power supply percentage data, first organize and summarize all relevant data parts, and use the database's JOIN operation to integrate the data, calculate the total power supply and total power consumption, and calculate the total power supply percentage data, total power supply percentage data = (total power supply / total power consumption) * 100%, and store the calculated percentage in the database.

[0059] The stored power and the remaining power are obtained from the supply and demand comparison database, the stored power is integrated and calculated to obtain the total stored power, the remaining power is integrated and calculated to obtain the total remaining power, and the remaining power percentage data is obtained according to the ratio of the total remaining power to the total stored power.

[0060] Specifically, when establishing the remaining power percentage data, the total stored power and the total remaining power are sorted and summarized, and the remaining power percentage = (total remaining power / total stored power) * 100%, and the calculated percentage is stored in the database.

[0061] S3. Processing the comparison difference of the supply and demand comparison database by big data calculation.

[0062] The comparison difference of the supply and demand comparison database is split, and the data of the same time, the same area, the same temperature, the same rainfall and the same daily average temperature are divided into independent tasks to obtain multiple subtasks;

[0063] A big data calculation method is used to calculate the first median of the power in each of the subtasks, the subtasks are merged, and a second median of the power is selected from multiple first medians to obtain a processing result.

[0064] Specifically, in the big data calculation process, the tasks to be calculated are split, and the tasks with the same time, the same area, the same temperature, the same rainfall, and the same daily average temperature are divided into independent tasks, which are divided into multiple subtasks respectively. The median of the electricity in multiple subtasks is taken, and then the multiple subtasks are merged. Through the merging, the median of multiple independent medians is selected twice.

[0065] In this embodiment, the big data calculation method adopts the Hadoop MapReduce framework for calculation, which processes large amounts of data in a reliable and fault-tolerant manner. MapReduce is divided into the Map stage and the Reduce stage. The input data is divided into independent blocks. The divided blocks are processed by Map in a completely parallel manner, and the output of Map is sorted and then input into the Reduce task, which is responsible for scheduling tasks, monitoring tasks and re-executing failed tasks. In the Hadoop cluster, the computing nodes and storage nodes are the same. The Hadoop and MapReduce distributed file systems both run on the same group of nodes, with a high degree of aggregation, which can effectively utilize computing resources.

[0066] S4. Combine the total power supply percentage data and the processing result of the comparison difference to reduce the supply and demand difference of the simulated power supply, establish a power supply simulation database, and encrypt the data in the power supply simulation database and transmit it to the target power grid.

[0067] S41. Obtain power supply indicators of each power supply location through the total power supply percentage data, allocate grid weights using a hierarchical analysis weighted method based on the power supply indicators, and simulate power distribution according to the grid weights.

[0068] Specifically, the total power supply percentage data can be used to clarify the indicator range of each region. In the simulation of power distribution, weights can be allocated according to the indicator range. In the allocation process, the hierarchical analysis weighted method is used to allocate the power grid weights.

[0069] In this embodiment, in the step of clarifying the indicator range of each region, the data is integrated and processed, and the total power supply percentage data is retrieved from the database. Comprehensive calculation and processing are performed according to the business needs and actual conditions of each region, and an appropriate indicator range is set. More than 70% is sufficient power supply, 50% to 70% is normal power supply, 30% to 50% is insufficient power supply, and less than 30% is seriously insufficient power supply. According to the total power supply percentage data of each region, it is classified into the corresponding indicator range, and the indicator range of each region is visualized using charts or map annotations, which can be quickly and intuitively understood.

[0070] Among them, the hierarchical analysis method is used to divide the evaluation objectives into several levels and several indicators, and a comprehensive evaluation is performed according to different weights. By analyzing the relationship between the various factors in the system, the hierarchical structure is determined, a target tree diagram is established, a two-phase comparison judgment matrix is ​​established, the relative weights are determined, and the weights of the sub-goals are calculated. The consistency of the weights is further verified. At the same time, the combined weights of each indicator are calculated, and then the comprehensive index is calculated and ranked. By establishing the target tree diagram, reasonable combined weights can be calculated, and finally the comprehensive index is obtained, making the evaluation intuitive and reliable. The conventional hierarchical analysis weighting method is modified by the three-scale (-1, 0, 1) matrix method. By comparing the two indicators, a comparison matrix is ​​established, the optimal transfer matrix is ​​calculated, and the consistency of the matrix is ​​determined, that is, the matrix is ​​judged. This method meets the consistency requirements and does not require consistency testing. Compared with other scales, it has good judgment transfer and scale value rationality. It simply and intuitively presents the required judgment information and makes judgment accuracy, which is conducive to improving accuracy in two-phase comparison judgment.

[0071] S42. When the simulated power supply is greater than the simulated power demand, the simulated power supply is decreased according to the processing result; when the simulated power supply is less than the simulated power demand, the simulated power supply is increased according to the processing result, so that the simulated power supply and the simulated power demand are balanced, and multi-dimensional power supply allocation is performed according to the supply and demand relationship to obtain a power supply simulation database.

[0072] Specifically, the power distribution of the enterprise priority power supply part, the civilian stable power supply part, the commercial power supply part and the remaining power supply parts is calculated based on the supply and demand relationship.

[0073] S43. Set an encryption key and generate a random key, use the encryption key to encrypt the data in the power supply simulation database for the first time to obtain a first ciphertext, and use the random key to encrypt the data in the power supply simulation database for a second time to obtain a second ciphertext, transmit the first ciphertext and the second ciphertext to the target power grid, so that the target power grid decrypts the first ciphertext and the second ciphertext. If the decryption results of the first ciphertext and the second ciphertext are the same, the decryption result is correct data.

[0074] Specifically, in the step of encrypting and transmitting data, a corresponding encryption key is obtained when encrypting the transmitted data, and a random key is generated. The encryption key is used for the first encryption, and then the random key is used for the second encryption. The first ciphertext after the first encryption and the second ciphertext after the second encryption are sent to the receiving terminal, and the first ciphertext and the second ciphertext are decrypted. At the same time, the decrypted first ciphertext and the second ciphertext are compared to obtain the required plaintext.

[0075] Therefore, the use of the analytic hierarchy process to allocate the weight of electricity in this embodiment has the beneficial effects of being systematic and objective, improving decision-making accuracy, optimizing resource allocation, and promoting decision-making transparency:

[0076] 1. Systematicity: The AHP considers the object as a system and makes decisions based on the thinking mode of decomposition, comparison, judgment and synthesis. It helps to consider various factors comprehensively and systematically, thus better formulating power management strategies.

[0077] 2. Objectivity: The AHP is a decision analysis method that combines qualitative and quantitative analysis. It uses the decision maker's experience to judge the relative importance of the achievable standards among various measurement indicators, and reasonably assigns weights to each measurement indicator for each decision plan. The weights are then used to determine the order of merit among the various plans, which is beneficial for subsequent quantitative analysis.

[0078] 3. Improve decision-making accuracy: By assigning weights to various factors in power management through the analytic hierarchy process, decision makers can more accurately assess the importance of various factors and thus formulate more reasonable and accurate power management strategies;

[0079] 4. Optimize resource allocation: By assigning weights to various factors, the AHP can help decision makers better understand resource allocation, thereby optimizing resource allocation and improving the efficiency of power management;

[0080] 5. Promote decision-making transparency: It can help all parties better understand the decision-making process and results, thereby improving the transparency of decision-making.

[0081] Moreover, in this embodiment, the stability and security of the transmitted data are improved through dual key transmission. In the above management method, the supply and demand of the distribution network in each region and at each time can be effectively simulated, the supply and demand gap can be narrowed, the operational reliability of the distribution network can be improved, and the operation and maintenance costs of the distribution network equipment can be effectively reduced.

[0082] Example 2

[0083] Please refer to Figure 2 A distribution network supply and demand reliability management terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of a distribution network supply and demand reliability management method of embodiment 1 is implemented.

[0084] In summary, the present invention provides a distribution network supply and demand reliability management method and terminal, in which the user's electricity meter records the electricity consumption of each household, and transmits the meter data to the corresponding power supply department through the Internet of Things, and the power supply department collects data uniformly; and marks the special electricity consumption points in the user's electricity consumption data; and transmits the collected data to the municipal and provincial levels; an accurate historical power supply database is established based on the above-mentioned collected data; and a comparison database is established between the historical power supply database and the historical simulation database, and the difference between the previous same time period, the same area, the same temperature range, the rainfall and the daily average temperature is calculated by comparing the database. The calculated difference is processed by the big data system and directly affects the current data of the simulation database. When the power supply is greater than the power demand, it is appropriately reduced according to the calculation of the same time period, the same area, the same temperature range, the rainfall and the daily average temperature. When the power supply is less than the power demand, it is appropriately increased according to the same time period, the same area, the same temperature range, the rainfall and the daily average temperature to achieve a supply and demand balance in the time period and the area, and is respectively allocated to the enterprise priority power supply part, the civilian stable power supply part, the commercial power supply part and the remaining power supply parts according to the supply and demand relationship. In this way, the supply and demand of the distribution network in various regions and at various times can be effectively simulated, the supply and demand gap can be narrowed, the operational reliability of the distribution network can be improved, the operation and maintenance costs of the distribution network equipment can be effectively reduced, and the problem of excessive supply and demand in the distribution network due to large differences in economic development, grid infrastructure and electricity consumption in different regions can be solved, which adds a lot of distribution network operation and maintenance costs in the supply and demand reliability management.

[0085] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network supply and demand reliability management method, characterized in that: Including steps: Establishing a historical power supply database, and establishing a supply and demand comparison database based on the historical power supply database and the historical simulation database; Establishing total power supply percentage data according to the supply-demand comparison database: obtaining power supply data and power consumption data from the supply-demand comparison database, integrating and calculating the power supply data to obtain a total power supply, integrating and calculating the power consumption data to obtain a total power consumption, and obtaining the total power supply percentage data according to a ratio of the total power supply to the total power consumption; The comparison difference of the supply and demand comparison database is processed by a big data calculation method: the comparison difference of the supply and demand comparison database is split, and the data of the same time, the same area, the same temperature, the same rainfall and the same daily average temperature are divided into independent tasks to obtain multiple subtasks; Calculating the first median of the power in each of the subtasks using a big data calculation method, merging the subtasks, and selecting a second median of the power from the plurality of first medians of the power to obtain a processing result; The supply-demand difference of the simulated power supply is reduced by combining the processing results of the total power supply percentage data and the comparison difference, and a power supply simulation database is established: the power supply index of each power supply point is obtained through the total power supply percentage data, the hierarchical analysis weighted method is used to allocate power grid weights based on the power supply index, and simulated power distribution is performed according to the power grid weights; when the simulated power supply is greater than the simulated power demand, the simulated power supply is decreased according to the processing result; when the simulated power supply is less than the simulated power demand, the simulated power supply is increased according to the processing result, so that the simulated power supply and the simulated power demand are balanced, and multi-dimensional power distribution is performed according to the supply and demand relationship to obtain a power supply simulation database; The data in the power supply simulation database is encrypted and transmitted to the target power grid.

2. A distribution network supply and demand reliability management method according to claim 1, characterized in that: The establishing of the historical power supply database includes: The electricity usage of each household is collected at the power supply station, special electricity usage in the electricity usage is marked, and a historical power supply database is established. The special electricity usage is the electricity usage that exceeds the preset electricity usage range.

3. A distribution network supply and demand reliability management method according to claim 1, characterized in that: Encrypting and transmitting the data in the power supply simulation database to the target power grid includes: Set encryption key and generate random key; Using the encryption key to encrypt the data in the power supply simulation database for the first time to obtain a first ciphertext; Using the random key to encrypt the data in the power supply simulation database for a second time to obtain a second ciphertext; The first ciphertext and the second ciphertext are transmitted to a target power grid so that the target power grid decrypts the first ciphertext and the second ciphertext. If the decryption results of the first ciphertext and the second ciphertext are the same, the decryption results are correct data.

4. A distribution network supply and demand reliability management method according to any one of claims 1 to 3, characterized in that: The supply and demand comparison database includes the enterprise priority power supply part, the civilian stable power supply part, the commercial power supply part and the remaining power supply parts.

5. A distribution network supply and demand reliability management terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: Establishing a historical power supply database, and establishing a supply and demand comparison database based on the historical power supply database and the historical simulation database; Establishing total power supply percentage data according to the supply-demand comparison database: obtaining power supply data and power consumption data from the supply-demand comparison database, integrating and calculating the power supply data to obtain a total power supply, integrating and calculating the power consumption data to obtain a total power consumption, and obtaining the total power supply percentage data according to a ratio of the total power supply to the total power consumption; The comparison difference of the supply and demand comparison database is processed by a big data calculation method: the comparison difference of the supply and demand comparison database is split, and the data of the same time, the same area, the same temperature, the same rainfall and the same daily average temperature are divided into independent tasks to obtain multiple subtasks; Calculating the first median of the power in each of the subtasks using a big data calculation method, merging the subtasks, and selecting a second median of the power from the plurality of first medians of the power to obtain a processing result; The supply-demand difference of the simulated power supply is reduced by combining the processing results of the total power supply percentage data and the comparison difference, and a power supply simulation database is established: the power supply index of each power supply point is obtained through the total power supply percentage data, the hierarchical analysis weighted method is used to allocate power grid weights based on the power supply index, and simulated power distribution is performed according to the power grid weights; when the simulated power supply is greater than the simulated power demand, the simulated power supply is decreased according to the processing result; when the simulated power supply is less than the simulated power demand, the simulated power supply is increased according to the processing result, so that the simulated power supply and the simulated power demand are balanced, and multi-dimensional power distribution is performed according to the supply and demand relationship to obtain a power supply simulation database; The data in the power supply simulation database is encrypted and transmitted to the target power grid.

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