Power distribution network optimization method and system based on power distribution automation, and storage medium

Abstract

The invention relates to a power distribution network optimization method and system based on power distribution automation and a storage medium, and belongs to the technical field of industrial data processing, and the method comprises the following steps: a, obtaining and storing power utilization feedback data of a user; b, extracting and identifying the electricity utilization feedback data of the user obtained in each time period; c, performing inspection and data acquisition on the field fault line area, and analyzing the obtained field fault line area data; step d, carrying out inspection and data acquisition on the field fault line area, and carrying out data decision updating on the obtained field fault line area data; e, solving the data of the field fault line area after decision updating to obtain the solving data of the power distribution network; the method has the advantages that compared with a line distribution point position analysis method, the vector opposite analysis method can achieve large-range fault detection and obtain large-range detection data.

Description

Technical Field

[0001] The present invention belongs to the technical field of industrial data processing, and in particular relates to a distribution network optimization method, system and storage medium based on distribution automation. Background Art

[0002] As an important part of the power system, the distribution network is an important infrastructure of the power system and a hub connecting power sources and users. The reliability of the distribution network has always been the most important indicator of the power system and a comprehensive reflection of the planning, construction, operation and management capabilities of the power grid. Therefore, it is necessary to continuously upgrade and transform the distribution network to improve its reliability.

[0003] At present, most distribution networks are based on fragmented management. Fragmented management may lead to information islands and information decision-making lags. It is impossible to understand users' needs and opinions in a timely manner, and it is impossible to optimize the operation and service of the distribution network in a timely manner based on users' electricity consumption behavior and feedback from users. This reduces users' satisfaction and participation in the distribution network. Because users' electricity consumption feedback can be used to timely understand whether the distribution network is operating reliably, optimizing the distribution network through users' electricity consumption feedback is an issue that needs to be urgently addressed. Summary of the invention

[0004] The present invention mainly solves the technical problem of how to optimize the distribution network through users' electricity consumption feedback data. The present invention provides a distribution network optimization method, system and storage medium based on distribution automation. The present invention further optimizes the detection range and obtains a wide range of detection data.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] A distribution network optimization method based on distribution automation comprises the following steps:

[0007] Step a: obtaining the user's electricity consumption feedback data and storing the user's electricity consumption feedback data; wherein the user's electricity consumption feedback data is obtained at every time period;

[0008] Step b: extracting the power consumption feedback data of users obtained in each time period, and classifying the power consumption feedback data of users obtained;

[0009] Step c: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is analyzed using the line distribution point location analysis method;

[0010] Step d: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is updated with data decision; wherein, the vector counter analysis method is used to update the data decision of the on-site fault line area;

[0011] Step e: Based on step c or step d, solve the updated data of the on-site fault line area decision to obtain the solution data of the distribution network.

[0012] Optionally, in step a, a segmented time interval method is used to obtain the user's power consumption feedback data at every time period. The segmented time interval method is the following formula (1):

[0013] (1);

[0014] in, For the total time, For the total time The time period, For the total time Find the Time period , To detect the Time period The time size;

[0015] For the Time period The user's electricity consumption feedback data obtained in Is the representative Time period Detects user's power consumption feedback data , For calibration Time period The user's electricity consumption feedback data obtained , For the Time period Detects user's power consumption feedback data , and marked in the Time period Contains user's electricity consumption feedback data ;

[0016] For the record Time period Get the user's electricity consumption feedback data , It represents the electricity consumption feedback data of users obtained in each time period.

[0017] Optionally, in step b, the electricity consumption feedback data of users obtained in each time period is classified and identified; wherein, the electricity consumption feedback data of users obtained is classified and identified by a category separation method.

[0018] Optional, category separation method, is the following steps:

[0019] Step 01: Since the electricity consumption feedback data of different categories of users have different frequencies, the frequencies of different sizes are used as classification standards for the electricity consumption feedback data of users;

[0020] Step 02: Extract the frequency signal in each time period and detect the frequency size in each time period;

[0021] Step 03: According to the frequency in each time period, the user's electricity consumption feedback data obtained in each time period is categorized.

[0022] Optionally, in step c, the line point location analysis method is the following formula (2):

[0023] (2);

[0024] in, In order to detect from one end to the other end of the distribution network in the entire distribution network, the first There is a fault in the section, or, in the entire distribution network, detection is performed from one end of the distribution network to the other end to obtain the first The segment is faulty. For fault identification operation, To identify the first The segment is faulty;

[0025] For the distribution network The first paragraph The charge at the point, To detect and collect the first The charge at each point on the segment, To determine the distribution network Whether there is charge at each point on the segment.

[0026] Optionally, in step d, the vector subtending analysis method is the following formula (3):

[0027] (3);

[0028] in, The length of the entire distribution network from one end to the other end is , For The number of detection segments on the length of The length recorded from one end of the distribution network to the other end during detection is There is The segment is detected;

[0029] The length of the entire distribution network from one end to the other end is , For The number of detection segments on the length of The length recorded from the other end of the distribution network to the detection end is There is The segment is detected;

[0030] To add the number of detection segments in each of the two lengths, To record the number of segments added in the two lengths;

[0031] or The length of the entire distribution network is the sum of the number of detection sections in each of the two lengths.

[0032] Optionally, in step e, it is checked whether a fault occurs in each detection section, and if a fault occurs, solution data of the distribution network is obtained.

[0033] A distribution network optimization system based on distribution automation, comprising:

[0034] A data storage module, used to store the acquired user's electricity consumption feedback data;

[0035] A data identification module is used to identify the category of the obtained user's electricity consumption feedback data;

[0036] A data analysis module is used to analyze the acquired on-site fault line area data, or to make data decision updates on the on-site fault line area;

[0037] The distribution network data solving module is used to determine whether the user's power consumption feedback data is accurate;

[0038] The data storage module circuit is connected to the data identification module and the data analysis module, and the data analysis module circuit is connected to the distribution network data solution module.

[0039] Optionally, the data analysis module includes: a fault line area data analysis module for analyzing on-site fault line area data and a data decision update module for making decisions on-site fault line area data, the two ends of the fault line area data analysis module are respectively circuit-connected to the data storage module and the distribution network data solution module, and the two ends of the data decision update module are respectively circuit-connected to the data storage module and the distribution network data solution module.

[0040] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned distribution network optimization method based on distribution automation.

[0041] Beneficial effects of the present invention:

[0042] After the present invention classifies the obtained user's electricity consumption feedback data, it then inspects and collects data on the on-site fault line area, and analyzes the obtained on-site fault line area data; or, after the obtained user's electricity consumption feedback data is classified, it then inspects and collects data on the on-site fault line area, and updates the data decision on the obtained on-site fault line area data; through comparison of the two methods, it can be seen that the optimization of the vector subtend analysis method relative to the line point location analysis method lies in that the vector subtend analysis method is that as long as the user has made a fault feedback on a certain section, the entire distribution network is detected, while the line point location analysis method only detects a certain section of the user's fault feedback. The vector subtend analysis method can realize a large range of fault detection and obtain a large range of detection data. The vector subtend analysis method further optimizes the detection range relative to the line point location analysis method and obtains a large range of detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0045] Figure 2 It is a schematic diagram of the internal structure of the fault line area data analysis module of the present invention;

[0046] Figure 3 A schematic diagram of the internal structure of the data decision update module of the present invention;

[0047] Figure 4 It is the work flow chart of the present invention;

[0048] Figure 5 It is a working principle diagram of the line point location analysis method of the present invention;

[0049] Figure 6 It is a working principle diagram of the vector analysis method of the present invention. DETAILED DESCRIPTION

[0050] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0051] Example 1

[0052] like Figure 1 As shown, this embodiment provides a distribution network optimization system based on distribution automation, including: a data acquisition module, a data storage module, a data identification module, a data analysis module and a distribution network data solving module.

[0053] The data acquisition module is used to obtain the user's power consumption feedback data;

[0054] The data storage module is used to store the acquired user's electricity consumption feedback data;

[0055] The data identification module is used to classify the obtained user's electricity consumption feedback data;

[0056] The data analysis module is used to analyze the acquired on-site fault line area data, or to make data decision updates on the on-site fault line area;

[0057] The distribution network data solving module is used to determine whether the user's power consumption feedback data is accurate;

[0058] The data acquisition module is connected to the data identification module and the data analysis module through the data storage module circuit, and the data analysis module circuit is connected to the distribution network data solution module.

[0059] The user's electricity consumption feedback data is obtained through the data acquisition module, the data storage module stores the obtained user's electricity consumption feedback data, the data identification module extracts the obtained user's electricity consumption feedback data and identifies the user's electricity consumption feedback data category. After the data identification module identifies the user's electricity consumption feedback data category, the identified user's electricity consumption feedback data category is sent to the data analysis module through the data storage module. The data analysis module analyzes or makes decisions on the fault line area data at the site, and finally determines whether the user's electricity consumption feedback data is accurate through the distribution network data solution module.

[0060] Specifically, Figure 1As shown, the data analysis module includes: a fault line area data analysis module and a data decision update module. The two ends of the fault line area data analysis module are respectively circuit-connected to the data storage module and the distribution network data solution module. The two ends of the data decision update module are respectively circuit-connected to the data storage module and the distribution network data solution module.

[0061] Among them, Figure 2 As shown, the fault line area data analysis module is used to analyze the fault line area data on site, and the fault line area data analysis module comprises: a point distribution data acquisition unit and a point distribution data analysis unit, and the point distribution data acquisition unit circuit is connected to the point distribution data analysis unit;

[0062] The point distribution data acquisition unit is used to detect or collect the points distribution in the distribution network, and the point distribution data analysis unit is used to analyze the point distribution situation in the distribution network;

[0063] like Figure 3 As shown, the data decision updating module is used to make decisions on the fault line area data on site, and the data decision updating module also has: a point data acquisition unit and a point data analysis unit, and the point data acquisition unit circuit is connected to the point data analysis unit;

[0064] Similarly, the point distribution data acquisition unit is used to detect or collect the points distribution in the distribution network, and the point distribution data analysis unit is used to analyze the point distribution situation in the distribution network;

[0065] The point distribution data acquisition unit and the point distribution data analysis unit in the data decision update module have the same purpose as the point distribution data acquisition unit and the point distribution data analysis unit in the fault line area data analysis module, but the working procedures are different.

[0066] In addition, the fault line area data analysis module and the data decision updating module work separately, either the fault line area data analysis module works or the data decision updating module works.

[0067] Example 2

[0068] Based on Example 1, Figure 4 As shown, this embodiment provides a distribution network optimization method based on distribution automation, comprising the following steps:

[0069] Step a: obtaining the user's electricity consumption feedback data and storing the user's electricity consumption feedback data; wherein the user's electricity consumption feedback data is obtained at intervals of a time period (the time period can be determined according to the setting, generally within 10 minutes);

[0070] Step b: extracting the power consumption feedback data of users obtained in each time period, and classifying the power consumption feedback data of users obtained;

[0071] Step c: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is analyzed using the line distribution point location analysis method;

[0072] Step d: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is updated with data decision; wherein, the vector counter analysis method is used to update the data decision of the on-site fault line area;

[0073] Step e: Based on step c or step d, solve the updated data of the on-site fault line area decision to obtain the solution data of the distribution network.

[0074] Example 3

[0075] Based on Example 2, in step a, the segmented time interval method is used to obtain the user's power consumption feedback data in every time period. The segmented time interval method is the following formula (1):

[0076] (1);

[0077] in, For the total time, For the total time The time period, For the total time Find the Time period , In total time Find the time periods in To detect the Time period The time size of each time period can be different or the same, so through The time size of each time period can be detected to determine whether the time spent in each time period is more or less;

[0078] For the Time period The user's electricity consumption feedback data obtained within Is the representative Time period Detects user's power consumption feedback data , For calibration Time period The user's electricity consumption feedback data obtained from the internal , only in Time period Detects user's power consumption feedback data , can be calibrated in the Time period The user's electricity consumption feedback data obtained , if in the Time period No user power consumption feedback data was detected , then in Time period The interior cannot be marked in the first Time period The user's electricity consumption feedback data obtained Yes, because in Time period There is no user's electricity consumption feedback data , so it cannot be calibrated. For the Time period Detects user's power consumption feedback data , and marked in the Time period Contains user's electricity consumption feedback data ;

[0079] For the record Time period Get the user's electricity consumption feedback data , or Represents the record of each time period (i.e. The electricity consumption feedback data of each user obtained in each time period .

[0080] The electricity consumption feedback data of each user can be obtained in each time period However, there may not be user feedback information in every time period. Therefore, there may not be user feedback information in some time periods. Of course, formula (1) only reflects that the power consumption feedback data of each user is obtained in each time period. , and obtaining the electricity consumption feedback data of each user in each time period is determined based on whether the user has information feedback.

[0081] Example 4

[0082] Based on Example 2, in step b, the power consumption feedback data of users obtained in each time period is classified and identified; wherein, the power consumption feedback data of users obtained is classified and identified by a category separation method.

[0083] Further, the category separation method is as follows:

[0084] Step 01: Due to the power consumption feedback data of different categories of users (i.e., the first section of the distribution network has a fault... the first section of the distribution network has a fault There is a fault in the section… The different frequencies are used as the classification standard of the user's power consumption feedback data;

[0085] Step 02: Extract the frequency signal in each time period and detect the frequency size in each time period;

[0086] Step 03: According to the frequency in each time period, the user's electricity consumption feedback data obtained in each time period is categorized.

[0087] In this embodiment, in step 01, according to the power consumption feedback data of different categories of users, the classification standard for the power consumption feedback data of users can be understood as follows: the user inputs or feedbacks the first section of the distribution network that there is a fault to the data acquisition module by voice or typing, and the data acquisition module converts the ten words "the first section of the distribution network has a fault" into the first frequency signal and sends it out, and the user inputs or feedbacks the first section of the distribution network by voice or typing. If there is a fault in the segment, the data acquisition module will send the fault information to the data acquisition module. The ten words "There is a fault in the segment" are transformed into The frequency signal is sent out. Similarly, the user inputs or feedbacks the first frequency signal of the distribution network through voice or typing. If there is a fault in the segment, the data acquisition module will send the fault information to the data acquisition module. The ten words "There is a fault in the segment" are transformed into Therefore, the frequency magnitudes of the various frequency signals can be set to be different.

[0088] In addition, the first frequency signal indicates that the first section of the distribution network has a fault, and the The frequency signal represents the There is a fault in the segment, The frequency signal represents the first The segment is faulty, based on the different frequency sizes of various frequency signals, the user's power consumption feedback data category is identified according to the frequency sizes of various frequency signals.

[0089] Example 5

[0090] Based on Example 2 and Example 4, Figure 5 As shown, in step c, the line point location analysis method is as follows:

[0091] (2);

[0092] in, In order to detect from one end of the distribution network to the other end (i.e. Figure 4 As shown, the first section of the distribution network... Section… of the distribution network The first or, in the entire distribution network, from one end to the other end of the distribution network (i.e. Figure 4 As shown in the figure, the distribution network Section… of the distribution network Segment…Segment 1 of the distribution network The first section of the distribution network is tested) and the The segment is faulty. For fault identification operation, To identify the first The segment is faulty;

[0093] For the distribution network The first paragraph The charge at the point, To detect and collect the first At each point on the segment (i.e. The charge at each point of To determine the distribution network Is there charge at each point on the segment? There is charge at each point on the segment, so the first If there is no fault in the first section of the distribution network, There is no charge at any point on the segment, then the first There is a fault in the section (because the As long as there is no charge at one point on the segment, the first segment will not be powered).

[0094] Formula (2) is used to detect whether there is charge at each point in a certain section of the distribution network.

[0095] In addition, the detection of formula (2) is to power off the entire distribution network and then power on both ends of each section of the distribution network for detection (that is, power is supplied to both ends of the first section of the distribution network, and the other parts of the distribution network are not powered on, and the first section of the distribution network is tested; the second section of the distribution network is powered on ... If both ends of the segment are powered, the other parts of the distribution network are not powered. Section; If both ends of the segment are powered, the other parts of the distribution network are not powered. Segments), to detect whether there is charge at each point in each segment of the distribution network.

[0096] Based on the above formula (2), if Figure 6 As shown, in step d, the vector analysis method is as follows:

[0097] (3);

[0098] in, In the entire distribution network, from one end of the distribution network to the other end (i.e. from the first section of the distribution network to the second section of the distribution network), The length of the detection in the segment direction is , For The number of detection segments on the length of The length recorded from one end of the distribution network to the other end during detection is There is The segment is detected;

[0099] In the entire distribution network, from one end of the distribution network to one end (i.e., from the first end of the distribution network) The length of the detection (the direction of the first section of the distribution network) is , For The number of detection segments on the length of The length recorded from the other end of the distribution network to the detection end is There is The segment is detected;

[0100] To convert two lengths (i.e. The length and The length of each segment is added together. To record the number of segments added in the two lengths;

[0101] or The length of the entire distribution network is the sum of the number of detection sections in each of the two lengths.

[0102] Formula (3) detects in opposite directions and simultaneously. Therefore, the detection will eventually converge at the middle position of the distribution network and detect the middle position of the distribution network (such as: the first The segments converge and detect the part).

[0103] The detection of formula (3) is to power off the entire distribution network and then energize the two ends of the detected distribution network, such as: powering on the first section of the distribution network to the second section of the distribution network, and powering on the second section of the distribution network. Section to the distribution network The first section of the distribution network is powered on, and the other parts of the distribution network are not powered on. The first section of the distribution network and the second section of the distribution network are tested. Section and distribution network Similarly, the first section of the distribution network is energized to the third section of the distribution network, and the Section 1 to the distribution network The first section of the distribution network is powered on, and the other parts of the distribution network are not powered on. The first section of the distribution network and the third section of the distribution network are tested. Section and distribution network Detection is performed between segments; and so on.

[0104] Formula (3) also detects whether there is charge at each point in each section of the distribution network.

[0105] In addition, in step e, according to the detection method of formula (2) or formula (3), it is checked whether each detection section has a fault. If a fault occurs, the solution data of the distribution network is obtained (that is, it is known whether the user's power consumption feedback data is accurate, because the detection method of formula (2) or formula (3) is used to detect each section of the distribution network, and it can be known whether a specific section in the distribution network has a fault).

[0106] Example 6

[0107] Based on Example 5, the present invention is an optimization of the vector subtend analysis method relative to the line point location analysis method in that the vector subtend analysis method is to detect the entire distribution network as long as the user provides fault feedback on a certain section, while the line point location analysis method only detects a certain section of the user's fault feedback. The vector subtend analysis method can achieve a wide range of fault detection and obtain a wide range of detection data. The vector subtend analysis method further optimizes the detection range relative to the line point location analysis method and obtains a wide range of detection data.

[0108] A person of ordinary skill in the art can understand that all or part of the steps in realizing the above-mentioned facts and methods can be completed by instructing the relevant hardware through a program, and the program or programs involved can be stored in a computer-readable storage medium. When the program is executed, it includes execution steps, and the execution steps lead to corresponding method steps. The storage medium can be ROM / RAM, a disk, an optical disk, etc.

[0109] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distribution network optimization method based on distribution automation, characterized in that: The steps include: Step a: obtaining the user's electricity consumption feedback data and storing the user's electricity consumption feedback data; wherein the user's electricity consumption feedback data is obtained at every time period; Step b: extracting the power consumption feedback data of users obtained in each time period, and classifying the power consumption feedback data of users obtained; Step c: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is analyzed using the line distribution point location analysis method; Step d: After classifying the obtained user's power consumption feedback data, the on-site fault line area is inspected and data is collected, and the obtained on-site fault line area data is updated with data decision; wherein, the vector counter analysis method is used to update the data decision of the on-site fault line area; Step e: Based on step c or step d, solve the updated data of the on-site fault line area decision to obtain the solution data of the distribution network.

2. A distribution network optimization method based on distribution automation according to claim 1, characterized in that: In step a, a segmented time interval method is used to obtain the user's power consumption feedback data at every other time period. The segmented time interval method is the following formula (1): (1)； in, For the total time, For the total time The time period, For the total time Find the Time period , To detect the Time period The time size; For the Time period The user's electricity consumption feedback data obtained in Is the representative Time period Detects user's power consumption feedback data , For calibration Time period The user's electricity consumption feedback data obtained , For the Time period Detects user's power consumption feedback data , and marked in the Time period Contains user's electricity consumption feedback data ; For the record Time period Get the user's electricity consumption feedback data , It represents the electricity consumption feedback data of users obtained in each time period.

3. A distribution network optimization method based on distribution automation according to claim 1, characterized in that: In the step b, the power consumption feedback data of the users obtained in each time period are classified and identified; wherein the power consumption feedback data of the users obtained are classified and identified by a classification separation method.

4. A distribution network optimization method based on distribution automation according to claim 3, characterized in that: The category separation method comprises the following steps: Step 01: Since the power consumption feedback data of the users of different categories have different frequencies, the frequencies of different sizes are used as classification standards for the power consumption feedback data of the users; Step 02: Extract the frequency signal in each time period and detect the frequency size in each time period; Step 03: According to the frequency in each time period, the user's electricity consumption feedback data obtained in each time period is categorized.

5. The method for optimizing a distribution network based on distribution automation according to claim 1, characterized in that: In step c, the line point location analysis method is the following formula (2): (2)； in, In order to detect from one end to the other end of the distribution network in the entire distribution network, the first There is a fault in the section, or, in the entire distribution network, detection is performed from one end of the distribution network to the other end to obtain the first The segment is faulty. For fault identification operation, To identify the first The segment is faulty; For the distribution network The first paragraph The charge at the point location, To detect and collect the distribution network The charge at each point on the segment, To determine the distribution network Whether there is charge at each point on the segment.

6. A distribution network optimization method based on distribution automation according to claim 1, characterized in that: In the step d, the vector analysis method is the following formula (3): (3)； in, The length of the entire distribution network from one end to the other end is , For The number of detection segments on the length of The length recorded from one end of the distribution network to the other end during detection is There is The segment is detected; The length of the entire distribution network from one end to the other end is , For The number of detection segments on the length of The length recorded from the other end of the distribution network to the end under detection is There is The segment is detected; To add the number of detection segments in each of the two lengths, To record the number of segments added in the two lengths; or The length of the entire distribution network is the sum of the number of detection sections in each of the two lengths.

7. A distribution network optimization method based on distribution automation according to claim 1, characterized in that: In the step e, it is checked whether a fault occurs in each detection section. If a fault occurs, the solution data of the distribution network is obtained.

8. A distribution network optimization system based on distribution automation, used to execute a distribution network optimization method based on distribution automation according to any one of claims 1 to 7, characterized in that: include: A data storage module, used to store the acquired user's electricity consumption feedback data; A data identification module is used to identify the category of the obtained user's electricity consumption feedback data; A data analysis module is used to analyze the acquired on-site fault line area data, or to make data decision updates on the on-site fault line area; The distribution network data solving module is used to determine whether the user's power consumption feedback data is accurate; The data storage module circuit is connected to the data identification module and the data analysis module, and the data analysis module circuit is connected to the distribution network data solution module.

9. A distribution network optimization system based on distribution automation according to claim 8, characterized in that: The data analysis module includes: a fault line area data analysis module for analyzing on-site fault line area data and a data decision update module for making decisions on on-site fault line area data, the two ends of the fault line area data analysis module are respectively circuit-connected to the data storage module and the distribution network data solution module, and the two ends of the data decision update module are respectively circuit-connected to the data storage module and the distribution network data solution module.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a distribution network optimization method based on distribution automation as described in any one of claims 1 to 7 are implemented.

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