Artificial intelligence multistage cooperation system and method for power industry

Abstract

The invention discloses an artificial intelligence multistage cooperation system and method for the power industry, and the system comprises a data collection module which is used for collecting the power utilization information data of a user. In the actual electric power industry, electricity consumption information of different users is different, for example, the electricity consumption of a large factory is higher than that of a medium factory, and the electricity consumption of the medium factory is higher than that of a normal family, electricity consumption information data of the users are collected and divided into a plurality of groups of types; the power utilization information data of the same user are fused, such as a school type, a family type, a factory type and the like, then classification is carried out according to the importance and the emergency degree of the power utilization information data of different users, the power utilization distribution priority is determined, and a power system distributes power for the users according to the distribution priority. And the power distribution efficiency is improved under the condition of ensuring normal power utilization requirements of users, and the condition of excessive power supply or insufficient power supply is reduced.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an artificial intelligence multi-level collaborative system and method for the electric power industry. Background Art

[0002] In the power industry, the transmission of electric energy, together with power transformation, distribution and power consumption, constitutes the overall function of the power system. Through power transmission, power plants and load centers that are far apart (up to thousands of kilometers) are connected, so that the development and utilization of electric energy can transcend geographical limitations. Compared with the transmission of other energy sources (such as coal and oil), power transmission has low losses, high efficiency, flexibility, convenience, easy regulation and less environmental pollution: power transmission can also connect power plants in different locations to implement peak and valley regulation. Power transmission is an important manifestation of the superiority of electric energy utilization. In modern society, it is an important energy artery. In the whole process, electricity needs to be managed and distributed, otherwise, the output power will not be used reasonably, which will increase the waste of electricity.

[0003] For example, a "power distribution system and power distribution method" disclosed in Chinese patent literature, with publication number CN105226731A, includes a power distribution method that cannot match the power consumption habits of power users with the power supply situation, resulting in excess or insufficient power supply in some areas, and at the same time cannot provide power consumption support to power supply users on a large scale, and cannot accurately receive feedback and process users' power consumption information. Summary of the invention

[0004] In order to solve the above technical problems, an artificial intelligence multi-level collaborative system and method for the power industry are provided. This technical solution solves the problem that the power distribution method proposed in the above background technology cannot match the power consumption habits of power users with the power supply situation, resulting in excess or insufficient power supply in part, and at the same time cannot provide power consumption support to power supply users on a large scale, and cannot accurately receive feedback and process users' power consumption information.

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

[0006] The artificial intelligence multi-level collaborative system for the power industry includes:

[0007] A data collection module, wherein the data collection module is used to collect user electricity consumption information data;

[0008] A data fusion module, which is electrically connected to the data acquisition module, calculates the concentration trend and dispersion degree of the user's electricity consumption information data, divides the user's electricity consumption information data into several groups, and fuses the same type of user's electricity consumption information data;

[0009] A classification module, which is electrically connected to the data fusion module and is used to classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power consumption allocation;

[0010] A power distribution module, which is electrically connected to the classification module and distributes power to users according to power allocation priorities;

[0011] A database is used to store and encrypt all power information data.

[0012] Preferably, the calculation of the concentration trend and dispersion degree of the user's electricity consumption information data specifically includes the following steps:

[0013] Calculate the average value of the data in the user's electricity consumption information data, and calculate the variance of the data according to the variance formula;

[0014] Setting the variance as the central trend of the user's electricity consumption information data, and assigning a trend identification code to the central trend;

[0015] Pair each data in the user's electricity consumption information data with the remaining data in the complete data to obtain at least one pairing group;

[0016] Calculate the absolute value of the difference between two data in each paired group of the complete data, and accumulate the absolute values ​​of the difference between two data in all paired groups to obtain a discrete value;

[0017] The discrete value is set as the discrete degree of the data, and a discrete identification code is assigned to the discrete degree.

[0018] Preferably, the calculation formula of the variance is as follows:

[0019]

[0020] Among them, D is the variance, n is the number of data, x i is the value of the data, and a is the average value of the data.

[0021] Preferably, the step of dividing the user's electricity usage information data into several groups comprises the following steps:

[0022] Retrieve the historical data classification and discrimination standard 1 from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification and discrimination standard 1;

[0023] Calculate the ratio of the central tendency of the data to the mean of the central tendency of the sample data;

[0024] Calculate the ratio of the degree of dispersion of the data to the mean of the degree of dispersion of the sample data;

[0025] Compare whether ratio 1 belongs to the range of central tendency ratio;

[0026] Compare the ratio 2 to see if it belongs to the range of the dispersion ratio;

[0027] If the first ratio belongs to the range of the central tendency ratio and the second ratio belongs to the range of the dispersion ratio, the classification in the classification discrimination group 1 is called, the classification is set as the first classification, and the data is classified into the first classification;

[0028] If the first ratio does not belong to the range of the central tendency ratio or the second ratio does not belong to the range of the dispersion ratio, the remaining classification discrimination groups in the data type determination model are called;

[0029] Find the historical data classification discrimination criterion 2 in the remaining classification discrimination groups, and the classification discrimination criterion 2 satisfies that the data ratio 1 belongs to the range interval of the central tendency ratio of the classification discrimination group 2 and the data ratio 2 belongs to the range interval of the dispersion ratio of the classification discrimination group 2, call the classification in the classification discrimination group 2, set the classification as the second classification, and classify the data into the second classification.

[0030] Preferably, the fusion of the same user electricity consumption information data specifically includes the following steps:

[0031] Retrieve the user electricity consumption information data belonging to the first category and the second category;

[0032] Fusion the user electricity consumption information data of the first category to obtain first category fusion data;

[0033] The user electricity consumption information data of the second category is integrated to obtain the second category integrated data.

[0034] Furthermore, during fusion, the original data position remains unchanged, but the position of the data group to be fused is not determined. Therefore, the smallest judgment sum is calculated and selected, and the target data group one corresponding to the smallest judgment sum is associated with the data group to be fused, because the two must be the data with the strongest correlation. Therefore, during fusion, the two are set adjacent to each other. Since the position of the target data group one is determined, the fusion position of the data group to be fused can be determined.

[0035] Preferably, the step of grading the power consumption information data of different types of users according to their importance and urgency to determine the power consumption allocation priority specifically comprises the following steps:

[0036] For users who will suffer significant economic losses due to power outage, the user level will be assessed as a first-level electricity user;

[0037] For users who will suffer great economic losses due to power outage, the user level will be rated as a second-level electricity user;

[0038] For electricity users that do not belong to the first and second levels, the user level is rated as a third-level electricity user.

[0039] Preferably, the determination rules for major losses and serious damages specifically include the following steps:

[0040] comparing the economic loss value with a first preset threshold and a second preset threshold, the first preset threshold being greater than the second preset threshold;

[0041] If the economic loss value is greater than or equal to the first preset threshold, it is determined to be a major loss;

[0042] If the economic loss value is greater than the second preset threshold value and less than the first preset threshold value, it is determined to be a major loss.

[0043] Furthermore, a power outage that causes economic losses of more than 2 million per hour is considered a major loss, and a power outage that causes economic losses of more than 500,000 but not more than 2 million per hour is considered a large loss; for those that cause losses of 500,000 per hour due to power outages, almost all of them are industrial enterprises or high-tech enterprises, and their electricity must be guaranteed, especially industrial enterprises, which have extremely high requirements for electricity and cannot be easily cut off from power. Tertiary electricity users are usually residents or small businesses, which do not have high demands for electricity and can therefore withstand short-term power outages.

[0044] Preferably, the first-level electricity users have a higher electricity priority than second-level electricity users, and the second-level electricity users have a higher electricity priority than third-level electricity users.

[0045] Preferably, distributing electricity to users according to the electricity allocation priority specifically includes the following steps:

[0046] Obtaining information on first-level electricity users, second-level electricity users, and third-level electricity users;

[0047] The power distribution module uses multiple circuits to distribute power to primary power users;

[0048] The power distribution module uses dual circuits to distribute power to secondary power users;

[0049] The power distribution module adopts a single circuit to distribute power to the third-level power users.

[0050] In addition, the present invention also proposes an artificial intelligence multi-level collaboration method for the power industry, including:

[0051] Collect user electricity usage information data;

[0052] Calculate the concentration trend and dispersion of user electricity consumption information data, divide the user electricity consumption information data into several groups, and merge the same user electricity consumption information data;

[0053] Classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power allocation;

[0054] Power is distributed to users based on power usage priorities.

[0055] Compared with the prior art, the present invention provides an artificial intelligence multi-level collaborative system and method for the power industry, which has the following beneficial effects:

[0056] In the actual power industry, the electricity consumption information of different users is different. For example, the electricity consumption of a large factory is higher than that of a medium-sized factory, and the electricity consumption of a medium-sized factory is higher than that of a normal household. Therefore, the power system distributes electricity to large factories higher than that to medium-sized factories and normal households. The present invention first collects user electricity consumption information data and divides it into several groups, and integrates the electricity consumption information data of the same user, such as school type, family type, factory type, etc., and then classifies the electricity consumption information data of different types of users according to their importance and urgency, determines the electricity allocation priority, and the power system distributes electricity to users according to the allocation priority, thereby improving the distribution efficiency while ensuring the normal electricity demand of users, while reducing the situation of excess or insufficient power supply, and adopts a multi-level distribution method to reduce the requirements for the accuracy of single distribution, reduce equipment costs and distribution method computing costs, and reduce resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the intelligent multi-level collaborative method in the present invention;

[0058] Figure 2 A schematic diagram of a method for calculating the central tendency and dispersion degree of user power consumption information data in the present invention;

[0059] Figure 3 A schematic diagram of a method for classifying user power consumption information data into several groups in the present invention;

[0060] Figure 4 It is a schematic diagram of a method for fusing the same user's electricity consumption information data in the present invention;

[0061] Figure 5 A schematic diagram of a method for determining electricity allocation priority in the present invention;

[0062] Figure 6 It is a schematic diagram of the method for determining the major losses and major injuries in the present invention. DETAILED DESCRIPTION

[0063] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0064] Example 1

[0065] Please refer to Figure 1-Figure 6 As shown in the figure, the artificial intelligence multi-level collaborative system for the power industry includes:

[0066] Data collection module, the data collection module is used to collect user electricity consumption information data;

[0067] The data fusion module is electrically connected to the data acquisition module, calculates the concentration trend and dispersion degree of the user's electricity consumption information data, divides the user's electricity consumption information data into several groups, and fuses the same user's electricity consumption information data;

[0068] A classification module, which is electrically connected to the data fusion module and is used to classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power distribution;

[0069] A power distribution module, which is electrically connected to the classification module and distributes power to users according to the power allocation priority;

[0070] Database, the database is used to store all power information data and encrypt it.

[0071] People skilled in the art can understand that in the actual power industry, the electricity consumption information of different users is different. For example, the electricity consumption of a large factory is higher than that of a medium-sized factory, and the electricity consumption of a medium-sized factory is higher than that of a normal household. Therefore, the power system distributes electricity to large factories higher than that to medium-sized factories and normal households. The present invention first collects user electricity consumption information data and divides it into several groups, and integrates the electricity consumption information data of the same user, such as school type, family type, factory type, etc., and then grades the electricity consumption information data of different types of users according to their importance and urgency, determines the electricity allocation priority, and the power system distributes electricity to users according to the allocation priority, thereby improving the distribution efficiency while ensuring the normal electricity demand of users, while reducing the situation of excess or insufficient power supply, and adopts a multi-level distribution method to reduce the requirements for the accuracy of a single distribution, reduce equipment costs and distribution method computing costs, and reduce resource waste.

[0072] Calculating the concentration trend and dispersion of user electricity consumption information data specifically includes the following steps:

[0073] Calculate the average value of the data in the user's electricity consumption information data, and calculate the variance of the data according to the variance formula;

[0074] Set the variance as the central trend of the user's electricity consumption information data, and assign a trend identification code to the central trend;

[0075] Pair each data in the user's electricity consumption information data with the remaining data in the complete data to obtain at least one pairing group;

[0076] Calculate the absolute value of the difference between two data in each paired group of the complete data, and accumulate the absolute values ​​of the difference between two data in all paired groups to obtain a discrete value;

[0077] Set the discrete value to the discrete degree of the data and assign a discrete identification code to the discrete degree.

[0078] The formula for calculating variance is as follows:

[0079]

[0080] Among them, D is the variance, n is the number of data, x i is the value of the data, and a is the average value of the data.

[0081] Classifying the user's electricity consumption information data into several groups specifically includes the following steps:

[0082] Retrieve the historical data classification and discrimination standard 1 from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification and discrimination standard 1;

[0083] Calculate the ratio of the central tendency of the data to the mean of the central tendency of the sample data;

[0084] Calculate the ratio of the degree of dispersion of the data to the mean of the degree of dispersion of the sample data;

[0085] Compare whether ratio 1 belongs to the range of central tendency ratio;

[0086] Compare the ratio 2 to see if it belongs to the range of the dispersion ratio;

[0087] If the ratio 1 belongs to the range of the central tendency ratio and the ratio 2 belongs to the range of the dispersion ratio, the classification in the classification discrimination group 1 is called, the classification is set to the first classification, and the data is classified into the first classification;

[0088] If the first ratio does not belong to the range of the central tendency ratio or the second ratio does not belong to the range of the dispersion ratio, the remaining classification discrimination groups in the data type determination model are called;

[0089] Find the historical data classification and discrimination criterion 2 in the remaining classification and discrimination groups. The classification and discrimination criterion 2 satisfies that the data ratio 1 belongs to the range interval of the central tendency ratio of the classification and discrimination group 2 and the data ratio 2 belongs to the range interval of the dispersion ratio of the classification and discrimination group 2. Call the classification in the classification and discrimination group 2, set the classification to the second classification, and classify the data into the second classification.

[0090] The integration of the same user's electricity consumption information data specifically includes the following steps:

[0091] Retrieve the user electricity consumption information data belonging to the first category and the second category;

[0092] Fusion the user electricity consumption information data of the first category to obtain first category fusion data;

[0093] The user electricity consumption information data of the second category is integrated to obtain the second category integrated data.

[0094] It can be understood by those skilled in the art that, during fusion, the original data position remains unchanged, but the position of the data group to be fused is not determined, therefore, the smallest judgment sum is calculated and selected, and the target data group one corresponding to the smallest judgment sum is associated with the data group to be fused, because the two must be the data with the strongest correlation, therefore, during fusion, the two are set adjacent to each other, and since the position of the target data group one is determined, the fusion position of the data group to be fused is determined.

[0095] According to the importance and urgency of different types of user electricity information data, the electricity allocation priority is determined by grading, which specifically includes the following steps:

[0096] For users who will suffer significant economic losses due to power outage, the user level will be assessed as a first-level electricity user;

[0097] For users who will suffer great economic losses due to power outage, the user level will be rated as a second-level electricity user;

[0098] For electricity users that do not belong to the first and second levels, the user level is rated as a third-level electricity user.

[0099] The specific rules for determining major losses and serious injuries include the following steps:

[0100] The economic loss value is compared with a first preset threshold and a second preset threshold, the first preset threshold being greater than the second preset threshold;

[0101] If the economic loss value is greater than or equal to the first preset threshold, it is determined to be a major loss;

[0102] If the economic loss value is greater than the second preset threshold value and less than the first preset threshold value, it is determined to be a major loss.

[0103] It is understood by those skilled in the art that a power outage causing economic losses of more than 2 million per hour is considered a major loss, and a power outage causing economic losses of more than 500,000 but not more than 2 million per hour is considered a large loss;

[0104] For enterprises that suffer losses of 500,000 per hour due to power outages, they are almost all industrial enterprises or high-tech enterprises. Their electricity must be guaranteed, especially industrial enterprises that have extremely high requirements for electricity and cannot easily be cut off from power. Tertiary electricity users are usually residents or small businesses who do not have high demands for electricity and can therefore withstand short-term power outages.

[0105] The electricity priority of first-level electricity users is higher than that of second-level electricity users, and the electricity priority of second-level electricity users is higher than that of third-level electricity users.

[0106] Distributing electricity to users according to electricity allocation priorities specifically includes the following steps:

[0107] Obtaining information on first-level electricity users, second-level electricity users, and third-level electricity users;

[0108] The power distribution module uses multiple circuits to distribute power to primary power users;

[0109] The power distribution module uses dual circuits to distribute power to secondary power users;

[0110] The power distribution module adopts a single circuit to distribute power to the third-level power users.

[0111] It is understandable to those skilled in the art that first-level electricity users use multiple circuits for power supply, and second-level electricity users use dual circuits for power supply. Thus, when a line fails, other lines can supply power to first-level and second-level electricity users to ensure their normal production operations. However, since first-level electricity users have large electricity demands, they require several additional lines to supply power at the same time.

[0112] In addition, the present invention also proposes an artificial intelligence multi-level collaboration method for the power industry, including:

[0113] Collect user electricity usage information data;

[0114] Calculate the concentration trend and dispersion of user electricity consumption information data, divide the user electricity consumption information data into several groups, and merge the same user electricity consumption information data;

[0115] Classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power allocation;

[0116] Power is distributed to users based on power usage priorities.

[0117] To sum up, in the actual power industry, the electricity consumption information of different users is different. For example, the electricity consumption of a large factory is higher than that of a medium-sized factory, and the electricity consumption of a medium-sized factory is higher than that of a normal household. Therefore, the power system distributes electricity to large factories higher than that to medium-sized factories and normal households. The present invention first collects user electricity consumption information data and divides it into several groups, and integrates the electricity consumption information data of the same user, such as school type, family type, factory type, etc., and then classifies the electricity consumption information data of different types of users according to their importance and urgency, determines the electricity allocation priority, and the power system distributes electricity to users according to the allocation priority, thereby improving the distribution efficiency while ensuring the normal electricity demand of users, while reducing the situation of excess or insufficient power supply, and adopts a multi-level distribution method to reduce the requirements for the accuracy of a single distribution, reduce equipment costs and distribution method computing costs, and reduce resource waste.

[0118] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An artificial intelligence multi-level collaborative system for the power industry, characterized by: include: A data collection module, wherein the data collection module is used to collect user electricity consumption information data; A data fusion module, which is electrically connected to the data acquisition module, calculates the concentration trend and dispersion degree of the user's electricity consumption information data, divides the user's electricity consumption information data into several groups, and fuses the same type of user's electricity consumption information data; A classification module, which is electrically connected to the data fusion module and is used to classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power consumption allocation; A power distribution module, which is electrically connected to the classification module and distributes power to users according to power allocation priorities; A database is used to store and encrypt all power information data.

2. The artificial intelligence multi-level collaborative system for the power industry according to claim 1 is characterized in that: The calculation of the concentration trend and dispersion degree of the user's electricity consumption information data specifically includes the following steps: Calculate the average value of the data in the user's electricity consumption information data, and calculate the variance of the data according to the variance formula; Setting the variance as the central trend of the user's electricity consumption information data, and assigning a trend identification code to the central trend; Pair each data in the user's electricity consumption information data with the remaining data in the complete data to obtain at least one pairing group; Calculate the absolute value of the difference between two data in each paired group of the complete data, and accumulate the absolute values ​​of the difference between two data in all paired groups to obtain a discrete value; The discrete value is set as the discrete degree of the data, and a discrete identification code is assigned to the discrete degree.

3. The artificial intelligence multi-level collaborative system for the power industry according to claim 2 is characterized in that: The calculation formula of the variance is as follows: Among them, D is the variance, n is the number of data, x i is the value of the data, and a is the average value of the data.

4. The artificial intelligence multi-level collaborative system for the power industry according to claim 3 is characterized in that: The method of dividing the user's electricity consumption information data into several groups specifically includes the following steps: Retrieve the historical data classification and discrimination standard 1 from the database, and obtain the range interval of the central tendency ratio and the range interval of the dispersion ratio in the classification and discrimination standard 1; Calculate the ratio of the central tendency of the data to the mean of the central tendency of the sample data; Calculate the ratio of the degree of dispersion of the data to the mean of the degree of dispersion of the sample data; Compare whether ratio 1 belongs to the range of central tendency ratio; Compare the ratio 2 to see if it belongs to the range of the dispersion ratio; If the first ratio belongs to the range of the central tendency ratio and the second ratio belongs to the range of the dispersion ratio, the classification in the classification discrimination group 1 is called, the classification is set as the first classification, and the data is classified into the first classification; If the first ratio does not belong to the range of the central tendency ratio or the second ratio does not belong to the range of the dispersion ratio, the remaining classification discrimination groups in the data type determination model are called; Find the historical data classification discrimination criterion 2 in the remaining classification discrimination groups, and the classification discrimination criterion 2 satisfies that the data ratio 1 belongs to the range interval of the central tendency ratio of the classification discrimination group 2 and the data ratio 2 belongs to the range interval of the dispersion ratio of the classification discrimination group 2, call the classification in the classification discrimination group 2, set the classification as the second classification, and classify the data into the second classification.

5. The artificial intelligence multi-level collaborative system for the power industry according to claim 4 is characterized in that: The fusion of the same user electricity consumption information data specifically includes the following steps: Retrieve the user electricity consumption information data belonging to the first category and the second category; Fusion the user electricity consumption information data of the first category to obtain first category fusion data; The user electricity consumption information data of the second category is integrated to obtain the second category integrated data.

6. The artificial intelligence multi-level collaborative system for the power industry according to claim 5 is characterized in that: The step of grading the power consumption information data of different users according to their importance and urgency and determining the power consumption allocation priority specifically includes the following steps: For users who will suffer significant economic losses due to power outage, the user level will be assessed as a first-level electricity user; For users who will suffer great economic losses due to power outage, the user level will be rated as a second-level electricity user; For electricity users that do not belong to the first and second levels, the user level is rated as a third-level electricity user.

7. The artificial intelligence multi-level collaborative system for the power industry according to claim 6 is characterized in that: The rules for determining major losses and serious damages specifically include the following steps: comparing the economic loss value with a first preset threshold and a second preset threshold, the first preset threshold being greater than the second preset threshold; If the economic loss value is greater than or equal to the first preset threshold, it is determined to be a major loss; If the economic loss value is greater than the second preset threshold value and less than the first preset threshold value, it is determined to be a major loss.

8. The artificial intelligence multi-level collaborative system for the power industry according to claim 7 is characterized in that: The first-level electricity users have a higher electricity priority than the second-level electricity users, and the second-level electricity users have a higher electricity priority than the third-level electricity users.

9. The artificial intelligence multi-level collaborative system for the power industry according to claim 8 is characterized in that: The method of distributing electricity to users according to the electricity allocation priority specifically includes the following steps: Obtaining information on first-level electricity users, second-level electricity users, and third-level electricity users; The power distribution module uses multiple circuits to distribute power to primary power users; The power distribution module uses dual circuits to distribute power to secondary power users; The power distribution module adopts a single circuit to distribute power to the third-level power users.

10. An artificial intelligence multi-level collaboration method for the power industry, used to implement an artificial intelligence multi-level collaboration system for the power industry as described in any one of claims 1 to 9, characterized in that: include: Collect user electricity usage information data; Calculate the concentration trend and dispersion of user electricity consumption information data, divide the user electricity consumption information data into several groups, and merge the same user electricity consumption information data; Classify the power consumption information data of different users according to their importance and urgency, and determine the priority of power allocation; Power is distributed to users based on power usage priorities.

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