Power acquisition terminal data interaction method

Abstract

The invention relates to the technical field of electric power data acquisition interaction, and discloses an electric power acquisition terminal data interaction method, which comprises the following steps of: storing electricity consumption information of a user by using a database, dividing the electricity consumption information of the user according to different weathers, the electric power acquisition terminal performs screening calculation on the electricity utilization information in the database according to the obtained user interaction information and feeds back target information to the user, when the interaction information is query operation, historical data with the same weather type or quarter can be obtained according to the time period to be queried, the average value of the historical data is calculated, and the user experience is improved. And when the interaction information is the value setting operation, firstly querying the historical data of the weather type or the quarterly to which the time period needs to be set, calculating the average value according to the historical data, and feeding the average value back to the user.

Description

Technical Field

[0001] The present invention relates to the technical field of power data collection interaction, and in particular to a power collection terminal data interaction method. Background Art

[0002] The principle of data acquisition is to convert the parameters in the power system into electrical signals or digital signals, collect and process them through data acquisition equipment, and finally transmit them to the data center through the communication module for monitoring and analysis; power data acquisition is to monitor and collect the user's power consumption data. In the context of big data, with the use of the Internet of Things and cloud computing technologies, power consumption monitoring and power management can be achieved, and analysis and prediction can be carried out through big data technology to provide two-way decision-making service support for power companies and users; The interaction of power data collection requires the use of key technologies such as big data and databases. Through key technologies such as big data and databases, information interaction between users and power companies can be achieved, improving the efficiency and safety of the power system; However, with the known electricity data collection and interaction, users’ understanding of electricity usage is too one-sided. They can only query the current remaining power and previous power consumption, but cannot obtain the user’s peak power consumption time period. Moreover, they cannot analyze power consumption information according to weather and season, and thus cannot set subsequent power consumption more accurately. In summary, there is an urgent need for a power collection terminal data interaction method to solve the above problems. Summary of the invention

[0003] The present invention provides a data interaction method for a power collection terminal, which is used to promote solving the problems mentioned in the above background technology.

[0004] The present invention provides the following technical solution: a data interaction method of a power collection terminal, comprising: S1 obtains the electricity usage information of the user's electricity meter and enters the electricity usage information into the database. The electricity usage information is updated once every moment, and the moment is one hour; S2 obtains the daily weather information of the user's location, divides the weather information by type, and sets the weather type of the user's daily electricity consumption information, dividing it into quarters according to the month of the day; S3 users send interactive information to the power collection terminal according to their personal needs; S4 adopts different ways of information screening and calculation on the database according to the interactive information sent by the user to the power collection terminal to obtain information for different purposes. The interactive information includes the monthly peak power consumption interval, the cumulative power consumption of the month, the expected power consumption of the next day and the expected power consumption of the next month. The expected power consumption of the next day is the expected power consumption of the next day based on the current day, and the expected power consumption of the next month is the expected power consumption of the next month based on the current month; S5: If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data; S6: If the user interaction information is the estimated electricity consumption for the next month, then according to the quarter in which the current month is located, the electricity consumption data of the historical months in the same quarter are obtained, and the estimated electricity consumption for the next month is obtained by calculating the electricity consumption data of the historical months; S7 If the user interaction information is the monthly peak power consumption range and the cumulative power consumption of the month, the corresponding power consumption information in the database is filtered to determine the first peak value of power consumption every day, and the peak power consumption range of the month is determined by counting the first peak value of power consumption every day.

[0005] Optionally, the user's electricity usage information is obtained and the electricity usage information is entered into a database: Obtain the user's electricity consumption information, including the total electricity consumption per day and the electricity consumption at each time of the day; Get the current weather information and historical weather information of the user's location, classify the weather information by type, including rainy days, cloudy days and sunny days, and set the weather type of daily electricity consumption information; The year is divided into four quarters, as follows: January to March is the first quarter; April to June is the second quarter; July to September is the third quarter; October to December is the fourth quarter.

[0006] Optionally, if the user interaction information is the monthly peak power consumption interval and the cumulative power consumption of the month, the corresponding power consumption information in the database is filtered to determine the first peak value of power consumption every day, and the peak power consumption interval of the month is determined by counting the first peak value of power consumption every day: If the interactive information sent by the user is the cumulative electricity consumption of the current month, the daily electricity consumption of the users in the current month in the database is obtained, and the sum of the daily electricity consumption of the users in the current month is recorded as g, which is the cumulative electricity consumption of the current month; If the user interaction information is the monthly peak power consumption interval, then the power consumption at every time of every day of the current month is obtained; Determine the peak power consumption interval of each day in the current month, that is, the time period with the highest power consumption each day in the current month; Taking the power consumption at 1 a.m. as the starting value, the power consumption at each subsequent time is judged in turn. If the power consumption at the subsequent time is greater than or equal to the starting value, the power consumption at the subsequent time is taken as the starting value. If the power consumption at the subsequent time is less than the starting value, the starting value and the corresponding time are recorded in the peak value set. Traverse and compare the peak value set, select the maximum power consumption of the peak value set as the first peak value, record the time corresponding to the first peak value as the first peak time, and obtain the adjacent time of the first peak time; If there are two adjacent moments, the interval between the adjacent moments is recorded as the first peak interval; If there is only one adjacent moment, the interval between the adjacent moment and the first peak moment is recorded as the first peak interval. Enter the first peak interval of each day in the current month into the set h, and select the element with the highest number of repetitions as the peak power consumption interval of the current month; If there are i elements with the highest number of repetitions, the first peak values ​​corresponding to the elements are compared, and the first peak interval corresponding to the largest first peak value is selected as the peak power consumption interval of the current month; By determining the monthly peak power consumption range and cumulative power consumption, users can better understand their power consumption habits and thus effectively manage power consumption and save energy.

[0007] Optionally, if the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the interactive information sent by the user is the estimated electricity consumption for the next day, the user's historical electricity consumption data is collated and calculated; Obtain the weather information of the next day, determine the weather category of the next day as the target type, obtain all daily electricity consumption information in the database of the current month, filter and retain the electricity consumption information with the same target category, and record it as the same weather set; If the number of elements in the same weather set is less than Q, then select all electricity consumption information of the previous month, filter and retain electricity consumption information with the same target category, and enter the same weather set. If the number of elements in the same weather set is greater than or equal to Q, sort them from early to late according to the element date; If the electricity consumption growth trend of the elements in the same weather set is increasing successively, then the absolute value of the difference in electricity consumption growth between two adjacent elements in the set is calculated, recorded as a1, a2, a3...an, n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows:

[0008] In the above formula, s represents the average value of the absolute value of the difference in electricity consumption growth between two adjacent elements in the same weather set, n represents the number of the difference, and |an| represents the absolute value of the nth difference; The sum of yesterday's electricity consumption and s is recorded as m, and m is the electricity consumption of the next day.

[0009] Optionally, if the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the power consumption growth trend of the elements in the same weather set is decreasing successively, then the absolute value of the power consumption difference between two adjacent elements in the set is calculated, recorded as b1, b2, b3...bn, where n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows:

[0010] In the above formula, z represents the average value of the absolute value of the difference in power consumption between two adjacent elements in the same weather set, n represents the number of the difference, and |bn| represents the absolute value of the nth difference; The difference between yesterday's electricity consumption and s is recorded as c, and c is the electricity consumption of the next day.

[0011] Optionally, if the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the electricity consumption growth trend of the elements in the same weather set is irregular, that is, there is an increasing trend and a decreasing trend, then all the electricity consumption in the current month is obtained, recorded as d1, d2, d3...de, e is the number of days in the current month as of today, and the average electricity consumption of users in the current month is calculated by the following formula, as follows:

[0012] In the above formula, f represents the average power consumption of users in the current month, e represents the number of days in the current month up to today, and de represents the power consumption on the eth day of the current month; Let f be the electricity consumption for the next day.

[0013] Optionally, if the user interaction information is the estimated power consumption for the next month, then according to the quarter in which the current month is located, the power consumption data of the historical months in the same quarter is obtained, and the estimated power consumption for the next month is obtained by calculating the power consumption data of the historical months: Determine the quarter you are in based on the current month and record that quarter as the target quarter; Select the same quarter as last year and the current quarter as the first month set, and obtain the cumulative power consumption of all elements in the first month set, recorded as k1, k2 and k3; pass Calculate the average value of the month in the first month set, denoted as r, where p is the number of elements in the first month set and r is the expected electricity consumption for the next month.

[0014] The present invention has the following beneficial effects: 1. This method for interacting with power collection terminal data ensures the real-time and accuracy of power consumption data in the database by updating the power consumption information of the user's power meter every moment.

[0015] 2. This method of data interaction of a power collection terminal allows users to send interactive information according to personal needs and obtain personalized power consumption data services, such as peak power consumption intervals, cumulative power consumption, etc., thereby improving user experience.

[0016] 3. This method of data interaction at an electric power collection terminal obtains the real-time power consumption of each day of the current month, takes the power consumption at 1 a.m. as the starting value, and compares the power consumption at subsequent times with the starting value in turn. If it is greater than the starting value, the power consumption at the current time is set as the starting value, and all starting values ​​are compared to obtain the largest starting value as the first peak value. If there are multiple largest starting values, the one with the most repeated times is selected as the first peak value, and the adjacent times of the first peak value are used as the peak power consumption interval of the current month. This can improve the accuracy of user query data, enhance the user's control and participation in power use, and solve the previous one-sided understanding of power use by users.

[0017] 4. A data interaction method for a power collection terminal determines the weather category of the next day, filters and retains the user's electricity consumption information with the same weather category in the current month. If the amount of electricity consumption information is insufficient, the electricity consumption information with the same weather category in the previous month is selected, and the electricity consumption information is sorted from early to late according to the date. Different calculation methods are selected according to the changing trend of the electricity consumption information. In this way, filtering historical data according to the weather can enhance the reliability of the prediction. By adopting different calculation methods for different changing trends, the accuracy of the prediction can be improved, making the prediction more in line with actual changes.

[0018] 5. This method of data interaction of power collection terminals takes into account quarterly factors when predicting the power consumption of the next month and improves the accuracy of the prediction by comparing historical data of the same quarter.

[0019] 6. A method for interacting with power collection terminal data, by determining the monthly peak power consumption interval and cumulative power consumption, enables users to better understand their own power consumption habits, thereby effectively managing power consumption and saving energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1, see Figure 1 , this embodiment provides a data interaction method for a power collection terminal, including: S1 obtains the electricity usage information of the user's electricity meter and enters the electricity usage information into the database. The electricity usage information is updated once every moment, and the moment is one hour; By updating the electricity consumption information of the user's electricity meter every moment, the real-time and accuracy of the electricity consumption data in the database is ensured; S2 obtains the daily weather information of the user's location, divides the weather information by type, and sets the weather type of the user's daily electricity consumption information, dividing it into quarters according to the month of the day; S3 users send interactive information to the power collection terminal according to their personal needs; S4 adopts different ways of information screening and calculation on the database according to the interactive information sent by the user to the power collection terminal to obtain information for different purposes. The interactive information includes the monthly peak power consumption interval, the cumulative power consumption of the month, the expected power consumption of the next day and the expected power consumption of the next month. The expected power consumption of the next day is the expected power consumption of the next day based on the current day, and the expected power consumption of the next month is the expected power consumption of the next month based on the current month; Users can send interactive information according to their personal needs and obtain personalized electricity consumption data services, such as peak power consumption intervals and cumulative power consumption, which improves user experience; S5: If the user interaction information is the estimated power consumption for the next day, then query the corresponding historical power consumption data through the database and predict the power consumption for the next day based on the historical data; S6: If the user interaction information is the estimated electricity consumption for the next month, then according to the quarter in which the current month is located, the electricity consumption data of the historical months in the same quarter are obtained, and the estimated electricity consumption for the next month is obtained by calculating the electricity consumption data of the historical months; S7 If the user interaction information is the monthly peak power consumption range and the cumulative power consumption of the month, the corresponding power consumption information in the database is filtered to determine the first peak value of power consumption every day, and the peak power consumption range of the month is determined by counting the first peak value of power consumption every day.

[0023] The user's electricity usage information is obtained and the electricity usage information is entered into a database: Obtain the user's electricity consumption information, including the total electricity consumption per day and the electricity consumption at each time of the day; Get the current weather information and historical weather information of the user's location, classify the weather information by type, including rainy days, cloudy days and sunny days, and set the weather type of daily electricity consumption information; By acquiring and classifying the weather information at the user's location, the impact of weather on electricity consumption patterns is taken into account, thus improving the accuracy of electricity consumption forecasts; The year is divided into four quarters, as follows: January to March is the first quarter; April to June is the second quarter; July to September is the third quarter; October to December is the fourth quarter.

[0024] If the user interaction information is the monthly peak power consumption interval and the cumulative power consumption of the month, the corresponding power consumption information in the database is screened to determine the first peak value of power consumption every day, and the peak power consumption interval of the month is determined by counting the first peak value of power consumption every day: If the interactive information sent by the user is the cumulative electricity consumption of the current month, the daily electricity consumption of the users in the current month in the database is obtained, and the sum of the daily electricity consumption of the users in the current month is recorded as g, which is the cumulative electricity consumption of the current month; If the user interaction information is the monthly peak power consumption interval, then the power consumption at every time of every day of the current month is obtained; Determine the peak power consumption interval of each day in the current month, that is, the time period with the highest power consumption each day in the current month; Taking the power consumption at 1 a.m. as the starting value, the power consumption at each subsequent time is judged in turn. If the power consumption at the subsequent time is greater than or equal to the starting value, the power consumption at the subsequent time is taken as the starting value. If the power consumption at the subsequent time is less than the starting value, the starting value and the corresponding time are recorded in the peak value set. Traverse and compare the peak value set, select the maximum power consumption of the peak value set as the first peak value, record the time corresponding to the first peak value as the first peak time, and obtain the adjacent time of the first peak time; If there are two adjacent moments, the interval between the adjacent moments is recorded as the first peak interval; If there is only one adjacent moment, the interval between the adjacent moment and the first peak moment is recorded as the first peak interval. Enter the first peak interval of each day in the current month into the set h, and select the element with the highest number of repetitions as the peak power consumption interval of the current month; If there are i elements with the highest number of repetitions, the first peak values ​​corresponding to the elements are compared, and the first peak interval corresponding to the largest first peak value is selected as the peak power consumption interval of the current month.

[0025] If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the interactive information sent by the user is the estimated electricity consumption for the next day, the user's historical electricity consumption data is collated and calculated; Obtain the weather information of the next day, determine the weather category of the next day as the target type, obtain all daily electricity consumption information in the database of the current month, filter and retain the electricity consumption information with the same target category, and record it as the same weather set; If the number of elements in the same weather set is less than Q, then select all electricity consumption information of the previous month, filter and retain electricity consumption information with the same target category, and enter the same weather set. If the number of elements in the same weather set is greater than or equal to Q, sort them from early to late according to the element date; If the electricity consumption growth trend of the elements in the same weather set is increasing successively, then the absolute value of the difference in electricity consumption growth between two adjacent elements in the set is calculated, recorded as a1, a2, a3...an, n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows:

[0026] In the above formula, s represents the average value of the absolute value of the difference in electricity consumption growth between two adjacent elements in the same weather set, n represents the number of the difference, and |an| represents the absolute value of the nth difference; The sum of yesterday's electricity consumption and s is recorded as m, and m is the electricity consumption of the next day.

[0027] If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the power consumption growth trend of the elements in the same weather set is decreasing successively, then the absolute value of the power consumption difference between two adjacent elements in the set is calculated, recorded as b1, b2, b3...bn, where n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows:

[0028] In the above formula, z represents the average value of the absolute value of the difference in power consumption between two adjacent elements in the same weather set, n represents the number of the difference, and |bn| represents the absolute value of the nth difference; The difference between yesterday's electricity consumption and s is recorded as c, and c is the electricity consumption of the next day.

[0029] If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the electricity consumption growth trend of the elements in the same weather set is irregular, that is, there is an increasing trend and a decreasing trend, then all the electricity consumption in the current month is obtained, recorded as d1, d2, d3...de, e is the number of days in the current month as of today, and the average electricity consumption of users in the current month is calculated by the following formula, as follows:

[0030] In the above formula, f represents the average power consumption of users in the current month, e represents the number of days in the current month up to today, and de represents the power consumption on the eth day of the current month; Let f be the electricity consumption of the next day; By obtaining the real-time electricity consumption of each day in the current month, taking the electricity consumption at 1 a.m. as the starting value, the electricity consumption at subsequent times is compared with the starting value. If it is greater than the starting value, the electricity consumption at the current time is set as the starting value. All starting values ​​are compared, and the largest starting value is obtained as the first peak value. If there are multiple largest starting values, the one with the most repeated times is selected as the first peak value, and the adjacent times of the first peak are used as the peak electricity consumption interval of the current month. This can improve the accuracy of user query data, enhance user control and participation in electricity use, and solve the previous one-sided understanding of electricity use by users.

[0031] If the user interaction information is the estimated electricity consumption for the next month, then according to the quarter in which the current month is located, the electricity consumption data of the historical months in the same quarter is obtained, and the estimated electricity consumption for the next month is obtained by calculating the electricity consumption data of the historical months: Determine the quarter you are in based on the current month and record that quarter as the target quarter; Select the same quarter as last year and the current quarter as the first month set, and obtain the cumulative power consumption of all elements in the first month set, recorded as k1, k2 and k3; pass Calculate the average value of the month in the first month set, recorded as r, p is the number of elements in the first month set, and r is the expected electricity consumption for the next month; By taking the quarterly factor into account when predicting the electricity consumption for the next month, the accuracy of the prediction can be improved by comparing the historical data of the same quarter.

[0032] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0033] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A data interaction method for a power collection terminal, characterized in that: include, S1 obtains the electricity usage information of the user's electricity meter and enters the electricity usage information into the database. The electricity usage information is updated once every moment, and the moment is one hour; S2 obtains the daily weather information of the user's location, divides the weather information by type, and sets the weather type of the user's daily electricity consumption information, dividing it into quarters according to the month of the day; S3 users send interactive information to the power collection terminal according to their personal needs; S4 adopts different ways of information screening and calculation on the database according to the interactive information sent by the user to the power collection terminal to obtain information for different purposes. The interactive information includes the monthly peak power consumption interval, the cumulative power consumption of the month, the expected power consumption of the next day and the expected power consumption of the next month. The expected power consumption of the next day is the expected power consumption of the next day based on the current day, and the expected power consumption of the next month is the expected power consumption of the next month based on the current month; S5: If the user interaction information is the estimated power consumption for the next day, then query the corresponding historical power consumption data through the database and predict the power consumption for the next day based on the historical data; S6: If the user interaction information is the estimated electricity consumption for the next month, then according to the quarter in which the current month is located, the electricity consumption data of the historical months in the same quarter are obtained, and the estimated electricity consumption for the next month is obtained by calculating the electricity consumption data of the historical months; S7 If the user interaction information is the monthly peak power consumption range and the cumulative power consumption of the month, the corresponding power consumption information in the database is filtered to determine the first peak value of power consumption every day, and the peak power consumption range of the month is determined by counting the first peak value of power consumption every day.

2. A method for interacting with power collection terminal data according to claim 1, characterized in that: The user's electricity usage information is obtained and the electricity usage information is entered into a database: Obtain the user's electricity consumption information, including the total electricity consumption per day and the electricity consumption at each time of the day; Get the current weather information and historical weather information of the user's location, classify the weather information by type, including rainy days, cloudy days and sunny days, and set the weather type of daily electricity consumption information; The year is divided into four quarters, as follows: January to March is the first quarter; April to June is the second quarter; July to September is the third quarter; October to December is the fourth quarter.

3. A method for interacting with power collection terminal data according to claim 1, characterized in that: If the user interaction information is the monthly peak power consumption interval and the cumulative power consumption of the month, the corresponding power consumption information in the database is screened to determine the first peak value of power consumption every day, and the peak power consumption interval of the month is determined by counting the first peak value of power consumption every day: If the interactive information sent by the user is the cumulative electricity consumption of the current month, the daily electricity consumption of the users in the current month in the database is obtained, and the sum of the daily electricity consumption of the users in the current month is recorded as g, which is the cumulative electricity consumption of the current month; If the user interaction information is the monthly peak power consumption interval, then the power consumption at every time of every day of the current month is obtained; Determine the peak power consumption interval of each day in the current month, that is, the time period with the highest power consumption each day in the current month; Taking the power consumption at 1 a.m. as the starting value, the power consumption at each subsequent time is judged in turn. If the power consumption at the subsequent time is greater than or equal to the starting value, the power consumption at the subsequent time is taken as the starting value. If the power consumption at the subsequent time is less than the starting value, the starting value and the corresponding time are recorded in the peak value set. Traverse and compare the peak value set, select the maximum power consumption of the peak value set as the first peak value, record the time corresponding to the first peak value as the first peak time, and obtain the adjacent time of the first peak time; If there are two adjacent moments, the interval between the adjacent moments is recorded as the first peak interval; If there is only one adjacent moment, the interval between the adjacent moment and the first peak moment is recorded as the first peak interval. Enter the first peak interval of each day in the current month into the set h, and select the element with the highest number of repetitions as the peak power consumption interval of the current month; If there are i elements with the highest number of repetitions, the first peak values ​​corresponding to the elements are compared, and the first peak interval corresponding to the largest first peak value is selected as the peak power consumption interval of the current month.

4. A method for interacting with power collection terminal data according to claim 1, characterized in that: If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the interactive information sent by the user is the estimated electricity consumption for the next day, the user's historical electricity consumption data is collated and calculated; Obtain the weather information of the next day, determine the weather category of the next day as the target type, obtain all daily electricity consumption information in the database of the current month, filter and retain the electricity consumption information with the same target category, and record it as the same weather set; If the number of elements in the same weather set is less than Q, then select all electricity consumption information of the previous month, filter and retain electricity consumption information with the same target category, and enter the same weather set. If the number of elements in the same weather set is greater than or equal to Q, sort them from early to late according to the element date; If the electricity consumption growth trend of the elements in the same weather set is increasing successively, then the absolute value of the difference in electricity consumption growth between two adjacent elements in the set is calculated, recorded as a1, a2, a3...an, n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows: In the above formula, s represents the average value of the absolute value of the difference in electricity consumption growth between two adjacent elements in the same weather set, n represents the number of the difference, and |an| represents the absolute value of the nth difference; The sum of yesterday's electricity consumption and s is recorded as m, and m is the electricity consumption of the next day.

5. A power collection terminal data interaction method according to claim 1, characterized in that: If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the power consumption growth trend of the elements in the same weather set is decreasing successively, then the absolute value of the power consumption difference between two adjacent elements in the set is calculated, recorded as b1, b2, b3...bn, where n is the number of the above differences, and the average value of the difference is calculated by the following formula, as follows: In the above formula, z represents the average value of the absolute value of the difference in power consumption between two adjacent elements in the same weather set, n represents the number of the difference, and |bn| represents the absolute value of the nth difference; The difference between yesterday's electricity consumption and s is recorded as c, and c is the electricity consumption of the next day.

6. A method for interacting with power collection terminal data according to claim 1, characterized in that: If the user interaction information is the estimated power consumption for the next day, the corresponding historical power consumption data is queried through the database, and the power consumption for the next day is predicted based on the historical data: If the electricity consumption growth trend of the elements in the same weather set is irregular, that is, there is an increasing trend and a decreasing trend, then all the electricity consumption in the current month is obtained, recorded as d1, d2, d3...de, e is the number of days in the current month as of today, and the average electricity consumption of users in the current month is calculated by the following formula, as follows: In the above formula, f represents the average power consumption of users in the current month, e represents the number of days in the current month up to today, and de represents the power consumption on the eth day of the current month; Let f be the electricity consumption for the next day.

7. A power collection terminal data interaction method according to claim 1, characterized in that: If the user interaction information is the estimated electricity consumption for the next month, then according to the quarter in which the current month is located, the electricity consumption data of the historical months in the same quarter is obtained, and the estimated electricity consumption for the next month is obtained by calculating the electricity consumption data of the historical months: Determine the quarter you are in based on the current month and record that quarter as the target quarter; Select the same quarter as last year and the current quarter as the first month set, and obtain the cumulative power consumption of all elements in the first month set, recorded as k1, k2 and k3; pass Calculate the average value of the month in the first month set, denoted as r, where p is the number of elements in the first month set and r is the expected electricity consumption for the next month.

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