City power brain intelligent big data analysis processing method and system

Abstract

The invention provides an urban power brain intelligent big data analysis processing method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining the distribution data of target users of different house vacancy probability types in a target region, and carrying out the determination of a risk analysis period of the target region based on the distribution data, based on the risk analysis cycle, determining power consumption data of different target users in a preset time period, determining power consumption risk coefficients of different target users and risk users by using deviation conditions between the power consumption data and historical power consumption data of the target users, dividing the risk users into different sub-regions, and determining the risk users according to the sub-regions. On the basis of the distribution data of the risk users of different house vacancy probability types in the sub-regions and the power utilization risk coefficients, whether the sub-regions need to be subjected to door-to-door care processing by the service institutions is determined, and the safety of home-based care personnel is improved.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a method and system for analyzing and processing intelligent big data of a city power brain. Background Art

[0002] How to support community service agencies in providing proactive care for elderly people living alone has become a technical issue that needs to be solved urgently.

[0003] Specifically, in the invention patent application CN202411147539.X "Early Warning Analysis System for Judging Risks of Home-based Elderly Care Workers Based on Water and Electricity Usage Behaviors", a comprehensive analysis and identification module is used to judge whether the current water, electricity and gas usage of the elderly is normal based on the normal behavior database, so that targeted feedback can be given to the guardian of the elderly to complete the risk warning for the elderly. However, the following technical problems exist through analysis: For community service agencies, the number of elderly people living at home to be managed is large and their distribution is relatively scattered. Therefore, if the care and treatment strategy cannot be determined based on the electricity consumption data and distribution data of the elderly people living at home, the targeted care and treatment of the elderly people living at home cannot be improved.

[0004] In response to the above-mentioned technical problems, the present application specifically provides a method and system for intelligent big data analysis and processing of a city power brain. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: A method for analyzing and processing intelligent big data of a city power brain, specifically comprising: S1 determines house vacancy data corresponding to different target users based on analysis results of electricity consumption data of different target users in the target area, and determines house vacancy probability types of different target users based on the house vacancy data; S2 obtains distribution data of target users of different house vacancy probability types in the target area, and determines a risk analysis period of the target area based on the distribution data; S3 determines the electricity consumption data of different target users within a preset time period based on the risk analysis cycle, and determines the electricity consumption risk coefficients of different target users and risky users by using the deviation between the electricity consumption data and the historical electricity consumption data of the target users; S4 divides the risk users into different sub-areas, and determines whether the sub-areas need door-to-door care by a service agency based on the distribution data of risk users of different house vacancy probability types in the sub-areas and the electricity risk coefficient.

[0006] The beneficial effects of the present invention are: The distribution data of target users with different house vacancy probability types in the target area are used to determine the risk analysis period of the target area, thereby realizing the determination of the risk analysis period based on the house vacancy situation of the target users and the number of target users, and fully considering the occurrence of abnormal electricity consumption of different target users due to house vacancy, thereby realizing the determination of differentiated risk analysis periods according to the differences in vacancy probabilities, thereby ensuring the reliability of the target area for analysis and processing of target users with lower vacancy probabilities.

[0007] Based on the distribution data of risk users with different house vacancy probability types in the sub-area and the electricity consumption risk coefficient, it is determined whether the sub-area needs door-to-door care by the service agency. Not only the electricity consumption risk of risk users in the sub-area is taken into account, but also the impact of house vacancy on abnormal electricity consumption of different risk users is taken into account. This realizes the screening of sub-areas for targeted door-to-door care, thereby improving the targeted nature of the door-to-door care.

[0008] A further technical solution is that the target area is an area including users who live at home for the elderly, which is specifically determined according to the management area of ​​the service structure.

[0009] A further technical solution is that the target users are users who live at home for the elderly.

[0010] A further technical solution is that the house vacancy data includes the number of house vacancy times of the target user and the vacancy durations of different house vacancy times.

[0011] A further technical solution is that the house vacancy data is determined based on the historical electricity consumption data of the target user, and specifically the date when the historical electricity consumption data is less than the preset electricity consumption is used as the house vacancy date.

[0012] A further technical solution is that the method for determining the housing vacancy probability type of the target user is: Based on the house vacancy data, determining the number of house vacancies of the target user; Determine the total vacancy time based on the vacancy time of different house vacancy times; The vacancy probability type of the target user's house is determined based on the proportion of the total vacancy time.

[0013] A further technical solution is to divide risky users into different sub-areas, including: According to the matching result between the address of the risky user and the sub-area, the risky user is divided into different sub-areas.

[0014] A further technical solution is to determine whether the sub-area needs a service agency to provide door-to-door care, specifically including: Based on the electricity consumption risk coefficients of risk users with different house vacancy probability types and in combination with the number of risk users, the sum of the electricity consumption risk coefficients of risk users with different house vacancy probability types is determined; Based on the preset weight coefficients corresponding to the house vacancy probability types, determining the weight value of the sum of the electricity risk coefficients of risk users of different house vacancy probability types; The regional risk coefficient of the sub-area is determined based on the sum of the electricity risk coefficients of risk users of different house vacancy probability types and the product of their corresponding weight values, and the regional risk coefficient is used to determine whether the sub-area needs a service agency to provide on-site care.

[0015] A further technical solution is that when the regional risk coefficient of the sub-region is greater than a preset coefficient threshold, it is determined that the sub-region requires a service agency to provide on-site care.

[0016] On the other hand, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for intelligent big data analysis and processing of a city power brain.

[0017] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0020] Figure 1 It is a flow chart of a method for analyzing and processing intelligent big data of a city power brain; Figure 2 is a flow chart of a method for determining a housing vacancy probability type of a target user; Figure 3 It is a flow chart of a method for determining a risk analysis cycle of a target area. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0022] The scheme will be described below through Example 1 and Example 2.

[0023] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, according to one aspect of the present invention, a method for analyzing and processing intelligent big data of a city power brain is provided, which specifically includes: S1 determines house vacancy data corresponding to different target users based on analysis results of electricity consumption data of different target users in the target area, and determines house vacancy probability types of different target users based on the house vacancy data; Furthermore, the target area is an area including users who are caring for the elderly at home, and is specifically determined according to a management area of ​​the service structure.

[0024] Specifically, the target users are users who care for their elderly at home.

[0025] It should be noted that the house vacancy data includes the number of house vacancy times of the target user and the vacancy durations of different house vacancy times.

[0026] Furthermore, the house vacancy data is determined based on the historical electricity consumption data of the target user, and specifically the date when the historical electricity consumption data is less than the preset electricity consumption is used as the house vacancy date.

[0027] It is understandable that if Figure 2 As shown, the method for determining the housing vacancy probability type of the target user is: Based on the house vacancy data, determining the number of house vacancies of the target user; Determine the total vacancy time based on the vacancy time of different house vacancy times; The vacancy probability type of the target user's house is determined based on the proportion of the total vacancy time.

[0028] Further, determining the vacancy probability type of the target user's house based on the proportion of the total vacancy time specifically includes: When the proportion of the total vacancy time is greater than the preset time proportion, it is determined that the vacancy probability type of the target user's house is a vacancy type; When the proportion of the total vacancy time is not greater than the preset time proportion, it is determined that the vacancy probability type of the target user's house is the second vacancy type.

[0029] In another embodiment, the method for determining the vacancy probability type of the target user's house is: Based on the house vacancy data, the number of house vacancy times of the target user is determined, and the total vacancy time is determined based on the vacancy time of different house vacancy times, and when the total vacancy time does not meet the requirement, the vacancy probability type of the house of the target user is determined to be a type of vacancy type; When the total vacancy time meets the requirements: Acquire the number of vacancy times of the target user's house, and when the number of vacancy times of the target user's house is less than a preset vacancy times threshold, determine that the vacancy probability type of the target user's house is a second vacancy type; When the number of vacancy times of the target user's house is not less than the preset vacancy number threshold and the total vacancy time is within the preset time interval: determining the vacancy probability type of the target user's house as a type of vacancy type; When the total control duration is not within the preset duration interval: Determine the number of house vacancy times with a vacancy time greater than a preset vacancy time threshold value according to the vacancy time corresponding to different house vacancy times, and use the number of house vacancy times with a vacancy time greater than the preset vacancy time threshold value as the screening vacancy times, and when the screening vacancy times is greater than the vacancy times limit value, determine that the house vacancy probability type of the target user is a type of vacancy type; When the screening vacancy times is not greater than the vacancy times limit value: Based on the number of screening controls in different divided time periods and the vacancy durations of different house vacancy times, the vacancy coefficients in different divided time periods are determined; when the number of divided time periods in which the vacancy coefficients do not meet the requirements is greater than the number of preset time periods, the house vacancy probability type of the target user is determined to be a type of vacancy type; When the number of time periods where the vacancy coefficient does not meet the requirements is not greater than the preset number of time periods: The weight coefficients of different divided time periods are determined by the interval lengths between different divided time periods and the current moment, and the comprehensive vacancy coefficient is determined in combination with the vacancy coefficients in different divided time periods, and the vacancy probability type of the target user's house is determined using the comprehensive vacancy coefficient.

[0030] Further, the housing vacancy probability type of the target user is determined by using the comprehensive vacancy coefficient, specifically including: When the comprehensive vacancy coefficient is greater than a preset vacancy coefficient threshold, it is determined that the vacancy probability type of the target user's house is a vacancy type; When the comprehensive vacancy coefficient is not greater than the preset vacancy coefficient threshold, it is determined that the vacancy probability type of the target user's house is the second vacancy type.

[0031] S2 obtains distribution data of target users of different house vacancy probability types in the target area, and determines a risk analysis period of the target area based on the distribution data; Specifically, the distribution data of the target users includes the distribution quantity of the target users.

[0032] Specifically, Figure 3 As shown, the method for determining the risk analysis period of the target area is: Determine the number of target users of different housing vacancy probability types based on the distribution data of target users of different housing vacancy probability types; Based on the house vacancy probability types of different target users, determine the preset power consumption abnormality risk coefficients corresponding to different target users; The power consumption abnormality comprehensive risk coefficient is determined according to the sum of the preset power consumption abnormality risk coefficients corresponding to different target users, and the risk analysis period of the target area is determined by using the power consumption abnormality comprehensive risk coefficient.

[0033] Further, the risk analysis period of the target area is determined by using the comprehensive risk coefficient of power consumption anomaly, specifically including: The preset analysis period corresponding to the comprehensive risk factor of abnormal power consumption is used to determine the risk analysis period of the target area.

[0034] In another embodiment, the method for determining the risk analysis period of the target area is: Determine the number of target users in the target area based on the distribution data of target users of different house vacancy probability types, and when the number of target users in the target area is greater than a preset number of target users, determine the risk analysis period of the target area to be a preset analysis period; When the number of target users in the target area is not greater than the preset number of target users: When the number of target users in the target area is not within a preset number range, determining a risk analysis period for the target area based on a benchmark analysis period; When the number of target users in the target area is within a preset number range: Determining the number of target users of different housing vacancy probability types based on the distribution data of target users of different housing vacancy probability types, and when the number of target users of a preset housing vacancy probability type is greater than a preset number threshold, determining the risk analysis period of the target area to be a preset analysis period; When the number of target users of the preset housing vacancy probability type is not greater than the preset number threshold: When the number of target users of a preset housing vacancy probability type accounts for less than a preset number of the target users, determining a risk analysis period for the target area based on a benchmark analysis period; When the number of target users of the preset housing vacancy probability type accounts for no less than the preset number of target users: Based on the house vacancy probability types of different target users, the preset power consumption abnormality risk coefficients corresponding to different target users are determined, and the comprehensive power consumption abnormality risk coefficient is determined according to the sum of the preset power consumption abnormality risk coefficients corresponding to different target users. The risk analysis period of the target area is determined using the comprehensive power consumption abnormality risk coefficient.

[0035] Specifically, the preset house vacancy probability type is the second type of vacancy.

[0036] Optionally, the benchmark analysis period is shorter than a preset analysis period.

[0037] S3 determines the electricity consumption data of different target users within a preset time period based on the risk analysis cycle, and determines the electricity consumption risk coefficients of different target users and risky users by using the deviation between the electricity consumption data and the historical electricity consumption data of the target users; Specifically, the method for determining the electricity consumption risk coefficient of the target user is: Based on the historical electricity consumption data of the target user, determine the historical electricity consumption of the target user on different dates, and determine the number of dates within different electricity consumption intervals according to the historical electricity consumption on different dates; Utilizing the number of dates within different power consumption intervals to determine the screened power consumption intervals, and determining the benchmark power consumption based on the screened power consumption intervals; Based on the electricity consumption data in a preset time period, the difference between the electricity consumption data and the benchmark electricity consumption is used to determine the electricity consumption deviation date in the preset time period, and based on the proportion of the electricity consumption deviation dates, the electricity consumption risk coefficient of the target user is determined.

[0038] Furthermore, the reference power consumption is a power consumption reference value corresponding to a screening power consumption interval corresponding to a minimum power consumption range.

[0039] It should be noted that the electricity consumption deviation date is a date within a preset time period when the electricity consumption is less than the benchmark electricity consumption and the deviation from the benchmark electricity consumption does not meet the requirements.

[0040] It can be understood that the value range of the electricity usage risk coefficient of the target user is between 0 and 1, wherein when the electricity usage risk coefficient of the target user is greater than a preset risk coefficient threshold, the target user is determined to be a risky user.

[0041] S4 divides the risk users into different sub-areas, and determines whether the sub-areas need door-to-door care by a service agency based on the distribution data of risk users of different house vacancy probability types in the sub-areas and the electricity risk coefficient.

[0042] Furthermore, risky users are divided into different sub-areas, including: According to the matching result between the address of the risky user and the sub-area, the risky user is divided into different sub-areas.

[0043] It is understandable that determining whether the sub-area requires a service agency to provide door-to-door care specifically includes: Based on the electricity consumption risk coefficients of risk users with different house vacancy probability types and in combination with the number of risk users, the sum of the electricity consumption risk coefficients of risk users with different house vacancy probability types is determined; Based on the preset weight coefficients corresponding to the house vacancy probability types, determining the weight value of the sum of the electricity risk coefficients of risk users of different house vacancy probability types; The regional risk coefficient of the sub-area is determined based on the sum of the electricity risk coefficients of risk users of different house vacancy probability types and the product of their corresponding weight values, and the regional risk coefficient is used to determine whether the sub-area needs a service agency to provide on-site care.

[0044] Furthermore, when the regional risk coefficient of the sub-region is greater than a preset coefficient threshold, it is determined that the sub-region requires a service agency to provide on-site care.

[0045] In another embodiment, determining whether the sub-area requires a service organization to provide door-to-door care specifically includes: Acquire the number of risk users in the sub-area, and when the number of risk users in the sub-area does not meet the requirement, determine that the sub-area requires a service agency to provide on-site care; When the number of risky users in the sub-area meets the requirement: Based on the electricity risk coefficients of different risk users, the sum of the electricity risk coefficients of the risk users in the sub-area is determined, and when the sum of the electricity risk coefficients of the risk users in the sub-area does not meet the requirements, it is determined that the sub-area needs door-to-door care by the service agency; When the sum of the electricity risk coefficients of the risk users in the sub-area meets the requirements: Based on the electricity risk coefficients of risk users of different house vacancy probability types and in combination with the number of risk users, the sum of the electricity risk coefficients of risk users of different house vacancy probability types is determined; when the sum of the electricity risk coefficients of risk users of preset house vacancy probability types does not meet the requirements, it is determined that the sub-area needs on-site care by the service agency; When the sum of the electricity risk coefficients of risk users of the preset housing vacancy probability type meets the requirements: Based on the preset weight coefficient corresponding to the housing vacancy probability type, and based on the product of the electricity consumption risk coefficient of different risk users and the preset weight coefficient, determining the modified risk coefficient of different risk users; When the number of risk users whose modified risk coefficients do not meet the requirements is greater than the preset number of screened users, it is determined that the sub-area requires a service agency to provide door-to-door care; When the number of risk users whose corrected risk coefficients do not meet the requirements is not greater than the preset number of screened users: Based on the preset weight coefficient corresponding to the house vacancy probability type, the weight value of the sum of the electricity risk coefficients of risk users of different house vacancy probability types is determined, and the regional risk coefficient of the sub-area is determined based on the sum of the products of the sum of the electricity risk coefficients of risk users of different house vacancy probability types and their corresponding weight values, and the regional risk coefficient is used to determine whether the sub-area needs a service agency to provide on-site care.

[0046] Example 2 On the other hand, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for intelligent big data analysis and processing of a city power brain.

[0047] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0048] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for analyzing and processing intelligent big data of a city power brain, characterized in that: Specifically include: Based on the analysis results of the electricity consumption data of different target users in the target area, determine the house vacancy data corresponding to the different target users, and determine the house vacancy probability types of the different target users based on the house vacancy data; Obtaining distribution data of target users of different house vacancy probability types in the target area, and determining a risk analysis period of the target area based on the distribution data; Based on the risk analysis cycle, determine the electricity consumption data of different target users within a preset time period, and use the deviation between the electricity consumption data and the historical electricity consumption data of the target users to determine the electricity consumption risk coefficients of different target users and risky users; The risk users are divided into different sub-areas, and based on the distribution data of risk users of different house vacancy probability types in the sub-areas and the electricity risk coefficient, it is determined whether the sub-areas need door-to-door care by the service agency.

2. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: The target area is an area including users who live in their homes for the elderly, and is specifically determined according to the management area of ​​the service structure.

3. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: The target users are users who are caring for their elderly at home.

4. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: The house vacancy data includes the number of times the house of the target user is vacant and the vacancy durations of different house vacancy numbers.

5. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: The method for determining the housing vacancy probability type of the target user is: Based on the house vacancy data, determining the number of house vacancies of the target user; Determine the total vacancy time based on the vacancy time of different house vacancy times; The vacancy probability type of the target user's house is determined based on the proportion of the total vacancy time.

6. A method for analyzing and processing intelligent big data of a city power brain as claimed in claim 5, characterized in that: Determining the housing vacancy probability type of the target user based on the proportion of the total vacancy time specifically includes: When the proportion of the total vacancy time is greater than the preset time proportion, it is determined that the vacancy probability type of the target user's house is a vacancy type; When the proportion of the total vacancy time is not greater than the preset time proportion, it is determined that the vacancy probability type of the target user's house is the second vacancy type.

7. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: Divide risky users into different sub-areas, including: According to the matching result between the address of the risky user and the sub-area, the risky user is divided into different sub-areas.

8. The method for analyzing and processing intelligent big data of a city power brain according to claim 1, characterized in that: Determine whether the sub-area requires a service agency to provide on-site care, including: Based on the electricity consumption risk coefficients of risk users with different house vacancy probability types and in combination with the number of risk users, the sum of the electricity consumption risk coefficients of risk users with different house vacancy probability types is determined; Based on the preset weight coefficients corresponding to the house vacancy probability types, determining the weight value of the sum of the electricity risk coefficients of risk users of different house vacancy probability types; The regional risk coefficient of the sub-area is determined based on the sum of the electricity risk coefficients of risk users of different house vacancy probability types and the product of their corresponding weight values, and the regional risk coefficient is used to determine whether the sub-area needs a service agency to provide on-site care.

9. A method for analyzing and processing intelligent big data of a city power brain as claimed in claim 8, characterized in that: When the regional risk coefficient of the sub-region is greater than a preset coefficient threshold, it is determined that the sub-region requires a service agency to perform on-site care.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes a city power brain intelligent big data analysis and processing method as described in any one of claims 1-9.

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