Intelligent community load intelligent management method and system based on big data

Abstract

The present invention belongs to the technical field of smart community management, and discloses a smart community load intelligent management method and system based on big data. The present invention divides a community into n management partitions, calculates the power load index of each management partition in the current time period according to the power consumption parameters of each management partition, and then compares the power load index of each management partition in the current time period with the power load index threshold interval preset for the management partition, and determines whether to issue an early warning signal according to the comparison result. If no early warning signal is issued, the current power consumption strategy is maintained, and if an early warning is issued, the current power consumption strategy is adjusted, and then, the load optimization index of each partition is calculated according to the accumulated power consumption of each management partition every day, and the load optimization indexes of all management partitions are sorted from large to small. According to the actual situation, the top x management partitions are intercepted for optimization, so as to improve energy utilization efficiency, reduce energy consumption costs, and provide strong support for the construction of smart communities.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart community management, and specifically relates to a smart community load intelligent management method and system based on big data. Background Art

[0002] With the acceleration of urbanization, the construction of smart communities has become an important trend. However, traditional community load management methods have problems such as scattered data, insufficient analysis, and inaccurate scheduling, making it difficult to cope with the growing electricity demand and complex electricity consumption environment. In the existing technology, although some systems can realize the monitoring and preliminary analysis of power loads, they lack the deep mining and intelligent decision-making capabilities supported by big data, and it is difficult to meet the needs of smart communities for efficient and accurate load management. Summary of the invention

[0003] The purpose of the present invention is to provide a smart community load intelligent management method and system based on big data to solve the problems faced in the above-mentioned background technology.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A smart community load intelligent management method based on big data, the method comprising:

[0006] Step S1, dividing the community into multiple management zones, and numbering each management zone in the following order: 1, 2, 3...n;

[0007] Step S2: The power consumption parameters of the i-th management partition in the current time period collected in real time by the collection module, where i∈n;

[0008] Step S3: Calculate the power load index of the i-th management partition in the current time period according to the power consumption parameters of the i-th management partition in step S2. ;

[0009] Step S4: The power load index of the current time period of the i-th management partition The power load index threshold interval preset for the management partition Compare, and determine whether to issue a warning signal according to the comparison result. If no warning signal is issued, the current power usage strategy is maintained; if a warning signal is issued, the current power usage strategy is adjusted;

[0010] Step S5: Divide each day into m time periods, calculate the power load index of the m time periods of the ith management partition, sort the power load index of the m time periods of the ith management partition from small to large, and obtain the maximum power load index max in the m time periods of the ith management partition every day. ;

[0011] Step S6: Substitute the data obtained in step S5 into the mathematical model to calculate the load optimization index of the i-th management partition , and calculate all management partition load optimization indicators in the same way ;

[0012] Step S7: Optimize the load of all management partitions Sort from largest to smallest, and select the top x management partitions for optimization based on actual conditions.

[0013] As a further description of the scheme of the present invention, the electricity consumption parameters of the i-th management zone in step S2 include: the time-varying data of household electricity consumption and the time-varying data of public electricity consumption in the i-th management area in the current period, where i∈n.

[0014] As a further description of the solution of the present invention, the working process of step S3 includes:

[0015] Construct a function of household electricity consumption over time in the ith management area based on the household electricity consumption over time data in the ith management area during the current time period ;

[0016] Construct the time-varying function of the public electricity consumption in the ith management area according to the time-varying data of the public electricity consumption in the ith management area in the current time period ;

[0017] Substitute the acquired data into the following formula to calculate the power load index of the i-th management partition in the current time period: :

[0018] ;

[0019] In the formula, is the standard function of household electricity consumption over time in the ith management area, is the standard function of the public electricity consumption of the ith management area over time, and are the weight coefficients corresponding to the home area and the public area, and is the preset reference coefficient.

[0020] As a further description of the solution of the present invention, the specific working steps of step S4 include:

[0021] The power load index of the i-th management partition in the current time period The power load index threshold interval preset for the management partition Compare;

[0022] like This indicates that the power load of the i-th management zone in the current time period is a first-level load, an early warning is issued, and the power allocation of the i-th management zone is reduced;

[0023] like This means that the power load of the i-th management zone in the current time period is a secondary load, no warning is issued, and the power allocation of the i-th management zone remains unchanged;

[0024] like This indicates that the power load situation of the i-th management partition in the current time period is level 3 load, an early warning is issued, and the power allocation of the i-th management partition is increased.

[0025] As a further description of the solution of the present invention, the specific working steps of step S6 include:

[0026] Construct the power load index of the i-th management zone Equation of the curve with m ;

[0027] Through i management partition power load indicators Equation of the curve with m , the maximum power load index of the i-th management partition in m time periods every day and the average daily power consumption of the ith management partition Calculate the load optimization index of the i-th management partition :

[0028] ;

[0029] In the formula, is the preset standard daily power consumption of the ith management partition, is the conversion factor.

[0030] A smart community load intelligent management system based on big data, the system includes a data acquisition module, a data processing module, a data analysis module and an early warning module;

[0031] The data collection module is used to divide the community into multiple management zones and collect real-time electricity consumption information of each management zone in real time;

[0032] The data processing module is used to process the real-time power consumption information data of each management partition collected by the data collection module and store it in the system database;

[0033] The analysis module is used to analyze the power load of each management partition according to the real-time power consumption information data of each management partition in the system database.

[0034] The early warning module is used to issue a real-time early warning according to the analysis result of the analysis module.

[0035] As a further description of the solution of the present invention, the early warning module is provided with two early warning modes, namely, an automatic early warning mode and an active early warning mode.

[0036] As a further description of the scheme of the present invention, the automatic warning mode is to automatically issue a warning based on the analysis results of the analysis module, and the active warning mode is to actively issue a warning when the staff finds abnormal electricity consumption in the community, and the active warning mode has priority over the automatic warning mode.

[0037] Beneficial effects of the present invention:

[0038] The present invention divides a community into n management zones, calculates the power load index of each management zone in the current time period according to the power consumption parameters of each management zone, and then compares the power load index of each management zone in the current time period with the power load index threshold interval preset for the management zone, and determines whether to issue an early warning signal according to the comparison result. If no early warning signal is issued, the current power consumption strategy is maintained; if an early warning is issued, the current power consumption strategy is adjusted, and then, the load optimization index of each zone is calculated according to the accumulated power consumption of each management zone every day, and the load optimization indexes of all management zones are sorted from large to small. According to the actual situation, the top x management zones are intercepted for optimization, thereby improving energy utilization efficiency, reducing energy consumption costs, and providing strong support for the construction of smart communities.

[0039] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 It is a partial flow chart of the intelligent management method of smart community load based on big data of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] See also Figure 1 As shown, a smart community load intelligent management method based on big data is disclosed, and the method includes:

[0044] Step S1, dividing the community into multiple management zones, and numbering each management zone in the following order: 1, 2, 3...n;

[0045] Step S2: The power consumption parameters of the i-th management partition in the current time period collected in real time by the collection module, where i∈n;

[0046] The electricity consumption parameters of the ith management zone in step S2 include: the household electricity consumption change data and the public electricity consumption change data of the ith management area in the current period, where i∈n.

[0047] Through the above technical solution, this embodiment divides the entire smart community into several management zones according to actual conditions in order to realize intelligent prediction, optimized scheduling and efficient management of power load in the community. Taking into account the actual distribution of household electricity consumption and public electricity consumption in each management zone, the time-varying data of household electricity consumption and public electricity consumption in each management area are collected respectively.

[0048] Step S3: Calculate the power load index of the i-th management partition in the current time period according to the power consumption parameters of the i-th management partition in step S2. ;

[0049] The working process of step S3 includes:

[0050] Construct a function of household electricity consumption over time in the ith management area based on the household electricity consumption over time data in the ith management area during the current time period ;

[0051] Construct the time-varying function of the public electricity consumption in the ith management area according to the time-varying data of the public electricity consumption in the ith management area in the current time period ;

[0052] Substitute the acquired data into the following formula to calculate the power load index of the i-th management partition in the current time period: :

[0053] ;

[0054] In the formula, is the standard function of household electricity consumption over time in the ith management area, is the standard function of the public electricity consumption of the ith management area over time, and are the weight coefficients corresponding to the home area and the public area, and is the preset reference coefficient.

[0055] Step S4: The power load index of the current time period of the i-th management partition The power load index threshold interval preset for the management partition Compare, and determine whether to issue a warning signal according to the comparison result. If no warning signal is issued, the current power usage strategy is maintained; if a warning signal is issued, the current power usage strategy is adjusted;

[0056] The specific working steps of step S4 include:

[0057] The power load index of the i-th management partition in the current time period The power load index threshold interval preset for the management partition Compare;

[0058] like This indicates that the power load of the i-th management zone in the current time period is a first-level load, an early warning is issued, and the power allocation of the i-th management zone is reduced;

[0059] like This means that the power load of the i-th management zone in the current time period is a secondary load, no warning is issued, and the power allocation of the i-th management zone remains unchanged;

[0060] like This indicates that the power load situation of the i-th management partition in the current time period is level 3 load, an early warning is issued, and the power allocation of the i-th management partition is increased.

[0061] Through the above technical solution, this embodiment comprehensively considers the impact of household electricity consumption and public electricity consumption on the electricity load indicators of each region. First, according to the household electricity consumption change over time data of the i-th management area in the current time period, a household electricity consumption change over time function of the i-th management area is constructed. , according to the time-varying data of the public electricity consumption in the ith management area in the current time period, the time-varying function of the public electricity consumption in the ith management area is constructed , then substitute the obtained function into the formula Calculate the power load index of the i-th management partition in the current time period , where Indicates the difference between the accumulated household electricity consumption in the current period and the preset standard accumulated amount. Indicates the difference between the accumulated public electricity consumption in the current period and the preset standard accumulated consumption. and The actual distribution of household electricity and public electricity in the ith area is determined as an empirical value. Finally, the electricity load index of the ith management partition in the current time period is The power load index threshold interval preset for the management partition Compare; if This means that the power load of the i-th management partition in the current time period is a first-level load, an early warning is issued, and the power allocation of the i-th management partition is reduced; if This means that the power load of the i-th management zone in the current time period is a secondary load, no warning is issued, and the power allocation of the i-th management zone remains unchanged; if This indicates that the power load situation of the i-th management partition in the current time period is level 3 load, an early warning is issued, and the power allocation of the i-th management partition is increased to improve energy utilization efficiency and reduce energy consumption costs.

[0062] Step S5: Divide each day into m time periods, calculate the power load index of the m time periods of the ith management partition, sort the power load index of the m time periods of the ith management partition from small to large, and obtain the maximum power load index max in the m time periods of the ith management partition every day. ;

[0063] Step S6: Substitute the data obtained in step S5 into the mathematical model to calculate the load optimization index of the i-th management partition , and calculate all management partition load optimization indicators in the same way ;

[0064] The specific working steps of step S6 include:

[0065] Construct the power load index of the i-th management zone Equation of the curve with m ;

[0066] Through i management partition power load indicators Equation of the curve with m , the maximum power load index of the i-th management partition in m time periods every day and the average daily power consumption of the ith management partition Calculate the load optimization index of the i-th management partition :

[0067] ;

[0068] In the formula, is the preset standard daily power consumption of the ith management partition, is the conversion factor.

[0069] Step S7: Optimize the load of all management partitions Sort from largest to smallest, and select the top x management partitions for optimization based on actual conditions.

[0070] Through the above technical solution, this embodiment takes into account the inconsistency of the peak power consumption time period of each management partition. First, each day is divided into m time periods, the power load index of the m time period of the i-th management partition is calculated, and the power load index of the m time period of the i-th management partition is sorted from small to large, and the maximum power load index max in the m time period of the i-th management partition is obtained. Then, construct the curve equation f(m, ) and substitute into the formula Calculate the load optimization index of the i-th management partition , where And the maximum power load index of each partition max Proportional, The cumulative change in power load for each partition every day. is the ratio of the standard daily electricity consumption of the ith management zone to the standard amount, both Proportional to, finally, all management partition load optimization indicators Sort from largest to smallest, and select the top x management partitions for optimization based on actual conditions.

[0071] A smart community load intelligent management system based on big data, the system includes a data acquisition module, a data processing module, a data analysis module and an early warning module;

[0072] The data collection module is used to divide the community into multiple management zones and collect real-time electricity consumption information of each management zone in real time;

[0073] The data processing module is used to process the real-time power consumption information data of each management partition collected by the data collection module and store it in the system database;

[0074] The analysis module is used to analyze the power load of each management partition according to the real-time power consumption information data of each management partition in the system database.

[0075] The early warning module is used to issue a real-time early warning according to the analysis result of the analysis module.

[0076] The early warning module is provided with two early warning modes, namely, an automatic early warning mode and an active early warning mode.

[0077] The automatic warning mode is to automatically issue a warning based on the analysis results of the analysis module, and the active warning mode is to actively issue a warning when the staff finds abnormal electricity consumption in the community, and the active warning mode has priority over the automatic warning mode.

[0078] Working principle: The present invention divides the community into n management zones, calculates the power load index of each management zone in the current time period according to the power consumption parameters of each management zone, and then compares the power load index of each management zone in the current time period with the power load index threshold interval preset for the management zone, and determines whether to issue a warning signal based on the comparison result. If no warning signal is issued, the current power consumption strategy is maintained; if a warning is issued, the current power consumption strategy is adjusted, and then, the load optimization index of each zone is calculated based on the accumulated power consumption of each management zone every day, and the load optimization indexes of all management zones are sorted from large to small. According to the actual situation, the top x management zones are intercepted for optimization, thereby improving energy utilization efficiency, reducing energy consumption costs, and providing strong support for the construction of smart communities.

[0079] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. Intelligent management method of smart community load based on big data, It is characterized in that The method comprises: Step S1: Divide the community into multiple management zones, and number each management zone in the following order: 1, 2, 3...n; Step S2: The power consumption parameters of the i-th management partition in the current time period collected in real time by the collection module, where i∈n; Step S3: Calculate the power load index σ of the i-th management zone in the current time period according to the power consumption parameters of the i-th management zone in step S2. i ; Step S4: The power load index σ of the current time period of the i-th management partition is i The threshold interval of power load index preset for this management partition [σ i1 , σ i2 ] comparison, and judging whether to issue an early warning signal according to the comparison result; if no early warning signal is issued, the current power usage strategy is maintained; if an early warning signal is issued, the current power usage strategy is adjusted; Step S5: Divide each day into m time periods, calculate the power load index of the m time periods of the ith management partition, sort the power load index of the m time periods of the ith management partition from small to large, and obtain the maximum power load index maxσ in the m time periods of the ith management partition every day i ; Step S6: Substitute the data obtained in step S5 into the mathematical model to calculate the load optimization index ρ of the i-th management partition i , and at the same time calculate the load optimization index ρ of all management partitions using the same method n ; Step S7: Optimize the load of all management partitions n Sort from largest to smallest, and select the top x management partitions for optimization based on actual conditions; The power consumption parameters of the i-th management zone in step S2 include: the time-varying data of household power consumption and the time-varying data of public power consumption in the i-th management area in the current period, where i∈n; The working process of step S3 includes: According to the household electricity consumption variation data of the ith management area in the current time period, the household electricity consumption variation function f of the ith management area is constructed. i (t); According to the time-varying data of the public power consumption in the ith management area in the current time period, the time-varying function g of the public power consumption in the ith management area is constructed. i (t); Substitute the acquired data into the following formula to calculate the power load index σ of the i-th management zone in the current time period: i : In the formula, f i0 (t) is the standard function of household electricity consumption in the ith management area over time, g i0 (t) is the standard function of the public electricity consumption in the ith management area over time, k 1 and k 2 are the weight coefficients corresponding to the home area and the public area, d f and d g is the preset reference coefficient; The specific working steps of step S4 include: The power load index σ of the i-th management partition in the current time period i The threshold interval of power load index preset for this management partition [σ i1 , σ i2 ]Compare; If σ i ∈[-∞,σ i1 ] indicates that the power load of the i-th management partition in the current time period is a first-level load, an early warning is issued, and the power allocation of the i-th management partition is reduced; If σ i ∈[σ i1 , σ i2 ] indicates that the power load of the i-th management partition in the current time period is a secondary load, no warning is issued, and the power allocation of the i-th management partition remains unchanged; If σ i ∈[σ i2 , +∞] indicates that the power load of the i-th management partition in the current time period is the third-level load, an early warning is issued, and the power allocation of the i-th management partition is increased; The specific working steps of step S6 include: Construct the power load index σ of the i-th management zone i The curve equation f(m, σ i ); Through the i-management zone power load index σ i The curve equation f(m, σ i ), the maximum power load index maxσ in the m time period of the ith management zone every day i and the average daily power consumption Q of the ith management partition i Calculate the load optimization index ρ of the i-th management partition i : In the formula, Q th is the preset standard daily electricity consumption of the ith management zone, and α is the conversion coefficient.

2. A smart community load intelligent management system based on big data, the system being applicable to the smart community load intelligent management method based on big data as claimed in claim 1, It is characterized in that The system includes a data acquisition module, a data processing module, a data analysis module and an early warning module; The data collection module is used to divide the community into multiple management zones and collect real-time electricity consumption information of each management zone in real time; The data processing module is used to process the real-time power consumption information data of each management partition collected by the data collection module and store it in the system database; The analysis module is used to analyze the power load of each management partition according to the real-time power consumption information data of each management partition in the system database. The early warning module is used to issue a real-time early warning according to the analysis result of the analysis module.

3. According to the big data-based intelligent community load intelligent management system of claim 2, It is characterized in that The early warning module is provided with two early warning modes, namely, an automatic early warning mode and an active early warning mode.

4. According to the big data-based intelligent community load intelligent management system of claim 3, It is characterized in that The automatic warning mode is to automatically issue a warning based on the analysis results of the analysis module, and the active warning mode is to actively issue a warning when the staff finds abnormal electricity consumption in the community, and the active warning mode has priority over the automatic warning mode.

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