Energy-saving management system and method based on cloud computing

Abstract

The invention provides an energy-saving management system and method based on cloud computing, and belongs to the field of energy-saving management. The problem of low energy-saving management efficiency is solved; specifically, the data acquisition module acquires power consumption data; the data analysis module is used for acquiring historical electricity consumption A and historical electricity consumption B; constructing an electricity consumption change equation based on the historical electricity consumption A; calculating an electricity consumption coefficient based on the historical electricity consumption B; the power amplifier adjusting module is used for acquiring power data and an energy-saving coefficient, calculating expected power consumption according to the power consumption data and a power consumption change equation, and judging whether the energy-saving coefficient is met or not by combining the power data; if yes, the power supply power is adjusted according to the energy-saving coefficient; if not, recalculating the energy-saving coefficient in combination with the power consumption coefficient, and adjusting the receiving power; the continuous monitoring module is used for continuously adjusting the power supply power or the receiving power; by acquiring, analyzing and processing the power utilization data of the target area, the power supply power and the receiving power are adjusted, and energy conservation is achieved.

Description

Technical Field

[0001] The present invention discloses an energy-saving management system and method based on cloud computing, and relates to the field of energy-saving management. Background Art

[0002] The existing systems and methods for energy-saving management have the following deficiencies:

[0003] Dependence on third parties: Existing systems are highly dependent on cloud service providers. Different cloud service providers may provide different technical support, service quality, pricing structure, etc. Enterprises need to choose the most suitable cloud service provider according to their own needs. If the choice is inappropriate, they may face problems such as service interruption and cost surge.

[0004] System integration and compatibility issues: Many traditional energy-saving management systems are often designed based on localized hardware and software architectures, while cloud computing platforms need to be seamlessly connected with a variety of different hardware and software systems. In actual applications, enterprises need to carry out a lot of system transformation and technical integration work, which will increase the complexity of initial investment and system deployment.

[0005] High maintenance cost: For some complex energy-saving management systems, enterprises need to rely on third-party technical teams to carry out subsequent maintenance and optimization; if these technical teams do not have sufficient experience and technical capabilities, it will affect the performance and stability of the system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention aims to provide an energy-saving management system and method based on cloud computing, aiming to solve the problem of low energy-saving management efficiency.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions: an energy-saving management system based on cloud computing includes:

[0008] Data acquisition module: used to obtain the number of urban districts in the target area; obtain the current time, obtain the power consumption of each urban district in the current time, and obtain the power consumption data;

[0009] Data analysis module: used to obtain the historical power consumption of the target area, and obtain the historical power consumption A and historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each municipal district over time, and construct the power consumption change equation of each municipal district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each municipal district;

[0010] Power amplifier adjustment module: used to obtain the power supply power and the received power of each city district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each city district according to the power consumption data and the power consumption change equation, and judge whether the energy saving coefficient is met in combination with the power data; if it is met, adjust the power supply power according to the energy saving coefficient; if it is not met, recalculate the energy saving coefficient in combination with the power consumption coefficient, and adjust the received power of each city district;

[0011] Continuous monitoring module: used to continuously obtain power consumption data and adjust the power supply or the receiving power of each municipal area.

[0012] Furthermore, the workflow of the data analysis module is as follows:

[0013] Process A1: Count the number of all the districts in the target area, record it as hn; get the current month, record it as month m;

[0014] Obtain the hourly electricity consumption of all urban areas in the past three days as historical electricity consumption A;

[0015] Process A2: Define triple A-1: ​​he (x，y，z) ;he (x，y，z) It represents the electricity consumption of the x-th municipal district at the z-th hour on the y-th day;

[0016] According to triple A-1, the electricity consumption of the first city district in the historical electricity consumption A from the first and second hours of the first day to the 24th hour is recorded as: (1，1，1) 、he (1，1，2) ~he (1，1，24) ;

[0017] The electricity consumption at the 1st and 2nd hour of the next day until the 24th hour is recorded as: (1，2，1) 、he (1，2，2) ~he (1，2，24) ;

[0018] The electricity consumption at the 1st and 2nd hour of the third day until the 24th hour is recorded as: (1，3，1) 、he (1，3，2) ~he (1，3，24) ;

[0019] Based on (1，1，1) ~he (1，3，24) , analyze the hourly electricity consumption change of the first city district, and construct the electricity consumption change equation of the first city district;

[0020] Process A23: Analyze the change in electricity consumption in the first district from the first to the second hour and calculate the Kalman gain Kdk (1-2) ;

[0021] Process A24: Based on triple A-1, record the electricity consumption of the first city district on the third day and the third hour as he (1，3，3) , the power consumption in the third hour of the second day is recorded as he (1，2，3) , the power consumption in the third hour of the first day is recorded as he (1，1，3) ;

[0022] Analyze the change in electricity consumption in the first city district from the second to the third hour and calculate the Kalman gain Kdk (2-3) ;

[0023] Process A25: Similarly, according to triple A-1, the electricity consumption of the first district at the 23rd hour on the third day is recorded as he (1，3，23) , the power consumption in the 23rd hour of the next day is recorded as he (1，2，23) , the electricity consumption in the 23rd hour of the first day is recorded as he (1，1，23) ;

[0024] Analyze the change in electricity consumption in the first district from the 23rd to the 24th hour and calculate the Kalman gain Kdk (23-24) .

[0025] Furthermore, the subsequent process of the process A25 is as follows:

[0026] Process A26: Summarize processes A23 to A25 to obtain Kalman gain: Kdk (1-2) ~Kdk (23-24) , construct the electricity consumption change equation of the first municipal district;

[0027] Process A261: Calculation of mean Kdk (1-2) ~Kdk (23-24) , denoted as aKk;

[0028] Assume that the total electricity consumption of the city area at the kth hour is ee (k) , the power consumption at k hour and d minute is de (k,d) , the power consumption in the (k-1) hour is ee (k-1) , construct equation A (1) ; A (1) The mathematical expression is:

[0029]

[0030] Wherein, QC represents the error coefficient, and the initial value of QC is 0;

[0031] Process A262: Set the value of d to 60 and set he (1，1，1) ~he (1，1，23) As de (k,d) andee (k-1) , will he(1，1，1) ~he (1，1，23) As ee (k) , substitute into equation A (1) The QC value was calculated by MATLAB software to obtain CC;

[0032] Substituting CC into equation A (1) In the equation A, we get (2) ; A (2) The mathematical expression is:

[0033]

[0034] Equation A (2) As the electricity consumption change equation for the first municipal district;

[0035] Process A27: Repeat the same process of constructing the electricity consumption change equation for the first city district to construct the electricity consumption change equation for the second to hn-th city districts;

[0036] Get the number of days in month (m-1), recorded as td; get the total daily electricity consumption of all municipal districts in month (m-1) as the historical electricity consumption B;

[0037] The total electricity consumption of the 1st, 2nd, and hnth municipal districts on the 1st day of month (m-1) in the historical electricity consumption B is recorded as: (1，1) 、ne (1，2) ~ne (1，hn) ;

[0038] On the 2nd day of month (m-1), the total electricity consumption of the 1st, 2nd, and up to the hnth municipal districts is recorded as: ne (2，1) 、ne (2，2) ~ne (2，hn) ;

[0039] Similarly, on day td of month (m-1), the total electricity consumption of the 1st, 2nd, and even hnth municipal districts is recorded as: ne (td，1) 、ne (td，2) ~ne (td，hn) ;

[0040] The electricity consumption coefficient of each city district is calculated based on the historical electricity consumption B, and enters the power amplifier adjustment module.

[0041] Furthermore, the specific process of the process A23 is as follows:

[0042] Process A231: Construct the electricity consumption matrix of the city area in the first hour and record it as B (1) , the power consumption matrix in the second hour is recorded as B (2) ;

[0043] Matrix B (1)The mathematical representation of is:

[0044] Matrix B (2) The mathematical representation of is:

[0045] Process A232: Calculate matrix B (1) Becomes matrix B (2) The state transfer matrix of (1-2) ; Matrix B (1-2) The calculation formula is:

[0046] B (1-2) =[(B (1) ) T ×B (2) ] -1 ×(B (2) ) T ×B (2) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0047] Process A233: Calculate matrix B (1) The difference matrix of (1) ; Matrix RB (1) The calculation formula is:

[0048] Among them, - represents matrix subtraction;

[0049] Calculate the matrix B (2) The difference matrix of (2) ; Matrix RB (2) The calculation formula is:

[0050]

[0051] Calculate the matrix B (1) Becomes matrix B (2) The error transition matrix is ​​denoted as matrix QB (1-2) ; Matrix QB (1-2) The mathematical formula is:

[0052] QB (1-2) =RB (2) -RB (1) ;

[0053] Process A234: Calculate matrix B (1) The error covariance matrix of (1) ; Matrix Pd (1) The calculation formula is:

[0054] Pd (1) =(1 / 3)×(APd(1) T ×APd (1) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (1) Represents the matrix Pd (1) The transition matrix, APd (1) The calculation formula is:

[0055] AP (1) =B (1) -[(1 / 3)×I×B (1) ]; where - represents matrix subtraction and I represents a one-matrix;

[0056] Process A235: Calculate matrix B (2) The pre-error covariance matrix of (2) ;Pk (2) The calculation formula is:

[0057]

[0058] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0059] Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the first to the second hour (1-2) ,Kdk (1-2) The calculation formula is:

[0060]

[0061] Furthermore, the specific process of the process A24 is as follows:

[0062] Process A241: Construct the electricity consumption matrix of the city area in the third hour, denoted as B (3) Matrix B (3) The mathematical representation of is:

[0063]

[0064] Process A242: Calculate matrix B (2) Becomes matrix B (3) The state transfer matrix of (2-3) ; Matrix B (2-3) The calculation formula is:

[0065] B (2-3) =[(B (2) ) T ×B (3) ] -1 ×(B (3) ) T ×B (3); Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0066] Process A243: Calculate matrix B (3) The difference matrix of (3) ; Matrix RB (3) The calculation formula is:

[0067] Among them, - represents matrix subtraction;

[0068] Calculate the matrix B (2) Becomes matrix B (3) The error transition matrix is ​​denoted as matrix QB (2-3) ; Matrix QB (2-3) The mathematical formula is:

[0069] QB (2-3) =RB (3) -RB (2) ;

[0070] Process A244: Calculate matrix B (2) The error covariance matrix of (2) ; Matrix Pd (2) The calculation formula is:

[0071] Pd (2) =(1 / 3)×(APd (2) T ×APd (2) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (2) Represents the matrix Pd (2) The transition matrix, APd (2) The calculation formula is:

[0072] AP (2) =B (2) -[(1 / 3)×I×B (2) ]; where - represents matrix subtraction and I represents a one-matrix;

[0073] Process A245: Calculate matrix B (3) The pre-error covariance matrix of (3) ;Pk (3) The calculation formula is:

[0074]

[0075] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0076] Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the second to the third hour(2-3) ,Kdk (2-3) The calculation formula is:

[0077]

[0078] Furthermore, the specific process of the process A27 is as follows:

[0079] Process A271: define a zero matrix of (td×1) as an initial matrix;

[0080] The total electricity consumption of the first municipal district from the 1st day of month (m-1) to the td day is ne (1，1) ~ne (td，1) Fill in the initial matrix to get the power consumption matrix BZ of the first district (1) ;

[0081] Fill the total power consumption of the second city district from the 1st day of (m-1) month to td day into the initial matrix, and get the power consumption matrix BZ of the second city district (2) ;

[0082] Similarly, the total power consumption of the hn-th city district from the 1st day of month (m-1) to the td day is filled into the initial matrix to obtain the power consumption matrix BZ of the hn-th city district (hn) ;

[0083] Process A272: Corresponding calculation formula A-2: Among them, ae (i) It represents the average electricity consumption of all municipal districts on day i of month (m-1), where the value range of i is: 1~td;

[0084] ne (i，j) represents the total electricity consumption of the j-th municipal district on day i of month (m-1), and the value range of j is: 1~hn;

[0085] Based on calculation formula A-2, according to ne (1，1) 、ne (1，2) ~ne (1，ht) Calculate the average electricity consumption of all city districts on the first day of month (m-1) and get ae (1) ;

[0086] According to ne (2，1) 、ne (2，2) ~ne (2，ht) Calculate the average electricity consumption of all city districts on the 2nd day of month (m-1), and get ae (2) ;

[0087] By analogy, according to ne (td，1) 、ne (td，2) ~ne (td，hn)Calculate the average electricity consumption of all urban areas in the target area on day (m-1) of the month td, and get ae (td) ;

[0088] AE (1) ~ae (td) Substitute into the initial matrix to obtain matrix AB;

[0089] Process A273: The covariance matrix of the p-th district is denoted as xB (p) ; The value range of p is: 1~hn; Define calculation formula A-3:

[0090] Among them, - represents matrix subtraction; BZ(q) represents the power consumption matrix of the qth municipal district, and the minimum value of (p-1) is 1;

[0091] The power consumption matrix BZ of the first, second, and even hn-th districts is (1) , BZ (2) ~BZ (hn) Substituting into formula A-3, we get the covariance matrix xB of the first, second, and even hn-th districts. (1) ,xB (2) ~xB (hn) ;

[0092] Process A274: Define calculation formula A-4: be (i) =(xB (i) ) T × (i) ; Among them, be (i) represents the electricity consumption coefficient of the first municipal district, and T represents the transpose of the matrix;

[0093] Substitute into xB (1) ,xB (2) ~xB (hn) Substituting into the calculation formula A-4, we can get the electricity consumption coefficient be for the 1st, 2nd, and up to the hnth municipal districts. (1) 、be (2) ~be (hn) .

[0094] Furthermore, the working process of the power amplifier adjustment module is as follows:

[0095] Process B1: Obtained energy saving coefficient, denoted as jn;

[0096] The power supply power of the target area is recorded as PU, and the receiving power corresponding to the first, second, and even hn-th municipal districts in the target area is recorded as PO. (1) ,PO (2) ~PO (hn) ;

[0097] Calculate PO (1) ,PO (2) ~PO (hn) The sum of is denoted as aPO; determine whether [1-aPO( / PU)]≥jn holds;

[0098] If it is established, the active power of the power grid in the target area is changed from PU to [(1-jn)×PU], and the process B2 to process B5 are skipped;

[0099] If not, recalculate the energy saving coefficient and adjust the active power of the grid received by the first to the hnth municipal districts, and enter process B2;

[0100] Process B2: Let the current time be ho hours and mi minutes; let the electricity consumption of the first, second, and finally hn-th municipal districts at ho hours and mi minutes be: ei (1) ,ei (2) ~ei (hn) ;

[0101] Get the total electricity consumption of the 1st, 2nd, and hnth municipal districts at (ho-1), and get: (1) 、eg (2) ~eg (hn) ;

[0102] will mi、ei (1) ~ei (hn) and (1) ~eg (hn) Substituting into the electricity consumption change equation of the 1st to hnth districts, we can get the expected electricity consumption of the 1st, 2nd, and hnth districts at time ho, denoted as qe (1) 、qe (2) ~qe (hn) ;

[0103] Process B3: Define the transition parameter as gd, and the calculation formula of gd is: gd = [1-aPO( / PU)]-jn; compare the positive and negative values ​​of gd to determine whether the power supply in the target area needs to be adjusted;

[0104] If gd is positive, it means that the power supply in the target area is sufficient, and the power supply power is not adjusted. The energy saving coefficient of the target area is recalculated, and the receiving power of all municipal areas is adjusted, and process B4 is entered;

[0105] If gd is negative, it means that the power supply in the target area is insufficient, and the power supply is adjusted, and the receiving power of some municipal areas is adjusted, and the process B5 is entered;

[0106] Process B4: Adequate power supply in the target area; define calculation formula B-1:

[0107] Among them, qe (i) represents the expected electricity consumption of the ith municipal district at time ho; ei (i) represents the electricity consumption of the ith municipal district at ho hour and mi minute; Δe (i) represents the remaining power of the ith municipal district at time ho;

[0108] Will (1) ~ei (hn) and qe (1) ~qe (hn) Substituting into the calculation formula B-1, calculate the remaining power of the first, second, and finally hn-th municipal districts at time ho, and obtain: Δe (1) , Δe (2) ~Δe (hn) ;

[0109] Based on Δe (1) ~Δe (hn) Recalculate the energy saving factor and adjust the receiving power of all municipal areas;

[0110] Process B5: The target area has insufficient power supply; the adjusted power supply is recorded as tPU, and the calculation formula of tPU is: tPU = (1-gd) × PU;

[0111] Get the electricity consumption coefficient be of the 1st, 2nd, and up to the hnth municipal district (1) 、be (2) ~be (hn) ; Calculate be (1) ~be (hn) The sum of is written as abe;

[0112] Get the remaining power Δe of the 1st to hnth municipal districts at time ho (1) ~Δe (hn) ;

[0113] The municipal districts with positive residual power are classified as Class A districts, and the municipal districts with negative residual power are classified as Class B districts;

[0114] According to the data in process B1 to process B5, the receiving power of some urban areas is adjusted.

[0115] Furthermore, the specific process of process B4 is as follows:

[0116] Process B41: the municipal districts with positive remaining electricity are regarded as quasi-allocated municipal districts, and the municipal districts with negative remaining electricity are regarded as pending allocation municipal districts;

[0117] According to Δe (1) ~Δe (hn), the number of municipal districts to be allocated is recorded as zu, and the number of municipal districts to be allocated is recorded as du; where zu+du=hn;

[0118] The remaining electricity from the 1st to the zuth quasi-allocated municipal area is ez (1) ~ez (zu) ;

[0119] The remaining electricity of the first to duth municipal districts to be allocated is recorded as ed (1) ~ez (du) ;

[0120] (Among them, ({ez (1) ~ez (zu)}∩{ed (1) ~ez (du)})={Δe (1) ~Δe (hn)})

[0121] Process B42: Calculate ez (1) ~ez (zu) The sum of is denoted as aez; calculate ed (1) ~ez (du) The sum of is written as aed;

[0122] The adjusted energy saving factor is recorded as jjn, and the calculation formula of jjn is determined by comparing aez and |aed|.

[0123] If aez>|aed|, the calculation formula of jjn is: jjn=[1-(|aed| / aez)]×jn;

[0124] If aez≤|aed|, then the formula for calculating jjn is: jjn=[1-(aez / |aed|)]×jn;

[0125] Process B43: The received power of the first to the zuth quasi-allocated municipal area is recorded as Pz (1) ~Pz (zu) ; The received power of the first to duth municipal districts to be allocated is recorded as Pd (1) ~Pd (du) ;

[0126] The received power of the xth quasi-allocated municipal area is denoted as Pz (x) , the received power of the yth city district to be allocated is recorded as Pd (y) ;

[0127] The adjusted received power of the xth quasi-allocated municipal area is recorded as tPz (x) , the adjusted receiving power of the yth city district to be allocated is recorded as tPd (y); The value range of x is: 1~zu, the value range of y is: 1~du;

[0128] Define calculation formula B-2: tPz (x) =(1-jjn)×Pz (x) ;

[0129] Definition of calculation formula B-3: tPd (y) =(1+jjn)×Pd (y) ;

[0130] Process B44: Pz (1) ~Pz (zu) Substitute into the calculation formula B-2 to calculate the adjusted receiving power tPz of the 1st to zuth quasi-allocated municipal areas (1) ~tPz (zu) ;

[0131] Pd (1) ~Pd (du) Substitute into the calculation formula B-3 to calculate the adjusted receiving power tPd of the first to duth municipal districts to be allocated (1) ~tPd (du) ;

[0132] According to tPz (1) ~tPz (zu) and tPd (1) ~tPd (du) , adjust the receiving power of the 1st to hnth urban districts.

[0133] Furthermore, the specific process of process B5 is as follows:

[0134] Process B51: The number of Class A areas is recorded as au, and the number of Class B areas is recorded as bu; (where au = zu, bu = du)

[0135] The received power of the 1st to auth A-type areas is denoted as Pa (1) ~Pa (au) Calculate Pa (1) ~Pa (au) The sum of is denoted as aPa;

[0136] The received power of the first to bu-th Class B area is denoted as Pb (1) ~Pb (bu) ;

[0137] Process B52: Class B area is classified as Pb (1) ~Pb (bu) Arrange in ascending order to obtain the C-type area;

[0138] The received power of the first to the buth C-type area is denoted as Pc (1)~Pc (bu) ; Among them, {Pb (1) ~Pb (bu)}={Pc (1) ~Pc (bu)};

[0139] Define relation B-4: Among them, Pc (i) represents the receiving power of the i-th Class C area, and the value range of i is: 1~l; where l represents the segmentation parameter, and the value range of l is: 1~bu;

[0140] Process B53: Pc (1) ~Pc (bu) Substitute into equation B-4, find the first segmentation parameter that does not satisfy equation B-4, denoted as nl;

[0141] Extract the B-type areas corresponding to the 1st to (nl-1)th C-type areas as BⅠ-type areas, and the B-type areas that do not belong to the BⅠ-type areas as BⅡ-type areas;

[0142] The number of BⅠ areas is recorded as bua, and the number of BⅡ areas is recorded as bub;

[0143] Process B54: The receiving power of the first to au-th Class A areas is not adjusted;

[0144] Get the expected power consumption of the 1st to buath BⅠ area, denoted as qP1 (1) ~qP1 (bua) ;

[0145] Define calculation formula B-5: tP1 (g) =qP1 (g) / 3600; among which, qP1 (g) represents the expected power consumption of the g-th BI zone, tP1 (g) It represents the adjusted receiving power of the g-th Class BⅠ area;

[0146] qP1 (1) ~qP1 (bua) Substituting into the calculation formula B-5, we can get the adjusted receiving power tP1 of the 1st to buath BⅠ class areas (1) ~tP1 (bua) ;

[0147] Process B55: Obtain the electricity consumption coefficient of the 1st to bubth BⅡ area, recorded as bb (1) ~bb (bub) ;

[0148] Get the expected power consumption of the 1st to bubth BⅡ area, denoted as qP2 (1) ~qP2 (bub);

[0149] Define calculation formula B-6: tP2 (g) =(qP2 (g) / 3600)×[1+(bb (g) / abe)]; among them, qP2 (h) represents the expected power consumption of the hth BⅡ area, tP2 (h) represents the adjusted receiving power of the g-th BⅡ area; bb (g) It represents the electricity consumption coefficient of the g-th BⅡ area;

[0150] qP2 (1) ~qP2 (bub) and bb (1) ~bb (bub) Substituting into the calculation formula B-6, we can get the adjusted receiving power tP2 of the 1st to bubth BⅡ area. (1) ~tP2 (bub) ;

[0151] According to tP1 (1) ~tP1 (bua) and tP2 (1) ~tP2 (bub) , adjust the receiving power corresponding to the 1st to hnth urban districts.

[0152] A cloud computing-based energy-saving management method includes:

[0153] Step S1: Obtain the number of districts in the target area; obtain the current time, obtain the power consumption of each district in the current time, and obtain power consumption data;

[0154] Step S2: Obtain the historical power consumption of the target area to obtain the historical power consumption A and the historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each city district over time, and construct the power consumption change equation of each city district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each city district;

[0155] Step S3: Obtain the power supply power and the received power of each city district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each city district according to the power consumption data and the power consumption change equation, and judge whether the energy saving coefficient is satisfied in combination with the power data; if satisfied, adjust the power supply power according to the energy saving coefficient; if not satisfied, recalculate the energy saving coefficient in combination with the power consumption coefficient, and adjust the received power of each city district;

[0156] Step S4: continuously obtain power consumption data and adjust the power supply or the received power of each city district.

[0157] Compared with the prior art, the present invention has the following beneficial effects:

[0158] Centralized data management and real-time monitoring: The present invention can centrally store and manage the power resource usage data of each municipal district in the target area. Through data centralization, the power resource usage in different locations and at different times can be realized, thereby improving the availability and accuracy of the data. At the same time, the present invention can automatically analyze the electricity consumption of all municipal districts, adjust the electricity distribution in the target area, and avoid the cost increase caused by energy waste.

[0159] Efficient data analysis: The present invention uses data analysis, data modeling and other technologies to conduct in-depth analysis of large amounts of electricity usage data; predict future energy demand based on historical data, and automatically adjust energy supply in target areas by optimizing electricity distribution to achieve the goal of energy saving and consumption reduction.

[0160] Reduce initial investment and operating costs: The present invention does not require users to purchase and maintain expensive hardware facilities and IT infrastructure. Users only need to select appropriate monitoring objects according to actual needs. The system can obtain real-time electricity consumption data of the target area, analyze and process the electricity consumption data, and automatically adjust the power distribution, thereby avoiding high initial investment and greatly reducing the operating costs of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0161] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0162] Figure 1 It is a schematic diagram of the system of the present invention;

[0163] Figure 2 It is a schematic diagram of the method of the present invention;

[0164] Figure 3 This is a schematic diagram of data processing of the present invention. DETAILED DESCRIPTION

[0165] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0166] Embodiment 1

[0167] See also Figure 1 and Figure 3 , an energy-saving management system based on cloud computing includes:

[0168] Data acquisition module: used to obtain the number of (all) municipal districts in the target area; obtain the current time, obtain the power consumption of each municipal district (in the target area) in the current time, and obtain the power consumption data;

[0169] It should be noted that the "target area" in the present invention means: the city-level area where the present invention is used for energy-saving management;

[0170] Data analysis module: used to obtain the historical power consumption of the target area, and obtain the historical power consumption A and historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each municipal district over time, and construct the power consumption change equation of each municipal district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each municipal district;

[0171] Process A: The workflow of the data analysis module is as follows:

[0172] Process A1: Count the number of all the districts in the target area, record it as hn; get the current month, record it as month m;

[0173] Obtain the hourly electricity consumption of all urban areas in the target area for the past three days as historical electricity consumption A;

[0174] Process A2: Define triple A-1: ​​he (x，y，z) ;he (x，y，z) It represents the electricity consumption of the x-th municipal district at the z-th hour on the y-th day;

[0175] According to triple A-1, the electricity consumption of the first city district in the historical electricity consumption A from the first and second hours of the first day to the 24th hour is recorded as: (1，1，1) 、he (1，1，2) ~he (1，1，24) ;

[0176] The electricity consumption at the 1st and 2nd hour of the next day until the 24th hour is recorded as: (1，2，1) 、he (1，2，2) ~he (1，2，24) ;

[0177] The electricity consumption at the 1st and 2nd hour of the third day until the 24th hour is recorded as: (1，3，1) 、he (1，3，2) ~he (1，3，24) ;

[0178] Based on (1，1，1) ~he (1，3，24) , analyze the hourly electricity consumption change of the first city district, and construct the electricity consumption change equation of the first city district;

[0179] Process A23: Analyze the change in electricity consumption in the first district from the first to the second hour and calculate the Kalman gain Kdk (1-2) ;

[0180] Process A231: Construct the electricity consumption matrix of the city area (in the past three days) in the first hour and record it as B (1) , the power consumption matrix in the second hour is recorded as B (2) ;

[0181] Matrix B (1) The mathematical representation of is:

[0182] Matrix B (2) The mathematical representation of is:

[0183] Process A232: Calculate matrix B (1) Becomes matrix B (2) The state transfer matrix of (1-2) ; Matrix B (1-2) The calculation formula is:

[0184] B (1-2) =[(B (1) ) T ×B (2) ] -1 ×(B (2) ) T ×B (2) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0185] Process A233: Calculate matrix B (1) The difference matrix of (1) ; Matrix RB (1) The calculation formula is:

[0186] Among them, - represents matrix subtraction;

[0187] Calculate the matrix B (2) The difference matrix of (2) ; Matrix RB (2) The calculation formula is:

[0188]

[0189] Calculate the matrix B (1) Becomes matrix B (2) The error transition matrix is ​​denoted as matrix QB (1-2) ; Matrix QB (1-2) The mathematical formula is:

[0190] QB (1-2) =RB (2) -RB (1) ;

[0191] Process A234: Calculate matrix B(1) The error covariance matrix of (1) ; Matrix Pd (1) The calculation formula is:

[0192] Pd (1) =(1 / 3)×(APd (1) T ×APd (1) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (1) Represents the matrix Pd (1) The transition matrix, APd (1) The calculation formula is:

[0193] AP (1) =B (1) -[(1 / 3)×I×B (1) ]; where - represents matrix subtraction and I represents a one-matrix;

[0194] Process A235: Calculate matrix B (2) The pre-error covariance matrix of (2) ;Pk (2) The calculation formula is:

[0195]

[0196] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0197] Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the first to the second hour (1-2) ,Kdk (1-2) The calculation formula is:

[0198]

[0199] Process A24: Based on triple A-1, record the electricity consumption of the first city district on the third day and the third hour as he (1，3，3) , the power consumption in the third hour of the second day is recorded as he (1，2，3) , the power consumption in the third hour of the first day is recorded as he (1，1，3) ;

[0200] Analyze the change in electricity consumption in the first city district from the second to the third hour and calculate the Kalman gain Kdk (2-3) ;

[0201] Process A241: Construct the electricity consumption matrix of the city area (in the past three days) in the third hour, recorded as B (3) Matrix B (3) The mathematical representation of is:

[0202]

[0203] Process A242: Calculate matrix B (2) Becomes matrix B (3) The state transfer matrix of (2-3) ; Matrix B (2-3) The calculation formula is:

[0204] B (2-3) =[(B (2) ) T ×B (3) ] -1 ×(B (3) ) T ×B (3) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0205] Process A243: Calculate matrix B (3) The difference matrix of (3) ; Matrix RB (3) The calculation formula is:

[0206] Among them, - represents matrix subtraction;

[0207] Calculate the matrix B (2) Becomes matrix B (3) The error transition matrix is ​​denoted as matrix QB (2-3) ; Matrix QB (2-3) The mathematical formula is:

[0208] QB (2-3) =RB (3) -RB (2) ;

[0209] Process A244: Calculate matrix B (2) The error covariance matrix of (2) ; Matrix Pd (2) The calculation formula is:

[0210] Pd (2) =(1 / 3)×(APd (2) T ×APd (2) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (2) Represents the matrix Pd (2) The transition matrix, APd (2) The calculation formula is:

[0211] AP (2) =B (2) -[(1 / 3)×I×B(2) ]; where - represents matrix subtraction and I represents a one-matrix;

[0212] Process A245: Calculate matrix B (3) The pre-error covariance matrix of (3) ;Pk (3) The calculation formula is:

[0213]

[0214] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0215] Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the second to the third hour (2-3) ,Kdk (2-3) The calculation formula is:

[0216]

[0217] Process A25: Similarly, according to triple A-1, the electricity consumption of the first district at the 23rd hour on the third day is recorded as he (1，3，23) , the power consumption in the 23rd hour of the next day is recorded as he (1，2，23) , the electricity consumption in the 23rd hour of the first day is recorded as he (1，1，23) ;

[0218] Analyze the change in electricity consumption in the first district from the 23rd to the 24th hour and calculate the Kalman gain Kdk (23-24) ;

[0219] Process A251: Construct the electricity consumption matrix of the city area (in the past three days) in the first hour and record it as B (23) , the power consumption matrix in the second hour is recorded as B (24) ;

[0220] Matrix B (23) The mathematical representation of is:

[0221] Matrix B (24) The mathematical representation of is:

[0222] Process A252: Calculate matrix B (23) Becomes matrix B (24) The state transfer matrix of (23-24) ; Matrix B (23-24) The calculation formula is:

[0223] B (1-2) =[(B (1) ) T ×B(2) ] -1 ×(B (2) ) T ×B (2) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix;

[0224] Process A253: Calculate matrix B (23) The difference matrix of (23) ; Matrix RB (23) The calculation formula is:

[0225] Among them, - represents matrix subtraction;

[0226] Calculate the matrix B (24) The difference matrix of (24) ; Matrix RB (24) The calculation formula is:

[0227]

[0228] Calculate the matrix B (23) Becomes matrix B (24) The error transition matrix is ​​denoted as matrix QB (23-24) ; Matrix QB (23-24) The mathematical formula is:

[0229] QB (23-24) =RB (24) -RB (23) ;

[0230] Process A254: Calculate matrix B (23) The error covariance matrix of (23) ; Matrix Pd (23) The calculation formula is:

[0231] Pd (23) =(1 / 3)×(APd (23) T ×APd (23) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (23) Represents the matrix Pd (23) The transition matrix, APd (23) The calculation formula is:

[0232] AP (23) =B (23) -[(1 / 3)×I×B (23) ]; where - represents matrix subtraction and I represents a one-matrix;

[0233] Process A255: Calculate matrix B(24) The pre-error covariance matrix of (24) ;Pk (24) The calculation formula is:

[0234]

[0235] Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition;

[0236] Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the 23rd to the 24th hour (23-24) ,Kdk (23-24) The calculation formula is:

[0237]

[0238] Process A26: Summarize processes A23 to A25 to obtain Kalman gain: Kdk (1-2) ~Kdk (23-24) , construct the electricity consumption change equation of the first municipal district;

[0239] Process A261: Calculation of mean Kdk (1-2) ~Kdk (23-24) , denoted as aKk;

[0240] Assume that the total electricity consumption of the city area at the kth hour is ee (k) , the power consumption at k hour and d minute is de (k,d) , the power consumption in the (k-1) hour is ee (k-1) , construct equation A (1) ; A (1) The mathematical expression is:

[0241]

[0242] Wherein, QC represents the error coefficient, and the initial value of QC is 0;

[0243] Process A262: Set the value of d to 60 and set he (1，1，1) ~he (1，1，23) As de (k,d) andee (k-1) , will he (1，1，1) ~he (1，1，23) As ee (k) , substitute into equation A (1) The QC value was calculated by MATLAB software to obtain CC;

[0244] Substituting CC into equation A (1) In the equation A, we get (2) ; A (2) The mathematical expression is:

[0245]

[0246] Equation A (2) As the electricity consumption change equation for the first municipal district;

[0247] Process A27: Repeat the same process of constructing the electricity consumption change equation for the first city district to construct the electricity consumption change equation for the second to hn-th city districts;

[0248] Get the number of days in month (m-1), recorded as td; get the total daily electricity consumption of all urban districts (in the target area) in month (m-1) as the historical electricity consumption B;

[0249] The total electricity consumption of the 1st, 2nd, and hnth municipal districts on the 1st day of month (m-1) in the historical electricity consumption B is recorded as: (1，1) 、ne (1，2) ~ne (1，hn) ;

[0250] On the 2nd day of month (m-1), the total electricity consumption of the 1st, 2nd, and up to the hnth municipal districts is recorded as: ne (2，1) 、ne (2，2) ~ne (2，hn) ;

[0251] Similarly, on day td of month (m-1), the total electricity consumption of the 1st, 2nd, and even hnth municipal districts is recorded as: ne (td，1) 、ne (td，2) ~ne (td，hn) ;

[0252] Calculate the electricity consumption coefficient of each municipal district based on the historical electricity consumption B;

[0253] It should be noted that if month m is January of a certain year, then month (m-1) is December of the previous year; for example: if month m is January of 2024, then month (m-1) is December of 2023;

[0254] Process A271: define a zero matrix of (td×1) as an initial matrix;

[0255] The total electricity consumption of the first municipal district from the 1st day of month (m-1) to the td day is ne (1，1) ~ne (td，1) Fill in the initial matrix to get the power consumption matrix BZ of the first district (1) ;

[0256] The total electricity consumption of the second municipal district from the 1st day of month (m-1) to the td day (i.e., ne (1，2) 、~ne (td，2)) is filled into the initial matrix to obtain the power consumption matrix BZ of the second city district (2) ;

[0257] Similarly, the total electricity consumption of the hn-th city district from the 1st day of month (m-1) to the td day (i.e., ne (1，hn) ~ne (td，hn) ) is filled into the initial matrix to obtain the power consumption matrix BZ of the hn-th municipal district (hn) ;

[0258] Process A272: Corresponding calculation formula A-2: Among them, ae (i) It represents the average electricity consumption of all urban districts in the target area on day i of month (m-1), where the value range of i is: 1~td;

[0259] ne (i，j) represents the total electricity consumption of the jth district in the target area on day i of month (m-1), and the value range of j is: 1~hn;

[0260] Based on calculation formula A-2, according to ne (1，1) 、ne (1，2) ~ne (1，ht) Calculate the average electricity consumption of all urban districts in the target area on the first day of month (m-1) to obtain ae (1) ;

[0261] According to ne (2，1) 、ne (2，2) ~ne (2，ht) Calculate the average electricity consumption of all urban areas in the target area on the 2nd day of month (m-1) to obtain ae (2) ;

[0262] By analogy, according to ne (td，1) 、ne (td，2) ~ne (td，hn) Calculate the average electricity consumption of all urban areas in the target area on day (m-1) of the month td, and get ae (td) ;

[0263] AE (1) ~ae (td) Substitute into the initial matrix to obtain matrix AB;

[0264] Process A273: The covariance matrix of the p-th district in the target area is denoted as xB (p) ; The value range of p is: 1~hn; Define calculation formula A-3:

[0265] Among them, - represents matrix subtraction; BZ(q) represents the power consumption matrix of the qth municipal district, and the minimum value of (p-1) is 1;

[0266] The power consumption matrix BZ of the first, second, and even hn-th districts is (1) , BZ (2) ~BZ (hn) Substituting into the calculation formula A-3, we can obtain the covariance matrix xB of the first, second, and even hn-th districts in the target area. (1) ,xB (2) ~xB (hn) ;

[0267] Process A274: Define calculation formula A-4: be (i) =(xB (i) ) T × (i) ; Among them, be (i) represents the electricity consumption coefficient of the first municipal district, and T represents the transpose of the matrix;

[0268] Substitute into xB (1) ,xB (2) ~xB (hn) Substituting into the calculation formula A-4, we can get the electricity consumption coefficient be for the 1st, 2nd, and up to the hnth municipal districts. (1) 、be (2) ~be (hn) .

[0269] Power amplifier adjustment module: used to obtain the power supply power (of the target area) and the received power of each city district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each city district according to the power consumption data and the power consumption change equation (of each city district), and judge whether the energy saving coefficient is met in combination with the power data; if it is met, adjust the power supply power according to the energy saving coefficient; if it is not met, recalculate the energy saving coefficient in combination with the power consumption coefficient (of each city district), and adjust the received power of each city district;

[0270] Process B: The workflow of the power amplifier adjustment module is as follows:

[0271] Process B1: Obtain the energy saving coefficient of (target area), denoted as jn;

[0272] (The power grid of the target area is used as the power supply grid, and the active power of the power supply grid of the target area is used as the power supply power;

[0273] The power grid that receives power from the power supply grid in each municipal district of the target area is regarded as the receiving power grid, and the active power corresponding to the receiving power grid in each municipal district is regarded as the receiving power;)

[0274] The power supply power of the target area is recorded as PU, and the receiving power corresponding to the first, second, and even hn-th municipal districts in the target area is recorded as PO. (1),PO (2) ~PO (hn) ;

[0275] (PU and PO (1) ,PO (2) ~PO (hn) , as power data for the target area)

[0276] Calculate PO (1) ,PO (2) ~PO (hn) The sum of is denoted as aPO; determine whether [1-aPO( / PU)]≥jn holds;

[0277] If it is established, the active power of the power grid in the target area is changed from PU to [(1-jn)×PU], and the process B2 to process B5 are skipped;

[0278] If not, recalculate the energy saving coefficient (of the target area) and adjust the active power of the receiving grid of the first to the hn-th municipal districts, and enter process B2;

[0279] Process B2: Let the current time be ho hours and mi minutes; let the electricity consumption of the first, second, and even hn-th municipal districts in the target area at ho hours and mi minutes be: ei (1) ,ei (2) ~ei (hn) ;

[0280] Get the total electricity consumption of the first, second, and even hn-th municipal districts at (ho-1) in the target area, and get: (1) 、eg (2) ~eg (hn) ;

[0281] will mi、ei (1) ~ei (hn) and (1) ~eg (hn) Substituting into the electricity consumption change equation of the 1st to hnth municipal districts, we can obtain the expected electricity consumption of the 1st, 2nd, and hnth municipal districts (in the target area) at time ho, denoted as qe (1) 、qe (2) ~qe (hn) ;

[0282] It should be noted that if ho is 0 o'clock (or 24 o'clock) on a certain day, then (ho-1) is 23 o'clock yesterday; for example, if ho is 0 o'clock on the 12th, then (ho-1) is 23 o'clock on the 11th;

[0283] Process B3: Define the transition parameter as gd, and the calculation formula of gd is: gd = [1-aPO( / PU)]-jn; compare the positive and negative values ​​of gd to determine whether the power supply in the target area needs to be adjusted;

[0284] If gd is positive, it means that the power supply in the target area is sufficient, and the power supply power is not adjusted. The energy saving coefficient of the target area is recalculated, and the receiving power of all municipal districts (within the target area) is adjusted, and process B4 is entered;

[0285] If gd is negative, it means that the power supply in the target area is insufficient, and the power supply is adjusted, and the receiving power of some municipal areas (within the target area) is adjusted, and the process B5 is entered;

[0286] Process B4: Adequate power supply in the target area; define calculation formula B-1:

[0287] Among them, qe (i) represents the expected electricity consumption of the ith municipal district (in the target area) at time ho; ei (i) represents the electricity consumption of the i-th district in the target area at ho hour and mi minute; Δe (i) represents the remaining power of the ith municipal district (in the target area) at time ho;

[0288] Will (1) ~ei (hn) and qe (1) ~qe (hn) Substituting into the calculation formula B-1, calculate the remaining power of the first, second, and even hn-th municipal districts (in the target area) at time ho, and obtain: Δe (1) , Δe (2) ~Δe (hn) ;

[0289] Based on Δe (1) ~Δe (hn) Recalculate the energy saving factor and adjust the receiving power of all municipal areas (within the target area);

[0290] Process B41: the municipal districts with positive remaining electricity are regarded as quasi-allocated municipal districts, and the municipal districts with negative remaining electricity are regarded as pending allocation municipal districts;

[0291] According to Δe (1) ~Δe (hn) , the number of municipal districts to be allocated is recorded as zu, and the number of municipal districts to be allocated is recorded as du; where zu+du=hn;

[0292] The remaining electricity from the 1st to the zuth quasi-allocated municipal area is ez (1) ~ez (zu) ;

[0293] The remaining electricity of the first to duth municipal districts to be allocated is recorded as ed (1) ~ez (du) ;

[0294] (Among them, ({ez (1) ~ez (zu)}∩{ed (1) ~ez (du)})={Δe (1) ~Δe (hn)})

[0295] Process B42: Calculate ez (1) ~ez (zu) The sum of is denoted as aez; calculate ed (1) ~ez (du) The sum of is written as aed;

[0296] The adjusted energy saving factor is recorded as jjn, and the calculation formula of jjn is determined by comparing aez and |aed|.

[0297] If aez>|aed|, the calculation formula of jjn is: jjn=[1-(|aed| / aez)]×jn;

[0298] If aez≤|aed|, then the formula for calculating jjn is: jjn=[1-(aez / |aed|)]×jn;

[0299] Process B43: The received power of the first to the zuth quasi-allocated municipal area is recorded as Pz (1) ~Pz (zu) ; The received power of the first to duth municipal districts to be allocated is recorded as Pd (1) ~Pd (du) ;

[0300] The received power of the xth quasi-allocated municipal area is denoted as Pz (x) , the received power of the yth city district to be allocated is recorded as Pd (y) ;

[0301] The adjusted received power of the xth quasi-allocated municipal area is recorded as tPz (x) , the adjusted receiving power of the yth city district to be allocated is recorded as tPd (y) ; The value range of x is: 1~zu, the value range of y is: 1~du;

[0302] Define calculation formula B-2: tPz (x) =(1-jjn)×Pz (x) ;

[0303] Definition of calculation formula B-3: tPd(y) =(1+jjn)×Pd (y) ;

[0304] Process B44: Pz (1) ~Pz (zu) Substitute into the calculation formula B-2 to calculate the adjusted receiving power tPz of the 1st to zuth quasi-allocated municipal areas (1) ~tPz (zu) ;

[0305] Pd (1) ~Pd (du) Substitute into the calculation formula B-3 to calculate the adjusted receiving power tPd of the first to duth municipal districts to be allocated (1) ~tPd (du) ;

[0306] According to tPz (1) ~tPz (zu) and tPd (1) ~tPd (du) , adjust the receiving power of the 1st to hnth municipal districts;

[0307] Process B5: The target area has insufficient power supply; the adjusted power supply is recorded as tPU, and the calculation formula of tPU is: tPU = (1-gd) × PU;

[0308] Get the electricity consumption coefficient be of the first, second, and even hn-th municipal districts (in the target area) (1) 、be (2) ~be (hn) ; Calculate be (1) ~be (hn) The sum of is written as abe;

[0309] Get the remaining power Δe of the 1st to hnth municipal districts (in the target area) at time ho (1) ~Δe (hn) ;

[0310] The municipal districts with positive residual power are classified as Class A districts, and the municipal districts with negative residual power are classified as Class B districts;

[0311] According to the data in process B1 to process B5, the receiving power of some municipal areas (within the target area) is adjusted;

[0312] Process B51: The number of Class A areas is recorded as au, and the number of Class B areas is recorded as bu; (where au = zu, bu = du)

[0313] The received power of the 1st to auth A-type areas is denoted as Pa (1) ~Pa (au) Calculate Pa (1)~Pa (au) The sum of is denoted as aPa;

[0314] The received power of the first to bu-th Class B area is denoted as Pb (1) ~Pb (bu) ;

[0315] Process B52: Class B area is classified as Pb (1) ~Pb (bu) Arrange in ascending order to obtain the C-type area;

[0316] The received power of the first to the buth C-type area is denoted as Pc (1) ~Pc (bu) ; Among them, {Pb (1) ~Pb (bu)}={Pc (1) ~Pc (bu)}; (i.e., the meaning of Class B area and Class C area is the same, but the order is different)

[0317] Define relation B-4: Among them, Pc (i) represents the receiving power of the i-th Class C area, and the value range of i is: 1~l; where l represents the segmentation parameter, and the value range of l is: 1~bu;

[0318] Process B53: Pc (1) ~Pc (bu) Substitute into equation B-4, find the first segmentation parameter that does not satisfy equation B-4, denoted as nl;

[0319] Extract the B-type areas corresponding to the 1st to (nl-1)th C-type areas as BⅠ-type areas, and the B-type areas that do not belong to the BⅠ-type areas as BⅡ-type areas;

[0320] The number of BⅠ areas is recorded as bua, and the number of BⅡ areas is recorded as bub;

[0321] Process B54: The receiving power of the first to au-th Class A areas is not adjusted;

[0322] Get the expected power consumption of the 1st to buath BⅠ area, denoted as qP1 (1) ~qP1 (bua) ;

[0323] Define calculation formula B-5: tP1 (g) =qP1 (g) / 3600; among which, qP1 (g) represents the expected power consumption of the g-th BI zone, tP1 (g) It represents the adjusted receiving power of the g-th Class BⅠ area;

[0324] qP1(1) ~qP1 (bua) Substituting into the calculation formula B-5, we can get the adjusted receiving power tP1 of the 1st to buath BⅠ class areas (1) ~tP1 (bua) ;

[0325] Process B55: Obtain the electricity consumption coefficient of the 1st to bubth BⅡ area, recorded as bb (1) ~bb (bub) ;

[0326] Get the expected power consumption of the 1st to bubth BⅡ area, denoted as qP2 (1) ~qP2 (bub) ;

[0327] Define calculation formula B-6: tP2 (g) =(qP2 (g) / 3600)×[1+(bb (g) / abe)]; among them, qP2 (h) represents the expected power consumption of the hth BⅡ area, tP2 (h) represents the adjusted receiving power of the g-th BⅡ area; bb (g) It represents the electricity consumption coefficient of the g-th BⅡ area;

[0328] qP2 (1) ~qP2 (bub) and bb (1) ~bb (bub) Substituting into the calculation formula B-6, we can get the adjusted receiving power tP2 of the 1st to bubth BⅡ area. (1) ~tP2 (bub) ;

[0329] According to tP1 (1) ~tP1 (bua) and tP2 (1) ~tP2 (bub) , adjust the receiving power corresponding to the 1st to hnth urban districts.

[0330] Continuous monitoring module: used to continuously obtain power consumption data and adjust the power supply (in the target area) or the received power in each municipal area.

[0331] Embodiment 2

[0332] See also Figure 2 , an energy-saving management method based on cloud computing includes:

[0333] Step S1: Obtain the number of (all) municipal districts in the target area; obtain the current time, obtain the power consumption of each municipal district (in the target area) in the current time, and obtain power consumption data;

[0334] Step S2: Obtain the historical power consumption of the target area to obtain the historical power consumption A and the historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each city district over time, and construct the power consumption change equation of each city district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each city district;

[0335] Step S3: Obtain the power supply power (of the target area) and the received power of each municipal district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each municipal district according to the power consumption data and the power consumption change equation (of each municipal district), and judge whether the energy saving coefficient is satisfied in combination with the power data; if satisfied, adjust the power supply power according to the energy saving coefficient; if not satisfied, recalculate the energy saving coefficient in combination with the power consumption coefficient (of each municipal district), and adjust the received power of each municipal district;

[0336] Step S4: Continuously obtain power consumption data and adjust the power supply (of the target area) or the received power of each municipal area.

[0337] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technical personnel in this field according to actual conditions. For example, if there are weight coefficients and proportional coefficients, their set sizes are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. Regarding the size of the weight coefficient and the proportional coefficient, it is sufficient as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0338] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An energy-saving management system based on cloud computing, characterized in that: The system comprises: Data acquisition module: used to obtain the number of urban districts in the target area; obtain the current time, obtain the power consumption of each urban district in the current time, and obtain the power consumption data; Data analysis module: used to obtain the historical power consumption of the target area, and obtain the historical power consumption A and historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each municipal district over time, and construct the power consumption change equation of each municipal district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each municipal district; Power amplifier adjustment module: used to obtain the power supply power and the received power of each city district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each city district according to the power consumption data and the power consumption change equation, and judge whether the energy saving coefficient is met in combination with the power data; if it is met, adjust the power supply power according to the energy saving coefficient; if it is not met, recalculate the energy saving coefficient in combination with the power consumption coefficient, and adjust the received power of each city district; Continuous monitoring module: used to continuously obtain power consumption data and adjust the power supply or the receiving power of each municipal area.

2. The cloud computing-based energy-saving management system according to claim 1, characterized in that: The workflow of the data analysis module is as follows: Process A1: Count the number of all the districts in the target area, record it as hn; get the current month, record it as month m; Obtain the hourly electricity consumption of all urban areas in the past three days as historical electricity consumption A; Process A2: Define triple A-1: ​​he (x，y，z) ;he (x，y，z) It represents the electricity consumption of the x-th municipal district at the z-th hour on the y-th day; According to triple A-1, the electricity consumption of the first city district in the historical electricity consumption A from the first and second hours of the first day to the 24th hour is recorded as: (1，1，1) 、he (1，1，2) ~he (1，1，24) ; The electricity consumption at the 1st and 2nd hour of the next day until the 24th hour is recorded as: (1，2，1) 、he (1，2，2) ~he (1，2，24) ; The electricity consumption at the 1st and 2nd hour of the third day until the 24th hour is recorded as: (1，3，1) 、he (1，3，2) ~he (1，3，24) ; Based on (1，1，1) ~he (1，3，24) , analyze the hourly electricity consumption change of the first city district, and construct the electricity consumption change equation of the first city district; Process A23: Analyze the change in electricity consumption in the first district from the first to the second hour and calculate the Kalman gain Kdk (1-2) ; Process A24: Based on triple A-1, record the electricity consumption of the first city district on the third day and the third hour as he (1，3，3) , the power consumption in the third hour of the second day is recorded as he (1，2，3) , the power consumption in the third hour of the first day is recorded as he (1，1，3) ; Analyze the change in electricity consumption in the first city district from the second to the third hour and calculate the Kalman gain Kdk (2-3) ; Process A25: Similarly, according to triple A-1, the electricity consumption of the first district at the 23rd hour on the third day is recorded as he (1，3，23) , the power consumption in the 23rd hour of the next day is recorded as he (1，2，23) , the electricity consumption in the 23rd hour of the first day is recorded as he (1，1，23) ; Analyze the change in electricity consumption in the first district from the 23rd to the 24th hour and calculate the Kalman gain Kdk (23-24) .

3. The cloud computing-based energy-saving management system according to claim 2, characterized in that: The subsequent process of the process A25 is as follows: Process A26: Summarize processes A23 to A25 to obtain Kalman gain: Kdk (1-2) ~Kdk (23-24) , construct the electricity consumption change equation of the first municipal district; Process A261: Calculation of mean Kdk (1-2) ~Kdk (23-24) , denoted as aKk; Assume that the total electricity consumption of the city area at the kth hour is ee (k) , the power consumption at k hour and d minute is de (k,d) , the power consumption in the (k-1) hour is ee (k-1) , construct equation A (1) ; A (1) The mathematical expression is: Wherein, QC represents the error coefficient, and the initial value of QC is 0; Process A262: Set the value of d to 60 and set he (1，1，1) ~he (1，1，23) As de (k,d) andee (k-1) , will he (1，1，1) ~he (1，1，23) As ee (k) , substitute into equation A (1) The QC value was calculated by MATLAB software to obtain CC; Substituting CC into equation A (1) In the equation A, we get (2) ; A (2) The mathematical expression is: Equation A (2) As the electricity consumption change equation for the first municipal district; Process A27: Repeat the same process of constructing the electricity consumption change equation for the first city district to construct the electricity consumption change equation for the second to hn-th city districts; Get the number of days in month (m-1), recorded as td; get the total daily electricity consumption of all municipal districts in month (m-1) as the historical electricity consumption B; The total electricity consumption of the 1st, 2nd, and hnth municipal districts on the 1st day of month (m-1) in the historical electricity consumption B is recorded as: (1，1) 、ne (1，2) ~ne (1，hn) ; On the 2nd day of month (m-1), the total electricity consumption of the 1st, 2nd, and up to the hnth municipal districts is recorded as: ne (2，1) 、ne (2，2) ~ne (2，hn) ; Similarly, on day td of month (m-1), the total electricity consumption of the 1st, 2nd, and even hnth municipal districts is recorded as: ne (td，1) 、ne (td，2) ~ne (td，hn) ; The electricity consumption coefficient of each city district is calculated based on the historical electricity consumption B, and enters the power amplifier adjustment module.

4. The cloud computing-based energy-saving management system according to claim 2, characterized in that: The specific process of process A23 is as follows: Process A231: Construct the electricity consumption matrix of the city area in the first hour and record it as B (1) , the power consumption matrix in the second hour is recorded as B (2) ; Matrix B (1) The mathematical representation of is: Matrix B (2) The mathematical representation of is: Process A232: Calculate matrix B (1) Becomes matrix B (2) The state transfer matrix of (1-2) ; Matrix B (1-2) The calculation formula is: B (1-2) =[(B (1) ) T ×B (2) ] -1 ×(B (2) ) T ×B (2) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A233: Calculate matrix B (1) The difference matrix of (1) ; Matrix RB (1) The calculation formula is: Among them, - represents matrix subtraction; Calculate the matrix B (2) The difference matrix of (2) ; Matrix RB (2) The calculation formula is: Calculate the matrix B (1) Becomes matrix B (2) The error transition matrix is ​​denoted as matrix QB (1-2) ; Matrix QB (1-2) The mathematical formula is: QB (1-2) =RB (2) -RB (1) ; Process A234: Calculate matrix B (1) The error covariance matrix of (1) ; Matrix Pd (1) The calculation formula is: Pd (1) =(1 / 3)×(APd (1) T ×APd (1) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (1) Represents the matrix Pd (1) The transition matrix, APd (1) The calculation formula is: AP (1) =B (1) -[(1 / 3)×I×B (1) ]; where - represents matrix subtraction and I represents a one-matrix; Process A235: Calculate matrix B (2) The pre-error covariance matrix of (2) ;Pk (2) The calculation formula is: Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the first to the second hour (1-2) ,Kdk (1-2) The calculation formula is:

5. The cloud computing-based energy-saving management system according to claim 2, characterized in that: The specific process of process A24 is as follows: Process A241: Construct the electricity consumption matrix of the city area in the third hour, denoted as B (3) Matrix B (3) The mathematical representation of is: Process A242: Calculate matrix B (2) Becomes matrix B (3) The state transfer matrix of (2-3) ; Matrix B (2-3) The calculation formula is: B (2-3) =[(B (2) ) T ×B (3) ] -1 ×(B (3) ) T ×B (3) ; Where × represents matrix multiplication, T represents the transpose of the matrix, and -1 represents the inverse of the matrix; Process A243: Calculate matrix B (3) The difference matrix of (3) ; Matrix RB (3) The calculation formula is: Among them, - represents matrix subtraction; Calculate the matrix B (2) Becomes matrix B (3) The error transition matrix is ​​denoted as matrix QB (2-3) ; Matrix QB (2-3) The mathematical formula is: QB (2-3) =RB (3) -RB (2) ; Process A244: Calculate matrix B (2) The error covariance matrix of (2) ; Matrix Pd (2) The calculation formula is: Pd (2) =(1 / 3)×(APd (2) T ×APd (2) ), where × represents matrix multiplication, T represents the transpose of the matrix, and APd (2) Represents the matrix Pd (2) The transition matrix, APd (2) The calculation formula is: AP (2) =B (2) -[(1 / 3)×I×B (2) ]; where - represents matrix subtraction and I represents a one-matrix; Process A245: Calculate matrix B (3) The pre-error covariance matrix of (3) ;Pk (3) The calculation formula is: Among them, × represents matrix multiplication, T represents matrix transpose, and + represents matrix addition; Calculate the Kalman gain Kdk of the electricity consumption in the first city district from the second to the third hour (2-3) ,Kdk (2-3) The calculation formula is:

6. The cloud computing-based energy-saving management system according to claim 3, characterized in that: The specific process of process A27 is as follows: Process A271: define a zero matrix of (td×1) as an initial matrix; The total electricity consumption of the first municipal district from the 1st day of month (m-1) to the td day is ne (1，1) ~ne (td，1) Fill in the initial matrix to get the power consumption matrix BZ of the first district (1) ; Fill the total power consumption of the second city district from the 1st day of (m-1) month to td day into the initial matrix, and get the power consumption matrix BZ of the second city district (2) ; Similarly, the total power consumption of the hn-th city district from the 1st day of month (m-1) to the td day is filled into the initial matrix to obtain the power consumption matrix BZ of the hn-th city district (hn) ; Process A272: Corresponding calculation formula A-2: Among them, ae (i) It represents the average electricity consumption of all municipal districts on day i of month (m-1), where the value range of i is: 1~td; ne (i，j) represents the total electricity consumption of the j-th municipal district on day i of month (m-1), and the value range of j is: 1~hn; Based on calculation formula A-2, according to ne (1，1) 、ne (1，2) ~ne (1，ht) Calculate the average electricity consumption of all city districts on the first day of month (m-1) and get ae (1) ; According to ne (2，1) 、ne (2，2) ~ne (2，ht) Calculate the average electricity consumption of all city districts on the 2nd day of month (m-1), and get ae (2) ; By analogy, according to ne (td，1) 、ne (td，2) ~ne (td，hn) Calculate the average electricity consumption of all municipal districts on day (m-1) of month td, and get ae (td) ; AE (1) ~ae (td) Substitute into the initial matrix to obtain matrix AB; Process A273: The covariance matrix of the p-th district is denoted as xB (p) ; The value range of p is: 1~hn; Define calculation formula A-3: Among them, - represents matrix subtraction; BZ(q) represents the power consumption matrix of the qth municipal district, and the minimum value of (p-1) is 1; The power consumption matrix BZ of the first, second, and even hn-th districts is (1) , BZ (2) ~BZ (hn) Substituting into formula A-3, we get the covariance matrix xB of the first, second, and even hn-th districts. (1) ,xB (2) ~xB (hn) ; Process A274: Define calculation formula A-4: be (i) =(xB (i) ) T × (i) ; Among them, be (i) represents the electricity consumption coefficient of the first municipal district, and T represents the transpose of the matrix; Substitute into xB (1) ,xB (2) ~xB (hn) Substituting into the calculation formula A-4, we can get the electricity consumption coefficient be for the 1st, 2nd, and up to the hnth municipal districts. (1) 、be (2) ~be (hn) .

7. The cloud computing-based energy-saving management system according to claim 3, characterized in that: The working process of the power amplifier adjustment module is as follows: Process B1: Obtained energy saving factor, denoted as jn; The power supply power of the target area is recorded as PU, and the receiving power corresponding to the first, second, and even hn-th municipal districts in the target area is recorded as PO. (1) ,PO (2) ~PO (hn) ; Calculate PO (1) ,PO (2) ~PO (hn) The sum of is denoted as aPO; determine whether [1-aPO( / PU)]≥jn holds; If it is established, the active power of the power grid in the target area is changed from PU to [(1-jn)×PU], and the process B2 to process B5 are skipped; If not, recalculate the energy saving coefficient and adjust the active power of the grid received by the first to the hnth municipal districts, and enter process B2; Process B2: Let the current time be ho hours and mi minutes; let the electricity consumption of the first, second, and finally hn-th municipal districts at ho hours and mi minutes be: ei (1) ,ei (2) ~ei (hn) ; Get the total electricity consumption of the 1st, 2nd, and hnth municipal districts at (ho-1), and get: (1) 、eg (2) ~eg (hn) ; will mi、ei (1) ~ei (hn) and (1) ~eg (hn) Substituting into the electricity consumption change equation of the 1st to hnth districts, we can get the expected electricity consumption of the 1st, 2nd, and hnth districts at time ho, denoted as qe (1) 、qe (2) ~qe (hn) ; Process B3: Define the transition parameter as gd, and the calculation formula of gd is: gd = [1-aPO( / PU)]-jn; compare the positive and negative values ​​of gd to determine whether the power supply in the target area needs to be adjusted; If gd is positive, it means that the power supply in the target area is sufficient, and the power supply power is not adjusted. The energy saving coefficient of the target area is recalculated, and the receiving power of all municipal areas is adjusted, and process B4 is entered; If gd is negative, it means that the power supply in the target area is insufficient, and the power supply is adjusted, and the receiving power of some municipal areas is adjusted, and the process B5 is entered; Process B4: Adequate power supply in the target area; define calculation formula B-1: Among them, qe (i) represents the expected electricity consumption of the ith municipal district at time ho; ei (i) represents the electricity consumption of the ith municipal district at ho hour and mi minute; Δe (i) represents the remaining power of the ith municipal district at time ho; Will (1) ~ei (hn) and qe (1) ~qe (hn) Substituting into the calculation formula B-1, calculate the remaining power of the first, second, and finally hn-th municipal districts at time ho, and obtain: Δe (1) , Δe (2) ~Δe (hn) ; Based on Δe (1) ~Δe (hn) Recalculate the energy saving factor and adjust the receiving power of all municipal areas; Process B5: The target area has insufficient power supply; the adjusted power supply is recorded as tPU, and the calculation formula of tPU is: tPU = (1-gd) × PU; Get the electricity consumption coefficient be of the 1st, 2nd, and up to the hnth municipal district (1) 、be (2) ~be (hn) ; Calculate be (1) ~be (hn) The sum of is written as abe; Get the remaining power Δe of the 1st to hnth municipal districts at time ho (1) ~Δe (hn) ; The municipal districts with positive residual power are classified as Class A districts, and the municipal districts with negative residual power are classified as Class B districts; According to the data in process B1 to process B5, the receiving power of some urban areas is adjusted.

8. The cloud computing-based energy-saving management system according to claim 7, characterized in that: The specific process of process B4 is as follows: Process B41: the municipal districts with positive remaining electricity are regarded as quasi-allocated municipal districts, and the municipal districts with negative remaining electricity are regarded as pending allocation municipal districts; According to Δe (1) ~Δe (hn) , the number of municipal districts to be allocated is recorded as zu, and the number of municipal districts to be allocated is recorded as du; where zu+du=hn; The remaining electricity from the 1st to the zuth quasi-allocated municipal area is ez (1) ~ez (zu) ; The remaining electricity of the first to duth municipal districts to be allocated is recorded as ed (1) ~ez (du) ; Process B42: Calculate ez (1) ~ez (zu) The sum of is denoted as aez; calculate ed (1) ~ez (du) The sum of is written as aed; The adjusted energy saving factor is recorded as jjn, and the calculation formula of jjn is determined by comparing aez and |aed|. If aez>|aed|, the calculation formula of jjn is: jjn=[1-(|aed| / aez)]×jn; If aez≤|aed|, then the formula for calculating jjn is: jjn=[1-(aez / |aed|)]×jn; Process B43: The received power of the first to the zuth quasi-allocated municipal area is recorded as Pz (1) ~Pz (zu) ; The received power of the first to duth municipal districts to be allocated is recorded as Pd (1) ~Pd (du) ; The received power of the xth quasi-allocated municipal area is denoted as Pz (x) , the received power of the yth city district to be allocated is recorded as Pd (y) ; The adjusted received power of the xth quasi-allocated municipal area is recorded as tPz (x) , the adjusted receiving power of the yth city district to be allocated is recorded as tPd (y) ; The value range of x is: 1~zu, the value range of y is: 1~du; Define calculation formula B-2: tPz (x) =(1-jjn)×Pz (x) ; Definition of calculation formula B-3: tPd (y) =(1+jjn)×Pd (y) ; Process B44: Pz (1) ~Pz (zu) Substitute into the calculation formula B-2 to calculate the adjusted receiving power tPz of the 1st to zuth quasi-allocated municipal areas (1) ~tPz (zu) ; Pd (1) ~Pd (du) Substitute into the calculation formula B-3 to calculate the adjusted receiving power tPd of the first to duth municipal districts to be allocated (1) ~tPd (du) ; According to tPz (1) ~tPz (zu) and tPd (1) ~tPd (du) , adjust the receiving power of the 1st to hnth urban districts.

9. The cloud computing-based energy-saving management system according to claim 7, characterized in that: The specific process of process B5 is as follows: Process B51: The number of Class A areas is counted and recorded as au, and the number of Class B areas is counted and recorded as bu; The received power of the 1st to auth A-type areas is denoted as Pa (1) ~Pa (au) Calculate Pa (1) ~Pa (au) The sum of is denoted as aPa; The received power of the first to bu-th Class B area is denoted as Pb (1) ~Pb (bu) ; Process B52: Class B area is classified as Pb (1) ~Pb (bu) Arrange in ascending order to obtain the C-type area; The received power of the first to the buth C-type area is denoted as Pc (1) ~Pc (bu) ; Among them, {Pb (1) ~Pb (bu) }={Pc (1) ~Pc (bu) }; Define relation B-4: Among them, Pc (i) represents the receiving power of the i-th Class C area, and the value range of i is: 1~l; where l represents the segmentation parameter, and the value range of l is: 1~bu; Process B53: Pc (1) ~Pc (bu) Substitute into equation B-4, find the first segmentation parameter that does not satisfy equation B-4, denoted as nl; Extract the B-type areas corresponding to the 1st to (nl-1)th C-type areas as BⅠ-type areas, and the B-type areas that do not belong to the BⅠ-type areas as BⅡ-type areas; The number of BⅠ areas is recorded as bua, and the number of BⅡ areas is recorded as bub; Process B54: The receiving power of the first to au-th Class A areas is not adjusted; Get the expected power consumption of the 1st to buath BⅠ area, denoted as qP1 (1) ~qP1 (bua) ; Define calculation formula B-5: tP1 (g) =qP1 (g) / 3600; among which, qP1 (g) represents the expected power consumption of the g-th BI zone, tP1 (g) It represents the adjusted receiving power of the g-th Class BⅠ area; qP1 (1) ~qP1 (bua) Substituting into the calculation formula B-5, we can get the adjusted receiving power tP1 of the 1st to buath BⅠ class areas (1) ~tP1 (bua) ; Process B55: Obtain the power consumption coefficient of the 1st to bubth BⅡ area, recorded as bb (1) ~bb (bub) ; Get the expected power consumption of the 1st to bubth BⅡ area, denoted as qP2 (1) ~qP2 (bub) ; Define calculation formula B-6: tP2 (g) =(qP2 (g) / 3600)×[1+(bb (g) / abe)]; among them, qP2 (h) represents the expected power consumption of the hth BⅡ area, tP2 (h) represents the adjusted receiving power of the g-th BⅡ area; bb (g) It represents the electricity consumption coefficient of the g-th BⅡ area; qP2 (1) ~qP2 (bub) and bb (1) ~bb (bub) Substituting into the calculation formula B-6, we can get the adjusted receiving power tP2 of the 1st to bubth BⅡ area. (1) ~tP2 (bub) ; According to tP1 (1) ~tP1 (bua) and tP2 (1) ~tP2 (bub) , adjust the receiving power corresponding to the 1st to hnth urban districts.

10. A cloud computing-based energy-saving management method, applicable to a cloud computing-based energy-saving management system according to any one of claims 1 to 9, characterized in that: The method comprises: Step S1: Obtain the number of districts in the target area; obtain the current time, obtain the power consumption of each district in the current time, and obtain power consumption data; Step S2: Obtain the historical power consumption of the target area to obtain the historical power consumption A and the historical power consumption B; based on the historical power consumption A, calculate the Kalman gain of the power consumption of each city district over time, and construct the power consumption change equation of each city district; based on the historical power consumption B, construct the initial matrix, and use data fitting to calculate the power consumption coefficient of each city district; Step S3: Obtain the power supply power and the received power of each city district to obtain power data; obtain the energy saving coefficient, calculate the expected power consumption of each city district according to the power consumption data and the power consumption change equation, and judge whether the energy saving coefficient is satisfied in combination with the power data; if satisfied, adjust the power supply power according to the energy saving coefficient; if not satisfied, recalculate the energy saving coefficient in combination with the power consumption coefficient, and adjust the received power of each city district; Step S4: continuously obtain power consumption data and adjust the power supply or the received power of each city district.

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