Intelligent power supply system data integrated management method and system

Abstract

The invention relates to the technical field of power supply control, and discloses an intelligent power supply system data integrated management method and system, and the method comprises the steps: S01, collecting historical power demand data; s02, constructing a power demand prediction model, and predicting the power demand data; s03, obtaining the optimal power supply amount; s04, obtaining a power supply combination of the traditional energy and the alternative energy, calculating a benefit index, and obtaining an optimal power supply combination based on a genetic algorithm; s05, the optimal power supply combination data are transmitted back to the power supply system, and the power supply quantity and the power supply mode of the power supply system are adjusted and controlled; s06, performing real-time monitoring, marking abnormal load data, transmitting the abnormal load data to a background data center, and performing early warning; by setting the step S02, the step S04 and the step S05, the power supply condition of the power supply system is adjusted and controlled to maximize the benefit while the purposes of reducing the dependence on traditional energy and reasonably utilizing new energy and renewable energy are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply control, and more specifically to a method and system for integrated data management of an intelligent power supply system. Background Art

[0002] The power supply system is a system composed of a power supply system and a transmission and distribution system that generates electrical energy and supplies and delivers it to electrical equipment. At present, the power company has made it clear that it will build an electric power Internet of Things to create an electric power system that is coordinated, complementary, and integrated with the smart grid, and has the characteristics of intelligence, diversity, and ecology, to move towards the realization of a ubiquitous electric power Internet of Things demonstration project with pioneering significance.

[0003] However, the existing management of power supply system data is mostly used to monitor the power usage and data anomalies of the power supply system, so as to grasp the power supply load status of the power supply system in real time and ensure the normal operation of the power supply system; it is impossible to predict the power demand based on the power supply system data, and it is impossible to reasonably allocate the power supply to different power supply methods based on the prediction results, and thus it is impossible to reasonably utilize new energy and renewable energy to reduce dependence on traditional energy. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent power supply system data integrated management method and system to solve the problems existing in the above-mentioned background technology.

[0005] The present invention provides the following technical solution: an intelligent power supply system data comprehensive management method, comprising the following steps:

[0006] Step S01: Collect historical power demand data of each time period in the power supply system, record each time period as t1, t2, t3...tn, and record the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ;

[0007] Step S02: constructing a power demand forecasting model to forecast power demand data for T time periods in the future;

[0008] Step S03: based on the power demand data predicted in step S02, obtaining the optimal power supply;

[0009] Step S04: Based on the optimal power supply obtained in step S03, obtain the power supply combination of traditional energy and alternative energy, calculate the benefit index of all power supply combinations, and obtain the optimal power supply combination based on the genetic algorithm, and at the same time obtain the benefit index of the optimal power supply combination;

[0010] Step S05: transmitting the optimal power supply combination data in step S04 back to the power supply system, and adjusting and controlling the power supply amount and power supply mode of the power supply system;

[0011] Step S06: Monitor the power supply load of the power supply system in real time, mark abnormal load data, transmit it to the background data center and issue an early warning.

[0012] Preferably, the step of constructing the power demand prediction model in step S02 includes:

[0013] Step S11: Take each time period in step S01 as a time series, which is the original series; then the time series contains n sample data, recorded as X (0) ={x (0) (1),x (0) (2),x (0) (3),…,x (0) (n)}, where x (0) (i) is the i-th data sample, i.e., the power demand data S ti , i=1, 2, 3...n;

[0014] Step S12: Check the sample data. If the check fails, pre-process the sample data. The formula for checking the sample data is: Where J(j) is the level ratio of the sample data, j = 2, 3, 4...n, if J(j) satisfies If the test is successful, then the test is passed; otherwise, the test is failed. is the allowable coverage interval;

[0015] Step S13: X (0) Perform an accumulation to obtain an accumulation sequence X (1) :X (1) ={x (1) (1),x (1 )(2),x (1) (3),…,x (1) (n)}, where

[0016] Let x (1) (1)Satisfy the first-order ordinary differential equation: Among them, a and u are constants, a is called the development gray number, and u is called the endogenous control gray number; the estimated values ​​a′ and u′ are calculated by the least squares method, and the calculation formula is: Where Y = {x (0) (2),x (0) (3),…,x (0) (n)},

[0017] Step S14: Substitute the estimated values ​​a′ and u′ calculated in step S13 into the first-order ordinary differential equation At this time, a′=a, u′=u; the time response equation is obtained by solving the first-order ordinary differential equation: Among them, when k ≥ n, is the predicted value, when k=1, 2, 3……n-1, is the fitted value of a cumulative sequence;

[0018] The data is subjected to cumulative subtraction processing to obtain the fitting value and predicted value of the original sequence; the formula for the cumulative subtraction processing is:

[0019] Step S15: Perform residual test on the time series. The residual test includes calculating the residual and calculating the relative error. The residual calculation formula is: The calculation formula of the relative error is: Perform residual correction on sample data in the time series that fail the residual test.

[0020] Preferably, the formula for obtaining the optimal power supply in step S03 is: g 优 =M+M 0 , where M is the predicted power demand data corresponding to the time period, M 0 For transmission loss.

[0021] Preferably, the calculation formula of the benefit index is: Among them, β Zr is the efficiency index of the power supply combination Zr, g 优 is the optimal power supply obtained in step S03, d is the unit power sales price, g is 1 Power supply for traditional energy, c 1 is the unit electricity cost of traditional energy, g ρ is the ρth energy among the alternative energy sources, c ρ is the unit electricity cost price of the ρth energy among the alternative energy sources; r=1, 2, 3……B, D is the total number of alternative energy types, ρ=2, 3……D.

[0022] Preferably, obtaining the optimal power supply combination based on the improved genetic algorithm in step S04 includes the following steps:

[0023] Step S21: Obtain the power supply combination number;

[0024] Step S22: Encode the power supply combination, obtain chromosomes, and construct an initial population;

[0025] Step S23: determining the fitness function;

[0026] Step S24: performing natural selection on chromosomes in the population;

[0027] Step S25: performing crossover recombination on chromosomes in the population;

[0028] Step S26: mutating the chromosomes in the population;

[0029] Step S27: Obtain a new population, preset the population generation to be L, the fitness threshold to be Q, L is an integer greater than 0, and Q is a real number greater than 0; loop steps S24 to S26 until the generation corresponding to the new population is L or the fitness corresponding to the chromosome in the new population is greater than or equal to the fitness threshold Q, the loop ends, and the power supply combination corresponding to the chromosome with the maximum fitness in the new population is obtained.

[0030] Preferably, the fitness function formula is: y =β y , where f y is the fitness corresponding to the yth chromosome, β y is the benefit index corresponding to the yth chromosome, y = 1, 2, 3...N;

[0031] The natural selection is carried out by combining the elite method and the rotation method, wherein the elite method produces F1 offspring chromosomes, and for a population with a capacity of N, the fitness corresponding to the N chromosomes are arranged from large to small, and the F1 chromosomes at the front each produce one offspring chromosome; the rotation method produces F2 offspring chromosomes, that is, N chromosomes produce F2 offspring chromosomes according to the corresponding rotation probability; F1+F2=N, so as to keep the offspring population capacity N unchanged and the population increases algebraically.

[0032] Preferably, the expression of the rotation probability is: Among them, P y is the rotation probability corresponding to the y-th chromosome.

[0033] Intelligent power supply system data integrated management system, including data acquisition module, data prediction module, power supply analysis module, power supply combination analysis module, regulation control module, data monitoring module and human-computer interaction module;

[0034] The data acquisition module is used to collect historical power demand data of each time period in the power supply system and transmit it to the data prediction module, recording each time period as t1, t2, t3...tn, and recording the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ;

[0035] The data prediction module is used to construct a power demand prediction model, predict the power demand data for the next T time periods and transmit it to the power supply analysis module;

[0036] The power supply analysis module is used to obtain the optimal power supply and transmit it to the power supply combination analysis module and the human-computer interaction module;

[0037] The power supply combination analysis module obtains the power supply combination of traditional energy and alternative energy based on the optimal power supply of the power supply analysis module, calculates the benefit index of all power supply combinations, and obtains the best power supply combination based on the genetic algorithm, and at the same time obtains the benefit index of the best power supply combination, which is then transmitted to the regulation control module and the human-computer interaction module;

[0038] The regulating control module is used to regulate the power supply amount and power supply mode of the power supply system and send monitoring instructions to the data monitoring module;

[0039] The data monitoring module is used to receive the monitoring instructions transmitted by the regulation control module, monitor the power supply load in real time, mark the abnormal load data, and transmit it to the human-computer interaction module;

[0040] The human-computer interaction module is used to perform human-computer interaction display on the data.

[0041] Technical effects and advantages of the present invention:

[0042] The present invention is provided with step S02, step S04 and step S05, which is conducive to predicting the power demand data of any future time period by constructing a power demand prediction model, and supplying power according to the predicted power demand data, so as to ensure that the power supply of the power supply system meets the power demand, and obtain the corresponding optimal power supply based on the predicted power demand data, and reasonably combine the power supply energy through the power supply data, reasonably allocate the power supply to different power supply modes according to the prediction results, and use the genetic algorithm to obtain the best power supply combination, so as to maximize the benefit index under the premise of meeting the power supply, so as to achieve the purpose of reducing dependence on traditional energy and reasonably utilizing new energy and renewable energy, and regulate and control the power supply of the power supply system to maximize the benefit; at the same time, the power supply load data in the power supply system is monitored in real time, and abnormal load data is warned to ensure the normal operation of the power supply system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the intelligent power supply system data comprehensive management method of the present invention.

[0044] Figure 2 It is a flow chart of the intelligent power supply system data integrated management system of the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following implementation modes are merely illustrative. The intelligent power supply system data integrated management method and system involved in the present invention are not limited to the various structures recorded in the following implementation modes. All other implementation modes obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0046] The present invention provides an intelligent power supply system data integrated management method, comprising the following steps:

[0047] Step S01: Collect historical power demand data of each time period in the power supply system, record each time period as t1, t2, t3...tn, the power demand data is power consumption, record the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ;

[0048] Step S02: constructing a power demand forecasting model to forecast power demand data for T time periods in the future;

[0049] Step S03: Based on the power demand data predicted in step S02, the optimal power supply is obtained, and the power supply is controlled to be optimal under the condition of meeting the power demand, so as to ensure that the power supply matches the demand and effectively improve the energy utilization efficiency;

[0050] Step S04: Based on the optimal power supply obtained in step S03, a power supply combination of traditional energy and alternative energy is obtained, where the alternative energy is new energy and renewable energy, including but not limited to solar energy and wind energy, the benefit indexes of all power supply combinations are calculated, and the optimal power supply combination is obtained based on a genetic algorithm, and the benefit index of the optimal power supply combination is obtained;

[0051] Step S05: transmitting the optimal power supply combination data in step S04 back to the power supply system, and adjusting and controlling the power supply amount and power supply mode of the power supply system;

[0052] Step S06: Monitor the power supply load of the power supply system in real time, mark the abnormal load data, transmit it to the background data center and issue an early warning; the abnormal load data includes the abnormal load change value, abnormal load change trend and abnormal load time point; the background data center is the background management personnel, and the management personnel can directly obtain any visual data from the background data center.

[0053] In this embodiment, it should be specifically explained that the step of constructing the power demand prediction model in step S02 includes:

[0054] Step S11: Take each time period in step S01 as a time series, which is the original series; then the time series contains n sample data, recorded as X (0) ={x (0) (1),x (0) (2),x (0) (3),…,x (0) (n)}, where x (0) (i) is the i-th data sample, i.e., the power demand data S ti , i=1, 2, 3...n;

[0055] Step S12: Check the sample data. If the check fails, pre-process the sample data. The formula for checking the sample data is: Where J(j) is the level ratio of the sample data, j = 2, 3, 4...n, if J(j) satisfies If the test is successful, then the test is passed; otherwise, the test is failed. The sample data is preprocessed, including but not limited to translation transformation and moving average, so that the level ratio of the transformed sample data falls within the acceptable coverage interval;

[0056] Step S13: X (0) Perform an accumulation to obtain an accumulation sequence X (1) :X (1) ={x (1) (1),x( 1 )(2),x( 1 )(3),…,x( 1 )(n)}, where

[0057] Let x (1) (1)Satisfy the first-order ordinary differential equation: Among them, a and u are constants, a is called the development gray number, and u is called the endogenous control gray number, which is a constant input to the system; the estimated values ​​a′ and u′ are calculated by the least squares method, and the calculation formula is: Where Y = {x (0) (2),x (0 )(3),…,x (0) (n)},

[0058] Step S14: Substitute the estimated values ​​a′ and u′ calculated in step S13 into the first-order ordinary differential equation At this time, a′=a, u′=u; the time response equation is obtained by solving the first-order ordinary differential equation: Among them, when k ≥ n, is the predicted value, when k=1, 2, 3……n-1, is the fitted value of a cumulative sequence;

[0059] The data is subjected to cumulative subtraction processing to obtain the fitting value and predicted value of the original sequence; the formula for the cumulative subtraction processing is:

[0060] Step S15: Perform residual test on the time series. The residual test includes calculating the residual and calculating the relative error. The residual calculation formula is: The calculation formula of the relative error is: The sample data in the time series that fail the residual test are corrected for residuals. The time series is stabilized through residual correction to eliminate residual values.

[0061] In this embodiment, it should be specifically explained that the formula for obtaining the optimal power supply in step S03 is: g 优 =M+M 0 , where M is the predicted power demand data corresponding to the time period, M 0 It is the transmission loss; the transmission loss has different calculation formulas according to the difference between a single line and a three-phase power line. The calculation formula is an existing formula, and this embodiment will not be described in detail.

[0062] In this embodiment, it should be specifically explained that the power supply combination of traditional energy and alternative energy in step S04 can be one or more of the combination forms of traditional energy plus solar energy, traditional energy plus wind energy, traditional energy plus solar energy plus wind energy, and traditional energy only, which are sequentially numbered Z1, Z2, Z3, Z4...ZB;

[0063] The calculation formula of the benefit index is: Among them, β Zr is the efficiency index of the power supply combination Zr, g 优 is the optimal power supply obtained in step S03, d is the unit power sales price, g is 1 Power supply for traditional energy, c 1 is the unit electricity cost of traditional energy, g ρ is the ρth energy among the alternative energy sources, c ρ is the unit electricity cost price of the ρth energy in the alternative energy; r = 1, 2, 3 ... B, D is the total number of alternative energy types, ρ = 2, 3 ... D; if one or more of the alternative energy sources does not exist in the power supply combination, the cost price corresponding to the energy is zero; illustratively, the benefit index formula of the four power supply combinations Z1, Z2, Z3 and Z4 is: Among them, β Z1 is the benefit index of power supply combination Z1, βZ2 is the benefit index of power supply combination Z2, β Z3 is the benefit index of power supply combination Z3, β Z4 It is the efficiency index of power supply combination Z4.

[0064] In this embodiment, it should be specifically explained that obtaining the optimal power supply combination based on the improved genetic algorithm in step S04 includes the following steps:

[0065] Step S21: Obtain the power supply combination number;

[0066] Step S22: Encode the power supply combination, obtain chromosomes, and construct an initial population;

[0067] The power supply combination is encoded as Y, where Y is the chromosome. Y is the same as the combination number. N chromosomes are randomly generated to form the initial population A. A={Y 1 ,Y 2 ,Y 3 ,…,Y N};

[0068] Step S23: determining the fitness function;

[0069] The fitness function formula is: y =β y , where f y is the fitness corresponding to the yth chromosome, β y is the benefit index corresponding to the yth chromosome, y = 1, 2, 3...N;

[0070] Step S24: performing natural selection on chromosomes in the population;

[0071] The natural selection is carried out by combining the elite method and the rotation method, wherein the elite method produces F1 offspring chromosomes, for a population with a capacity of N, the fitness corresponding to the N chromosomes is arranged from large to small, and the F1 chromosomes at the front each produce a offspring chromosome; the rotation method produces F2 offspring chromosomes, that is, N chromosomes produce F2 offspring chromosomes according to the corresponding rotation probability; F1+F2=N, so as to keep the offspring population capacity N unchanged and the population generation increases;

[0072] The expression of rotation probability is: Among them, P y is the rotation probability corresponding to the yth chromosome;

[0073] Step S25: performing crossover recombination on chromosomes in the population;

[0074] Randomly select U chromosomes from the population for crossover recombination to obtain U new chromosomes; the crossover recombination adopts the PMX method, which is a prior art method and will not be described in detail in this embodiment; after the chromosome crossover recombination, the fitness of the U new chromosomes is calculated, and the fitness of the U new chromosomes and the fitness of the U chromosomes are sorted from large to small to generate a sorting table, and the U chromosomes in the sorting table are replaced with the U chromosomes for crossover recombination in the population in positive order; in this embodiment, U=0.7N is selected, and if the calculated U is not an integer, U is rounded up to ensure that the calculated U is an integer;

[0075] Step S26: mutating the chromosomes in the population;

[0076] The mutation probability is preset to G, and the N chromosomes in the population are mutated according to the mutation probability. The mutation method is to randomly select the positions of two genes in the chromosome and exchange the two gene values. In this embodiment, G=0.02 is selected. The mutation probability is preset by those skilled in the art according to the algorithm efficiency and algorithm accuracy.

[0077] Step S27: Obtain a new population, the preset population generation number is L, the fitness threshold is Q, L is an integer greater than 0, and Q is a real number greater than 0; loop steps S24 to S26 until the generation number corresponding to the new population is L or the fitness corresponding to the chromosome in the new population is greater than or equal to the fitness threshold Q, the loop ends, and the power supply combination corresponding to the chromosome with the maximum fitness in the new population is obtained; illustratively, if the preset population generation number is 1, the chromosomes in the initial population are subjected to natural selection, crossover recombination and mutation to obtain a new population, and the generation number corresponding to the new population is 1 at this time, so the loop ends;

[0078] The fitness threshold Q is preset by those skilled in the art according to the accuracy of the algorithm. The population algebra L is obtained by those skilled in the art under multiple different power demand data by using a genetic algorithm multiple times to obtain the corresponding warranty date. In each process of using the genetic algorithm, when the fitness corresponding to the chromosome in the new population is greater than or equal to the fitness threshold Q, the loop ends, the algebra corresponding to the new population is obtained, and the largest algebra among the multiple algebras is used as the population algebra L.

[0079] In this embodiment, it should be specifically explained that the intelligent power supply system data integrated management system for implementing the intelligent power supply system data integrated management method includes: a data acquisition module, a data prediction module, a power supply analysis module, a power supply combination analysis module, a regulation control module, a data monitoring module and a human-computer interaction module;

[0080] The data acquisition module is used to collect historical power demand data of each time period in the power supply system and transmit it to the data prediction module, recording each time period as t1, t2, t3...tn, and recording the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ;

[0081] The data prediction module is used to construct a power demand prediction model, predict the power demand data for the next T time periods and transmit it to the power supply analysis module;

[0082] The power supply analysis module is used to obtain the optimal power supply and transmit it to the power supply combination analysis module and the human-computer interaction module;

[0083] The power supply combination analysis module obtains the power supply combination of traditional energy and alternative energy based on the optimal power supply of the power supply analysis module, calculates the benefit index of all power supply combinations, and obtains the best power supply combination based on the genetic algorithm, and at the same time obtains the benefit index of the best power supply combination, which is then transmitted to the regulation control module and the human-computer interaction module;

[0084] The regulating control module is used to regulate the power supply amount and power supply mode of the power supply system and send monitoring instructions to the data monitoring module;

[0085] The data monitoring module is used to receive the monitoring instructions transmitted by the regulation control module, monitor the power supply load in real time, mark the abnormal load data, and transmit it to the human-computer interaction module;

[0086] The human-computer interaction module is used to perform human-computer interaction display on the data.

[0087] In the present embodiment, it should be specifically explained that the difference between the present embodiment and the prior art mainly lies in that the present embodiment has step S02, step S04 and step S05, which is conducive to predicting the power demand data of any time period in the future by constructing a power demand prediction model, and supplying power according to the predicted power demand data, so as to ensure that the power supply of the power supply system meets the power demand, and obtain the corresponding optimal power supply based on the predicted power demand data, and reasonably combine the power supply energy through the power supply data, reasonably allocate the power supply for different power supply modes according to the prediction results, and use the genetic algorithm to obtain the best power supply combination, so as to maximize the benefit index under the premise of meeting the power supply, so as to achieve the purpose of reducing dependence on traditional energy and rationally utilizing new energy and renewable energy, and to adjust and control the power supply of the power supply system to maximize the benefit; at the same time, the power supply load data in the power supply system is monitored in real time, and abnormal load data is warned to ensure the normal operation of the power supply system.

[0088] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent power supply system data comprehensive management method, characterized by: The following steps are involved: Step S01: Collect historical power demand data of each time period in the power supply system, record each time period as t1, t2, t3...tn, and record the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ; Step S02: constructing a power demand forecasting model to forecast power demand data for the next T time periods; Step S03: based on the power demand data predicted in step S02, obtaining the optimal power supply; Step S04: Based on the optimal power supply obtained in step S03, obtain the power supply combination of traditional energy and alternative energy, calculate the benefit index of all power supply combinations, and obtain the optimal power supply combination based on the genetic algorithm, and at the same time obtain the benefit index of the optimal power supply combination; Step S05: transmitting the optimal power supply combination data in step S04 back to the power supply system, and adjusting and controlling the power supply amount and power supply mode of the power supply system; Step S06: Monitor the power supply load of the power supply system in real time, mark abnormal load data, transmit it to the background data center and issue an early warning.

2. The intelligent power supply system data integrated management method according to claim 1 is characterized in that: The step of constructing the power demand prediction model in step S02 includes: Step S11: Take each time period in step S01 as a time series, which is the original series; then the time series contains n sample data, recorded as X (0) ={x (0) (1),x (0) (2),x (0) (3),…,x (0) (n)}, where x (0) (i) is the i-th data sample, i.e., the power demand data S ti , i=1, 2, 3...n; Step S12: Check the sample data. If the check fails, pre-process the sample data. The formula for checking the sample data is: Where J(j) is the level ratio of the sample data, j = 2, 3, 4...n, if J(j) satisfies If the test is successful, then the test is passed; otherwise, the test is failed. is the allowable coverage interval; Step S13: X (0) Perform an accumulation to obtain an accumulation sequence X (1) :X (1) ={x (1) (1),x (1) (2),x (1) (3),…,x (1) (n)}, where Let x (1) (1)Satisfy the first-order ordinary differential equation: Among them, a and u are constants, a is called the development gray number, and u is called the endogenous control gray number; the estimated values ​​a′ and u′ are calculated by the least squares method, and the calculation formula is: Where Y = {x (0) (2),x (0) (3),…,x (0) (n)}, Step S14: Substitute the estimated values ​​a′ and u′ calculated in step S13 into the first-order ordinary differential equation At this time, a′=a, u′=u; the time response equation is obtained by solving the first-order ordinary differential equation: Among them, when k ≥ n, is the predicted value, when k=1, 2, 3……n-1, is the fitted value of a cumulative sequence; The data is subjected to cumulative subtraction processing to obtain the fitting value and predicted value of the original sequence; the formula for the cumulative subtraction processing is: Step S15: Perform residual test on the time series. The residual test includes calculating the residual and calculating the relative error. The residual calculation formula is: The calculation formula of the relative error is: Perform residual correction on sample data in the time series that fail the residual test.

3. The intelligent power supply system data integrated management method according to claim 1 is characterized in that: The formula for obtaining the optimal power supply in step S03 is: 优 =M+M0, where M is the predicted power demand data corresponding to the time period, and M0 is the transmission loss.

4. The intelligent power supply system data integrated management method according to claim 1 is characterized by: The calculation formula of the benefit index is: Among them, β Zr is the efficiency index of the power supply combination Zr, g 优 is the optimal power supply obtained in step S03, d is the unit power sales price, g1 is the traditional energy power supply, c1 is the traditional energy unit power cost price, g ρ is the ρth energy among the alternative energy sources, c ρ is the unit electricity cost price of the ρth energy among the alternative energy sources; r=1, 2, 3……B, D is the total number of alternative energy types, ρ=2, 3……D.

5. The intelligent power supply system data integrated management method according to claim 1 is characterized by: The step S04 of obtaining the best power supply combination based on the improved genetic algorithm includes the following steps: Step S21: Obtain the power supply combination number; Step S22: Encode the power supply combination, obtain chromosomes, and construct an initial population; Step S23: determining the fitness function; Step S24: performing natural selection on chromosomes in the population; Step S25: performing crossover recombination on chromosomes in the population; Step S26: mutating the chromosomes in the population; Step S27: Obtain a new population, preset the population generation to be L, the fitness threshold to be Q, L is an integer greater than 0, and Q is a real number greater than 0; loop steps S24 to S26 until the generation corresponding to the new population is L or the fitness corresponding to the chromosome in the new population is greater than or equal to the fitness threshold Q, the loop ends, and the power supply combination corresponding to the chromosome with the maximum fitness in the new population is obtained.

6. The intelligent power supply system data integrated management method according to claim 5 is characterized by: The fitness function formula is: y =β y , where f y is the fitness corresponding to the yth chromosome, β y is the benefit index corresponding to the yth chromosome, y = 1, 2, 3...N; The natural selection is carried out by combining the elite method and the rotation method, wherein the elite method produces F1 offspring chromosomes, and for a population with a capacity of N, the fitness corresponding to the N chromosomes are arranged from large to small, and the F1 chromosomes at the front each produce one offspring chromosome; the rotation method produces F2 offspring chromosomes, that is, N chromosomes produce F2 offspring chromosomes according to the corresponding rotation probability; F1+F2=N, so as to keep the offspring population capacity N unchanged and the population increases algebraically.

7. The intelligent power supply system data integrated management method according to claim 6 is characterized by: The expression of the rotation probability is: Among them, P y is the rotation probability corresponding to the y-th chromosome.

8. An intelligent power supply system data integrated management system, used to implement the intelligent power supply system data integrated management method described in any one of claims 1 to 7, comprising a data acquisition module, a data prediction module, a power supply analysis module, a power supply combination analysis module, a regulation control module, a data monitoring module and a human-computer interaction module; The data acquisition module is used to collect historical power demand data of each time period in the power supply system and transmit it to the data prediction module, recording each time period as t1, t2, t3...tn, and recording the power demand data of each time period as S t1 , S t2 , S t3 ……S tn ; The data prediction module is used to construct a power demand prediction model, predict the power demand data for the next T time periods and transmit it to the power supply analysis module; The power supply analysis module is used to obtain the optimal power supply and transmit it to the power supply combination analysis module and the human-computer interaction module; The power supply combination analysis module obtains the power supply combination of traditional energy and alternative energy based on the optimal power supply of the power supply analysis module, calculates the benefit index of all power supply combinations, and obtains the best power supply combination based on the genetic algorithm, and at the same time obtains the benefit index of the best power supply combination, which is then transmitted to the regulation control module and the human-computer interaction module; The regulating control module is used to regulate the power supply amount and power supply mode of the power supply system and send monitoring instructions to the data monitoring module; The data monitoring module is used to receive the monitoring instructions transmitted by the regulation control module, monitor the power supply load in real time, mark the abnormal load data, and transmit it to the human-computer interaction module; The human-computer interaction module is used to perform human-computer interaction display on the data.

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