System and method for analyzing comprehensive computing power data of power utilization management platform based on cloud computing

Abstract

The invention relates to the technical field of data analysis, and discloses a power consumption management platform comprehensive computing power data analysis system and method based on cloud computing. According to the method, Internet of Things equipment is installed in an area needing computing power analysis, an aggregation local area network is established for communication, meanwhile, historical electricity consumption data of a user are collected through the installed networking equipment, and the collected historical electricity consumption data of the user are processed; uploading the processed historical power consumption data of the user through an established aggregation local area network, and analyzing and predicting the uploaded historical power consumption data of the user through a power consumption management platform; meanwhile, average computing power data of the power consumption management platform are calculated in a mode of recording processing time, and finally comprehensive computing power data of the power consumption management platform are analyzed based on the calculated average computing power data of the power consumption management platform, user power consumption data collected in real time and a user power consumption data prediction calculation request. And the accuracy of comprehensive computing power data analysis of the power utilization management platform is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a comprehensive computing power data analysis system and method for an electricity management platform based on cloud computing. Background Art

[0002] With the advancement of smart grid technology, the power information collection system has accumulated a huge amount of power consumption information, and users' demand for transfer, calculation and storage is growing. However, there are still many problems in the deployment and scheduling of power management platform computing power. Many lightweight power management platforms have limited computing power, but they cannot accurately judge the power of the power management platform, which leads to the inability to meet user needs; and some large power management platforms waste computing resources due to the way computing power is allocated.

[0003] The existing Chinese patent application CN118170752A, this method collects relevant power grid data information, and uploads the collected relevant power grid data information to the cloud space for storage and backup; at the same time, the collected relevant power grid data information is processed and output by setting monitoring thresholds and database management technology, and finally the output analysis results are optimized; but due to ignoring the scale of relevant power grid data information and the computing power of cloud computing, it cannot accurately meet user needs and has certain limitations. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a comprehensive computing power data analysis system and method for an electricity management platform based on cloud computing, which has the advantages of accurate, real-time and efficient analysis, and solves the problem of not being able to accurately judge the computing power of the electricity management platform, resulting in the inability to meet user needs.

[0006] (II) Technical solution

[0007] In order to solve the above-mentioned technical problem that the computing power of the power management platform cannot be accurately determined, resulting in the inability to meet user needs, the present invention provides the following technical solutions:

[0008] This embodiment discloses a method for analyzing comprehensive computing power data of a power consumption management platform based on cloud computing, which specifically includes the following steps:

[0009] S1. Install IoT devices in the area where computing power analysis is required, and form an aggregated local area network based on the location information and wireless communication mode of the installed IoT devices;

[0010] S2. Collecting historical electricity consumption data of users through installed networking devices, constructing a sample set, and preprocessing the historical electricity consumption data of users in the sample set to obtain preprocessed historical electricity consumption data of users, and uploading the preprocessed historical electricity consumption data of users to the electricity consumption management platform in real time through cloud computing based on the established aggregation local area network;

[0011] S3. The power management platform performs analysis and calculation of the user's historical power consumption data, prediction and calculation of the user's power consumption data, and calculation of the average computing power data of the power management platform based on the received pre-processed user's historical power consumption data;

[0012] S4. Collect user electricity consumption data and user electricity consumption data prediction and calculation requests in real time, and conduct comprehensive computing power data analysis on the electricity consumption management platform.

[0013] The present invention installs Internet of Things devices in areas where computing power analysis is required and forms an aggregated local area network for communication, collects historical electricity usage data of users through the installed networking devices, and processes the collected historical electricity usage data of users; then uploads the processed historical electricity usage data of users through the formed aggregated local area network, and analyzes and predicts the uploaded historical electricity usage data of users through an electricity management platform; at the same time, calculates the average computing power data of the electricity management platform by recording the processing time, and finally analyzes the comprehensive computing power data of the electricity management platform based on the calculated average computing power data of the electricity management platform, the real-time collection of user electricity usage data, and the user electricity usage data prediction calculation request, thereby improving the accuracy of the comprehensive computing power data analysis of the electricity management platform.

[0014] Preferably, the step of installing IoT devices in the area where computing power analysis is required and forming an aggregated local area network based on the location information and wireless communication mode of the installed IoT devices comprises the following steps:

[0015] S11. Divide the power lines in the area where computing power analysis is required, and set a power consumption monitoring point at the intersection of each divided power line;

[0016] S12. Set each electricity monitoring point to install an IoT device at the beginning of each household line;

[0017] S13. Collect the location information of the power consumption monitoring points set up on each power line, the location information of the installed Internet of Things devices and the wireless communication mode, establish an aggregated local area network and save it.

[0018] The present invention divides the power lines in the area where computing power analysis is required and installs Internet of Things devices. At the same time, by constructing an aggregated local area network, a connection is established between the Internet of Things devices and the power management platform, thereby determining the management scope of the power management platform.

[0019] Preferably, the steps of collecting historical electricity usage data of users through installed networking devices, constructing a sample set, preprocessing the historical electricity usage data of users in the sample set, obtaining the preprocessed historical electricity usage data of users, and uploading the preprocessed historical electricity usage data of users to the electricity management platform in real time through cloud computing based on the established aggregated local area network include the following steps:

[0020] S21. Collecting historical electricity consumption data of users through installed networking devices and constructing a sample set;

[0021] S22, preprocessing the historical electricity consumption data of users in the sample set to obtain the preprocessed historical electricity consumption data of users;

[0022] S23. Set the network address of the electricity management platform, and at the same time, use the electricity management platform to number the network address of the aggregated local area network. After the network address numbering is completed, establish a wireless data connection between the two network addresses and perform real-time data exchange.

[0023] Preferably, the collecting of historical electricity consumption data of users through installed networked devices and constructing a sample set comprises the following steps:

[0024] Set the data collected at the monitoring point b a for:

[0025]

[0026] Among them, n a Expressed as the number of types of electrical equipment on the ath power line, n e It is expressed as the number of class e electrical equipment on the ath power line, c i Represents the collected data of the i-th type of electrical equipment, d j Indicates the data collected from the jth electrical equipment in the i-type electrical equipment, b a It represents the historical electricity consumption data of users collected on the ath power line;

[0027] Summarize the historical electricity consumption data of users collected on each power line and construct a sample set B = {b1, b2, ..., b a};

[0028] Among them, b1 represents the user's historical electricity consumption data collected on the first power line in the sample set.

[0029] Preferably, the preprocessing of the user historical electricity usage data in the sample set to obtain the preprocessed user historical electricity usage data comprises the following steps:

[0030] The historical electricity consumption data of users in the sample set is processed by the data standardization method as follows:

[0031]

[0032] Among them, b min represents the minimum value of the user's historical electricity consumption data in the sample set, b max represents the maximum value of the user's historical electricity consumption data in the sample set, b a represents the historical electricity consumption data of users collected on the ath power line in the sample set, b N Indicates the standardized historical electricity consumption data of users;

[0033] The standardized user historical electricity usage data is set as the pre-processed user historical electricity usage data.

[0034] The present invention collects user historical electricity usage data and constructs a sample set through installed networking equipment, and completes the processing of user historical electricity usage data by standardizing the user historical electricity usage data in the sample set, thereby ensuring the reliability of the user historical electricity usage data.

[0035] Preferably, the power management platform performs user historical power consumption data analysis and calculation, user power consumption data prediction and calculation, and power management platform average computing power data calculation based on the received pre-processed user historical power consumption data, including the following steps:

[0036] S31, after receiving the pre-processed historical electricity consumption data of the user, the electricity consumption management platform performs analysis and calculation of the historical electricity consumption data of the user based on the received pre-processed historical electricity consumption data of the user;

[0037] S32, performing user power consumption data prediction calculation based on the received pre-processed user historical power consumption data;

[0038] S33, recording the data processing time of the analysis and calculation of the historical electricity consumption data of each group of users and the prediction and calculation of the user electricity consumption data, and calculating the average computing power data based on the recorded time;

[0039] Average computing power data = aggregated data processing time / number of processed user historical electricity consumption data.

[0040] Preferably, the analyzing and calculating the user's historical electricity usage data based on the received pre-processed user's historical electricity usage data comprises the following steps:

[0041] Since the received pre-processed user historical electricity consumption data is discrete, the discrete pre-processed user historical electricity consumption data is analyzed by the calculus principle;

[0042] The effective value of the user's historical electricity consumption data after preprocessing I rms The calculation formula is as follows:

[0043]

[0044] Where, T represents the time period; I 2 (t) represents the user's historical electricity consumption data collected at time t;

[0045] The calculated pre-processed user historical electricity consumption data effective value I rms Multiplying by the standard mains voltage 220V, we can get the load power of each group of users’ historical power consumption data;

[0046] Set a load power threshold of the user's historical power consumption data. When the calculated load power of the user's historical power consumption data exceeds the set load power threshold, it indicates dangerous power consumption;

[0047] When the calculated load power of the user's historical power consumption data does not exceed the set load power threshold, it indicates safe power consumption.

[0048] Preferably, the calculation of user power consumption data prediction based on the received pre-processed user historical power consumption data comprises the following steps:

[0049] S321, initializing hybrid genetic algorithm parameters, performing chromosome encoding on user power consumption data, and constructing a population set;

[0050] Set each group of chromosome encoding to represent a group of user electricity consumption data, set the population size, and the maximum number of iterations d max ;

[0051] S322, setting an objective function of user electricity consumption data based on the constructed population set size;

[0052] Set the objective function:

[0053] Among them, Fit represents the objective function of the user's electricity consumption data, and Δω represents the change in the user's electricity consumption data;

[0054] S323, genetic operator selection operation;

[0055] Assume the probability of chromosome code being selected is p(z k );

[0056]

[0057] Among them, K is the population size, Fit(z k ) is the fitness encoded by the i-th chromosome;

[0058] S324, chromosome crossover operation;

[0059] A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population;

[0060] Remove duplicate chromosome codes in order and ensure that the length of chromosome codes is consistent to form daughter chromosomes;

[0061] S325, chromosome mutation operation;

[0062] The mutation process is set to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; the parent chromosome code is mutated based on the set mutation probability p, thereby generating a new generation of offspring chromosomes;

[0063] The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation.

[0064] S326, output the optimal solution to the problem;

[0065] Set the iteration threshold Y, set the iteration d max When the chromosome code output after the last iteration reaches the iteration threshold Y or when the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated and the chromosome code output for the last time is used as the predicted user electricity consumption data.

[0066] The present invention calculates the average computing power data of the power management platform by recording the power management platform to analyze and calculate the user's historical power consumption data and to perform predictive calculations on the user's power consumption data, thereby improving the efficiency of the power management platform's computing power calculations.

[0067] Preferably, the real-time collection of user electricity consumption data and user electricity consumption data prediction and calculation requests, and the analysis of the comprehensive computing power data of the electricity consumption management platform include the following steps:

[0068] Calculate the execution completion time of the power management platform based on the average computing power data, the total amount of user power consumption data collected in real time, and the number of user power consumption data prediction calculation requests;

[0069] The execution completion time of the power management platform = (the total amount of user power data collected in real time + the number of user power data prediction calculation requests) × average computing power data;

[0070] The power management platform is set to process the user power consumption data collected in real time in a sequential manner;

[0071] Set the cut-off time for user electricity consumption data;

[0072] After the user power consumption data collected in real time is uploaded to the power consumption management platform, if the execution completion time of the power consumption management platform exceeds the deadline of the user power consumption data, the currently uploaded user power consumption data will be rejected. Otherwise, the currently uploaded user power consumption data will be received and transmitted to the waiting queue;

[0073] Record the number of times the power management platform receives user power data and the number of times it rejects user power data, calculate the power management platform's acceptance probability, and set a power management platform acceptance probability threshold;

[0074] When the reception probability of the power management platform is greater than or equal to the set threshold, it means that the comprehensive computing power of the power management platform is qualified. When the reception probability of the power management platform is less than the set threshold, it means that the comprehensive computing power of the power management platform is unqualified.

[0075] The present invention calculates the execution completion time of the power management platform according to the average computing power data, the real-time collection of user power consumption data, and the number of user power consumption data prediction calculation requests, and records and analyzes the comprehensive computing power of the power management platform by setting a cutoff time for user power consumption data, thereby improving the real-time performance of the comprehensive computing power analysis of the power management platform.

[0076] This embodiment also discloses a cloud computing-based power management platform comprehensive computing power data analysis system, including: an Internet of Things device, a data preprocessing module, a power management platform, and a comprehensive computing power analysis module;

[0077] The IoT device is used to collect user electricity consumption data in real time;

[0078] The data preprocessing module is used to process the user electricity consumption data collected in real time and transmit the processed user electricity consumption data to the electricity consumption management platform;

[0079] The power consumption management platform is used to analyze and calculate the received processed user power consumption data;

[0080] The comprehensive computing power analysis module is used to record and analyze the comprehensive computing power of the power management platform.

[0081] (III) Beneficial effects

[0082] Compared with the prior art, the present invention provides a comprehensive computing power data analysis system and method for a power management platform based on cloud computing, which has the following beneficial effects:

[0083] 1. The invention installs IoT devices in areas where computing power analysis is required and forms an aggregated local area network for communication. At the same time, the user's historical electricity usage data is collected through the installed networked devices and the collected user's historical electricity usage data is processed; the processed user's historical electricity usage data is then uploaded through the formed aggregated local area network, and the uploaded user's historical electricity usage data is analyzed and predicted through the electricity management platform; at the same time, the average computing power data of the electricity management platform is calculated by recording the processing time, and finally the comprehensive computing power data of the electricity management platform is analyzed based on the calculated average computing power data of the electricity management platform, the real-time collection of user electricity usage data, and the user's electricity usage data prediction calculation request, thereby improving the accuracy of the comprehensive computing power data analysis of the electricity management platform.

[0084] 2. This invention divides the power lines in the area that requires computing power analysis and installs IoT devices. At the same time, by building an aggregated local area network, a connection is established between the IoT devices and the electricity management platform, thereby determining the management scope of the electricity management platform.

[0085] 3. The invention collects the user's historical electricity usage data and constructs a sample set through installed networked devices, and at the same time completes the processing of the user's historical electricity usage data by standardizing the user's historical electricity usage data in the sample set, thereby ensuring the reliability of the user's historical electricity usage data.

[0086] 4. The invention calculates the average computing power data of the power management platform by recording the power management platform's analysis and calculation of users' historical power consumption data and performing predictive calculations on users' power consumption data, thereby improving the efficiency of the power management platform's computing power calculations.

[0087] 5. The invention calculates the execution completion time of the power management platform by collecting user power consumption data in real time and predicting the number of calculation requests based on user power consumption data, and records and analyzes the comprehensive computing power of the power management platform by setting a deadline for user power consumption data, thereby improving the real-time performance of the comprehensive computing power analysis of the power management platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a schematic diagram of the structure of the comprehensive computing power data analysis process of the electricity management platform of the present invention. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0090] Example 1

[0091] See also Figure 1 This embodiment discloses a method for analyzing comprehensive computing power data of a power management platform based on cloud computing, which specifically includes the following steps:

[0092] S1. Install IoT devices in the area where computing power analysis is required, and form an aggregated local area network based on the location information and wireless communication mode of the installed IoT devices;

[0093] Installing IoT devices in the area where computing power analysis is required and forming an aggregated local area network based on the location information and wireless communication mode of the installed IoT devices includes the following steps:

[0094] S11. Divide the power lines in the area where computing power analysis is required, and set a power consumption monitoring point at the intersection of each divided power line;

[0095] S12. Set each electricity monitoring point to install an IoT device at the beginning of each household line;

[0096] S13, collecting the location information of the power consumption monitoring points set up on each power line, the location information of the installed Internet of Things devices and the wireless communication mode, establishing and saving the aggregated local area network;

[0097] S2. Collecting historical electricity consumption data of users through installed networking devices, constructing a sample set, and preprocessing the historical electricity consumption data of users in the sample set to obtain preprocessed historical electricity consumption data of users, and uploading the preprocessed historical electricity consumption data of users to the electricity consumption management platform in real time through cloud computing based on the established aggregation local area network;

[0098] Collecting historical electricity consumption data of users through installed networking devices, building a sample set, and preprocessing the historical electricity consumption data of users in the sample set to obtain the preprocessed historical electricity consumption data of users, and uploading the preprocessed historical electricity consumption data of users to the electricity consumption management platform in real time through cloud computing based on the established aggregation local area network, including the following steps:

[0099] S21. Collecting historical electricity consumption data of users through installed networking devices and constructing a sample set;

[0100] Set the data collected at the monitoring point b a for:

[0101]

[0102] Among them, n a Expressed as the number of types of electrical equipment on the ath power line, n e It is expressed as the number of class e electrical equipment on the ath power line, ci Represents the collected data of the i-th type of electrical equipment, d j Indicates the data collected from the jth electrical equipment in the i-type electrical equipment, b a It represents the historical electricity consumption data of users collected on the ath power line;

[0103] Furthermore, the historical electricity consumption data of users collected on each power line is summarized to construct a sample set B = {b1, b2, ..., b a};

[0104] Among them, b1 represents the historical electricity consumption data of users collected on the first power line in the sample set;

[0105] S22, preprocessing the historical electricity consumption data of users in the sample set to obtain the preprocessed historical electricity consumption data of users;

[0106] The historical electricity consumption data of users in the sample set is processed by the data standardization method as follows:

[0107]

[0108] Among them, b min represents the minimum value of the user's historical electricity consumption data in the sample set, b max represents the maximum value of the user's historical electricity consumption data in the sample set, b a represents the historical electricity consumption data of users collected on the ath power line in the sample set, b N Indicates the standardized historical electricity consumption data of users;

[0109] Further, the standardized user historical electricity usage data is set as the pre-processed user historical electricity usage data;

[0110] S23, setting the network address of the power management platform, and at the same time numbering the network address of the aggregated local area network formed through the power management platform. After the network address numbering is completed, a wireless data connection is established between the two network addresses and real-time data interaction is performed;

[0111] S3. The power management platform performs analysis and calculation of the user's historical power consumption data, prediction and calculation of the user's power consumption data, and calculation of the average computing power data of the power management platform based on the received pre-processed user's historical power consumption data;

[0112] The power management platform performs the following steps based on the received user historical power consumption data:

[0113] S31, after receiving the pre-processed historical electricity consumption data of the user, the electricity consumption management platform performs analysis and calculation of the historical electricity consumption data of the user based on the received pre-processed historical electricity consumption data of the user;

[0114] Since the received pre-processed user historical electricity consumption data is discrete, the discrete pre-processed user historical electricity consumption data is analyzed by the calculus principle;

[0115] The effective value of the user's historical electricity consumption data after preprocessing I rms The calculation formula is as follows:

[0116]

[0117] Where, T represents the time period; I 2 (t) represents the user's historical electricity consumption data collected at time t;

[0118] Furthermore, the calculated pre-processed historical power consumption data of the user is effectively rms Multiplying by the standard mains voltage 220V, we can get the load power of each group of users’ historical power consumption data;

[0119] Furthermore, a load power threshold of the user's historical power consumption data is set, and when the calculated load power of the user's historical power consumption data exceeds the set load power threshold, it indicates dangerous power consumption;

[0120] When the calculated load power of the user's historical power consumption data does not exceed the set load power threshold, it indicates safe power consumption;

[0121] S32, performing user power consumption data prediction calculation based on the received pre-processed user historical power consumption data;

[0122] S321, initializing hybrid genetic algorithm parameters, performing chromosome encoding on user power consumption data, and constructing a population set;

[0123] Set each group of chromosome encoding to represent a group of user electricity consumption data, set the population size, and the maximum number of iterations d max ;

[0124] S322, setting an objective function of user electricity consumption data based on the constructed population set size;

[0125] Set the objective function:

[0126] Among them, Fit represents the objective function of the user's electricity consumption data, and Δω represents the change in the user's electricity consumption data;

[0127] S323, genetic operator selection operation;

[0128] Assume the probability of chromosome code being selected is p(z k );

[0129]

[0130] Among them, K is the population size, Fit(z k ) is the fitness encoded by the i-th chromosome;

[0131] S324, chromosome crossover operation;

[0132] A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population;

[0133] Remove duplicate chromosome codes in order and ensure that the length of chromosome codes is consistent to form daughter chromosomes;

[0134] S325, chromosome mutation operation;

[0135] The mutation process is set to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; the parent chromosome code is mutated based on the set mutation probability p, thereby generating a new generation of offspring chromosomes;

[0136] The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation.

[0137] S326, output the optimal solution to the problem;

[0138] Set the iteration threshold Y, set the iteration d max When the chromosome code output after the last iteration reaches the iteration threshold Y or when the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated and the chromosome code output for the last time is used as the predicted user electricity consumption data;

[0139] S33, recording the data processing time of the analysis and calculation of the historical electricity consumption data of each group of users and the prediction and calculation of the user electricity consumption data, and calculating the average computing power data based on the recorded time;

[0140] Average computing power data = aggregated data processing time / number of processed user historical electricity consumption data;

[0141] S4: Collect user electricity consumption data and user electricity consumption data prediction and calculation requests in real time, and perform comprehensive computing power data analysis on the electricity consumption management platform;

[0142] Real-time collection of user electricity consumption data and user electricity consumption data prediction and calculation requests, and comprehensive computing power data analysis on the electricity consumption management platform include the following steps:

[0143] Calculate the execution completion time of the power management platform based on the average computing power data, the total amount of user power consumption data collected in real time, and the number of user power consumption data prediction calculation requests;

[0144] The execution completion time of the power management platform = (the total amount of user power data collected in real time + the number of user power data prediction calculation requests) × average computing power data;

[0145] The power management platform is set to process the user power consumption data collected in real time in a sequential manner;

[0146] Furthermore, a cut-off time for user electricity usage data is set;

[0147] After the user power consumption data collected in real time is uploaded to the power consumption management platform, if the execution completion time of the power consumption management platform exceeds the deadline of the user power consumption data, the currently uploaded user power consumption data will be rejected. Otherwise, the currently uploaded user power consumption data will be received and transmitted to the waiting queue;

[0148] Record the number of times the power management platform receives user power data and the number of times it rejects user power data, calculate the power management platform's acceptance probability, and set a power management platform acceptance probability threshold;

[0149] When the reception probability of the power management platform is greater than or equal to the set threshold, it means that the comprehensive computing power of the power management platform is qualified; when the reception probability of the power management platform is less than the set threshold, it means that the comprehensive computing power of the power management platform is unqualified;

[0150] Example 2

[0151] This embodiment also discloses a cloud computing-based power management platform comprehensive computing power data analysis system, including: an Internet of Things device, a data preprocessing module, a power management platform, and a comprehensive computing power analysis module;

[0152] The IoT device is used to collect user electricity consumption data in real time;

[0153] The data preprocessing module is used to process the user electricity consumption data collected in real time and transmit the processed user electricity consumption data to the electricity consumption management platform;

[0154] The power consumption management platform is used to analyze and calculate the received processed user power consumption data;

[0155] The comprehensive computing power analysis module is used to record and analyze the comprehensive computing power of the power management platform.

[0156] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing comprehensive computing power data of a power management platform based on cloud computing, characterized in that: The following steps are involved: S1. Install IoT devices in the area where computing power analysis is required, and form an aggregated local area network based on the location information and wireless communication mode of the installed IoT devices; S2. Collecting historical electricity consumption data of users through installed networking devices, constructing a sample set, and preprocessing the historical electricity consumption data of users in the sample set to obtain preprocessed historical electricity consumption data of users, and uploading the preprocessed historical electricity consumption data of users to the electricity consumption management platform in real time through cloud computing based on the established aggregation local area network; S3. The power management platform performs analysis and calculation of the user's historical power consumption data, prediction and calculation of the user's power consumption data, and calculation of the average computing power data of the power management platform based on the received pre-processed user's historical power consumption data; S4. Collect user electricity consumption data and user electricity consumption data prediction and calculation requests in real time, and conduct comprehensive computing power data analysis on the electricity consumption management platform.

2. According to claim 1, a method for analyzing comprehensive computing power data of a power management platform based on cloud computing is characterized in that: The S1 comprises the following steps: S11. Divide the power lines in the area where computing power analysis is required, and set a power consumption monitoring point at the intersection of each divided power line; S12. Set each electricity monitoring point to install an IoT device at the beginning of each household line; S13. Collect the location information of the power consumption monitoring points set up on each power line, the location information of the installed Internet of Things devices and the wireless communication mode, establish an aggregated local area network and save it.

3. According to the method for analyzing comprehensive computing power data of a power management platform based on cloud computing in claim 1, it is characterized in that: The S2 comprises the following steps: S21. Collecting historical electricity consumption data of users through installed networking devices and constructing a sample set; S22, preprocessing the historical electricity consumption data of users in the sample set to obtain the preprocessed historical electricity consumption data of users; S23. Set the network address of the electricity management platform, and at the same time, use the electricity management platform to number the network address of the aggregated local area network. After the network address numbering is completed, establish a wireless data connection between the two network addresses and perform real-time data exchange.

4. According to claim 3, a method for analyzing comprehensive computing power data of a power management platform based on cloud computing is characterized in that: The S21 comprises the following steps: Set the data collected at the monitoring point b a for: Among them, n a Expressed as the number of types of electrical equipment on the ath power line, n e It is expressed as the number of class e electrical equipment on the ath power line, c i Represents the collected data of the i-th type of electrical equipment, d j Indicates the data collected from the jth electrical equipment in the i-type electrical equipment, b a It represents the historical electricity consumption data of users collected on the ath power line; Summarize the historical electricity consumption data of users collected on each power line and construct a sample set B = {b1, b2, ..., b a }; Among them, b1 represents the user's historical electricity consumption data collected on the first power line in the sample set.

5. According to claim 4, a method for analyzing comprehensive computing power data of a power management platform based on cloud computing is characterized in that: The S22 The following steps are involved: The historical electricity consumption data of users in the sample set is processed by the data standardization method as follows: Among them, b min represents the minimum value of the user's historical electricity consumption data in the sample set, b max represents the maximum value of the user's historical electricity consumption data in the sample set, b a represents the historical electricity consumption data of users collected on the ath power line in the sample set, b N Indicates the standardized historical electricity consumption data of users; The standardized user historical electricity usage data is set as the pre-processed user historical electricity usage data.

6. According to claim 1, a method for analyzing comprehensive computing power data of a power management platform based on cloud computing is characterized in that: The S3 comprises the following steps: S31, after receiving the pre-processed historical electricity consumption data of the user, the electricity consumption management platform performs analysis and calculation of the historical electricity consumption data of the user based on the received pre-processed historical electricity consumption data of the user; S32, performing user power consumption data prediction calculation based on the received pre-processed user historical power consumption data; S33, recording the data processing time of the analysis and calculation of the historical electricity consumption data of each group of users and the prediction and calculation of the user electricity consumption data, and calculating the average computing power data based on the recorded time; Average computing power data = aggregated data processing time / number of processed user historical electricity consumption data.

7. The method for analyzing comprehensive computing power data of a power management platform based on cloud computing according to claim 6 is characterized in that: The S31 comprises the following steps: Receiving the pre-processed historical electricity consumption data of the user through the electricity consumption management platform, and analyzing the discrete pre-processed historical electricity consumption data of the user through the principle of calculus because the received pre-processed historical electricity consumption data of the user is discrete; The effective value of the user's historical electricity consumption data after preprocessing I rms The calculation formula is as follows: Where, T represents the time period; I 2 (t) represents the user's historical electricity consumption data collected at time t; The calculated pre-processed user historical electricity consumption data effective value I rms Multiplying by the standard mains voltage 220V, we can get the load power of each group of users’ historical power consumption data; Set a load power threshold of the user's historical power consumption data. When the calculated load power of the user's historical power consumption data exceeds the set load power threshold, it indicates dangerous power consumption; When the calculated load power of the user's historical power consumption data does not exceed the set load power threshold, it indicates safe power consumption.

8. The method for analyzing comprehensive computing power data of a power management platform based on cloud computing according to claim 6, characterized in that: The S32 comprises the following steps: S321, initializing hybrid genetic algorithm parameters, performing chromosome encoding on user power consumption data, and constructing a population set; Set each group of chromosome encoding to represent a group of user electricity consumption data, set the population size, and the maximum number of iterations d max ; S322, setting an objective function of user electricity consumption data based on the constructed population set size; Set the objective function: Among them, Fit represents the objective function of the user's electricity consumption data, and Δω represents the change in the user's electricity consumption data; S323, genetic operator selection operation; Assume the probability of chromosome code being selected is p(z k ); Among them, K is the population size, Fit(z k ) is the fitness encoded by the i-th chromosome; S324, chromosome crossover operation; A crossover point is randomly selected from the chromosome codes in the population set, and the chromosome code segments between the crossover points are exchanged to form a new chromosome code, thereby forming a new population; Remove duplicate chromosome codes in order and ensure that the length of chromosome codes is consistent to form daughter chromosomes; S325, chromosome mutation operation; The mutation process is set to replace the gene fragment of the parent chromosome to produce a chromosome code different from the parent; the parent chromosome code is mutated based on the set mutation probability p, thereby generating a new generation of offspring chromosomes; The fitness of all offspring chromosomes is calculated. If the offspring is better than the parent, the parent chromosome code is replaced. Otherwise, if the parent is better, it means that the mutation has failed, and the parent is selected to continue the iteration operation. S326, output the optimal solution to the problem; Set the iteration threshold Y, when iteration d max When the chromosome code output after the last iteration reaches the iteration threshold Y or when the hybrid genetic algorithm reaches the set maximum number of iterations, the hybrid genetic algorithm is terminated and the chromosome code output for the last time is used as the predicted user electricity consumption data.

9. The method for analyzing comprehensive computing power data of a power management platform based on cloud computing according to claim 1, characterized in that: The S4 comprises the following steps: Calculate the execution completion time of the power management platform based on the average computing power data, the total amount of user power consumption data collected in real time, and the number of user power consumption data prediction calculation requests; The execution completion time of the power management platform = (the total amount of user power data collected in real time + the number of user power data prediction calculation requests) × average computing power data; The power management platform is set to process the user power consumption data collected in real time in a sequential manner; Set the cut-off time for user electricity consumption data; After the user power consumption data collected in real time is uploaded to the power consumption management platform, if the execution completion time of the power consumption management platform exceeds the deadline of the user power consumption data, the currently uploaded user power consumption data will be rejected. Otherwise, the currently uploaded user power consumption data will be received and transmitted to the waiting queue; Record the number of times the power management platform receives user power data and the number of times it rejects user power data, calculate the power management platform's acceptance probability, and set a power management platform acceptance probability threshold; When the reception probability of the power management platform is greater than or equal to the set threshold, it means that the comprehensive computing power of the power management platform is qualified. When the reception probability of the power management platform is less than the set threshold, it means that the comprehensive computing power of the power management platform is unqualified.

10. A system for implementing the method for analyzing the comprehensive computing power data of a power management platform based on cloud computing according to any one of claims 1 to 9, characterized in that: include: Internet of Things devices, data preprocessing modules, power management platforms, and comprehensive computing power analysis modules; The IoT device is used to collect user electricity consumption data in real time; The data preprocessing module is used to process the user electricity consumption data collected in real time and transmit the processed user electricity consumption data to the electricity consumption management platform; The power consumption management platform is used to analyze and calculate the received processed user power consumption data; The comprehensive computing power analysis module is used to record and analyze the comprehensive computing power of the power management platform.

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