Control method of multi-source intelligent power manager based on 5G communication

Abstract

The present invention discloses a control method for a multi-source intelligent power manager based on 5G communication, and relates to the technical field of power manager control. The method includes: real-time monitoring of the power manager and integrated multi-channel energy inputs and data collection; data processing and analysis of the manager data and multi-channel energy data respectively; generating a control strategy based on the analysis results and intelligently controlling and scheduling the power manager and multi-channel energy inputs; abnormal monitoring of the power manager and feedback adjustment of the multi-channel energy inputs; and constructing a data defense mechanism during data transmission. The present invention ensures the energy utilization efficiency of the multi-source intelligent power manager and improves the stability and reliability of the multi-source intelligent power manager by real-time monitoring and analysis of the power manager and multi-source inputs, and intelligently controlling and scheduling energy based on the generated optimal control strategy; and also ensures the security of the data transmission process by constructing a data defense mechanism.

Description

Technical Field

[0001] The present invention belongs to the technical field of power manager control, and in particular to a control method for a multi-source intelligent power manager based on 5G communication. Background Art

[0002] The multi-source intelligent power manager is a system that integrates multiple energy inputs and intelligent control technologies. It has a wide range of applications, covering various scenarios such as homes, commercial buildings, and industrial facilities. It aims to reduce energy costs, optimize energy utilization efficiency, and enhance the stability and reliability of energy supply.

[0003] Controlling a multi-source intelligent power manager means efficiently managing and coordinating various input energies in real time to achieve optimized energy utilization and system stability. Although multi-source intelligent power managers have significant advantages in improving energy efficiency and system stability, effective control of them still faces many challenges, including real-time data processing, energy scheduling optimization, system fault handling, and data and network security maintenance. Therefore, how to ensure the energy efficiency and stability of multi-source intelligent power managers while solving the above problems has become a problem that needs to be solved urgently. Summary of the invention

[0004] The object of the present invention is to provide a control method for a multi-source intelligent power manager based on 5G communication, which can be implemented by the following technical solutions:

[0005] The embodiment of the present application provides a control method for a multi-source intelligent power manager based on 5G communication, comprising the following steps:

[0006] Integrate multiple energy inputs into the power manager;

[0007] Monitor the power manager and multiple energy inputs in real time and collect manager data and multiple energy data;

[0008] Performing data processing and data analysis on the manager data and the multi-channel energy data respectively;

[0009] Generate control strategies based on analysis results;

[0010] Intelligently controlling and scheduling the power manager and the multiple energy inputs according to the control strategy;

[0011] Monitor the power manager for abnormalities and make feedback adjustments to multiple energy inputs;

[0012] Build data defense mechanisms and use them to protect the data transmission process;

[0013] Wherein, the power manager includes a master manager and a plurality of slave managers;

[0014] The master manager includes a comprehensive control unit and an intelligent scheduling unit for power management, strategy formulation and regulation of the slave managers;

[0015] The slave manager includes a multi-channel energy unit and a multi-channel load unit, which is used to monitor and control the multi-channel load and multi-channel energy input;

[0016] The multi-channel energy input is: energy input input into the slave manager via multiple energy power supplies, including solar energy input, wind energy input, mechanical energy input, reserve energy input and main grid power input;

[0017] Among them, the manager data includes the main manager performance data, the slave manager performance data and the working environment data; the multi-channel energy data includes the multi-channel power supply load data, the multi-channel power supply operation data and the multi-channel power supply energy data.

[0018] Preferably, real-time monitoring of the power manager includes:

[0019] Monitoring the performance status of the master manager to obtain master manager performance data;

[0020] Monitoring the performance status of the slave manager to obtain slave manager performance data;

[0021] Monitoring the environmental status of the power manager to obtain working environment data;

[0022] Real-time monitoring of multiple energy inputs, including:

[0023] Monitor the multiple loads of the multiple energy power sources to obtain multiple power load data;

[0024] Monitoring the operating status of the multiple energy power supplies to obtain multiple power supply operating data;

[0025] Monitor the energy reserves of the multiple energy power supplies to obtain energy data of the multiple power supplies;

[0026] The multiple power supply operation data include power supply voltage data, power supply current data and power supply power data.

[0027] Preferably, data processing and data analysis are performed on the manager data, including the following contents:

[0028] Data cleaning: dealing with outliers and noise in manager data;

[0029] Data integration: Use timestamps to synchronize the data between the master manager and the slave manager and integrate them through data integration;

[0030] Data analysis: Analyze the performance data of the master manager and the slave manager and extract performance indicator information;

[0031] Data analysis: analyzing the performance indicator information and generating performance analysis results; analyzing the working environment data and obtaining the working environment temperature of the master manager and the slave manager;

[0032] The performance indicator information includes total output power, voltage regulation range, output current, load capacity, failure rate and response time of energy scheduling.

[0033] Preferably, data processing and data analysis of the multiple energy data include the following:

[0034] Data cleaning: Processing noise and outliers in multi-channel energy data;

[0035] Data completion: fill in the missing data during the data collection process;

[0036] Data synchronization: Process and synchronize the time series of multiple energy data;

[0037] Data fusion: Fusion of multiple energy data.

[0038] Preferably, the data analysis of the multiple energy data includes the following contents:

[0039] Analyze the multi-channel power supply load data, determine the voltage, current, load size and load operation status of the multi-channel loads, and generate multi-channel load analysis results based on this;

[0040] Analyze the operating data of the multiple power supplies, determine the voltage, current, power phase status and power harmonic status of the multiple energy power supplies, and generate multiple power supply analysis results based on this;

[0041] Analyze the energy data of the multiple power sources, determine the charging and discharging efficiency, energy storage efficiency, energy conversion efficiency, energy flow and power generation reserve of the multiple energy sources, and generate multiple energy analysis results based on this.

[0042] Preferably, a control strategy is generated based on the analysis results, including the following:

[0043] Based on the performance analysis results and the working environment temperature, the ε-constraint method is used to generate the first control decision;

[0044] Obtaining the power demand of multiple loads, and generating a second control decision based on the analysis results of the multiple loads and using the ε-constraint method;

[0045] Obtain the power supply quality of multiple energy sources, and generate a third control decision based on the analysis results of multiple power sources and using the ε-constraint method;

[0046] Obtaining the energy conversion quality of multiple energy sources, and generating a fourth control decision based on the multiple energy analysis results and using the ε-constraint method;

[0047] Based on the first regulation decision, the second regulation decision, the third regulation decision and the fourth regulation decision, a weighted average method is used to generate an optimal control strategy.

[0048] Preferably, the ε-constraint method is used to generate a control strategy, including the following:

[0049] Select the main goal: select a main goal as the primary optimization direction;

[0050] Set constraints: Set other goals besides the main goal as constraints and specify the constraint range ε;

[0051] Optimize the main objective: optimize the selected main objective under constraints;

[0052] Generate solution set: By adjusting the constraint range ε ​​and repeating the process of optimizing the main objective, multiple different solutions are generated and the target optimization solution set is formed;

[0053] Analysis and evaluation: Evaluate the generated target optimization solution set and select the best solution as the control decision.

[0054] Preferably, the weighted average method is used to generate the optimal control strategy, including the following:

[0055] Determine the goal: select multiple control decisions to be optimized;

[0056] Assign weights: Assign a weight to each regulatory decision;

[0057] Standardized goals: Standardize the goals of different regulatory decisions;

[0058] Calculate the weighted sum: Use the assigned weights to weight the standardized control decision objectives to form a comprehensive objective function;

[0059] Optimize comprehensive objectives: Optimize the comprehensive objective function to obtain the optimal control strategy;

[0060] Adjustment and evaluation: Adjust the weights and objectives according to the actual application effect of the optimal control strategy, and verify and evaluate the effectiveness of the optimal control strategy.

[0061] Preferably, abnormal monitoring of the power manager specifically includes:

[0062] Monitor the working environment of multiple slave managers in the power manager and determine whether they are in a high-temperature environment. If the slave managers are in a high-temperature working environment, cut off the power of the slave managers and use the master manager to replace the slave managers. After a set time threshold, re-enable the slave managers.

[0063] The usage status of multiple slave managers in the power manager is monitored and it is determined whether each slave manager is in an idle or overloaded state. If a slave manager is in an idle state, the idle slave manager will be shut down or restarted after a set time threshold; if each slave manager is in an overloaded state, the power supply of each slave manager will be cut off and the master manager will be used to replace each slave manager. After the set time threshold, each slave manager will be re-enabled.

[0064] Preferably, constructing the data defense mechanism specifically includes:

[0065] According to the importance of data, the permissions of manager data and multi-channel energy data are graded as follows:

[0066] The main manager performance data is divided into the highest level of authority; the slave manager performance data is divided into high-level authority; the multi-channel energy data is divided into medium-level authority; the working environment data is divided into low-level authority;

[0067] According to different levels of authority, the manager data and multi-channel energy data are encrypted and the encryption identification is obtained, specifically:

[0068] Using an asymmetric encryption algorithm to perform a first encryption on the manager data and the multi-channel energy data and obtain a first encryption identifier;

[0069] Use the KP-ABE algorithm to perform a second encryption on the slave manager performance data and obtain a second encryption identifier;

[0070] Use the national encryption algorithm to perform the third encryption on the main manager performance data and obtain the third encryption identification;

[0071] Generate the corresponding decryption identifier according to the encryption identifier and decrypt the data according to the decryption identifier, specifically:

[0072] Generate a first decryption identifier according to the first encryption identifier;

[0073] Generate a second decryption identifier according to the second encryption identifier;

[0074] A third decryption identifier is generated according to the third encryption identifier.

[0075] The beneficial effects of the present invention are:

[0076] (1) The present invention performs real-time monitoring and analysis on the power manager and multi-source inputs, and performs intelligent control and energy scheduling according to the generated optimal control strategy, thereby ensuring the energy utilization efficiency of the multi-source intelligent power manager and improving the stability and reliability of the multi-source intelligent power manager; and also ensures the security of the data transmission process by building a data defense mechanism.

[0077] (2) The present invention first integrates multiple energy inputs into the power manager; then monitors the power manager and multiple energy inputs in real time and collects manager data and multiple energy data; then processes and analyzes the manager data and multiple energy data respectively and generates a control strategy based on the analysis results; then intelligently controls and dispatches the power manager and multiple energy inputs based on the control strategy; also monitors and feedbacks the power manager and multiple energy inputs for abnormalities; and finally uses a data defense mechanism to protect the data transmission process. The present application improves the real-time data processing capability by real-time monitoring of the multi-source power manager and processing and analyzing the acquired data; performs comprehensive control and intelligent dispatch of the energy input of the multi-source power manager through the control strategy generated by the analysis results, thereby maximizing the energy utilization efficiency; also monitors and feedbacks the power manager and multiple energy inputs for abnormalities to continuously monitor the energy management effect and system operation status, and timely adjusts the energy management strategy based on real-time feedback to ensure efficient operation under various working conditions; finally uses a data defense mechanism to protect the data transmission process, thereby ensuring the security of data transmission and storage and the stability of system operation under unexpected circumstances. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0079] Figure 1 A flowchart of a control method for a multi-source intelligent power manager based on 5G communication provided in an embodiment of the present application;

[0080] Figure 2 A flow chart of the steps for generating a control strategy provided in an embodiment of the present application;

[0081] Figure 3 A flowchart of the steps for performing abnormal monitoring provided in an embodiment of the present application;

[0082] Figure 4 A flowchart of the steps for building a data defense mechanism provided in an embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0084] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to any or all possible combinations of one or more associated listed items.

[0085] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0086] Example 1

[0087] See also Figure 1 , the embodiment of the present application provides a control method of a multi-source intelligent power manager based on 5G communication, comprising the following steps:

[0088] S0, integrates multiple energy inputs into the power manager;

[0089] S1, real-time monitoring of the power manager and multiple energy inputs and collection of manager data and multiple energy data;

[0090] S2, performing data processing and data analysis on the manager data and the multi-channel energy data respectively;

[0091] S3, generating a control strategy based on the analysis results;

[0092] S4, intelligently controlling and scheduling the power manager and the multiple energy inputs according to the control strategy;

[0093] S5, monitoring the power manager for abnormalities and performing feedback adjustment on multiple energy inputs;

[0094] S6, build a data defense mechanism and use it to protect the data transmission process;

[0095] Wherein, the power manager includes a master manager and a plurality of slave managers;

[0096] The master manager includes a comprehensive control unit and an intelligent scheduling unit for power management, strategy formulation and regulation of the slave managers;

[0097] The slave manager includes a multi-channel energy unit and a multi-channel load unit, which is used to monitor and control the multi-channel load and multi-channel energy input;

[0098] The multi-channel energy input is: energy input into the slave manager via multi-channel energy power supplies, including solar energy input, wind energy input, mechanical energy input, reserve energy input and main grid power input.

[0099] Specifically, since there are many problems in the control of the current multi-source intelligent power manager, in order to ensure the energy utilization efficiency of the multi-source intelligent power manager and improve its stability, this embodiment first integrates multiple energy inputs into the power manager; wherein the power manager includes a master manager and a slave manager, and the multiple energy inputs include solar energy input, wind energy input, mechanical energy input, reserve energy input and main grid power input; then the power manager and the multiple energy inputs are monitored in real time and the manager data and the multiple energy data are collected; then the manager data and the multiple energy data are processed and analyzed respectively and a control strategy is generated based on the analysis results; then the power manager and the multiple energy inputs are intelligently controlled and energy dispatched based on the control strategy; the power manager and the multiple energy inputs are also monitored for abnormalities and feedback adjusted; finally, a data defense mechanism is used to provide security protection for the data transmission process. The present application improves the real-time data processing capability by real-time monitoring of the multi-source power manager and processing and analyzing the acquired data; comprehensively controls and intelligently schedules the energy input of the multi-source power manager through the control strategy generated by the analysis results, so as to maximize the energy utilization efficiency; and continuously monitors the energy management effect and system operation status by abnormal monitoring and feedback adjustment of the power manager and multiple energy inputs, and timely adjusts the energy management strategy according to real-time feedback to ensure efficient operation under various working conditions; finally, uses the data defense mechanism to securely protect the data transmission process, so as to ensure the security of data transmission and storage and the stability of the system operation under unexpected circumstances; in summary, the present application ensures the energy utilization efficiency of the multi-source intelligent power manager and improves the stability and reliability of the multi-source intelligent power manager through the above-mentioned control method.

[0100] It can be understood that a multi-energy power supply is a multi-power supply system formed by multiple energy sources, including but not limited to solar power supply, wind power supply, mechanical power supply, reserve energy and main grid power supply.

[0101] It should be noted that since the slave manager is used to monitor and control multiple loads and multiple energy inputs, there are multiple slave managers in the power manager.

[0102] In an embodiment provided in the present application, real-time monitoring of the power manager includes:

[0103] Monitoring the performance status of the master manager to obtain master manager performance data;

[0104] Monitoring the performance status of the slave manager to obtain slave manager performance data;

[0105] Monitoring the environmental status of the power manager to obtain working environment data;

[0106] Real-time monitoring of multiple energy inputs, including:

[0107] Monitor the multiple loads of the multiple energy power sources to obtain multiple power load data;

[0108] Monitoring the operating status of the multiple energy power supplies to obtain multiple power supply operating data;

[0109] Monitoring the energy reserves of the multiple energy power supplies to obtain energy data of the multiple power supplies;

[0110] Wherein, the multiple power supply operation data includes power supply voltage data, power supply current data and power supply power data;

[0111] Thus, the manager data includes master manager performance data, slave manager performance data and working environment data;

[0112] The multi-channel energy data includes multi-channel power supply load data, multi-channel power supply operation data and multi-channel power supply energy data.

[0113] Specifically, in this embodiment, the power manager includes a master manager and a slave manager, and the two different types of managers are used to perform different operations and play different roles. Such a configuration helps to improve the reliability, scalability and management efficiency of the power manager; and the multi-channel energy input is that the multi-channel energy power supply outputs the energy in each energy channel to the slave manager for management and regulation.

[0114] In an embodiment provided in the present application, data processing and data analysis are performed on the manager data, including the following contents:

[0115] Data cleaning: dealing with outliers and noise in manager data;

[0116] Data integration: Use timestamps to synchronize the data between the master manager and the slave manager and integrate them through data integration;

[0117] Data analysis: Analyze the performance data of the master manager and the slave manager and extract performance indicator information;

[0118] Data analysis: analyzing the performance indicator information and generating performance analysis results; analyzing the working environment data and obtaining the working environment temperature of the master manager and the slave manager;

[0119] The performance indicator information includes total output power, voltage regulation range, output current, load capacity, failure rate and response time of energy scheduling.

[0120] Specifically, by processing the above manager data and analyzing the processed data, the overall performance of the power manager is evaluated and analyzed, so as to provide data support for the generation of subsequent control strategies. It can be understood that the processing and analysis of the manager data mainly targets the performance indicators of the power manager itself, focusing on the performance status and fault conditions of the power manager, which is closely related to the scheduling of multiple energy sources and the regulation of the power manager.

[0121] In an embodiment provided in the present application, data processing and data analysis of the multiple energy data include the following contents:

[0122] Data cleaning: Processing noise and outliers in multi-channel energy data;

[0123] Data completion: fill in the missing data during the data collection process;

[0124] Data synchronization: Process and synchronize the time series of multiple energy data;

[0125] Data fusion: Fusion of multiple energy data.

[0126] In an embodiment provided in the present application, data analysis is performed on the multiple energy data, including the following contents:

[0127] Analyze the multi-channel power supply load data, determine the voltage, current, load size and load operation status of the multi-channel loads, and generate multi-channel load analysis results based on this;

[0128] Analyze the operating data of the multiple power supplies, determine the voltage, current, power phase status and power harmonic status of the multiple energy power supplies, and generate multiple power supply analysis results based on this;

[0129] Analyze the energy data of the multiple power sources, determine the charging and discharging efficiency, energy storage efficiency, energy conversion efficiency, energy flow and power generation reserve of the multiple energy sources, and generate multiple energy analysis results based on this.

[0130] Specifically, this embodiment analyzes multi-power loads, multi-power operations and multi-power energies, focusing mainly on the load status, energy conversion and power generation efficiency, etc. By analyzing multi-energy data, the usage of multiple energies and the operation of multiple loads are parsed therefrom, thereby providing data support for subsequent energy scheduling and energy control.

[0131] like Figure 2 As shown, in one embodiment provided in the present application, a control strategy is generated according to the analysis result, including the following contents:

[0132] Based on the performance analysis results and the working environment temperature, the ε-constraint method is used to generate the first control decision;

[0133] Obtaining the power demand of multiple loads, and generating a second control decision based on the analysis results of the multiple loads and using the ε-constraint method;

[0134] Obtain the power supply quality of multiple energy sources, and generate a third control decision based on the analysis results of multiple power sources and using the ε-constraint method;

[0135] Obtaining the energy conversion quality of multiple energy sources, and generating a fourth control decision based on the multiple energy analysis results and using the ε-constraint method;

[0136] Based on the first regulation decision, the second regulation decision, the third regulation decision and the fourth regulation decision, a weighted average method is used to generate an optimal control strategy.

[0137] Specifically, from a local perspective, this embodiment generates a first control decision based on the performance analysis results and the working environment temperature to control and adjust the power manager; and manages and controls the multiple loads by acquiring the power demand of the multiple loads and generating a second control decision based on the multiple load analysis results; and controls the power supply capacity and power supply quality of the multiple energy sources by acquiring the power supply quality of the multiple energy sources and generating a third control decision based on the multiple power analysis results; and schedules the energy or energy of the multiple energy inputs by acquiring the energy conversion quality of the multiple energy sources and generating a fourth control decision based on the multiple energy analysis results; and finally comprehensively analyzes the above four control decisions and generates the optimal control strategy by using the weighted average method, so as to control and schedule the power manager and the multiple energy inputs as a whole. This embodiment starts from the local and the whole respectively, first generates multiple local control decisions based on different analysis results, and finally comprehensively analyzes and judges the multiple decisions from the whole, so as to make the optimal control strategy from the whole, which improves the accuracy of the decision and further guarantees the comprehensiveness and reliability of the control of the multi-source power manager.

[0138] In an embodiment provided in the present application, an ε-constraint method is used to generate a control strategy, including the following contents:

[0139] Select the main goal: select a main optimization goal as the primary optimization direction;

[0140] Set constraints: Set other goals besides the main goal as constraints and specify the constraint range ε;

[0141] Optimize the main objective: optimize the selected main objective under constraints;

[0142] Generate solution set: By adjusting the constraint range ε ​​and repeating the process of optimizing the main objective, multiple different solutions are generated and the target optimization solution set is formed;

[0143] Analysis and evaluation: Evaluate the generated target optimization solution set and select the best solution as the control decision.

[0144] Specifically, this embodiment uses the ε-constraint method to generate multiple control strategies, which can handle complex multi-objective optimization problems in multiple analysis results. By adjusting the constraint range (ε) to explore different solutions, it can cover different parts of the target space and provide a more comprehensive solution set, with excellent flexibility and comprehensiveness.

[0145] In an embodiment provided in the present application, a weighted average method is used to generate an optimal control strategy, including the following contents:

[0146] Determine the goal: select multiple control decisions to be optimized;

[0147] Assign weights: Assign a weight to each control decision; the weight reflects the importance of each decision in the overall optimization;

[0148] Standardize objectives: Standardize the objectives of different regulatory decisions so that they have the same unit of measurement or range;

[0149] Calculate the weighted sum: Use the assigned weights to weight the standardized control decision objectives to form a comprehensive objective function;

[0150] Optimize comprehensive objectives: Optimize the comprehensive objective function to obtain the optimal control strategy;

[0151] Adjustment and evaluation: Adjust the weights and objectives according to the actual application effect of the optimal control strategy, verify and evaluate the effectiveness of the optimal control strategy, and ensure that the generated optimal control strategy achieves the best balance in each decision-making dimension.

[0152] Specifically, this embodiment adopts the weighted average method to generate the optimal control strategy. By assigning weights to the above-mentioned multiple control decisions and calculating the weighted sum, the optimal control strategy is obtained, which can flexibly reflect the importance of different control decisions; and the adjustment of weights and targets can quickly respond to changing needs or priorities, thereby improving the accuracy and reliability of control decisions.

[0153] In an embodiment provided in the present application, intelligent control and energy scheduling are performed according to a control strategy, specifically including:

[0154] From a local perspective:

[0155] If the performance and working environment of the power manager do not meet the manager performance requirements and the manager environment requirements, intelligently controlling the power manager through a first regulation decision;

[0156] If the performance of the multi-channel power load does not meet the load performance requirements and power demand, the multi-channel power load is intelligently controlled through the second control decision;

[0157] If the performance of multiple power sources does not meet the power performance requirements and power quality requirements, the multiple energy sources are intelligently controlled through the third control decision;

[0158] If the energy of the multiple power sources does not meet the energy conversion quality requirements, the multiple energy sources are dispatched through the fourth control decision;

[0159] Overall:

[0160] If either the power manager or the multi-channel energy input does not meet the actual demand, the optimal control strategy will be used to comprehensively intelligently regulate and dispatch the two.

[0161] Specifically, this embodiment adopts different decisions for regulation and scheduling based on different situations locally and overall, so that the control of the multi-source power manager has a sense of hierarchy, thereby improving the accuracy and comprehensiveness of the control.

[0162] like Figure 3 As shown, in one embodiment provided in the present application, abnormal monitoring of the power manager specifically includes:

[0163] S51, monitoring the working environment of multiple slave managers in the power manager and determining whether they are in a high temperature environment. If the slave managers are in a high temperature working environment, cutting off the power of the slave managers and using the master manager to replace the slave managers to run, and re-enabling the slave managers after a set time threshold has passed;

[0164] S52, monitor the usage status of multiple slave managers in the power manager and determine whether each slave manager is in an idle or overloaded state. If a slave manager is in an idle state, the idle slave manager will be shut down or restarted after a set time threshold; if each slave manager is in an overloaded state, cut off the power supply of each slave manager and use the master manager to replace each slave manager. After the set time threshold, re-enable each slave manager.

[0165] Specifically, this embodiment monitors the power manager for abnormalities, mainly monitoring multiple slave managers. If it is found that the working environment of the slave manager is in high temperature, or idle or overloaded, corresponding control measures are taken to maintain the operating safety and stability of the slave manager, thereby further improving the stability and security of the power manager.

[0166] like Figure 4 As shown, in one embodiment provided in the present application, constructing the data defense mechanism specifically includes:

[0167] S61, classify the permissions of the manager data and the multi-channel energy data according to the importance of the data, specifically:

[0168] The main manager performance data is divided into the highest level of authority; the slave manager performance data is divided into high-level authority; the multi-channel energy data is divided into medium-level authority; the working environment data is divided into low-level authority;

[0169] S62, encrypting the manager data and the multi-channel energy data according to different levels of authority and obtaining encryption identifiers, specifically:

[0170] Using an asymmetric encryption algorithm to perform a first encryption on the manager data and the multi-channel energy data and obtain a first encryption identifier;

[0171] Use the KP-ABE algorithm to perform a second encryption on the slave manager performance data and obtain a second encryption identifier;

[0172] Use the national encryption algorithm to perform the third encryption on the main manager performance data and obtain the third encryption identification;

[0173] S63, generating a corresponding decryption identifier according to the encryption identifier and decrypting the data according to the decryption identifier, specifically:

[0174] Generate a first decryption identifier according to the first encryption identifier;

[0175] Generate a second decryption identifier according to the second encryption identifier;

[0176] A third decryption identifier is generated according to the third encryption identifier.

[0177] Specifically, this embodiment divides the manager data and multi-channel energy data into multiple permission levels according to the importance and privacy of the data, and uses multiple encryption methods to perform multiple encryption on data of different levels; for example: the main manager performance data is triple-encrypted using three different encryption algorithms, so if you want to obtain the main manager performance data by decryption, you first need to obtain the third decryption identifier through the third encryption identifier, and then obtain the second decryption identifier and the first decryption identifier in sequence. Only after obtaining all the third decryption identifiers, the second decryption identifiers and the first decryption identifiers can the main manager performance data be completely decrypted. Therefore, this embodiment greatly improves the security of the multi-source intelligent power manager during data transmission through the above-mentioned data defense mechanism.

[0178] Example 2

[0179] The embodiment of the present application provides a control system of a multi-source intelligent power manager based on 5G communication, and applies the control method of the multi-source intelligent power manager as described above, including:

[0180] A multi-channel energy integration module is used to integrate multi-channel energy input into the power manager;

[0181] Data monitoring and collection module, used to monitor the power manager and multiple energy inputs in real time and collect manager data and multiple energy data;

[0182] A data processing and analysis module, used for processing and analyzing the manager data and the multi-channel energy data respectively;

[0183] A control strategy generation module is used to generate a control strategy based on the analysis results;

[0184] An intelligent control and scheduling module, used for intelligently controlling and scheduling the power manager and the multiple energy inputs according to a control strategy;

[0185] An abnormality monitoring feedback module is used to monitor the power manager for abnormalities and make feedback adjustments to multiple energy inputs;

[0186] Data security management module, used to build data defense mechanism and use it to protect the data transmission process;

[0187] Wherein, the power manager includes a master manager and a plurality of slave managers;

[0188] The master manager includes a comprehensive control unit and an intelligent scheduling unit for power management, strategy formulation and regulation of the slave managers;

[0189] The slave manager includes a multi-channel energy unit and a multi-channel load unit, which is used to monitor and control the multi-channel load and multi-channel energy input;

[0190] The multiple energy inputs output energy to the slave manager via multiple energy power supplies, including solar energy input, wind energy input, mechanical energy input, reserve energy input and main grid power input.

[0191] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0192] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A control method for a multi-source intelligent power manager based on 5G communication, characterized in that: The steps include: Integrate multiple energy inputs into the power manager; Monitor the power manager and multiple energy inputs in real time and collect manager data and multiple energy data; Performing data processing and data analysis on the manager data and the multi-channel energy data respectively; Generate control strategies based on analysis results; Intelligently controlling and scheduling the power manager and the multiple energy inputs according to the control strategy; Monitor the power manager for abnormalities and make feedback adjustments to multiple energy inputs; Build data defense mechanisms and use them to protect the data transmission process; Wherein, the power manager includes a master manager and a plurality of slave managers; The master manager includes a comprehensive control unit and an intelligent scheduling unit for power management, strategy formulation and regulation of the slave managers; The slave manager includes a multi-channel energy unit and a multi-channel load unit, which is used to monitor and control the multi-channel load and multi-channel energy input; The multi-channel energy input is: energy input input into the slave manager via multiple energy power supplies, including solar energy input, wind energy input, mechanical energy input, reserve energy input and main grid power input; The manager data includes the main manager performance data, the slave manager performance data and the working environment data; the multi-channel energy data includes the multi-channel power supply load data, the multi-channel power supply operation data and the multi-channel power supply energy data; Monitor the power manager for abnormalities, including: Monitor the working environment of multiple slave managers in the power manager and determine whether they are in a high-temperature environment. If the slave managers are in a high-temperature working environment, cut off the power of the slave managers and use the master manager to replace the slave managers. After a set time threshold, re-enable the slave managers. The usage status of multiple slave managers in the power manager is monitored and it is determined whether each slave manager is in an idle or overloaded state. If a slave manager is in an idle state, the idle slave manager will be shut down or restarted after a set time threshold; if each slave manager is in an overloaded state, the power supply of each slave manager will be cut off and the master manager will be used to replace each slave manager. After the set time threshold, each slave manager will be re-enabled.

2. The control method of the multi-source intelligent power manager based on 5G communication according to claim 1 is characterized in that: Real-time monitoring of the power manager, including: Monitoring the performance status of the master manager to obtain master manager performance data; Monitoring the performance status of the slave manager to obtain slave manager performance data; Monitoring the environmental status of the power manager to obtain working environment data; Real-time monitoring of multiple energy inputs, including: Monitor the multiple loads of the multiple energy power sources to obtain multiple power load data; Monitoring the operating status of the multiple energy power supplies to obtain multiple power supply operating data; Monitoring the energy reserves of the multiple energy power supplies to obtain energy data of the multiple power supplies; The multiple power supply operation data include power supply voltage data, power supply current data and power supply power data.

3. The control method of the multi-source intelligent power manager based on 5G communication according to claim 1 is characterized in that: Performing data processing and data analysis on the manager data includes the following contents: Data cleaning: dealing with outliers and noise in manager data; Data integration: Use timestamps to synchronize the data between the master manager and the slave manager and integrate them through data integration; Data analysis: Analyze the performance data of the master manager and the slave manager and extract performance indicator information; Data analysis: analyzing the performance indicator information and generating performance analysis results; analyzing the working environment data and obtaining the working environment temperature of the master manager and the slave manager; The performance indicator information includes total output power, voltage regulation range, output current, load capacity, failure rate and response time of energy scheduling.

4. The control method of the multi-source intelligent power manager based on 5G communication according to claim 1 is characterized in that: The multi-channel energy data is processed and analyzed, including the following contents: Data cleaning: Processing noise and outliers in multi-channel energy data; Data completion: fill in the missing data during the data collection process; Data synchronization: Process and synchronize the time series of multiple energy data; Data fusion: Fusion of multiple energy data.

5. The control method of the multi-source intelligent power manager based on 5G communication according to claim 4 is characterized in that: The data analysis of the multiple energy data includes the following contents: Analyze the multi-channel power supply load data, determine the voltage, current, load size and load operation status of the multi-channel loads, and generate multi-channel load analysis results based on this; Analyze the operating data of the multiple power supplies, determine the voltage, current, power phase status and power harmonic status of the multiple energy power supplies, and generate multiple power supply analysis results based on this; Analyze the energy data of the multiple power sources, determine the charging and discharging efficiency, energy storage efficiency, energy conversion efficiency, energy flow and power generation reserve of the multiple energy sources, and generate multiple energy analysis results based on this.

6. The control method of the multi-source intelligent power manager based on 5G communication according to claim 1, characterized in that: Generate a control strategy based on the analysis results, including the following: Based on the performance analysis results and the working environment temperature, the ε-constraint method is used to generate the first control decision; Obtaining the power demand of multiple loads, and generating a second control decision based on the analysis results of the multiple loads and using the ε-constraint method; Obtain the power supply quality of multiple energy sources, and generate a third control decision based on the analysis results of multiple power sources and using the ε-constraint method; Obtaining the energy conversion quality of multiple energy sources, and generating a fourth control decision based on the multiple energy analysis results and using the ε-constraint method; Based on the first regulation decision, the second regulation decision, the third regulation decision and the fourth regulation decision, a weighted average method is used to generate an optimal control strategy.

7. The control method of the multi-source intelligent power manager based on 5G communication according to claim 6 is characterized in that: The ε-constraint method is used to generate the control strategy, including the following: Select the main goal: select a main goal as the primary optimization direction; Set constraints: Set other goals besides the main goal as constraints and specify the constraint range ε; Optimize the main objective: optimize the selected main objective under constraints; Generate solution set: By adjusting the constraint range ε ​​and repeating the process of optimizing the main objective, multiple different solutions are generated and the target optimization solution set is formed; Analysis and evaluation: Evaluate the generated target optimization solution set and select the best solution as the control decision.

8. The control method of the multi-source intelligent power manager based on 5G communication according to claim 6, characterized in that: The weighted average method is used to generate the optimal control strategy, which includes the following: Determine the goal: select multiple control decisions to be optimized; Assign weights: Assign a weight to each regulatory decision; Standardized goals: Standardize the goals of different regulatory decisions; Calculate the weighted sum: Use the assigned weights to weight the standardized control decision objectives to form a comprehensive objective function; Optimize comprehensive objectives: Optimize the comprehensive objective function to obtain the optimal control strategy; Adjustment and evaluation: Adjust the weights and objectives according to the actual application effect of the optimal control strategy, and verify and evaluate the effectiveness of the optimal control strategy.

9. The control method of the multi-source intelligent power manager based on 5G communication according to claim 1, characterized in that: Constructing the data defense mechanism specifically includes: According to the importance of data, the permissions of manager data and multi-channel energy data are graded as follows: The main manager performance data is divided into the highest level of authority; the slave manager performance data is divided into high-level authority; the multi-channel energy data is divided into medium-level authority; the working environment data is divided into low-level authority; According to different levels of authority, the manager data and multi-channel energy data are encrypted and the encryption identification is obtained, specifically: Using an asymmetric encryption algorithm to perform a first encryption on the manager data and the multi-channel energy data and obtain a first encryption identifier; Use the KP-ABE algorithm to perform a second encryption on the slave manager performance data and obtain a second encryption identifier; Use the national encryption algorithm to perform the third encryption on the main manager performance data and obtain the third encryption identification; Generate the corresponding decryption identifier according to the encryption identifier and decrypt the data according to the decryption identifier, specifically: Generate a first decryption identifier according to the first encryption identifier; Generate a second decryption identifier according to the second encryption identifier; A third decryption identifier is generated according to the third encryption identifier.

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