A smart energy unit device and method for monitoring and managing user load energy resources

Abstract

The present invention discloses a smart energy unit device and method for monitoring and managing user load energy resources, belonging to the field of energy management technology. A smart energy unit device for monitoring and managing user load energy resources includes a data acquisition unit, a data analysis unit and an energy management unit. The present invention solves the problem that the prior art cannot predict future load trends. The present invention collects power equipment data in real time through a data acquisition unit, and compares and analyzes the power equipment data through a data analysis unit. At the same time, based on historical data in a database and statistical methods, the future load trend of user energy equipment is predicted. The energy management unit makes an abnormal alarm and generates management instructions based on the load analysis results and load prediction trends, thereby obtaining corresponding management strategies for management. By predicting the load trend, corresponding management measures can be taken in time for power equipment with load risks.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a smart energy unit device and method for monitoring and managing user load energy resources. Background Art

[0002] User load energy resource monitoring and management refers to the process of optimizing power supply and consumption by monitoring the power load of user electrical equipment and formulating corresponding management strategies to manage the user's load equipment, aiming to improve the efficiency and reliability of the power system, reduce energy waste, reduce damage to the environment, and achieve economic benefits. A smart energy management system and method with publication number CN116581873A includes a data acquisition module that collects energy equipment data from energy monitoring terminals, user systems, and user load equipment, and sends the energy equipment data to a data processing module; the data processing module receives the energy equipment data, and screens the energy equipment data according to the management instructions sent by the new power load management system to obtain management data corresponding to the management instructions; the intelligent management module generates an intelligent management strategy based on the management data, and manages the user load equipment according to the intelligent management strategy. The intelligent management strategy is used to control the operating status and information collection of each user load equipment according to the management instructions. The patent realizes that different intelligent management strategies are generated on the user side according to different management instructions to realize intelligent management and real-time control of user load equipment. The above patent still has the following problems in actual use: the above patent cannot predict the future load trend of users, and thus cannot take management measures in advance for electrical equipment with load risks; therefore, it does not meet existing needs. In this regard, we propose a smart energy unit device and method for user load energy resource monitoring and management. Summary of the invention

[0003] The purpose of the present invention is to provide a smart energy unit device and method for monitoring and managing user load energy resources. The data of electrical equipment is collected in real time by a data acquisition unit, and the data of electrical equipment is compared and analyzed by a data analysis unit. The load peak and valley values ​​are found according to the analysis results and the user's load equipment is determined. At the same time, the future load trend of the user's energy equipment is predicted based on the historical data in the database in combination with statistical methods. Corresponding management measures are taken in time through the prediction of load trends. The energy management unit makes an abnormal alarm and generates a management instruction at the same time according to the load analysis results and the load prediction trend. The corresponding management strategy is obtained according to the management instruction, so that the user's load equipment is managed according to the management strategy, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a smart energy unit device for monitoring and managing user load energy resources, comprising:

[0005] The data collection unit is used to monitor the user's electrical equipment in real time, thereby collecting relevant electrical equipment data in real time, and sending the collected user's electrical equipment data to the data processing module;

[0006] The data analysis unit is used to compare and analyze the sent power equipment data with the preset load threshold value to obtain the analysis result, find the load peak and valley value according to the analysis result, and determine the user's load equipment according to the analysis result. The current analysis result is stored in the database. At the same time, the trend of the user's energy equipment load in the future is predicted according to the historical data stored in the database in combination with statistical methods;

[0007] The energy management unit is used to make abnormal alarms based on the load analysis results and load forecast trends of the data analysis unit. At the same time, it generates management instructions based on the load analysis results and load forecast trends. The data processing unit is preset with a management strategy corresponding to the management instruction, so as to obtain the corresponding management strategy based on the management instruction and manage the user's load equipment according to the management strategy.

[0008] Furthermore, the electrical equipment data collected by the data collection unit includes electrical equipment information, electrical power and electrical consumption data of the electrical equipment.

[0009] Furthermore, the data acquisition unit also includes:

[0010] The time period setting module is used to set the data collection time period according to the length of the unit time, wherein the data collection time period is obtained by the following formula:

[0011] T=(1+λ)·M·T0

[0012]

[0013]

[0014] Wherein, T represents the data collection time period; T0 represents the unit time; λ represents the first correlation coefficient; M represents the second correlation coefficient; m represents the number of data types contained in the power equipment data; Ci represents the average data volume corresponding to the i-th data type per unit time; C0 represents the preset data baseline volume; C z Indicates the average amount of electrical equipment data received per unit time;

[0015] A real-time data collection module for electric equipment, used to collect data of electric equipment in real time during the data collection period;

[0016] A data set generation module, configured to classify the electric device data within the data collection period according to the electric device information, electric power and electric power consumption data of the electric devices in the electric device data, and form a data set corresponding to the electric device information, electric power and electric power consumption data;

[0017] A data volume information extraction module, used to extract the data volume of each data set within each collection time period at the end of the data collection time period;

[0018] A data packet generation module, used to package the electric device data collected in the data collection time period according to the data volume of each data set in each collection time period to form multiple data packets;

[0019] The communication transmission module is used to send the data packets to the data analysis unit in sequence through the communication channel.

[0020] Furthermore, the data packet generation module includes:

[0021] A remaining capacity retrieving module, used to retrieve the remaining capacity of the communication channel at the end of the data collection time period;

[0022] A variation range information extraction module, used to extract the variation range of the remaining capacity per unit time in the historical operation data of the communication channel at the end of the data collection time period;

[0023] The compensation coefficient acquisition module is used to set the data volume compensation coefficient by using the remaining capacity of the communication channel and the variation range of the remaining capacity per unit time in the historical operation data of the communication channel, wherein the data volume compensation coefficient is obtained by the following formula:

[0024]

[0025] Where X represents the data volume compensation coefficient; X0 represents the preset initial compensation coefficient; n represents the number of unit time in the historical operation data; C y Indicates that at the end of the data collection period, the remaining capacity of the communication channel is retrieved; C yi represents the remaining capacity in the i-th unit time in the historical operation data; λ represents the first correlation coefficient;

[0026] The unit data volume acquisition module is used to use the data volume compensation coefficient in combination with the data volume of each data set in each collection time period to determine the maximum unit data volume for data packaging, wherein the maximum unit data volume is obtained by the following formula:

[0027]

[0028] Among them, C max Indicates the maximum unit data volume; C max0 Indicates the preset initial maximum unit data volume; C ij represents the data volume of the jth data set in the i-th collection time period; k represents the total number of data in the collection time period; C zi represents the total amount of electrical equipment data collected in the i-th collection time period;

[0029] The data packaging processing module is used to package the power-consuming equipment data collected during the data collection time period using the maximum unit data volume as an upper limit value to form multiple data packets.

[0030] Furthermore, the data analysis unit comprises:

[0031] The analysis module is pre-set with a load threshold value, and is used to compare and analyze the collected power equipment data with the load threshold value, so as to obtain the analysis result, and determine the user's load equipment through the analysis result;

[0032] The database is used to store the current analysis results. The database contains real-time data and historical data. The real-time data is used to send to the energy management unit, and the historical data provides a basis for the prediction module's prediction;

[0033] The prediction module is used to retrieve historical data from the database and predict the future load trend of the user's energy equipment based on historical data and statistical methods.

[0034] Furthermore, the analysis module compares the data of the electrical equipment and generates charts and text reports of relevant energy consumption indicators of the electrical equipment data based on the comparison results for display, so as to find the load peak and valley values ​​according to the charts and reports.

[0035] Furthermore, the prediction module predicts future load trends through historical data and statistical methods, and generates statistical charts based on the prediction results for display.

[0036] Furthermore, the database contains real-time data and historical data, specifically:

[0037] Real-time data is used to obtain and store the current analysis results of the power equipment data. The real-time data is sent to the energy management unit. After the energy management unit performs management work, the real-time data is converted into historical data for storage;

[0038] Historical data is used to obtain converted historical data for storage, and the historical data is retrieved and used by the prediction module.

[0039] Furthermore, the energy management unit comprises:

[0040] An alarm module is used to make an abnormal alarm according to the load analysis results and load forecast trend of the data analysis unit, and send abnormal alarm information and load forecast trend information to the user at the same time;

[0041] A management module is used to generate management instructions according to the load analysis results and load forecast trends of the data analysis unit, obtain corresponding management strategies according to the generated management instructions, and manage the user's load equipment according to the management strategies;

[0042] The management strategy is pre-set in the management module, the management instruction has a corresponding management strategy, and the management module selects the corresponding management strategy according to the management instruction.

[0043] A method for implementing a smart energy unit device for monitoring and managing user load energy resources comprises the following steps:

[0044] S1: Monitor the user's electrical equipment through the data collection unit, thereby collecting relevant electrical equipment data in real time, and send the collected user's electrical equipment data to the data processing module;

[0045] S2: Compare and analyze the data of power equipment through the data analysis unit, find the load peak and valley value according to the analysis results, and determine the user's load equipment according to the analysis results. The current analysis results will be stored in the database, and the future load trend of the user's energy equipment will be predicted according to the historical data in the database and the statistical method;

[0046] S3: The energy management unit makes an abnormal alarm and generates a management instruction based on the load analysis results and load forecast trend, and obtains the corresponding management strategy based on the management instruction, so as to manage the user's load equipment according to the management strategy.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention can monitor the user's electrical equipment in real time through the data acquisition unit, so as to collect the electrical equipment data in real time, and the data analysis unit can compare and analyze the electrical equipment data through the analysis module, and generate charts and text reports of relevant energy consumption indicators of the electrical equipment data from the analysis results for display, so as to facilitate the discovery of load peaks and valleys, and the user's load equipment can also be determined according to the analysis results. At the same time, the historical data in the database can be retrieved through the prediction module, and the future load trend of the user's energy equipment can be predicted according to the historical data and statistical methods, and the prediction results can be generated into statistical charts for display. The statistical charts can more intuitively understand the user's electrical equipment with load risks, so as to facilitate the grasp of the possible load risks of the electrical equipment. Finally, the energy management unit makes an abnormal alarm and generates a management instruction at the same time according to the load analysis results and the load prediction trend. The generated management instruction has a corresponding management strategy, and the user's load equipment can be managed according to the management strategy. Through the prediction of the load trend, the energy management unit can take corresponding management measures for the electrical equipment with load risks in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0050] Figure 2 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0051] 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.

[0052] In order to solve the technical problem that the existing technology cannot predict the future load trend of users and thus cannot take management measures in advance for electrical equipment with load risks, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0053] A smart energy unit device for monitoring and managing user load energy resources, comprising:

[0054] The data collection unit is used to monitor the user's electrical equipment in real time, thereby collecting relevant electrical equipment data in real time, and sending the collected user's electrical equipment data to the data processing module;

[0055] The electrical equipment data collected by the data collection unit includes electrical equipment information, electrical power and electrical consumption data of the electrical equipment;

[0056] Specifically, the data acquisition unit also includes:

[0057] The time period setting module is used to set the data collection time period according to the length of the unit time, wherein the data collection time period is obtained by the following formula:

[0058] T=(1+λ)·M·T0

[0059]

[0060]

[0061] Wherein, T represents the data collection time period; T0 represents the unit time; λ represents the first correlation coefficient; M represents the second correlation coefficient; m represents the number of data types contained in the power equipment data; Ci represents the average data volume corresponding to the i-th data type per unit time; C0 represents the preset data baseline volume; C z Indicates the average amount of electrical equipment data received per unit time;

[0062] A real-time data collection module for electric equipment, used to collect data of electric equipment in real time during the data collection period;

[0063] A data set generation module, configured to classify the electric device data within the data collection period according to the electric device information, electric power and electric power consumption data of the electric devices in the electric device data, and form a data set corresponding to the electric device information, electric power and electric power consumption data;

[0064] A data volume information extraction module, used to extract the data volume of each data set within each collection time period at the end of the data collection time period;

[0065] A data packet generation module, used to package the electric device data collected in the data collection time period according to the data volume of each data set in each collection time period to form multiple data packets;

[0066] The communication transmission module is used to send the data packets to the data analysis unit in sequence through the communication channel.

[0067] The technical effect of the above technical solution is: through the time period setting module, the data collection time period is set according to the time length of the unit time, and the length of the data collection time period is adjusted by using parameters such as the first correlation coefficient and the second correlation coefficient, so that the frequency and scope of data collection can be flexibly adjusted to meet different needs.

[0068] The real-time data collection module for power consumption equipment can collect power consumption equipment data in real time during the data collection period to ensure the real-time and accuracy of the data.

[0069] The data set generation module can classify the collected power-consuming equipment data according to different types of power-consuming equipment information to form corresponding data sets, which is convenient for subsequent data processing and analysis.

[0070] The data volume information extraction module can extract the data volume of each data set at the end of the data collection time period, so as to understand the changes of each data set within the time period.

[0071] The data packet generation module can package the collected power-consuming equipment data according to the data volume of each data set to form multiple data packets, which facilitates data transmission and processing.

[0072] The communication transmission module can send data packets to the data analysis unit in sequence through the communication channel, thereby realizing the remote transmission and centralized analysis and processing of the power equipment data.

[0073] In summary, this technical solution can realize the functions of real-time collection, classification, quantification and transmission of power equipment data, improve the accuracy and efficiency of power equipment data collection, and provide strong support for subsequent data analysis and decision-making.

[0074] Specifically, the data packet generation module includes:

[0075] A remaining capacity retrieving module, used to retrieve the remaining capacity of the communication channel at the end of the data collection time period;

[0076] A variation range information extraction module, used to extract the variation range of the remaining capacity per unit time in the historical operation data of the communication channel at the end of the data collection time period;

[0077] The compensation coefficient acquisition module is used to set the data volume compensation coefficient by using the remaining capacity of the communication channel and the variation range of the remaining capacity per unit time in the historical operation data of the communication channel, wherein the data volume compensation coefficient is obtained by the following formula:

[0078]

[0079] Where X represents the data volume compensation coefficient; X0 represents the preset initial compensation coefficient; n represents the number of unit time in the historical operation data; C y Indicates that at the end of the data collection period, the remaining capacity of the communication channel is retrieved; C yirepresents the remaining capacity in the i-th unit time in the historical operation data; λ represents the first correlation coefficient;

[0080] The unit data volume acquisition module is used to use the data volume compensation coefficient in combination with the data volume of each data set in each collection time period to determine the maximum unit data volume for data packaging, wherein the maximum unit data volume is obtained by the following formula:

[0081]

[0082] Among them, C max Indicates the maximum unit data volume; C max0 Indicates the preset initial maximum unit data volume; C ij represents the data volume of the jth data set in the i-th collection time period; k represents the total number of data in the collection time period; C zi represents the total amount of electrical equipment data collected in the i-th collection time period;

[0083] The data packaging processing module is used to package the power-consuming equipment data collected during the data collection time period using the maximum unit data volume as an upper limit value to form multiple data packets.

[0084] The technical effect of the above technical solution is that the remaining capacity retrieval module can retrieve the remaining capacity information of the communication channel in real time at the end of the data collection time period, so as to grasp the real-time load situation of the communication channel and provide a reference for subsequent data transmission.

[0085] The variation range information extraction module can extract the variation range of the remaining capacity per unit time from the historical operation data of the communication channel, so as to understand the historical operation status of the communication channel and provide a reference for setting the data volume compensation coefficient.

[0086] The compensation coefficient acquisition module can set the data volume compensation coefficient by using the remaining capacity of the communication channel and the variation range of the remaining capacity per unit time in the historical operation data of the communication channel, so as to compensate the data volume according to the actual situation and improve the efficiency and accuracy of data transmission.

[0087] The unit data volume acquisition module can utilize the data volume compensation coefficient in combination with the maximum unit data volume of each data set in each collection time period for data packaging, thereby better controlling the size of data packaging and adapting to different network environments and data processing requirements.

[0088] The data packaging processing module can package the power equipment data collected during the data collection period with the maximum unit data volume as the upper limit value to form multiple data packets, so as to better control the size and quantity of the data and adapt to different network transmission requirements.

[0089] In summary, this technical solution can package and process the data of power-consuming equipment according to the real-time load conditions and historical operating status of the communication channel, improve the efficiency and accuracy of data transmission, and provide strong support for subsequent data analysis and decision-making.

[0090] The data analysis unit is used to compare and analyze the sent power equipment data with the preset load threshold value to obtain the analysis result, find the load peak and valley value according to the analysis result, and determine the user's load equipment according to the analysis result. The current analysis result is stored in the database. At the same time, the trend of the user's energy equipment load in the future is predicted according to the historical data stored in the database in combination with statistical methods;

[0091] The energy management unit is used to make abnormal alarms based on the load analysis results and load forecast trends of the data analysis unit. At the same time, it generates management instructions based on the load analysis results and load forecast trends. The data processing unit is preset with a management strategy corresponding to the management instruction, so as to obtain the corresponding management strategy based on the management instruction and manage the user's load equipment according to the management strategy.

[0092] The technical effect of the above content is: the data of electrical equipment is collected in real time through the data collection unit, and the electrical equipment data includes the electrical equipment information, electrical power and electricity consumption data of the electrical equipment. The collected electrical equipment data can be provided to the data analysis unit for analysis. The data analysis unit can compare and analyze the electrical equipment data, and find the load peak and valley values ​​according to the analysis results and determine the user's load equipment. At the same time, based on the historical data in the database and statistical methods, the future load trend of the user's energy equipment can be predicted. Finally, the energy management unit can make abnormal alarms according to the load analysis results and load prediction trends, so that the user can timely grasp the load situation of the electrical equipment and the electrical equipment that may have load risks. At the same time, management instructions are generated, and the corresponding management strategy is obtained according to the management instructions. The user's load equipment is managed according to the management strategy, thereby realizing the monitoring and management of the user's load energy resources.

[0093] Data Analysis Unit, including:

[0094] The analysis module is pre-set with a load threshold value, which is used to compare and analyze the collected power equipment data with the load threshold value, so as to obtain the analysis result, and determine the user's load equipment through the analysis result; the analysis module compares the power equipment data, and generates charts and text reports of relevant energy consumption indicators of the power equipment data based on the comparison results, so as to find the load peak and valley values ​​according to the charts and reports;

[0095] The database is used to store the current analysis results. The database contains real-time data and historical data. The real-time data is used to send to the energy management unit, and the historical data provides a basis for the prediction of the prediction module; the real-time data is used to obtain and store the current analysis results of the power equipment data. The real-time data is used to send to the energy management unit. The real-time data is converted into historical data for storage after the energy management unit performs management work; the historical data is used to obtain the converted historical data for storage, and the historical data is retrieved and used by the prediction module;

[0096] The prediction module is used to retrieve historical data from the database and predict the future load trend of the user's energy equipment based on historical data and statistical methods; the prediction module predicts the future load trend through historical data and statistical methods, and generates statistical charts for display of the prediction results.

[0097] The technical effect of the above content: the power equipment data can be analyzed through the analysis module, and the load threshold is pre-set in the analysis module. The analysis module compares and analyzes the power equipment data with the pre-set load threshold to obtain the analysis results of the power equipment data. Through the power consumption and electricity consumption data of the power equipment data, it is possible to analyze whether there is a load situation, and generate charts and text reports of relevant energy consumption indicators of the power equipment data based on the load situation results of the comparison analysis for display, so as to find the load peak and trough values ​​according to the charts and reports, generate charts and text reports of relevant energy consumption indicators, and understand the user's load situation more intuitively. Based on the load situation obtained by the analysis, the load equipment can be determined according to the power equipment information of the power equipment data, and the determination of the load equipment is convenient for the energy management unit Accurately manage load equipment. The current analysis results obtained by the analysis module will be stored in the database as real-time data, and converted into historical data storage after the energy management unit performs management work. The historical data can be retrieved and used by the prediction module. The prediction module retrieves the historical data in the database for use. The user's historical data combined with statistical methods can be used to predict the future load trend of the user's energy equipment, and the prediction results can be generated into statistical charts for display. The statistical charts can more intuitively understand the user's electrical equipment with load risks. Through the prediction of load trends by the prediction module, the load risks of electrical equipment can be grasped, and potential contradictions in electricity supply and demand can be discovered, so that the energy management unit can take corresponding management measures for electrical equipment with load risks in a timely manner according to the prediction results.

[0098] Energy management unit, including:

[0099] An alarm module is used to make an abnormal alarm according to the load analysis results and load forecast trend of the data analysis unit, and send abnormal alarm information and load forecast trend information to the user at the same time;

[0100] A management module is used to generate management instructions according to the load analysis results and load forecast trends of the data analysis unit, obtain corresponding management strategies according to the generated management instructions, and manage the user's load equipment according to the management strategies;

[0101] The management strategy is pre-set in the management module, the management instruction has a corresponding management strategy, and the management module selects the corresponding management strategy according to the management instruction.

[0102] The technical effect of the above content: according to the load analysis results and load forecast trends, the alarm module can make an abnormal alarm, and at the same time, it can send abnormal alarm information and load forecast trend information to the user, so that the user can understand the existing load situation of the electrical equipment, and understand the electrical equipment with load risks. According to the load analysis results and load forecast trends, the management module can generate management instructions. The management strategy is pre-set in the management module, and the management instructions have corresponding management strategies. The corresponding management strategy can be selected according to the generated management instructions, so as to manage the user's load equipment according to the management strategy. The management instruction contains two parts, one part is generated according to the load analysis results, so as to adjust the power generation and load accordingly, and the other part is based on the load forecast trend, so as to increase or reduce the scheduling of the power generation group, and take corresponding management measures in time to avoid load risks. The management module can optimize the supply and demand of electricity and reduce energy waste.

[0103] Specifically, this embodiment also provides an implementation method of a smart energy unit device for monitoring and managing user load energy resources, comprising the following steps:

[0104] S1: Monitor the user's electrical equipment through the data collection unit, thereby collecting relevant electrical equipment data in real time, and send the collected user's electrical equipment data to the data processing module;

[0105] S2: Compare and analyze the data of power equipment through the data analysis unit, find the load peak and valley value according to the analysis results, and determine the user's load equipment according to the analysis results. The current analysis results will be stored in the database, and the future load trend of the user's energy equipment will be predicted according to the historical data in the database and the statistical method;

[0106] S3: The energy management unit makes an abnormal alarm and generates a management instruction based on the load analysis results and load forecast trend, and obtains the corresponding management strategy based on the management instruction, so as to manage the user's load equipment according to the management strategy.

[0107] Working principle: The data acquisition unit can monitor the user's electrical equipment in real time, so as to collect the data of the electrical equipment in real time, and the data analysis unit can compare and analyze the data of the electrical equipment with the preset load threshold through the analysis module, and generate charts and text reports of the relevant energy consumption indicators of the electrical equipment data based on the analysis results for display, so that the load peak and trough values ​​can be found according to the charts and reports, and the prediction module can retrieve the historical data in the database and combine with statistical methods to predict the future load trend of the user's energy equipment, and generate statistical charts for display. The load risks of electrical equipment can be grasped through statistical charts, and potential contradictions in power supply and demand can be found. The prediction module predicts the load trend. The energy management unit can take corresponding management measures for electrical equipment with load risks in a timely manner. According to the load analysis results and load forecast trends, the alarm module can make abnormal alarms, and at the same time, it can send abnormal alarm information and load forecast trend information to the user, so that the user can understand the existing load conditions of the electrical equipment and the electrical equipment with load risks. At the same time, according to the load analysis results and load forecast trends, the management module can generate management instructions. The management strategy is pre-set in the management module, and the management instructions have corresponding management strategies. The corresponding management strategy can be selected according to the generated management instructions, so as to manage the user's load equipment according to the management strategy. The management module can optimize the supply and demand of electricity and reduce energy waste.

[0108] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0109] 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 smart energy unit device for monitoring and managing user load energy resources, characterized in that: include: The data collection unit is used to monitor the user's electrical equipment in real time, thereby collecting relevant electrical equipment data in real time, and sending the collected user's electrical equipment data to the data processing module; The data analysis unit is used to compare and analyze the sent power equipment data with the preset load threshold value to obtain the analysis result, find the load peak and valley value according to the analysis result, and determine the user's load equipment according to the analysis result. The current analysis result is stored in the database. At the same time, the trend of the user's energy equipment load in the future is predicted according to the historical data stored in the database in combination with statistical methods; The energy management unit is used to make an abnormal alarm according to the load analysis results and load forecast trends of the data analysis unit, and generate management instructions according to the load analysis results and load forecast trends. The data processing unit is preset with a management strategy corresponding to the management instruction, so as to obtain the corresponding management strategy according to the management instruction, and manage the user's load equipment according to the management strategy. The power equipment data collected by the data collection unit includes power equipment information, power consumption and power consumption data of the power equipment. The data collection unit also includes: The time period setting module is used to set the data collection time period according to the length of the unit time, wherein the data collection time period is obtained by the following formula: T=(1+λ)·M·T0 Wherein, T represents the data collection time period; T0 represents the unit time; λ represents the first correlation coefficient; M represents the second correlation coefficient; m represents the number of data types contained in the power equipment data; Ci represents the average data volume corresponding to the i-th data type per unit time; C0 represents the preset data baseline volume; C z Indicates the average amount of electrical equipment data received per unit time; A real-time data collection module for electric equipment, used to collect data of electric equipment in real time during the data collection period; A data set generation module, configured to classify the electric device data within the data collection period according to the electric device information, electric power and electric power consumption data of the electric devices in the electric device data, to form a data set corresponding to the electric device information, electric power and electric power consumption data; A data volume information extraction module, used to extract the data volume of each data set within each collection time period at the end of the data collection time period; A data packet generation module, used to package the electric device data collected in the data collection time period according to the data volume of each data set in each collection time period to form multiple data packets; The communication transmission module is used to send the data packets to the data analysis unit in sequence through the communication channel. The data packet generation module includes: A remaining capacity retrieving module, used to retrieve the remaining capacity of the communication channel at the end of the data collection time period; A variation range information extraction module, used to extract the variation range of the remaining capacity per unit time in the historical operation data of the communication channel at the end of the data collection time period; The compensation coefficient acquisition module is used to set the data volume compensation coefficient by using the remaining capacity of the communication channel and the variation range of the remaining capacity per unit time in the historical operation data of the communication channel, wherein the data volume compensation coefficient is obtained by the following formula: Where X represents the data volume compensation coefficient; X0 represents the preset initial compensation coefficient; n represents the number of unit time in the historical operation data; C y Indicates that at the end of the data collection period, the remaining capacity of the communication channel is retrieved; C yi represents the remaining capacity in the i-th unit time in the historical operation data; λ represents the first correlation coefficient; The unit data volume acquisition module is used to use the data volume compensation coefficient in combination with the data volume of each data set in each collection time period to determine the maximum unit data volume for data packaging, wherein the maximum unit data volume is obtained by the following formula: Among them, C max Indicates the maximum unit data volume; C max0 Indicates the preset initial maximum unit data volume; C ij represents the data volume of the jth data set in the i-th collection time period; k represents the total number of data in the collection time period; C zi represents the total amount of electrical equipment data collected in the i-th collection time period; The data packaging processing module is used to package the power-consuming equipment data collected during the data collection time period using the maximum unit data volume as an upper limit value to form multiple data packets.

2. According to claim 1, a smart energy unit device for monitoring and managing user load energy resources is characterized by: The data analysis unit comprises: The analysis module is pre-set with a load threshold value, and is used to compare and analyze the collected power equipment data with the load threshold value, so as to obtain the analysis result, and determine the user's load equipment through the analysis result; The database is used to store the current analysis results. The database contains real-time data and historical data. The real-time data is used to send to the energy management unit, and the historical data provides a basis for the prediction module's prediction; The prediction module is used to retrieve historical data from the database and predict the future load trend of the user's energy equipment based on historical data and statistical methods.

3. The smart energy unit device for monitoring and managing user load energy resources according to claim 2 is characterized in that: The analysis module compares the data of the power-consuming equipment and generates charts and text reports of the relevant energy consumption indicators of the power-consuming equipment data based on the comparison results for display, so as to find the load peak and valley values ​​according to the charts and reports.

4. The smart energy unit device for monitoring and managing user load energy resources according to claim 3 is characterized in that: The prediction module predicts future load trends through historical data and statistical methods, and generates statistical charts based on the prediction results for display.

5. The smart energy unit device for monitoring and managing user load energy resources according to claim 4 is characterized in that: The database contains real-time data and historical data, specifically: Real-time data is used to obtain and store the current analysis results of the power equipment data. The real-time data is sent to the energy management unit. After the energy management unit performs management work, the real-time data is converted into historical data for storage; Historical data is used to obtain converted historical data for storage, and the historical data is retrieved and used by the prediction module.

6. The smart energy unit device for monitoring and managing user load energy resources according to claim 5 is characterized in that: The energy management unit comprises: An alarm module is used to make an abnormal alarm according to the load analysis results and load forecast trend of the data analysis unit, and send abnormal alarm information and load forecast trend information to the user at the same time; A management module is used to generate management instructions according to the load analysis results and load forecast trends of the data analysis unit, obtain corresponding management strategies according to the generated management instructions, and manage the user's load equipment according to the management strategies; The management strategy is pre-set in the management module, the management instruction has a corresponding management strategy, and the management module selects the corresponding management strategy according to the management instruction.

7. A method for implementing the smart energy unit device for monitoring and managing user load energy resources as claimed in claim 6, characterized in that: The following steps are involved: S1: Monitor the user's electrical equipment through the data collection unit, thereby collecting relevant electrical equipment data in real time, and send the collected user's electrical equipment data to the data processing module; S2: Compare and analyze the data of power equipment through the data analysis unit, find the load peak and valley value according to the analysis results, and determine the user's load equipment according to the analysis results. The current analysis results will be stored in the database, and the future load trend of the user's energy equipment will be predicted according to the historical data in the database and the statistical method; S3: The energy management unit makes an abnormal alarm and generates a management instruction based on the load analysis results and load forecast trend, and obtains the corresponding management strategy based on the management instruction, so as to manage the user's load equipment according to the management strategy.

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