Power load management method and system based on acquisition communication module

Abstract

The present invention relates to the technical field of power supply management and control, and provides a power load management method and system based on an acquisition communication module. The method comprises: acquiring historical power consumption information, performing trend analysis, and generating a regional power consumption prediction result; further performing regional power consumption tiered identification; obtaining historical power consumption information of a target user and performing power consumption identification evaluation; generating a power consumption suggestion on the basis of a power consumption identification evaluation result, and sending the power consumption suggestion to the target user; acquiring real-time power consumption information of the target user; performing matching evaluation on the basis of the power consumption suggestion to acquire an evaluation result, and generating a power consumption evaluation control parameter; further performing power consumption tiered cumulative calculation to obtain a tiered cumulative calculation constraint parameter; and performing power consumption management of the target user in light of the tiered cumulative calculation constraint parameter. The present invention solves the technical problem in the prior art of final power consumption management and control strength and accuracy being insufficient due to the information analysis process being not strict enough and the management and control mode being not complete enough in a power management method, and realizes rational and accurate electric energy management and control.

Description

A method and system for managing power load based on acquisition and communication module Technical Field

[0001] The present invention relates to the technical field of power supply control, and in particular to a power load management method and system based on an acquisition and communication module. Background Art

[0002] Electricity is an indispensable energy source in daily life and maintains the normal operation of society. During peak hours, due to the excessive number of electrical equipment, it is necessary to reasonably distribute electricity to ensure the electricity needs of users. At the same time, to ensure the normal operation of the power system, the electricity consumption of users can be monitored, and on this basis, the user's electricity consumption indicators can be controlled to control the electricity load during peak hours. In the existing technology, when the power grid is overloaded, electricity dispatch instructions can be obtained and secondary planning of electricity can be carried out to solve the current problem of electricity shortage. However, the commonly used power management methods today have certain drawbacks, and there is still room for improvement in the control of electricity. Technical issues

[0003] The existing power management methods lack rigorous information analysis processes and incomplete control methods, resulting in insufficient power consumption control strength and accuracy. Technical Solutions

[0004] The present application provides a power load management method and system based on an acquisition and communication module, which is used to solve the technical problem that the power management method in the existing technology is not rigorous enough in the information analysis process and the control method is not complete enough, resulting in insufficient power consumption control strength and accuracy.

[0005] In view of the above problems, the present application provides a method and system for power load management based on an acquisition and communication module.

[0006] In the first aspect, the present application provides a power load management method based on an acquisition and communication module, the method comprising: collecting historical power consumption information, performing trend analysis on the historical power consumption information, and generating a regional power consumption forecast result based on the trend analysis result; performing regional power consumption classification identification based on the regional power consumption forecast result to obtain a regional power consumption classification identification result; obtaining historical power consumption information of a target user, performing power consumption identification evaluation based on the historical power consumption information, and generating a power consumption identification evaluation result; generating power consumption suggestions based on the power consumption identification evaluation result, and sending the power consumption suggestions to the target user; collecting real-time power consumption information of the target user through the acquisition and communication module to obtain a real-time power consumption information collection result of the target user; matching and evaluating the real-time power consumption information collection result and the power consumption suggestion, and generating power consumption evaluation control parameters based on the evaluation result; performing power consumption classification cumulative calculation based on the real-time power consumption information collection result and the regional power consumption classification identification result to obtain classification cumulative calculation constraint parameters; and performing power consumption management of the target user based on the power consumption evaluation control parameters and the classification cumulative calculation constraint parameters.

[0007] In the second aspect, the present application provides an electric load management system based on an acquisition and communication module, the system comprising: an information analysis and prediction module, the information analysis and prediction module being used to collect historical electricity consumption information, perform trend analysis on the historical electricity consumption information, and generate regional electricity consumption forecast results based on the trend analysis results; a regional identification module, the regional identification module being used to perform regional electricity consumption classification identification based on the regional electricity consumption forecast results, and obtain regional electricity consumption classification identification results; an identification evaluation module, the identification evaluation module being used to obtain historical electricity consumption information of target users, perform electricity consumption identification evaluation based on the historical electricity consumption information, and generate electricity consumption identification evaluation results; a suggestion generation module, the suggestion generation module being used to generate electricity consumption suggestions based on the electricity consumption identification evaluation results, and convert the electricity consumption The suggestion is sent to the target user; an information collection module, the information collection module is used to collect the real-time electricity consumption information of the target user through the collection and communication module, and obtain the real-time electricity consumption information collection result of the target user; a parameter generation module, the parameter generation module is used to match and evaluate the real-time electricity consumption information collection result and the electricity consumption suggestion, and generate electricity consumption evaluation control parameters according to the evaluation result; a result calculation module, the result calculation module is used to perform electricity consumption grading cumulative calculation through the real-time electricity consumption information collection result and the regional electricity consumption grading identification result, and obtain the grading cumulative calculation constraint parameters; a parameter management module, the parameter management module is used to manage the electricity consumption of the target user based on the electricity consumption evaluation control parameters and the grading cumulative calculation constraint parameters. Beneficial effects

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] An embodiment of the present application provides a power load management method based on an acquisition and communication module, which collects historical power consumption information and performs trend analysis, generates a regional power consumption forecast result based on the trend analysis result, further performs regional power consumption classification identification to obtain a regional power consumption classification identification result; obtains the historical power consumption information of the target user and performs power consumption identification evaluation to generate a power consumption identification evaluation result, generates power consumption suggestions on this basis, and sends the power consumption suggestions to the target user; collects the real-time power consumption information of the target user through the acquisition and communication module to obtain the real-time power consumption information collection result of the target user, matches and evaluates it with the power consumption suggestion, obtains the evaluation result and generates power consumption evaluation control parameters, performs a graded cumulative calculation of power consumption based on the real-time power consumption information collection result and the regional power consumption classification identification result to obtain a graded cumulative calculation constraint parameter, and manages the power consumption of the target user based on the power consumption evaluation control parameter and the graded cumulative calculation constraint parameter. This solves the technical problem in the power management method in the prior art that the final power consumption control strength and accuracy are insufficient due to the insufficiently rigorous information analysis process and the insufficient completeness of the control method, thereby realizing rational and precise control of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG1 is a flow chart of a method for managing power load based on an acquisition and communication module provided by the present application;

[0011] FIG2 is a schematic diagram of a hierarchical cumulative calculation constraint parameter acquisition process in a power load management method based on an acquisition communication module provided by the present application;

[0012] FIG3 is a schematic diagram of a power consumption control process during peak power consumption time intervals in a power load management method based on an acquisition and communication module provided by the present application;

[0013] FIG4 is a schematic diagram of the structure of an electric load management system based on an acquisition and communication module provided by the present application.

[0014] Explanation of the accompanying drawings: information analysis and prediction module a, area identification module b, identification evaluation module c, suggestion generation module d, information collection module e, parameter generation module f, result calculation module g, parameter management module h. Modes for Carrying Out the Invention

[0015] The present application provides an electric power load management method based on an acquisition and communication module, which collects historical electricity consumption information for trend analysis to generate regional electricity consumption forecast results, further performs regional electricity consumption classification and identification, obtains historical electricity consumption information of target users and performs electricity consumption identification evaluation, generates electricity consumption suggestions based on the electricity consumption identification evaluation results and sends them to target users, collects real-time electricity consumption information of target users and matches and evaluates it with the electricity consumption suggestions, obtains the evaluation results and generates electricity consumption evaluation control parameters, further performs electricity consumption classification and cumulative calculation to obtain classification and cumulative calculation constraint parameters, and combines the classification and cumulative calculation constraint parameters to manage the electricity consumption of target users, so as to solve the technical problem that the power management method in the prior art is not rigorous enough in the information analysis process and the control method is not complete enough, resulting in insufficient final electricity consumption control strength and accuracy. Example 1

[0016] As shown in FIG1 , the present application provides a method for managing power loads based on an acquisition and communication module. The method is applied to an intelligent control system, wherein the intelligent control system is communicatively connected to the acquisition and communication module. The method includes:

[0017] Step S100: collecting historical electricity usage information, performing trend analysis on the historical electricity usage information, and generating regional electricity usage forecast results based on the trend analysis results;

[0018] Specifically, the present application provides an electric load management method based on an acquisition and communication module, which is applied to the intelligent control system. The intelligent control system is communicatively connected to the acquisition and communication module. The intelligent control module is used to control and constrain the electricity consumption time and electricity consumption of the target user. The acquisition and communication module is used to collect electricity consumption information of the target user and obtain real-time information for subsequent control. First, the historical electricity consumption information is collected. The historical electricity consumption information refers to the electricity consumption information of a certain period of time in the past, including specific electricity consumption time and electricity consumption. A trend analysis is performed on the historical electricity consumption information to determine the change of the electricity consumption information. For example, the electricity consumption time can be used as the horizontal coordinate and the electricity consumption can be used as the vertical coordinate to construct a rectangular coordinate system, perform visual distribution of the historical electricity consumption information, obtain the trend analysis results, and further use the trend analysis results as a reference to perform regional electricity consumption forecasts to generate the regional electricity consumption forecast results. The regional electricity consumption forecast results are used as a benchmark to provide a reference basis for electricity consumption control of later users.

[0019] Step S200: performing regional electricity consumption classification identification based on the regional electricity consumption prediction result to obtain a regional electricity consumption classification identification result;

[0020] Specifically, based on the regional electricity consumption forecast result, the regional electricity consumption is divided into regions based on the electricity consumption time, and the peak and valley time periods of electricity consumption in the region are determined. The electricity consumption classification results are obtained based on this as the regional classification standard. For example, Xi'an is taken as the control area, and the peak electricity consumption time periods of various sections in the control area are determined. For example, the peak electricity consumption time period in Yanta District is 8-9 o'clock, and the peak electricity consumption time period in Weiyang District is 9-10 o'clock. The two districts are divided into regions with different electricity consumption levels. Furthermore, the electricity consumption classification results are marked based on a certain sequence for later identification and distinction. The regional electricity consumption classification identification results are obtained, and the historical electricity consumption information of the target user is collected, that is, the electricity consumption information of the user to be controlled, and the historical electricity consumption information of the target user is obtained. The electricity consumption identification evaluation is performed based on the historical electricity consumption information to generate the electricity consumption identification evaluation results. The acquisition of the electricity consumption identification evaluation results lays a solid foundation for the electricity consumption analysis and evaluation of the target user.

[0021] Step S300: Obtain historical electricity usage information of a target user, perform electricity usage identification evaluation based on the historical electricity usage information, and generate an electricity usage identification evaluation result;

[0022] Step S400: generating an electricity usage suggestion based on the electricity usage identification evaluation result, and sending the electricity usage suggestion to the target user;

[0023] Specifically, the electricity consumption classification identification area to which the target user belongs is determined, and the historical electricity consumption information of the target user is further evaluated for electricity consumption identification. The regional electricity consumption classification identification result expresses the general peak electricity consumption period within the classification range, which is highly generalized. There will be a certain time deviation when it is accurate to the target user. The specific peak and trough electricity consumption time of the target user is determined, and then the electricity consumption identification evaluation result is generated. Furthermore, based on the electricity consumption identification evaluation result, electricity consumption suggestions suitable for the target user are generated. Based on the historical electricity consumption information of the target user, the electricity consumption of the target user is adjusted within a certain limit without affecting the normal life, so that the electricity distribution in the control area is more reasonable, avoiding uneven electricity distribution due to excessive electricity consumption during peak electricity consumption time periods, and even affecting the normal operation of the circuit, and then sending the electricity consumption suggestion to the target user. The target user can reasonably plan the electricity consumption time based on the electricity consumption suggestion.

[0024] Step S500: collecting the real-time electricity consumption information of the target user through the collection and communication module to obtain the real-time electricity consumption information collection result of the target user;

[0025] Step S600: generating power consumption evaluation control parameters according to the real-time power consumption information collection results and the power consumption suggestions matching evaluation results;

[0026] Specifically, based on the acquisition and communication module, the real-time electricity consumption information of the target user is collected to obtain the specific electricity consumption direction, electricity consumption time and electricity consumption of the target user. For example, the target user has a peak electricity consumption period of 7-8 o'clock, and the electricity consumption direction is mainly electrical appliances and electronic products. The relevant information obtained is integrated and processed as the real-time electricity consumption information collection result of the target user. The real-time electricity consumption information collection result of the target user is used as a benchmark for electricity consumption regulation and constraint. Furthermore, the real-time electricity consumption information collection result and the electricity consumption suggestion are matched and analyzed. With time as the measurement standard, the real-time electricity consumption information collection result and the electricity consumption suggestion are mapped and matched, and the difference between the two is checked in each time period to obtain the evaluation result. The electricity consumption evaluation control parameter is further generated based on the evaluation result to control the electricity consumption distribution of the target user in each time period, laying the foundation for the subsequent overall electricity consumption distribution control in the control area.

[0027] Step S700: performing a hierarchical cumulative calculation of electricity consumption based on the real-time electricity consumption information collection result and the regional electricity consumption classification identification result to obtain a hierarchical cumulative calculation constraint parameter;

[0028] Step S800: performing electricity consumption management of the target user based on the electricity consumption evaluation control parameter and the hierarchical cumulative calculation constraint parameter.

[0029] Specifically, based on the real-time electricity consumption information collection results and the regional electricity consumption grading identification results, the real-time electricity consumption information collection results are mapped to the regional electricity consumption grading identification results, and then the graded electricity consumption data of the peak electricity consumption time interval is accumulated and calculated to obtain the accumulated points calculation results, determine the level corresponding to the accumulated points calculation results, and perform level distribution identification on them, and generate the graded cumulative calculation constraint parameters based on the accumulated points calculation results, which are one of the reference bases for the target user electricity consumption management.

[0030] Furthermore, the peak power consumption time interval within the real-time power consumption time interval is determined, and the power consumption data of the actual peak power consumption time is analyzed to determine whether the constraint requirements are met. If the power consumption data of the actual peak power consumption time does not meet the constraint requirements, the power consumption evaluation control parameters and the graded cumulative calculation constraint parameters are used as a basis to generate an early warning message and send it to the target user to issue an early warning alert. At the same time, a power control instruction is generated to manage the peak power consumption time of the target user and to control the use of some non-essential electrical appliances to reduce the power consumption in the peak power consumption time interval and to carry out reasonable planning and control of power consumption.

[0031] Furthermore, as shown in FIG2 , step 700 of the present application further includes:

[0032] Step 710: Construct a hierarchical evaluation value set;

[0033] Step 720: performing hierarchical matching of the electricity consumption data of the real-time electricity consumption information collection result according to the regional electricity consumption classification identification result to obtain hierarchical electricity consumption data;

[0034] Step 730: Calculating the accumulated points of the graded evaluation value set and the graded electricity consumption data to obtain an accumulated point calculation result, wherein the accumulated point calculation result includes a grade distribution identifier;

[0035] Step 740: Obtain the hierarchical cumulative calculation constraint parameters according to the cumulative integral calculation result.

[0036] Specifically, the hierarchical evaluation value set is constructed. The hierarchical evaluation value set refers to the information basis for hierarchical division of user electricity consumption time, and is composed of multiple hierarchical sets. Based on the regional electricity consumption hierarchical identification result, the collected multiple real-time electricity consumption information collection results are hierarchically matched. The real-time electricity consumption information collection results correspond to the regional hierarchical identification result, including the electricity consumption data information of the peak time period corresponding to the regional electricity consumption hierarchical identification result, and the hierarchical electricity consumption data is obtained. Further, based on the hierarchical evaluation value set and the hierarchical electricity consumption data, a cumulative score is calculated to determine the total electricity consumption of each level in the hierarchical evaluation set, obtain the cumulative score and determine the distribution of the score, that is, the distribution of peak and trough electricity consumption time of users in each level, including the proportion information of the peak electricity consumption time period. Further, a grade distribution identification is performed to obtain the cumulative score calculation result, providing a data basis for subsequent electricity consumption management and control. The hierarchical cumulative calculation constraint parameter is obtained based on the cumulative score calculation result. The hierarchical cumulative calculation constraint parameter refers to the total score constraint value for maintaining normal circuit operation. Subsequent electricity consumption management and control is performed based on the hierarchical cumulative calculation constraint parameter.

[0037] Furthermore, step 740 of the present application also includes:

[0038] Step 741: performing multi-level grading on the graded evaluation value set to obtain a first-level set, a second-level set, and a third-level set;

[0039] Step 742: Based on the accumulated score calculation result, the percentage of each level in the score is evaluated to obtain the percentage of the first level set, the percentage of the second level set, and the percentage of the third level set;

[0040] Step 743: Obtain the level distribution identifier according to the first-level set proportion, the second-level set proportion, and the third-level set proportion.

[0041] Specifically, the hierarchical evaluation value set is constructed, and the hierarchical evaluation value set is multi-level graded based on the peak power consumption time period, the first level set, the second level set and the third level set are obtained, and the proportion of points at each level of the hierarchical evaluation value set is evaluated based on the cumulative points calculation result, and the proportion of the cumulative points of each level to the total cumulative points is determined, and the proportion of the first level set, the second level set and the third level set are obtained. The proportion of the first level set, the second level set and the third level set are used as the basis for judgment, and the grade distribution is identified to determine the grade distribution identification result, so as to facilitate identification and analysis to complete subsequent management and control.

[0042] Furthermore, as shown in FIG3 , step S800 of the present application further includes:

[0043] Step S810: generating a peak power consumption constraint value according to the historical power consumption information;

[0044] Step S820: obtaining actual peak power consumption data of the peak power consumption time interval in the real-time power consumption information;

[0045] Step S830: determining whether the actual peak power consumption data satisfies the peak power consumption constraint value;

[0046] Step S840: When the actual peak power consumption data does not meet the peak power consumption constraint value, generating a power consumption control instruction;

[0047] Step S850: performing power consumption control during the peak power consumption time interval of the target user according to the power consumption control instruction.

[0048] Specifically, the historical electricity consumption information is obtained, and the peak electricity consumption constraint value is generated based on this information. The peak electricity consumption constraint value refers to the maximum electricity consumption required to maintain normal operation during the peak electricity consumption period. Furthermore, the peak electricity consumption time interval is determined from the real-time electricity consumption information, and the electricity consumption data corresponding to the actual peak electricity consumption time within the peak electricity consumption time interval is obtained. The actual peak electricity consumption data is further compared with the peak electricity consumption constraint value to determine whether the peak electricity consumption data meets the peak electricity consumption constraint value. When the actual peak electricity consumption data does not meet the peak electricity consumption constraint value, it means that the current peak electricity consumption data has exceeded the standard, and then the electricity consumption control instruction is generated. The peak electricity consumption time of the target user is controlled by the received electricity consumption control instruction. For example, some non-essential electricity consumption can be controlled, and other time intervals can be planned to complete it, so as to slow down the accumulation of electricity consumption data within the peak electricity consumption time interval.

[0049] Furthermore, step S830 of this application also includes:

[0050] Step S831: when the actual peak power consumption data satisfies the peak power consumption constraint value, obtaining a remaining peak power consumption value according to the actual peak power consumption data and the peak power consumption constraint value;

[0051] Step S832: Determine whether the remaining peak power consumption value meets the warning interval;

[0052] Step S833: When the remaining peak power consumption value meets the warning interval, generate warning information;

[0053] Step S834: Send the warning information to the target user.

[0054] Furthermore, step S833 of this application also includes:

[0055] Step S8331: Determine whether the remaining time between the current time node and the peak interval meets the preset time threshold;

[0056] Step S8332: When the remaining time meets the preset time threshold, the warning information is not generated.

[0057] Specifically, a comparison is performed between the actual peak power consumption data and the peak power consumption constraint value. When the actual peak power consumption data meets the peak power consumption constraint value, a difference calculation is performed between the actual peak power consumption data and the peak power consumption constraint value to obtain the remaining peak power consumption value. It is further determined whether the remaining peak power consumption value meets the warning interval. The warning interval refers to an interval for determining whether the remaining peak power consumption value exceeds the standard. When the remaining peak power consumption value meets the warning interval, it indicates that the current power consumption value has exceeded the standard, which may affect the subsequent power consumption. The remaining time between the current time node and the peak interval is further obtained to determine whether the remaining peak power consumption value meets the warning interval. Whether the remaining time meets the preset time threshold, the preset time threshold refers to the limit time set to adapt to the early warning interval. When the remaining time meets the preset time threshold, it means that the remaining time of the peak interval is short, and the remaining peak power consumption value can provide the power supply and demand within the time period, and the early warning information is not generated. When the remaining time does not meet the preset time threshold, it means that the remaining time of the peak interval is long, and the remaining peak power consumption value cannot meet the power supply and demand within the time period, and then the early warning information is generated, and the early warning information is further sent to the target user to issue an electricity usage warning to the target user.

[0058] Furthermore, step S850 of the present application also includes:

[0059] Step S851: Perform a cumulative control identification on the target user to obtain a cumulative control identification result;

[0060] Step S852: Using the accumulated control identification result, the target user's electrical appliance usage on / off constraints are performed during the peak power consumption time interval.

[0061] Specifically, when the actual peak power consumption data does not meet the peak power consumption constraint value, a power consumption control instruction is generated, and then the target user is identified for control, the power consumption direction of the target user is determined and cumulatively identified, and further classified to determine the necessary power consumption and non-essential power consumption of the target user, and the relevant information is classified and integrated. The power consumption of the target user is controlled based on the control cumulative identification result. When the current power consumption exceeds the standard, some non-essential power consumption can be constrained to open and close related electrical appliances within the peak power consumption time interval of the target user, such as washing machines, televisions, etc., to reduce the power consumption in the peak power consumption time interval. Example 2

[0062] Based on the same inventive concept as the power load management method based on the acquisition and communication module in the aforementioned embodiment, as shown in FIG4 , the present application provides a power load management system based on the acquisition and communication module, the system comprising:

[0063] An information analysis and prediction module a is configured to collect historical electricity usage information, perform trend analysis on the historical electricity usage information, and generate a regional electricity usage forecast result based on the trend analysis result;

[0064] A regional identification module b is configured to perform regional electricity consumption classification identification based on the regional electricity consumption forecast result to obtain a regional electricity consumption classification identification result;

[0065] an identification evaluation module c, which is used to obtain historical electricity usage information of a target user, perform an electricity usage identification evaluation based on the historical electricity usage information, and generate an electricity usage identification evaluation result;

[0066] A suggestion generating module d is configured to generate a power usage suggestion based on the power usage identification evaluation result and send the power usage suggestion to the target user;

[0067] An information collection module e is configured to collect the real-time electricity consumption information of the target user through the collection and communication module to obtain a collection result of the real-time electricity consumption information of the target user;

[0068] A parameter generation module f is configured to generate a power consumption evaluation control parameter based on the real-time power consumption information collection result and the power consumption suggestion matching evaluation result;

[0069] A result calculation module g, which is used to perform a hierarchical cumulative calculation of electricity consumption based on the real-time electricity consumption information collection results and the regional electricity consumption classification identification results to obtain a hierarchical cumulative calculation constraint parameter;

[0070] The parameter management module h is used to manage the electricity consumption of the target user based on the electricity consumption evaluation control parameter and the hierarchical cumulative calculation constraint parameter.

[0071] Furthermore, the system further comprises:

[0072] A set construction module, wherein the set construction module is used to construct a hierarchical evaluation value set;

[0073] a data matching module, configured to perform hierarchical matching of the electricity consumption data of the real-time electricity consumption information collection result according to the regional electricity consumption classification identification result, to obtain hierarchical electricity consumption data;

[0074] an integral calculation module, configured to calculate the accumulated integrals of the hierarchical evaluation value set and the hierarchical electricity consumption data to obtain an accumulated integral calculation result, wherein the accumulated integral calculation result includes a grade distribution identifier;

[0075] A constraint parameter acquisition module is used to obtain the hierarchical cumulative calculation constraint parameters according to the cumulative integral calculation result.

[0076] Furthermore, the system further comprises:

[0077] A set grading module, the set grading module is used to perform multi-level grading on the graded evaluation value set to obtain a first-level set, a second-level set, and a third-level set;

[0078] A proportion evaluation module is used to evaluate the proportion of the levels in the points based on the cumulative points calculation result, and obtain the proportion of the first level set, the proportion of the second level set, and the proportion of the third level set;

[0079] A distribution identification module is used to obtain the level distribution identification according to the proportion of the first level set, the proportion of the second level set and the proportion of the third level set.

[0080] Furthermore, the system further comprises:

[0081] A constraint value generation module, configured to generate a peak power consumption constraint value based on the historical power consumption information;

[0082] A data acquisition module, the data acquisition module is used to obtain actual peak power consumption data of the peak power consumption time interval in the real-time power consumption information;

[0083] a data judgment module, configured to judge whether the actual peak power consumption data satisfies the peak power consumption constraint value;

[0084] an instruction generation module, configured to generate an electricity consumption control instruction when the actual peak electricity consumption data does not satisfy the peak electricity consumption constraint value;

[0085] The power consumption control module is used to control the power consumption of the target user during the peak power consumption time interval according to the power consumption control instruction.

[0086] Furthermore, the system further comprises:

[0087] A power consumption value acquisition module, configured to obtain a remaining peak power consumption value according to the actual peak power consumption data and the peak power consumption constraint value when the actual peak power consumption data satisfies the peak power consumption constraint value;

[0088] A power consumption value judgment module, the power consumption value judgment module is used to judge whether the remaining peak power consumption value meets the warning interval;

[0089] a warning information generating module, configured to generate warning information when the remaining peak power consumption value meets the warning interval;

[0090] An information sending module is used to send the warning information to the target user.

[0091] Furthermore, the system further comprises:

[0092] A threshold judgment module is used to judge whether the remaining time between the current time node and the peak interval meets a preset time threshold;

[0093] An information generation and judgment module is used to not generate the warning information when the remaining time meets the preset time threshold.

[0094] Furthermore, the system further comprises:

[0095] A user identification module, the user identification module is used to perform management and control cumulative identification on the target user and obtain a management and control cumulative identification result;

[0096] The power consumption constraint module is used to constrain the on / off use of electrical appliances during the peak power consumption time interval of the target user according to the control cumulative identification result.

[0097] Through the above detailed description of the power load management method based on the acquisition and communication module in this specification, those skilled in the art can clearly understand the power load management method and system based on the acquisition and communication module in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0098] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A power load management method based on an acquisition and communication module, characterized in that: The method is applied to an intelligent control system, wherein the intelligent control system is communicatively connected to an acquisition communication module, and the method includes: Collecting historical electricity usage information, performing trend analysis on the historical electricity usage information, and generating regional electricity usage forecast results based on the trend analysis results; Performing regional electricity consumption classification identification based on the regional electricity consumption forecast result to obtain a regional electricity consumption classification identification result; Obtaining historical electricity usage information of a target user, performing an electricity usage identification evaluation based on the historical electricity usage information, and generating an electricity usage identification evaluation result; generating an electricity usage suggestion based on the electricity usage identification evaluation result, and sending the electricity usage suggestion to the target user; The real-time electricity consumption information of the target user is collected by the collection and communication module to obtain the real-time electricity consumption information collection result of the target user; According to the real-time electricity consumption information collection results and the electricity consumption suggestions matching evaluation, generating electricity consumption evaluation control parameters according to the evaluation results; Performing a graded cumulative calculation of electricity consumption based on the real-time electricity consumption information collection results and the regional electricity consumption grade identification results to obtain graded cumulative calculation constraint parameters; The target user's electricity consumption management is performed based on the electricity consumption evaluation control parameter and the hierarchical cumulative calculation constraint parameter.

2. The method according to claim 1, wherein The method further comprises: Constructing a set of hierarchical evaluation values; Performing hierarchical matching of the electricity consumption data of the real-time electricity consumption information collection result according to the regional electricity consumption classification identification result to obtain hierarchical electricity consumption data; Obtaining a cumulative integral calculation result based on the set of graded evaluation values ​​and the cumulative integral of the graded electricity consumption data, wherein the cumulative integral calculation result includes a grade distribution identifier; The hierarchical cumulative calculation constraint parameter is obtained according to the cumulative integral calculation result.

3. The method according to claim 2, wherein The method further comprises: Performing multi-level grading on the graded evaluation value set to obtain a first-level set, a second-level set, and a third-level set; Based on the cumulative score calculation result, the level proportion evaluation is performed to obtain the first level set proportion, the second level set proportion and the third level set proportion; The level distribution identifier is obtained according to the first-level set proportion, the second-level set proportion, and the third-level set proportion.

4. The method according to claim 1, wherein The method further comprises: generating a peak power consumption constraint value according to the historical power consumption information; Obtaining actual peak power consumption data of a peak power consumption time interval in the real-time power consumption information; Determining whether the actual peak power consumption data satisfies the peak power consumption constraint value; When the actual peak power consumption data does not meet the peak power consumption constraint value, generating a power consumption control instruction; The power consumption of the target user during the peak power consumption time interval is controlled according to the power consumption control instruction.

5. The method according to claim 4, wherein The method further comprises: When the actual peak power consumption data satisfies the peak power consumption constraint value, obtaining a remaining peak power consumption value according to the actual peak power consumption data and the peak power consumption constraint value; Determining whether the remaining peak power consumption value meets the warning interval; When the remaining peak power consumption value meets the warning interval, generating warning information; The warning information is sent to the target user.

6. The method according to claim 5, wherein The method further comprises: Determine whether the remaining time between the current time node and the peak interval meets the preset time threshold; When the remaining time meets the preset time threshold, the warning information is not generated.

7. The method according to claim 4, wherein The method further comprises: Performing a cumulative control identification on the target user to obtain a cumulative control identification result; The target user's electrical appliance use switching constraints are implemented during the peak power consumption time interval using the control cumulative identification result.

8. A power load management system based on an acquisition and communication module, characterized in that: The system is communicatively connected to the acquisition communication module, and the system includes: An information analysis and prediction module is used to collect historical electricity usage information, perform trend analysis on the historical electricity usage information, and generate regional electricity usage forecast results based on the trend analysis results; A regional identification module, the regional identification module being used to perform regional electricity consumption classification identification based on the regional electricity consumption forecast result to obtain a regional electricity consumption classification identification result; An identification evaluation module, which is used to obtain historical electricity usage information of a target user, perform an electricity identification evaluation based on the historical electricity usage information, and generate an electricity identification evaluation result; A suggestion generation module, configured to generate a power usage suggestion based on the power usage identification evaluation result, and send the power usage suggestion to the target user; An information collection module, configured to collect the target user's real-time electricity consumption information through the collection and communication module, and obtain a collection result of the target user's real-time electricity consumption information; A parameter generation module, the parameter generation module is used to generate power consumption evaluation control parameters according to the evaluation results based on the real-time power consumption information collection results and the power consumption suggestions; A result calculation module, which is used to perform a hierarchical cumulative calculation of electricity consumption based on the real-time electricity consumption information collection results and the regional electricity consumption classification identification results to obtain a hierarchical cumulative calculation constraint parameter; A parameter management module is used to manage the power consumption of the target user based on the power consumption evaluation control parameter and the hierarchical cumulative calculation constraint parameter.

Images