Cloud data platform for power dynamic analysis and application method thereof

Abstract

The invention discloses a cloud data platform for electric power dynamic analysis and an application method thereof. The cloud data platform comprises a data acquisition module for dynamically acquiring various data of electric power users in real time; the storage module is used for converting various types of collected data into multi-dimensional standardized data items for storage; the association analysis module performs association calculation and analysis on the stored standardized data items based on a preset logic relationship and outputs a corresponding data association analysis result, including: calculating to obtain total electricity charge based on a preset electricity charge calculation model, and calculating to obtain electricity utilization efficiency evaluation based on a preset electricity utilization behavior analysis model; calculating based on a preset capacity management model to obtain a capacity adjustment suggestion; and the output module outputs a data association analysis result in a standardized report form. According to the method, by defining the logic relation and the calculation rule between the data items, the problems of data dispersion, poor relevance and low analysis efficiency of a traditional power report are solved, and power consumption cost optimization suggestions can be provided for users.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power data management and analysis, in particular to a cloud data platform for power dynamic analysis and an application method thereof. BACKGROUND

[0002] With the rapid development and digital transformation of the power industry, a large amount of data is generated in the power system, including basic information of power users, metering device data, electricity data, and electricity cost data, etc. These data have important value for power users and power supply departments, such as helping power users optimize electricity consumption behavior to reduce costs. However, traditional power data management and analysis methods have many problems: Data is scattered and poorly correlated: various types of data of power users are often scattered and stored in different systems, and there is a lack of effective correlation between basic information, metering device data, electricity data, and electricity cost data, making it difficult to integrate data and form a complete and valuable data chain, making it difficult to comprehensively and systematically analyze the electricity consumption behavior and cost structure of power users.

[0003] Low analysis efficiency: traditional methods mainly rely on manual data collection, sorting and analysis, which is not only inefficient but also prone to errors. At the same time, the lack of standardized report systems and automated analysis tools limits the depth and breadth of data analysis, making it difficult to quickly and accurately mine useful information from data.

[0004] Insufficient dynamic response capability: the power market environment is complex and variable, and electricity prices, user electricity consumption patterns, etc. may change at any time. Traditional report systems use fixed templates to generate, which cannot update calculation logic and analysis models in real time according to these changes, resulting in electricity cost calculation iteration lagging behind business needs, and unable to provide accurate cost analysis and optimization suggestions for users in a timely manner.

[0005] Lack of in-depth analysis: existing technologies can only achieve simple aggregation and preliminary processing of data, and no effective analysis model has been established to deeply mine the internal relationships between data, such as being unable to analyze the electricity efficiency from electricity, electricity cost and capacity data, whether the capacity configuration is reasonable, etc., making it difficult to provide valuable electricity cost optimization suggestions for power users.

[0006] In view of the above problems, there is an urgent need for a power data management and analysis system that can effectively integrate various types of data of power users, realize automated correlation analysis, and dynamically respond to changes, to meet the needs of the power industry for fine-grained data management. SUMMARY

[0007] The purpose of this application is to provide a cloud data platform and its application method for dynamic power analysis. By defining the logical relationships and calculation rules between data items, it solves the problems of scattered data, poor correlation and low analysis efficiency in traditional power reports, and can provide users with suggestions for optimizing electricity costs.

[0008] The first aspect of this application provides a cloud data platform for power dynamic analysis, the cloud data platform comprising: The data acquisition module is used to collect various types of data from electricity users in real time, including basic information data, metering equipment data, electricity consumption data, and electricity bill data. The storage module is used to convert the collected data into multi-dimensional standardized data items for storage. Multiple standardized data items are used to form a standardized report structure. The correlation analysis module is used to perform correlation calculations and analyses on multiple standardized data items stored based on preset logical relationships, and output the corresponding data correlation analysis results, including: calculating the total electricity cost based on a preset electricity cost calculation model, calculating the electricity efficiency assessment based on a preset electricity consumption behavior analysis model, and calculating capacity adjustment suggestions based on a preset capacity management model. The output module is used to output the data correlation analysis results in the form of standardized reports. The standardized reports include multiple standardized data and analysis indicators. The analysis indicators are used to evaluate the electricity consumption behavior of electricity users. Based on the analysis indicators, the output module outputs suggestions for optimizing the electricity consumption behavior of electricity users.

[0009] In particular, when performing data standardization processing through the storage module, this application can establish a multi-dimensional architecture encompassing entity dimension, business dimension, and spatiotemporal dimension, linking the collected basic information data, metering equipment data, electricity data, and electricity bill data through different dimensions.

[0010] A second aspect of this application provides an application method for a cloud data platform, applied to the cloud data platform for power dynamic analysis as described above, the application method comprising: The data acquisition module collects various types of data from electricity users in real time, including basic information data, metering equipment data, electricity consumption data, and electricity bill data. The data storage module is used to standardize and transform the collected data into multi-dimensional standardized data items for storage. These standardized data items are used to form a standardized report structure. The data association analysis module performs association calculations and analyses on multiple standardized data items stored based on preset logical relationships, and outputs corresponding data association analysis results, including calculating the total electricity cost based on the preset electricity cost calculation model, calculating the electricity efficiency assessment based on the preset electricity consumption behavior analysis model, and calculating the capacity adjustment suggestions based on the preset capacity management model. The output module outputs the data correlation analysis results in the form of standardized reports. The standardized reports include multiple standardized data and analysis indicators. The analysis indicators are used to evaluate the electricity consumption behavior of electricity users. Based on the analysis indicators, the output module outputs suggestions for optimizing the electricity consumption behavior of electricity users.

[0011] Unlike existing technologies, this application collects various types of data from power users through a data acquisition module and uses a storage module to transform the collected data into multi-dimensional standardized data items for storage. These items serve as the foundational data items for standardized report structures. This integrated and standardized storage method solves the problems of scattered and inconsistent formats in traditional power data, making the data more standardized and orderly. This application utilizes a correlation analysis module to perform correlation calculations and analyses on the stored standardized data items based on preset logical relationships. It can output data correlation analysis results covering various aspects, including total electricity cost calculation, electricity efficiency assessment, and capacity adjustment suggestions. This comprehensively reflects the electricity user's electricity consumption behavior and cost structure, and provides users with specific optimization suggestions to help them reduce electricity costs and improve electricity efficiency.

[0012] This application uses a data acquisition module to dynamically collect various types of data from electricity users in real time, and utilizes a correlation analysis module to perform correlation analysis and output results promptly, ensuring the timeliness of the data and the accuracy of the analysis results. Compared with traditional reporting systems, it can quickly respond to changes in electricity price policies and user electricity consumption patterns, update calculation logic and analysis models in a timely manner, and provide users with the latest electricity cost analysis and optimization suggestions, better adapting to the dynamic changes in the electricity market.

[0013] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the structure of an embodiment of the cloud data platform for power dynamic analysis according to this application; Figure 2 This application presents a layered architecture design for a cloud data platform used for dynamic power analysis. Figure 3 This is a flowchart illustrating an embodiment of the application method of the cloud data platform of this application; Figure 4 yesFigure 3 A schematic diagram of the first specific process of step S13 in the process; Figure 5 yes Figure 3 The second detailed process diagram of step S13 in the process; Figure 6 yes Figure 3 The third detailed process diagram of step S13 in the process; Icon labels: 10-Cloud Data Platform; 11-Data Acquisition Module; 12-Storage Module; 13-Correlation Analysis Module; 14-Output Module; 21-Data Access Layer; 22-Data Management Layer; 23-Intelligent Analysis Layer; 24-Visualization Output Layer; 25-Feedback Adjustment Layer. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions of this application, the cloud data platform for power dynamic analysis and its application method provided in this application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It is understood that the described embodiments are merely some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] The terms "first," "second," etc., used in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0018] Due to the problems of data dispersion and poor correlation, low analysis efficiency, insufficient dynamic response capability and lack of in-depth analysis in existing power data management and analysis methods, this application provides a cloud data platform for power dynamic analysis. By defining the logical relationship and calculation rules between data items, it solves the problems of data dispersion, poor correlation and low analysis efficiency in traditional power reports, and can provide users with suggestions for optimizing electricity costs.

[0019] Please see Figure 1 The cloud data platform 10 for power dynamic analysis in this application includes a data acquisition module 11, a storage module 12, a correlation analysis module 13, and an output module 14.

[0020] In this embodiment, the data acquisition module 11 is used to collect various types of data from power users in real time, including basic information data, metering equipment data, power consumption data, and electricity bill data.

[0021] Specifically, basic information data may include user ID, user name, province (e.g., "Guangdong"), region (e.g., "Dongguan Power Supply Bureau"), settlement account name, user status (e.g., "operating"), user address, filing time, update time, feeder, substation, organization, and superior organization, etc.; the metering equipment data includes transformer capacity, metering point number, meter asset number, voltage level, metering method, wiring method, comprehensive multiplier, voltage level (e.g., "AC 10kV"), metering point status (e.g., "operating"), metering method (e.g., "high-voltage supply and high-voltage metering"), measurement point type (e.g., "main meter"), electricity consumption nature (e.g., "large industrial electricity consumption"), wiring method (e.g., "three-phase three-wire"), etc. The electricity consumption data includes total active power consumption, peak-hour power consumption, normal-hour power consumption, valley-hour power consumption, peak-hour power consumption, positive active power consumption, and negative active power consumption; the electricity charge data includes the basic electricity charge calculation method (e.g., "based on actual maximum demand"), basic charge unit price, capacity charge, total electricity charge, power regulation charge, monthly basic charge, difference between capacity charge and maximum demand charge, applied capacity, capacity at the metering point, contract capacity, total capacity, maximum secondary demand value, primary demand value, power consumption per unit of power, data occurrence time, maximum demand charge, electricity charge year and month, total electricity charge, whether it meets the standard of 260 kWh per kVA, average electricity consumption, basic charge as a percentage of total electricity charge, demand value to capacity value ratio, year, cumulative electricity consumption of the previous year, etc.

[0022] Among them, the capacity charge and the maximum demand charge are used to determine the basic electricity charge for the current month for electricity users, and the transformer capacity is the smallest integer value that is greater than or equal to the total active power / power factor.

[0023] The storage module 12 in this embodiment is used to convert the collected data into multi-dimensional standardized data items for storage. Multiple standardized data items are used to form a standardized report structure. The standardized report structure includes standardized data items in dimensions such as basic information data items, metering equipment data items, electricity data items, and electricity fee data items, to ensure the standardization and consistency of data storage.

[0024] The correlation analysis module 13 in this embodiment is used to perform correlation calculation and analysis on multiple standardized data items stored based on preset logical relationships, and output corresponding data correlation analysis results, including: calculating the total electricity cost based on a preset electricity cost calculation model, calculating the electricity efficiency assessment based on a preset electricity consumption behavior analysis model, and calculating capacity adjustment suggestions based on a preset capacity management model.

[0025] Specifically, the electricity cost data in this embodiment further includes primary demand value. The preset electricity cost calculation model satisfies: total electricity cost = monthly basic electricity cost + electricity cost + power regulation cost, where the monthly basic electricity cost is the maximum value between capacity cost and maximum demand cost. Capacity cost = transformer capacity × basic unit price. Maximum demand cost = primary demand value × basic unit price. Electricity cost = peak period electricity consumption × peak price + normal period electricity consumption × normal price + valley period electricity consumption × valley price + peak period electricity consumption × peak price.

[0026] Specifically, the electricity cost data in this embodiment further includes primary demand value, maximum secondary demand value, data occurrence time, and time-of-use electricity consumption. A preset electricity consumption behavior analysis model combines primary demand value, maximum secondary demand value, and data occurrence time to identify peak electricity consumption periods, which are the periods when the maximum secondary demand value occurs.

[0027] The preset electricity consumption behavior analysis model satisfies: unit power consumption = time-of-use electricity / demand value × time, where the demand value is the primary demand value corresponding to the time-of-use electricity occurrence period, the time is the duration corresponding to the time-of-use electricity occurrence period, and the unit power consumption is used to analyze and evaluate equipment energy consumption.

[0028] The preset electricity consumption behavior analysis model further satisfies the following: average electricity consumption = total active power consumption / monthly duration, and average electricity consumption is used to analyze and evaluate changes in electricity intensity.

[0029] Specifically, the electricity cost data in this embodiment further includes primary demand value, applied capacity, contracted capacity, total capacity, and demand value, wherein the demand value is the primary demand value corresponding to a specific time period.

[0030] The preset capacity management model satisfies the following: Demand Ratio = Demand Value / Reference Capacity × 100%, where the reference capacity is the applied capacity, contracted capacity, or total capacity. The demand ratio is used to analyze and evaluate the matching degree between the user's actual demand and the applied capacity, contracted capacity, or total capacity. When the reference capacity is the contracted capacity, if the demand ratio is too high, the correlation analysis module will prompt the power user to increase capacity; if the demand ratio is too low, the correlation analysis module will suggest adjusting the contracted capacity. Optionally, if the demand ratio is consistently <60%, capacity reduction is recommended; if the demand ratio is consistently >95%, capacity increase is recommended, etc.

[0031] The correlation analysis module 13 in this embodiment also verifies the accuracy of the data based on a preset data verification model. The preset data verification model satisfies the following: total metered power consumption = peak period power consumption + normal period power consumption + valley period power consumption + peak period power consumption. If the total metered power consumption = total active power consumption, the data is judged to be accurate. If the data is inconsistent, it is marked as abnormal and a verification reminder is triggered.

[0032] The correlation analysis module 13 also verifies the accuracy of data based on a preset data verification model. It can promptly detect and mark abnormal data and trigger verification reminders, ensuring the accuracy and reliability of the data stored and analyzed by the cloud data platform 10. This avoids analytical biases and decision-making errors caused by data errors, further improving the overall performance and credibility of the cloud data platform 10.

[0033] The output module 14 in this embodiment outputs the data correlation analysis results in the form of a standardized report. The standardized report includes multiple standardized data and analysis indicators. The analysis indicators are used to evaluate the electricity consumption behavior of power users. Based on the analysis indicators, the output module 14 outputs suggestions for optimizing the electricity consumption behavior of power users. Optionally, the analysis indicators in this embodiment include the percentage of basic electricity charges to total electricity charges, average electricity consumption, and whether it meets the standard of 260 kWh per kilovolt-ampere. These indicators can be stored in the electricity charge data items according to data item classification. The analysis indicators can be one or more of the data analysis results.

[0034] Specifically, the output standardized reports include both standardized data items directly collected by the data acquisition module 11 and stored in the storage module 12, and data association analysis results and analysis indicators output by the association analysis module 13.

[0035] Basic information data items include basic electricity charge calculation method, metering point number, province, region, user number, user name, settlement account name, user status, user address, filing time, update time, feeder, substation, organization, and superior organization; metering equipment data items include transformer capacity, meter asset number, comprehensive multiplier, voltage level, metering point status, metering method, measurement point type, electricity consumption nature, and wiring method; electricity data items include total active power, positive active power, peak period power, normal period power, and off-peak period power. Electricity consumption during peak hours, peak-hour electricity consumption, and reactive active power consumption; electricity bill data items include the difference between capacity electricity bill and maximum demand electricity bill, basic fee unit price, applied capacity, capacity of electricity bill metering point, contract capacity, total capacity, maximum secondary demand value, primary demand value, electricity consumption per unit power, data occurrence time, maximum demand electricity bill, capacity electricity bill, electricity bill month and year, total electricity bill, basic electricity bill for the current month, power regulation electricity bill, whether it meets the standard of 260 kWh per kVA, average electricity consumption, basic electricity bill as a percentage of total electricity bill, demand value as a percentage of capacity value, year, and cumulative electricity consumption of the previous year.

[0036] Output module 14 also outputs data association analysis charts generated based on the data association analysis results, including a pie chart of electricity cost composition (e.g., showing the proportion of basic electricity cost, electricity consumption cost, and power regulation cost), a line chart of electricity consumption trend (with time as the horizontal axis and demand value or average electricity consumption as the vertical axis), and a bar chart of equipment energy consumption. The data in the data association analysis charts comes from multiple standardized data from standardized reports.

[0037] The output module 14 of this application outputs the data correlation analysis results in a standardized report format and can generate a data correlation analysis graph. The analysis results are presented intuitively through visual output, which makes it convenient for users and power supply departments to quickly view and understand them, reduces the threshold for data use, and improves the application value of data and user experience.

[0038] In addition to the physical structure consisting of a data acquisition module 11, a storage module 12, a correlation analysis module 13, and an output module 14, the cloud data platform 10 of this application can also be as follows: Figure 2 It consists of a layered architecture. For example... Figure 2 As shown, the cloud data platform 10 of this application consists of a data access layer 21, a data management layer 22, an intelligent analysis layer 23, a visualization output layer 24, and a feedback adjustment layer 25.

[0039] To adapt to the data collection requirements of the power cloud data report, the data access layer 21 supports both DL / T645 protocol and direct database connection modes, enabling full collection of core data items in the report. Optionally, the various types of data collected by the data access layer 21 can be the same types of data collected by the data collection module 11.

[0040] Meanwhile, the data access layer 21 has a built-in data verification module that identifies and repairs abnormal data based on the report data logic rules, achieving a data cleaning accuracy rate of 99.97%. Optionally, the report data logic rules can be the preset data verification model in the above embodiment, and the specific process of the data verification module can be the same as the specific process of the correlation analysis module 13 for verifying data accuracy.

[0041] Specifically, the data access layer 21 deploys a Kafka cluster to receive real-time report data and uses Flink for stream processing. Its configuration is as follows: the data collection frequency is set to 5 minutes / time to ensure the real-time performance of the report data; the verification rule base has 28 built-in verification rules based on the electricity cloud report, such as "the transformer capacity shall not be less than the installed capacity" and "total electricity cost = basic electricity cost + electricity cost + power regulation cost".

[0042] The data association layer 22 constructs a three-dimensional association matrix to achieve deep integration of report data. The association model supports dynamic expansion and can automatically update association rules based on newly added data items in the report. The association model can perform item-by-item integration of various types of collected data based on entity association, business association, and spatiotemporal association. Optionally, the specific process of deep integration of report data by the data association layer 22 can be similar to the specific process of data transformation and storage performed by the storage module 12.

[0043] Specifically, the data association layer 22 uses a graph database to store association relationships. Nodes include "user", "metering point", "equipment", "electricity fee item", etc., and edges are defined as relationships such as "belongs to", "metering", "calculation basis".

[0044] The intelligent analysis layer 23 integrates three core algorithm modules and performs in-depth analysis based on report data. The three core algorithm modules are dynamic billing engine, electricity consumption behavior recognition module and capacity optimization model.

[0045] Optionally, the specific calculation process of the dynamic billing engine can be as follows: the specific calculation process of the correlation analysis module 13 using the preset electricity billing calculation model.

[0046] The electricity consumption behavior identification module analyzes the correlation between peak / flat / valley / peak electricity distribution and occurrence time in the reports using clustering algorithms to identify peak electricity consumption periods and calculate unit power consumption to assess equipment energy efficiency. The specific process can be seen in the calculation process performed by the correlation analysis module 13 using a preset electricity consumption behavior analysis model. Optionally, the electricity consumption behavior identification module deploys the electricity consumption behavior identification model using TensorFlow Serving. The inputs are features such as "primary demand value," "occurrence time," and "peak / valley electricity consumption" from the reports, and the output is the probability distribution of peak electricity consumption. The model's training data comes from historical report data over the past 12 months, with an iteration cycle of once a week, enabling dynamic data updates and improving the dynamic analysis capabilities of the cloud data platform 10.

[0047] The capacity optimization model is based on the "demand-capacity ratio" indicator in the report, combined with 90 days of historical data to build a capacity matching evaluation model and output capacity adjustment suggestions. The specific process can be seen in the calculation process of the pre-set capacity management model in the correlation analysis module 13.

[0048] The visualization output layer 24 supports dynamic report generation and multi-dimensional visualization, including custom reports, interactive charts, and analysis reports. Optionally, the process of the visualization output layer 24 outputting dynamic reports can be similar to the process of the output module 14 outputting standardized reports or data correlation analysis charts.

[0049] Custom reports allow users to configure report data items as needed, such as selecting to display indicators like "basic electricity cost as a percentage of total electricity cost" and "average electricity consumption"; interactive charts can be displayed as pie charts of electricity cost composition, based on data such as total electricity cost, basic electricity cost, and power regulation cost, or as line charts of demand trends, used to correlate occurrence time with primary demand values; analysis reports include electricity efficiency assessments, cost optimization suggestions, and more.

[0050] Specifically, the visualization output layer 24 is a dynamic reporting platform developed based on WebGL, which supports custom selection of report data items, such as selecting to display "power regulation electricity fee" and "cumulative electricity consumption of the previous year", drill-down in the time dimension, such as drilling down from "electricity fee year and month" to the detailed data of "occurrence time", and functions such as highlighting abnormal data, such as highlighting when the "demand value ratio to capacity value ratio" in the report exceeds the threshold.

[0051] The feedback adjustment layer 25 establishes a dynamic feedback mechanism between report data and analysis models. When key parameters in the report, such as basic fee unit price, voltage level, etc., change, the intelligent analysis layer algorithm parameters are automatically updated to ensure that the analysis results are accurate in real time.

[0052] This application also provides an application method for a cloud data platform; please refer to [link / reference]. Figure 3 , Figure 3 This is a flowchart illustrating an embodiment of the application method of the cloud data platform of this application. The executing entity of the application method of the cloud data platform of this application can be the cloud data platform 10 for power dynamic analysis described in the above embodiment. Specifically, the application method of the cloud data platform of this disclosure embodiment may include the following steps: Step S11: Collect various types of data from power users in real time through the data acquisition module.

[0053] Step S11 can be executed by the data acquisition module 11 or the data access layer 21 mentioned above, and is used to collect various types of data, including basic information data, metering equipment data, power consumption data and electricity cost data.

[0054] Step S12: Use the data storage module to perform data standardization transformation, converting the collected data into multi-dimensional standardized data items for storage.

[0055] Step S12 can be executed by the aforementioned storage module 12 or data management layer 22 to achieve data association and fusion, and store data according to different standard data items.

[0056] Step S13: The data association analysis module performs association calculations and analysis on multiple standardized data items stored based on preset logical relationships, and outputs the corresponding data association analysis results.

[0057] Step S13 can be executed by the aforementioned correlation analysis module 13 or intelligent analysis layer 23 to perform data correlation analysis and output data correlation analysis results including total electricity cost calculation, electricity efficiency assessment and capacity adjustment suggestions.

[0058] Optionally, the step of calculating the total electricity cost based on the preset electricity cost calculation model in this embodiment can be as follows: Figure 4 ,include: Step S131: Calculate the capacity charge and the maximum demand charge according to different basic electricity charge calculation methods, and determine the basic electricity charge for the month by taking the maximum value of the capacity charge and the maximum demand charge.

[0059] Step 132: Calculate the total of the basic electricity charge, the electricity consumption charge, and the power regulation charge for the current month to obtain the total electricity charge.

[0060] In this embodiment, the specific process of calculating the total electricity cost can be described as follows: the specific calculation process of the correlation analysis module 13 based on the preset electricity cost calculation model.

[0061] Optionally, the step in this embodiment of calculating the electricity efficiency assessment based on a preset electricity consumption behavior analysis model can be as follows: Figure 5 ,include: Step 231: Identify peak electricity consumption periods by combining primary demand value, maximum secondary demand value, and data occurrence time.

[0062] Step 232: Calculate the power consumption per unit of power by combining the time-of-use power consumption, the first demand value, and the time.

[0063] Step 233: Calculate the ratio of total active power consumption to the duration of the month to obtain the average power consumption.

[0064] In this embodiment, the specific process of calculating electricity efficiency, including identifying peak electricity consumption periods, calculating electricity consumption per unit power, and calculating average electricity consumption, can be described by the correlation analysis module 13 based on the specific calculation process of the preset electricity behavior analysis model, and the electricity efficiency assessment is completed based on the calculated data.

[0065] Optionally, the step in this embodiment of calculating capacity adjustment recommendations based on a preset capacity management model can be as follows: Figure 6 ,include: Step 331: Combine the initial demand value with the specific time period to obtain the second demand value.

[0066] Step 332: Calculate the percentage of the second demand value to the reference capacity.

[0067] In this embodiment, the specific process of calculating the demand ratio can be as follows: the correlation analysis module 13 calculates the demand ratio based on the preset capacity management model, and can provide capacity adjustment suggestions based on the calculated demand ratio.

[0068] Step S14: Use the output module to output the data correlation analysis results in the form of standardized reports.

[0069] Step S14 can be executed by the output module 14 or the visualization output layer 24, and the analysis results can be presented intuitively through the visualization output report.

[0070] The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A cloud data platform for power dynamic analysis, characterized in that, The utility model relates to a kind of power user behavior analysis system, including: Data acquisition module, for real-time dynamic acquisition of various data of electric power user, including basic information data, metering equipment data, electric quantity data and electricity data; Storage module, for converting the various data collected into multi-dimensional standardized data items for storage, multiple standardized data items are used to form a standardized report structure; Correlation analysis module, for correlation calculation and analysis of multiple standardized data items stored based on a predetermined logical relationship, and output corresponding data correlation analysis results, including: total electricity calculated based on a predetermined electricity calculation model, electricity efficiency evaluation calculated based on a predetermined electricity consumption behavior analysis model, and capacity adjustment suggestion calculated based on a predetermined capacity management model; Output module, for outputting the data correlation analysis results in the form of a standardized report, the standardized report includes multiple standardized data and analysis indicators, the analysis indicators are used to evaluate the electricity consumption behavior of the electric power user, and the output module outputs suggestions for optimizing the electricity consumption behavior of the electric power user based on the analysis indicators.

2. The cloud data platform of claim 1, wherein, The metering equipment data includes transformer capacity, the electric quantity data includes active total electric quantity, peak period electric quantity, flat period electric quantity, valley period electric quantity, sharp peak period electric quantity, positive active electric quantity and negative active electric quantity, and the electricity data includes basic electricity calculation method, basic price, maximum demand electricity, capacity electricity, total electricity, power adjustment electricity and monthly basic electricity. Wherein, the capacity electricity and the maximum demand electricity are used to determine the monthly basic electricity of the electric power user, and the transformer capacity is the minimum integer value greater than or equal to the total active power / power factor.

3. The cloud data platform of claim 2, wherein, The electricity data further includes primary demand value, The predetermined electricity calculation model satisfies: total electricity = monthly basic electricity + electricity + power adjustment electricity, wherein the monthly basic electricity is the maximum value of the capacity electricity and the maximum demand electricity, the capacity electricity = transformer capacity × basic price, the maximum demand electricity = primary demand value × basic price, and the electricity = peak period electric quantity × peak time price + flat period electric quantity × flat time price + valley period electric quantity × valley time price + sharp peak period electric quantity × sharp time price.

4. The cloud data platform of claim 2, wherein, The electricity data further includes primary demand value, maximum secondary demand value, data occurrence time and time-sharing electric quantity, The predetermined electricity consumption behavior analysis model identifies the electricity peak period by combining the primary demand value, the maximum secondary demand value and the data occurrence time, and the electricity peak period is the occurrence period of the maximum secondary demand value, The predetermined electricity consumption behavior analysis model satisfies: unit power consumption = time-sharing electric quantity / demand value × time, wherein the demand value is the primary demand value corresponding to the occurrence period of the time-sharing electric quantity, the time is the time length corresponding to the occurrence period of the time-sharing electric quantity, and the unit power consumption is used to analyze and evaluate equipment energy consumption, The predetermined electricity consumption behavior analysis model further satisfies: average electricity consumption = active total electric quantity / monthly time length, and the average electricity consumption is used to analyze and evaluate electricity intensity change.

5. The cloud data platform of claim 2, wherein, The electricity data further includes primary demand value, installed capacity, contract capacity, total capacity and demand value, wherein the demand value is the primary demand value corresponding to a specific period, The preset capacity management model satisfies: demand ratio = demand value / reference capacity * 100%, wherein the reference capacity is installed capacity, contract capacity or total capacity, and the demand ratio is used to analyze and evaluate the matching degree between the actual demand of the user and the installed capacity, the contract capacity or the total capacity, When the reference capacity is the contract capacity, if the demand ratio is too high, the correlation analysis module prompts the power user to increase the capacity, and if the demand ratio is too low, the correlation analysis module suggests adjusting the contract capacity.

6. The cloud data platform of claim 2, wherein, The correlation analysis module also performs data accuracy verification based on a preset data verification model, and the preset data verification model satisfies: total electric quantity = peak period electric quantity + flat period electric quantity + valley period electric quantity + peak-sharp period electric quantity, If the total electric quantity = total active electric quantity, the data is judged to be accurate, if the data is inconsistent, it is marked as abnormal and a verification reminder is triggered.

7. The cloud data platform of claim 1, wherein, The output module also outputs a data correlation analysis diagram generated based on the data correlation analysis result, including an electricity cost composition pie chart, an electricity consumption trend line chart and an equipment energy consumption column chart, and the data of the data correlation analysis diagram is derived from multiple standardized data of the standardized report.

8. The cloud data platform of claim 1, wherein, The standardized data items in the standardized report include basic information data items, metering equipment data items, electric quantity data items and electricity cost data items, The basic information data items include basic electricity cost calculation method, metering point number, province, region, user number, user name, settlement account name, user state, user address, filing time, update time, belonging feeder, belonging transformer substation, organization and superior organization; the metering equipment data items include transformer capacity, meter asset number, comprehensive ratio, voltage grade, metering point state, metering method, measurement point type, power consumption property and wiring method; the electric quantity data items include total active electric quantity, positive active electric quantity, peak period electric quantity, flat period electric quantity, valley period electric quantity, peak-sharp period electric quantity and negative active electric quantity; and the electricity cost data items include capacity electricity cost and maximum demand electricity cost difference, basic fee unit price, installed capacity, electricity cost single metering point capacity, contract capacity, total capacity, maximum secondary demand value, primary demand value, unit power consumption, data occurrence time, maximum demand electricity cost, capacity electricity cost, electricity cost year and month, total electricity cost, monthly basic electricity cost, power adjustment electricity cost, whether it meets the standard of 260 degrees of electricity per kilovolt-ampere, average power consumption, basic electricity cost proportion of total electricity cost, demand value and capacity value proportion, year and last year cumulative electric quantity.

9. An application method of a cloud data platform, applied to the cloud data platform for power dynamic analysis according to any one of claims 1-8, characterized in that, It includes: Real-time dynamic collection of various data of power users through a data collection module, including basic information data, metering equipment data, electric quantity data and electricity cost data; Standardized data items are stored in the form of multi-dimensional standardized data items by using a data storage module to standardize and transform the collected various data, wherein multiple standardized data items are used to form a standardized report structure; The data correlation analysis module performs correlation calculation and analysis on the stored multiple standardized data items based on a preset logical relationship, and outputs corresponding data correlation analysis results, including total electricity charges calculated based on a preset electricity charge calculation model, electricity efficiency evaluation calculated based on a preset electricity consumption behavior analysis model, and capacity adjustment suggestions calculated based on a preset capacity management model; The output module outputs the data correlation analysis results in the form of a standardized report, which includes multiple standardized data and analysis indexes for evaluating electricity consumption behavior of the power user, and outputs suggestions for optimizing the electricity consumption behavior of the power user based on the analysis indexes.

10. The use according to claim 8, characterized in that, The metering equipment data includes transformer capacity, the power data includes active total power, and the electricity charge data includes kilowatt-hour electricity charges, power regulation electricity charges, primary demand value, maximum secondary demand value, data occurrence time, time-of-use power, active total power, installed capacity, contract capacity, and total capacity, The step of calculating total electricity charges based on the preset electricity charge calculation model includes: According to different basic electricity charge calculation methods, capacity electricity charges and maximum demand electricity charges are calculated respectively, and the maximum value of the capacity electricity charges and the maximum demand electricity charges is determined as the monthly basic electricity charges; The total value of the monthly basic electricity charges, the kilowatt-hour electricity charges, and the power regulation electricity charges is calculated to obtain the total electricity charges; The step of calculating electricity efficiency evaluation based on the preset electricity consumption behavior analysis model includes: The electricity consumption peak period is identified in combination with the primary demand value, the maximum secondary demand value, and the data occurrence time; Unit power consumption is calculated in combination with the time-of-use power, the first demand value, and the time, where the first demand value is a primary demand value corresponding to an occurrence period of the time-of-use power, the time is a time length corresponding to the occurrence period of the time-of-use power, and the unit power consumption is used for analyzing and evaluating equipment energy consumption; The average power consumption is calculated by dividing the active total power by the monthly time length, and the average power consumption is used for analyzing and evaluating power intensity changes; The step of calculating capacity adjustment suggestions based on the preset capacity management model includes: The second demand value is obtained in combination with the primary demand value and the specific period; The demand ratio is calculated by taking the percentage of the second demand value to the reference capacity, and the reference capacity is the installed capacity, the contract capacity, or the total capacity. The demand ratio is used for analyzing and evaluating the matching degree between the actual demand of the user and the installed capacity, the contract capacity, or the total capacity.

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