Digitalized marketing management system based on amount and cost analysis

Abstract

The invention discloses a digital marketing management system based on amount and cost analysis. Relates to power systems. Comprising a data uploading unit, a data processing unit, a data analysis unit and a user interface, the data uploading unit is used for transmitting data to the data processing unit, the data processing unit is used for enabling the processed data to be used by the data analysis unit, and the data analysis unit is used for displaying an analysis result to a user through the user interface. And the user interface is used for a user to operate the interface and check and store the required analysis result. According to the invention, the electric charge accounting and issuing are no longer the end point of the marketing process, but the starting point of the front-end marketing link is fed back and supplemented; the defects of insufficient self-checking and self-correcting capability and demand side response lag caused by one-way transmission of the marketing process are overcome, reverse thinking and source tracing are achieved, and quality-effect re-improvement of each link of the marketing process is deduced.

Description

Technical Field

[0001] The invention relates to an electric power system, and in particular to a digital marketing management system based on quantity and fee analysis. Background Art

[0002] With the rapid development of the times, continuously improving the people's sense of gain in electricity has become a new direction for power marketing. While customers' electricity demand has extended in multiple dimensions, it has also put forward new challenges to the level of power grid marketing and management.

[0003] At present, relying on the energy Internet marketing service system and the electricity consumption information collection system, the digitalization of quantity and fee data collection has been basically realized. However, there are still problems such as scattered data acquisition channels, cumbersome data statistics methods, and low data analysis efficiency, which leads to the statistical analysis work taking up a lot of time and energy of the majority of marketing workers. Not only is the statistical time long, but the analysis quality is not high, which affects work efficiency and also reduces the timeliness of statistical analysis work.

[0004] This cannot meet the ever-expanding and increasingly diversified statistical analysis needs. Summary of the invention

[0005] In view of the above problems, the present invention provides a digital marketing management system based on quantity and fee analysis, which can accurately, conveniently and efficiently complete statistical work, deeply tap data resources, and help improve the level of power marketing management.

[0006] The technical solution of the present invention is: a digital marketing management system based on quantity and cost analysis, including a data uploading unit, a data processing unit, a data analysis unit and a user interface. The data uploading unit is used to transmit data to the data processing unit. The data processing unit is used to provide the processed data to the data analysis unit. The data analysis unit is used to display the analysis results to the user through the user interface, The user interface is used for the user to operate the interface, view and save the required analysis results.

[0007] The data uploading unit is used to collect local user power consumption and electricity fee data, including power consumption, timestamp, and electricity fee.

[0008] The data processing unit is used to perform data cleaning and format matching on the uploaded data, delete erroneous data rows with missing key column information, and complete preliminary information noise reduction.

[0009] The data analysis unit is based on the Pandas library and includes an average electricity price analysis module, an electricity quantity increase analysis module and a major project analysis module.

[0010] The average electricity price analysis module is used to regularly collect the electricity consumption and electricity fee data of all users, calculate the average electricity price of each household by using statistical methods, and draw a line chart of the average electricity price exclusive to each user with the year and month of the quantity and fee as the horizontal axis, so as to monitor the changes in the average electricity price of all users on a regular basis; Among them, for high-voltage users whose average electricity prices fluctuate abnormally on a month-on-month basis, the quantity and fee recalculation process is initiated, and meter calibration and electricity fee repayment are carried out in a timely manner.

[0011] The power increase analysis module is used to dynamically track the power consumption and electricity price trends of important customers over time and discover potential business expansion needs of customers; Among them, for users whose electricity consumption and electricity charges have similar trends every year, that is, users whose electricity consumption and electricity charges have periodic changes, their production capacity and load changes can be estimated based on the annual electricity consumption and charges, so as to discover the demand for capacity increase or reduction in advance; For high-voltage users whose electricity bills are chronically high, comprehensive considerations are conducted from multiple dimensions, including: basic electricity billing method, operating capacity, peak, flat and valley electricity usage habits, and reactive power compensation; If there are distributed photovoltaic equipment, consider the capacity of the photovoltaic equipment invested.

[0012] The major project analysis module is used to track and count major projects, conduct comparative analysis with the industry, and regularly predict electricity consumption trends and development trends of the city's industries through artificial intelligence.

[0013] In operation, the present invention utilizes automated data processing methods to realize real-time and rapid statistics of quantity and cost data, and relies on advanced data visualization technology to greatly improve the intuitiveness of analysis.

[0014] The present invention makes the electricity fee calculation and issuance no longer the end of the marketing process, but the starting point of the front-end marketing link for feedback and benefit. It makes up for the defects of insufficient self-inspection and self-correction capabilities and delayed response on the demand side caused by the one-way transmission of the marketing process, and reverse thinking, tracing the source, and forcing the quality and efficiency of each link in the marketing process to be further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the following briefly introduces the drawings required for the specific implementation or the prior art description. In the drawings, each part is not necessarily drawn according to the actual scale.

[0016] Figure 1 is a schematic diagram of the division of statistical analysis in the present invention, Figure 2 This is a schematic diagram of Case 1. Figure 3 This is a schematic diagram of Case 2. Figure 4 This is a schematic diagram of Case 4. DETAILED DESCRIPTION

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] In the description of the present invention, it should be understood that the terms "upper", "lower", "left", "right", "vertical", "horizontal" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0019] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0020] The present invention provides a digital marketing management system based on quantity and cost analysis, including a data uploading unit, a data processing unit, a data analyzing unit and a user interface. The data uploading unit transmits the data to the data processing unit.

[0021] The data processing unit provides the processed data to the data analysis unit.

[0022] The analysis results of the data analysis unit are displayed to the user through the user interface.

[0023] The user interface allows the user to operate the chart generator and view and save the desired analysis results.

[0024] This system adopts C / S architecture design and supports platform migration. Users can access the system through a browser to realize data processing and analysis at any time.

[0025] Specifically, Data upload unit: responsible for collecting local user electricity consumption and electricity fee data, including but not limited to electricity consumption, timestamp, electricity fee and other information.

[0026] Data processing unit: This unit cleans and formats the data in Excel format uploaded by the user, deletes the erroneous data rows with missing key column information, and completes the initial information noise reduction. In order to reduce the difficulty of subsequent information retrieval and improve the efficiency of data analysis, special data preprocessing will be carried out for different business scenarios. Taking the power increase analysis module as an example, the data processing unit will extract the required data columns from the user power data of two months respectively, combine them into new data objects for pre-storage, and prepare for subsequent data analysis.

[0027] Data analysis unit: This unit mainly implements data analysis and result return in the three major business scenarios of the system, and the input is the data object processed by the data processing unit.

[0028] In the early stage, we completed auxiliary work such as demand condition analysis, label design and module design for different business scenarios, and designed a data analysis process in combination with electricity bill professional knowledge. This unit is mainly implemented based on the Pandas library. Pandas is based on NumPy and provides flexible data structures (Series and DataFrame) and data operation functions. It is an indispensable tool in data science and machine learning. Using its efficient and convenient data analysis functions, we can complete the data analysis functions required by the system.

[0029] User Interface (UI): Provides an intuitive, easy-to-use interface that allows users to input parameters, select analysis modules, and display analysis results.

[0030] In application, the present invention (I) First, if Figure 1 As shown, starting from the demand side, three common statistical analysis demand conditions are set: regional division, industry classification, and quantity and cost year and month, which correspond to regional data, industry data, and time data in the system.

[0031] (II) Secondly, standardize statistical labels to lay a solid foundation for the logical architecture. Taking industry classification as an example, the classification of electricity consumption industries in our city is characterized by large number, variety, and fine branches. If the industry is simply divided into industries such as industry and service industry, it is too rough and lacks statistical value; if it is directly divided according to the industry on the user's business license, there is a problem of too few samples in a single industry, which also has no statistical value. In this regard, we have added a secondary label of the industry category with the characteristics of our city between the large label of the first, second and third industries and the small label of the industry classification based on the "NB / T 33030-2018 National Economic Industry Electricity Classification". A total of 253 first-level labels, 142 second-level labels, and 4 third-level labels are formed. In this way, we can meet the statistical needs of different granularity and count the quantity and fee of various industries, large and small.

[0032] (III) Finally, three major application modules were designed based on the actual work, corresponding to three common business scenarios in marketing work: average electricity price analysis to assist in monitoring abnormal quantity and fee, electricity increase forecast to dynamically analyze corporate energy consumption, and major project tracking to deeply perceive industry development.

[0033] (1) Average electricity price analysis assists in monitoring abnormal quantity and price (Introduction to average electricity price: The average electricity price parameter is easy to calculate and intuitive and easy to understand. In daily electricity fee accounting work, we find that whether it is an abnormal file or an abnormal meter reading, it will eventually affect the average electricity price parameter): This module focuses on points. By collecting the electricity consumption and electricity fee data of all users on a regular basis (according to the accounting cycle, generally monthly), the average electricity price of each household is calculated using statistical methods, and a line chart of the average electricity price exclusive to each user is drawn with the year and month of the quantity and fee as the horizontal axis, so as to monitor the changes in the average electricity price of all users on a regular basis. For high-voltage users whose average electricity prices fluctuate abnormally month-on-month, the quantity and fee recalculation process is quickly initiated, and analysis and investigation are carried out around the two core issues of "electricity metering" and "electricity fee calculation and collection". Timely meter calibration and electricity fee recovery, and rectification and improvement of possible problems one accounting cycle in advance can ensure the accurate issuance of the next month's electricity bill, greatly improving the first-time pass rate of electricity fee accounting.

[0034] Case 1: like Figure 2 As shown in the figure, in July 2023, the average electricity price analysis module found that the average electricity price of Yangzhou Qinyou Special Metal Materials Co., Ltd. in June 2023 was 0.7847 yuan / kWh, which was higher than the normal average electricity price in previous months. After sorting it into the abnormal quantity and fee database, the electricity fee professional quickly started the quantity and fee recalculation process. Combined with the actual operation mode of the customer, it was finally found that from 07:00 on June 19, 2023 to 18:00 on June 21, 2023, due to the maintenance and adjustment of the phase sequence of the 220kV Gaozuo 4H89 / 90 double-line, the provincial dispatch arranged the Qinte transformer 220kV busbar 2610 switch to close the ring operation. During this temporary operation, the flow direction of the Qinte transformer Youte 4H17 line and the Chengte 4H18 line changed. The flow flowed from Youte 4H17 line to Chengte 4H18 line through the Qinte transformer 220kV busbar, and the user load only accounted for a part of the flow of Youte 4H17 line. The maintenance caused the maximum demand of Youte 4H17 line to increase, and the power at the gateway metering point was over-measured. After the recalculation of the quantity and fee, it was concluded that the basic electricity fee of 2,707,543.18 yuan should be refunded. While refunding the basic electricity charges, in response to abnormal electricity charges caused by such changes in transportation parties, the electricity charge professionals will work together with the metering professionals and dispatching departments to dynamically grasp the user's electricity usage, investigate abnormal quantities and charges in real time, fully protect the interests of both electricity suppliers and users, and ensure that electricity charges are calculated accurately.

[0035] Case 2: like Figure 3 As shown in the figure, in October 2023, the average electricity price analysis module found that the average electricity price of Gaoyou Yusen Alloy Co., Ltd. in January 2023 was 0.35 lower than the average electricity price in 2023, exceeding the warning value of 20%. According to past experience, the average electricity price is often lower than that in normal months due to poor load factors in months with low production capacity. The electricity consumption in this month is significantly lower than that in other months, but the average electricity price is higher than that in other months. The quantity and fee in this month are obviously abnormal.

[0036] After coordinated inspection and measurement verification, it was found that the household applied for suspension of 1#2000kVA transformer on January 4, 2023, and applied for capacity reduction and continued suspension of 3#630kVA transformer on January 17, 2023. Since the de-rate meter showed negative peak and flat data from January 5 to January 29, 2023, the electricity consumption and electricity fee were calculated incorrectly. The additional electricity fee was 8716.63 yuan.

[0037] This case fully demonstrates the sensitivity of the average electricity price analysis module, effectively promotes the optimization of the pre-monitoring and early warning mechanism, and forces the quality and efficiency of each link in the marketing process to be further improved. "Relay protection" is added to the marketing service process, and finally the closed-loop control of the entire marketing process is achieved.

[0038] (2) Dynamic analysis of energy consumption by enterprises through forecast of electricity increase: This module focuses on the line, with time as the line, and conducts "energy efficiency checkups" for each important customer on a regular basis by dynamically tracking the trends of the electricity consumption and electricity charges of important customers. Combined with the results of the checkup, a comprehensive consideration is carried out in multiple dimensions: Is the basic electricity charge calculation method reasonable? Is the operating capacity reasonable? Are the peak, flat and valley electricity consumption habits reasonable? Is the reactive power compensation reasonable? If there are distributed photovoltaic equipment, is the capacity of the photovoltaic equipment invested reasonable? Then, from the perspective of the customer, combined with the actual production of the user, the "Rationalization Suggestions for Cost Reduction and Efficiency Increase" and "Suggestions for Potential Business Expansion Demand" are issued. Think about what the customer needs and what the customer wants, deeply analyze the composition structure of the electricity consumption and electricity charges of important customers, and fully explore the potential business expansion needs of customers.

[0039] Case 3: By tracking the composition and changing trend of a customer's electricity bill over a long period of time, we found that the customer's electricity cost was gradually increasing while its production capacity had not increased. Therefore, the staff immediately carried out an "energy efficiency check" focusing on the two core aspects of electricity price and line loss. The analysis results are shown in the following analysis report. Based on this, our staff further proposed the "Rationalization Suggestions for Reducing Costs and Increasing Efficiency" in combination with its actual production: 1. Adjust electricity usage habits and reduce the duration of electricity usage during peak hours without affecting normal production order. 2. Dedicated line transformation: The steel-core aluminum stranded wire used in its dedicated line has a long service life and large line loss. By reducing line loss, the electricity cost can be further reduced. After the "Suggestion" was adopted, the household's periodic electricity cost decreased by about 5% year-on-year.

[0040] Analysis report of a customer 1. Analysis of electricity price July September Large industrial electricity price (10kV) 0.669802 0.680772 Lighting electricity price (10kV) 0.704402 0.715372 Shared profit and loss electricity price -0.0002 0.0093 This user is a market-oriented user. The monthly electricity price fluctuates. The electricity price increased in September (the increase in the shared profit and loss price was the largest). For specific reasons, users can consult their power sales company (Datang Jiangsu Power Sales Co., Ltd.).

[0041] 2. Analysis of Peak, Flat and Valley Electricity Consumption Habits July September Peak electricity consumption ratio 30.72% 33.20% Electricity consumption ratio during normal period 32.41% 33.04% Electricity consumption during off-peak hours 36.88% 33.76% July September Peak electricity price 1.151792 1.170656 Normal electricity prices 0.669802 0.680772 Off-peak electricity price 0.280312 0.284903 Note: Peak period electricity price = normal period electricity price × (1 + floating ratio (71.96%)) Off-peak electricity price = normal electricity price × (1-downward ratio (58.15%)) This user implements the large industrial time-of-use electricity price. In September, the proportion of electricity consumption during the valley period decreased, while the proportion of electricity consumption during the peak and normal periods increased. The electricity prices during the peak and normal periods are higher, resulting in an increase in electricity bills.

[0042] 3. Line loss analysis The company provides power supply for 10KV dedicated line, the line length is about 4.7KM, steel core aluminum stranded wire LGJ-240 square millimeters Line resistance per kilometer: R=0.120P ohm / km (the DC resistance loss of 10KV dedicated line LGJ-240 square millimeter was found on Baidu. As the relevant line reactance loss parameters were not found, the power loss caused by line reactance was not calculated in the following line power loss) The power factor during normal electricity usage is 0.95. Meter reading account demand: 5700KW---6250KW per month. Calculate the power load used in the wire loss: take the value of 6000KW.

[0043] Line loss rate calculation: S=6000 / 0.95=6315KVA I=S / ( U)=6315 / ( *10)=364.6074 A R=r0L=0.1209*4.7=0.56823Ω ΔP =3*I 2 * R =3*364.60742*0.56823=226619W Loss rate r= *100%=(226619 / 6000000)*100%=3.777% The line loss rate on this line is 3.777% when the power load is 6000KW.

[0044] According to the analysis of the simultaneous meter readings at the beginning and end of the line, the cumulative loss of electricity from October 1 to 20 was 59,600 kWh, and the line loss was estimated to be 3.95058%.

[0045] Combined with the above line loss comparison: The line loss rate on this line is 3.777% when the power load is 6000KW. The cumulative meter readings from October 1 to 20 show a loss of 59600kWh, and the line loss is calculated to be 3.95058%. The theoretical data is basically consistent.

[0046] Case 4: There are 9 110kV power customers in the city, of which 6 are engaged in photovoltaic module manufacturing business and 2 are engaged in battery manufacturing business. In recent years, large industrial users have formed a new energy industry chain dominated by 110kV leading enterprises and coordinated by 10kV small and micro enterprises in the industrial park of the city's development zone. While the industrial chain resonates, its electricity demand also fluctuates at the same frequency. The demand for business expansion (capacity increase / reduction, suspension and activation of special transformers, etc.) shows a more obvious periodicity and synchronization, which is projected on the trend chart of electricity consumption and electricity charges, indicating the similarity of the trend of electricity consumption and electricity charges. Therefore, for such users, according to their energy consumption patterns in previous years and the current year's quantity and fee situation and the supply and demand situation of the upstream and downstream of the industrial chain, the production capacity and load changes in the next cycle can be predicted, and the demand for business expansion can be discovered in advance.

[0047] like Figure 4 As shown in the figure, the overall change in the customer's electricity consumption and electricity charges from 2021 to 2023 presents a linear function image with a constant slope. By using function fitting methods combined with the industry development trend of the year, it is predicted that the customer's existing transformer capacity will not be able to carry its electricity load by mid-2024. Therefore, at the end of 2023, the city's power supply company submitted the "Business Expansion Demand Proposal" to the customer, initiated the capacity increase process on November 13, 2023, and completed the capacity increase filing on May 6, 2024, increasing its operating capacity from 5000kVA to 7500kVA, laying a solid foundation for its expansion and revenue increase.

[0048] (3) Tracking major projects to deeply perceive industry development: This module focuses on the overall situation and expands the tracking statistics of major projects (i.e. the city's leading enterprises and newly completed projects of the year) from the previous vertical comparisons such as month-on-month and year-on-year to multi-dimensional horizontal comparisons at the industry level. This allows for a more three-dimensional analysis of the electricity consumption of our company's major tertiary industry projects after they are put into operation, as well as the development gap with customers in the same industry.

[0049] In addition, after expanding the data source to all users in the city, the module can also fully play the role of a weather vane and barometer of electricity consumption. For large-volume users and the city's leading industries, it can regularly predict electricity consumption trends and development trends of the city's industries through artificial intelligence algorithms, assist government and company decision-making, and help promote high-quality development of the local economy.

[0050] Regarding the contents disclosed in this case, there are a few points that need to be explained: (1) The drawings of the embodiments disclosed in this case only involve the structures involved in the embodiments disclosed in this case. Other structures can refer to the general design; (2) In the absence of conflict, the embodiments and features of the embodiments disclosed in this case may be combined with each other to obtain new embodiments; The above are only specific implementation methods disclosed in this case, but the protection scope of the present disclosure is not limited thereto. The protection scope disclosed in this case should be based on the protection scope of the claims.

Claims

1. A digital marketing management system based on quantity and cost analysis, characterized by: It includes a data uploading unit, a data processing unit, a data analyzing unit and a user interface. The data uploading unit is used to transmit data to the data processing unit. The data processing unit is used to provide the processed data to the data analysis unit. The data analysis unit is used to display the analysis results to the user through the user interface, The user interface is used for the user to operate the interface, view and save the required analysis results.

2. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The data uploading unit is used to collect local user power consumption and electricity fee data, including power consumption, timestamp, and electricity fee.

3. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The data processing unit is used to perform data cleaning and format matching on the uploaded data, delete erroneous data rows with missing key column information, and complete preliminary information noise reduction.

4. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The data analysis unit is based on the Pandas library and includes an average electricity price analysis module, an electricity quantity increase analysis module and a major project analysis module.

5. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The average electricity price analysis module is used to regularly collect the electricity consumption and electricity fee data of all users, calculate the average electricity price of each household by using statistical methods, and draw a line chart of the average electricity price exclusive to each user with the year and month of the quantity and fee as the horizontal axis, so as to monitor the changes in the average electricity price of all users on a regular basis; Among them, for high-voltage users whose average electricity prices fluctuate abnormally on a month-on-month basis, the quantity and fee recalculation process is initiated, and meter calibration and electricity fee repayment are carried out in a timely manner.

6. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The power increase analysis module is used to dynamically track the power consumption and electricity price trends of important customers over time and discover potential business expansion needs of customers; Among them, for users whose electricity consumption and electricity charges have similar trends every year, that is, users whose electricity consumption and electricity charges have periodic changes, their production capacity and load changes can be estimated based on the annual electricity consumption and charges, so as to discover the demand for capacity increase or reduction in advance; For high-voltage users whose electricity bills are chronically high, comprehensive considerations are conducted from multiple dimensions, including: basic electricity billing method, operating capacity, peak, flat and valley electricity usage habits, and reactive power compensation; If there are distributed photovoltaic equipment, consider the capacity of the photovoltaic equipment invested.

7. The digital marketing management system based on quantity and cost analysis according to claim 1 is characterized in that: The major project analysis module is used to track and count major projects, conduct comparative analysis with the industry, and regularly predict electricity consumption trends and development trends of the city's industries through artificial intelligence.

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