Visual business data analysis method and system

Abstract

The invention relates to the technical field of data analysis, in particular to a visual business data analysis method and system, and the method comprises the steps: collecting the information of a power consumption installation user, evaluating the power consumption level of the user according to the user information, carrying out the statistics of the power consumption data of a power enterprise in different time periods, and carrying out the analysis of the business data according to the power consumption data information of the power enterprise in different time periods. And the power supply enterprise publicizes and guides the power consumption enterprise to stagger peak power consumption, and controls the progress of publicizing and guiding the power consumption enterprise to stagger peak power consumption according to the publicized user quantity data in the power consumption participation time period. According to the method, the power supply companies guide the power utilization companies in part of peak time periods to adjust the power utilization time periods into the valley time periods under the condition permission, and meanwhile, value-added services are added to serve as welfare, so that the power supply amounts in different power supply time periods are in a balanced state, the power supply pressure of the power supply companies is relieved, and the power supply efficiency is improved. Excessive capacity at low ebb or capacity shortage at high peak of a power supply company is effectively prevented, and the cost of standby and capacity markets is reduced.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a method and system for visualizing business data analysis. Background Technology

[0002] Electricity has the physical characteristics of being "ready to use immediately and unable to be stored on a large scale". Therefore, the power grid must maintain an instantaneous balance between power generation, transmission, transformation, distribution and consumption at all times. In order to ensure that the power supply of the power supply company meets the needs of multiple power-consuming enterprises, the power supply company usually collects information on the electricity application amount of the power-consuming enterprises, and then produces the amount of electricity to meet the electricity demand based on the electricity application amount information. Otherwise, there will be problems of overcapacity or undercapacity. Currently, while power supply companies can achieve grid security, precise investment, and reduced line losses and energy consumption by analyzing electricity application data and electricity consumption business data from electricity-consuming enterprises, it is not easy to guide electricity-consuming enterprises to stagger their electricity consumption during peak hours based on the electricity consumption periods of user applications, so as to make the electricity consumption more balanced across different time periods. Otherwise, if multiple electricity-consuming enterprises start up at the same time, instantaneous overload can easily occur, increasing the power supply pressure on the power supply enterprise. Furthermore, in the process of analyzing electricity application data, it is not easy to use changes in electricity application data within the same time period to provide feedback on whether the electrical equipment of electricity-consuming enterprises is abnormal. Summary of the Invention

[0003] The purpose of this invention is to provide a visual business data analysis method and system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: Visualized business data analysis methods include: S1: Collect information on users who have applied for electricity connection and assess their electricity consumption level based on the information. S2: Statistics on electricity consumption data of enterprises at different time periods; S3: Based on the electricity consumption data of enterprises at different times, the power supply enterprise promotes and guides enterprises to avoid peak electricity consumption. S4: Control the progress of publicity and guidance for electricity-consuming enterprises to stagger peak electricity consumption based on the number of users participating in the publicity during the electricity consumption period; S5: Based on the electricity consumption data of enterprises in different time periods and the number of users participating in the promotion during the electricity consumption period, predict the electricity application volume and business data information for the next statistical period. S6: Information on planning business strategies when electricity consumption is at the equilibrium threshold in different time periods.

[0005] Furthermore, the information of the electricity application user includes electricity consumption information and user information. The electricity consumption information includes total electricity consumption, electricity consumption time period and electricity bill amount. The user information includes the type of electrical equipment of the electricity-using enterprise and the consumption level information of the electricity-using enterprise.

[0006] Furthermore, the consumption level of an electricity-consuming enterprise is obtained based on its consumption level information, wherein the consumption level is determined by the amount of electricity bills paid (a) and the amount of unpaid electricity bills (b).

[0007] Furthermore, the statistical analysis of electricity consumption data of enterprises in different time periods specifically includes: dividing the electricity consumption time into several electricity consumption time periods, counting the number of enterprises in each time period, sorting each electricity consumption time period from most to least according to the number of enterprises in different time periods, and then selecting the time periods with the most enterprises as the peak electricity consumption time periods.

[0008] Furthermore, the power supply company's promotion and guidance to power users to avoid peak hours specifically includes: the power supply company informing power users of specific peak hours via telephone or SMS, and advising power users to adjust their electricity usage time to off-peak hours unless necessary.

[0009] Furthermore, the suggestion to encourage electricity-consuming enterprises to adjust their electricity usage time to off-peak hours when not necessary includes providing value-added services to enterprises that agree to adjust their electricity usage time. These value-added services include issuing electricity fee recharge vouchers, installing backup power supplies for electricity-consuming enterprises, and providing free quality inspections of the electrical equipment used by electricity-consuming enterprises.

[0010] Furthermore, the process of controlling the publicity and guidance work specifically includes stopping the persuasion of electricity-using enterprises to adjust their electricity usage time periods to the peak and off-peak hours when the number of enterprises that have adjusted their electricity usage time periods to the peak and off-peak hours in accordance with regulations reaches a preset threshold.

[0011] Furthermore, the business data information for predicting the electricity application volume for the next statistical period specifically includes, based on the number of enterprises that have agreed to adjust their electricity usage time periods, predicting the number of enterprises using electricity in the same time period on the next working day, and predicting the target electricity volume that the power supply company should produce in the same time period on the next working day.

[0012] Furthermore, when electricity consumption in different time periods reaches the equilibrium threshold, the planning and operation strategy information includes: when the electricity consumption of enterprises during peak and off-peak periods is balanced, the adjustment of the electricity consumption time periods of enterprises can be stopped, and then the total electricity consumption and electricity bill amount can be calculated to decide whether the electricity company can continue to expand the transmission line to expand the transmission area, maintain the stability of power supply, and gradually increase the profit input.

[0013] Preferred: A visualized business data analysis system, comprising a data collection module, a strategy optimization module, a strategy feedback module, a data statistics module, a data prediction module, and a strategy implementation module. The strategy feedback module and the strategy optimization module are both connected to the data collection module. The data collection module is connected to the data statistics module. The data statistics module is connected to the data prediction module. The data prediction module is connected to the strategy implementation module. The strategy implementation module is connected to the data collection module.

[0014] Furthermore, the data collection module includes a memory and a smart meter. The memory can store the electricity consumption data of the electricity-consuming enterprise, and the smart meter can record the electricity consumption of the electricity-consuming enterprise. The strategy feedback module includes a mobile app, through which the electricity-consuming enterprise can upload electricity connection applications, pay electricity bills, adjust electricity usage time periods, receive value-added services, and provide feedback on electricity-related questions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By dividing electricity consumption into several time periods, recording the enterprises using electricity and their electricity consumption in each time period, and then sorting the multiple time periods from high to low based on the amount of electricity consumption, peak and off-peak electricity periods are determined. Then, through SMS, telephone, and other means, enterprises are informed that they can adjust their electricity consumption to off-peak periods when it is not necessary or when conditions permit, thereby increasing electricity consumption during off-peak periods to prevent excess electricity and reducing electricity consumption during peak periods to prevent overload. This ensures that the power company's electricity consumption is balanced across different time periods, reducing reserve and capacity market costs.

[0016] By adding value-added services to guide users to avoid peak electricity usage, these services include providing electricity recharge vouchers, installing backup power supplies, and free equipment inspections. Electricity recharge vouchers can offset part of the electricity bill for electricity-using companies. Installing backup power supplies helps users stop using the power company's electricity during peak hours. Free equipment inspections prevent overloading of damaged equipment, reducing electricity costs for electricity-using companies and preventing energy waste. These value-added services not only effectively guide some peak-hour users to adjust their electricity usage times, alleviating the power supply company's pressure, but also reduce the electricity costs for electricity-using companies, achieving a win-win situation for both. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the visualization business data analysis method in this invention; Figure 2 This is a schematic diagram of the method for collecting information from users applying for electricity connection in this invention; Figure 3This is a schematic diagram of the process of promoting and guiding users to avoid peak electricity consumption based on electricity data in this invention; Figure 4 This is a schematic diagram of the process of the visualized business data analysis system in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, please refer to Figure 1 - Figure 3 In this embodiment of the invention, the visualized business data analysis method includes collecting information on users who have applied for electricity connection, evaluating the user's consumption level based on the information on users who have applied for electricity connection, further statistically analyzing the user's electricity consumption data at different time periods based on the information on users who have applied for electricity connection, promoting and guiding users to avoid peak electricity consumption based on the user's electricity consumption data at different time periods, and controlling the progress of the promotion and guidance work based on the number of users participating in the promotion during the electricity consumption period. Based on the statistical data of user electricity consumption in different time periods and the data of the number of users participating in the promotion during the electricity consumption period, the progress of the promotion and guidance work is controlled to predict the electricity application volume for the next statistical period. Finally, when the electricity consumption in different time periods reaches the equilibrium threshold, the business strategy information is planned.

[0020] Specifically, by dividing the electricity application data of electricity-consuming enterprises into several time periods, and then statistically analyzing the electricity-consuming enterprises and their electricity consumption in each time period, the time periods with higher electricity consumption are designated as peak electricity consumption periods, and the remaining time periods are designated as off-peak electricity consumption periods. The power supply company guides some electricity-consuming enterprises during peak hours to adjust their electricity consumption periods to off-peak periods when conditions permit, while increasing value-added services as benefits. This helps to balance the power supply volume in different time periods, reduce the power supply pressure on the power supply company, effectively prevent the power supply company from having excess capacity during off-peak hours or insufficient capacity during peak hours, and reduce the costs of the reserve and capacity market.

[0021] like Figure 2 As shown, in this embodiment, the information collected from users applying for electricity connection includes electricity consumption information and user information. Electricity consumption information includes total electricity consumption, electricity consumption time period, and electricity bill amount. User information includes the type of electrical equipment used by the electricity user and the consumption level information of the electricity user. The consumption level of the electricity user is obtained based on the consumption level information of the electricity user. The consumption level is determined by the amount of electricity bill paid (a) and the amount of unpaid electricity bill (b).

[0022] In this embodiment, the consumption level is determined by the amount of electricity bill paid (a) and the amount of unpaid bill (b). Specifically, the consumption level can be recorded as the difference (d) between a and b. The larger the value of a, the more important the electricity-consuming enterprise is to the power supply company. In this case, additional business strategies can be adjusted, such as issuing electricity bill recharge coupons to important customers or providing free testing for customers to check whether the enterprise's electrical equipment is damaged and overloaded.

[0023] like Figure 1 and Figure 2 As shown in this embodiment, the statistics of electricity consumption data of enterprises in different time periods include: dividing the electricity consumption time into several electricity consumption time periods, counting the number of electricity consumption enterprises in each time period, sorting each electricity consumption time period from most to least according to the number of electricity consumption enterprises in different time periods, and then selecting the first few time periods with more electricity consumption enterprises as peak electricity consumption time periods.

[0024] In this embodiment, a day can be divided into multiple time periods, such as every two hours (00:00-02:00, 02:00-04:00, ..., 22:00-24:00). The electricity connection application volume in each time period is counted, and the multiple time periods are sorted according to the amount of electricity connection applications. In this case, every two hours of 24 hours is counted as a time period, for a total of 12 time periods. The first four time periods with the highest electricity consumption are extracted as peak periods, and the last four time periods with the lowest electricity consumption are extracted as off-peak periods. Then, some electricity-consuming enterprises during peak periods can be guided to consume electricity during off-peak periods.

[0025] like Figure 1 and Figure 2 As shown in this embodiment, the power supply company promotes and guides electricity users to avoid peak hours. Specifically, this includes: the power supply company informing electricity users of the specific peak hours via telephone or SMS, and advising them to adjust their electricity usage to off-peak hours unless necessary. This advising includes providing value-added services to companies that agree to adjust their usage, such as issuing electricity bill recharge vouchers, installing backup power supplies, and providing free quality inspections of their electrical equipment.

[0026] In this embodiment, the free quality inspection of electrical equipment for power-consuming enterprises is mainly based on the reported power consumption and actual power of the equipment. Actual power ÷ reported capacity = load rate η. Once η exceeds the normal operating load range, the equipment is considered to have potential damage. Specifically, a two-step method of "double threshold + machine learning" can be used to complete the detection. The specific detection-related content is as follows: I. Algorithm Selection: 3σ threshold initial screening + random forest fine judgment. Among them, 3σ statistical threshold - millisecond-level triggering, suitable for edge gateways, and random forest - using multi-dimensional features (current harmonics, temperature, η, etc.) to reduce false alarms, suitable for cloud verification. II. Process (Edge-Cloud Collaboration): Step 1: Data collection: active power P, current I, temperature T (uploaded every 10 seconds), equipment installed capacity S (kW) is taken from the marketing system and written into the local whitelist; Step 2: Construct features, η = P / S, Δη = η - η_history_mean, THD_I = Total Harmonic Distortion of Current (direct output of smart IoT meter), ΔT = Chassis Temperature - Ambient Temperature Step 3: 3σ initial screening (marginal), training set: 30 days of η samples from the same equipment, μ_η = mean(η), σ_η = std(η), upper limit UCL = μ_η + 3σ_η, if η > UCL for 3 consecutive points → preliminary anomaly flag Flag1 = 1; Step 3: Random Forest Refinement (Cloud-based), Input feature vector X = [η, Δη, THD_I, ΔT, runtime] Offline model training: Label 0 = normal, 1 = fault / overload. If Flag1 = 1 and RF probability > 0.8 → an anomaly is confirmed and an alarm is issued. Step 5: Complete the business loop by sending an SMS or pushing a notification to the device administrator via a mobile app. III. Example: Equipment file: Air compressor installed capacity S = 100 kW, 30-day historical average η value μ_η = 0.65, σ_η = 0.08 Real-time flow, t0: P = 95 kW → η = 0.95, t1: P = 98 kW → η = 0.98, t2: P = 103 kW → η = 1.03 Calculation shows that UCL = 0.65 + 3 × 0.08 = 0.89. For three consecutive points, η > 0.89 → Flag1 = 1, and the RF model outputs a probability of 0.87 → confirming an anomaly. As a result, the system immediately issued an alarm: the air compressor load rate was 103%, and the valve plate was suspected to be damaged, resulting in an additional power consumption of 18 kW. Please check on-site.

[0027] like Figure 1As shown in this embodiment, controlling the publicity and guidance process specifically includes stopping the persuasion of electricity users to adjust their electricity usage time to the peak and off-peak hours when the number of enterprises that adjust their electricity usage time to the peak and off-peak hours according to regulations reaches a preset threshold. Otherwise, continuously guiding electricity users to use electricity during off-peak hours will turn off-peak hours into peak hours.

[0028] like Figure 1 As shown in this embodiment, the business data information for predicting the electricity application volume for the next statistical period specifically includes: predicting the number of electricity-consuming enterprises in the same time period of the next working day based on the number of enterprises that have agreed to adjust their electricity consumption time periods; predicting the target electricity volume that the power supply company should produce in the same time period of the next working day; and planning business strategy information after the electricity consumption in different time periods is at the equilibrium threshold. Specifically, based on the predicted electricity consumption in the same time period of the next working day, planning to produce no less than the predicted electricity volume.

[0029] Please see Figure 4 In this embodiment of the invention, the visualized business data analysis system includes a data collection module, a strategy optimization module, a strategy feedback module, a data statistics module, a data prediction module, and a strategy implementation module. The strategy feedback module and the strategy optimization module are both connected to the data collection module. The data collection module is connected to the data statistics module. The data statistics module is connected to the data prediction module. The data prediction module is connected to the strategy implementation module. The strategy implementation module is connected to the data collection module.

[0030] In this embodiment, the data collection module includes a memory and a smart meter. The memory can store the electricity consumption data of the electricity-consuming enterprise, and the smart meter can record the electricity consumption of the electricity-consuming enterprise. The strategy feedback module includes a mobile app, through which the electricity-consuming enterprise can upload electricity connection applications, pay electricity bills, adjust electricity usage time periods, receive value-added services, and provide feedback on electricity-related questions.

[0031] In this embodiment, electricity-consuming enterprises can use a mobile app to report electricity connection requests, pay bills, and coordinate with the power supply company to adjust electricity usage periods. They can also use the app to receive electricity recharge coupons and schedule equipment quality inspections. At the same time, electricity-consuming enterprises can also report problems in the power supply company's power supply strategy. The memory on the data receiving module can store relevant information, and then classify and integrate the information and send it to the computer of the strategy optimization module. The power supply company can then check the relevant data information on the computer to adjust its strategy.

[0032] In this embodiment, the data collection module can also transmit the electricity application data, peak and off-peak electricity consumption data to the computer on the data statistics module. The computer can automatically divide multiple electricity consumption periods into peak and off-peak periods based on the electricity consumption, and count the electricity-consuming enterprises in different periods. This makes it easier for the power supply company to target users and guide them to adjust their electricity consumption periods. The data statistics module transmits the electricity application data to the data prediction module. The computer on the data prediction module can predict the electricity consumption for the same period on the next working day based on the application data, and then enable the strategy implementation module to control the power supply equipment to produce enough electricity to meet the application amount.

[0033] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0034] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A visual business data analysis method, characterized in that, Includes the following steps: Step 1: Collect information on users who have applied for electricity connection, and assess the users' electricity consumption level based on the information; Step 2: Compile electricity consumption data for enterprises at different time periods; Step 3: Based on the electricity consumption data of enterprises at different times, the power supply company promotes and guides enterprises to avoid peak electricity consumption. Step 4: Control the progress of publicity and guidance work to help electricity-consuming enterprises avoid peak electricity consumption based on the number of users participating in the publicity during the electricity consumption period; Step 5: Based on the electricity consumption data of enterprises in different time periods and the number of users participating in the electricity consumption period promotion data, predict the electricity application volume and business data information for the next statistical period. Step Six: Once electricity consumption reaches the equilibrium threshold in different time periods, plan operational strategy information.

2. The visualized business data analysis method according to claim 1, characterized in that, The information of the electricity application user includes electricity consumption information and user information. The electricity consumption information includes total electricity consumption, electricity consumption time period and electricity bill amount. The user information includes the type of electrical equipment of the electricity-using enterprise and the consumption level of the electricity-using enterprise.

3. The visualized business data analysis method according to claim 1, characterized in that, The consumption level of an electricity-consuming enterprise is obtained based on its consumption level information. The consumption level is determined by the amount of electricity bills paid (a) and the amount of unpaid electricity bills (b).

4. The visualized business data analysis method according to claim 1, characterized in that, The statistical analysis of electricity consumption data of enterprises in different time periods specifically includes: dividing the electricity consumption time into several electricity consumption time periods, counting the number of electricity consumption enterprises in each time period, sorting each electricity consumption time period from most to least, and then selecting the time periods with the most electricity consumption enterprises as the peak electricity consumption time periods.

5. The visualized business data analysis method according to claim 4, characterized in that, The power supply company's promotion and guidance to power users to avoid peak hours specifically includes: the power supply company informing power users of the specific peak hours of electricity consumption by phone or text message, and advising power users to adjust their electricity consumption time to off-peak hours unless necessary.

6. The visualized business data analysis method according to claim 5, characterized in that, The suggestion to encourage electricity-using enterprises to adjust their electricity usage time to off-peak hours when not necessary includes providing value-added services to enterprises that agree to adjust their electricity usage time. These value-added services include issuing electricity fee recharge vouchers, installing backup power supplies for electricity-using enterprises, and providing free quality inspections of the electrical equipment used by electricity-using enterprises.

7. The visualized business data analysis method according to claim 1, characterized in that, The process of controlling publicity and guidance work specifically includes stopping the persuasion of electricity-using enterprises to adjust their electricity usage time periods to peak and off-peak hours when the number of enterprises that have adjusted their electricity usage time periods to peak and off-peak hours in accordance with regulations reaches a preset threshold.

8. The visualized business data analysis method according to claim 1, characterized in that, The business data information for predicting electricity application volume for the next statistical period specifically includes, based on the number of enterprises that have agreed to adjust their electricity usage time periods, predicting the number of enterprises using electricity in the same time period on the next working day, and predicting the target electricity volume that the power supply company should produce in the same time period on the next working day.

9. A visual business data analysis system is applied to the visual business data analysis method described in any one of claims 1-8, characterized in that, It includes a data collection module, a strategy optimization module, a strategy feedback module, a data statistics module, a data prediction module, and a strategy implementation module. The strategy feedback module and the strategy optimization module are both connected to the data collection module. The data collection module is connected to the data statistics module. The data statistics module is connected to the data prediction module. The data prediction module is connected to the strategy implementation module. The strategy implementation module is connected to the data collection module.

10. The visualized business data analysis system according to claim 9, characterized in that, The data collection module includes a memory and a smart meter. The memory can store the electricity consumption data of the electricity-using enterprises, and the smart meter can record the electricity consumption of the electricity-using enterprises. The strategy feedback module includes a mobile app, through which electricity-using enterprises can upload electricity connection applications, pay electricity bills, adjust electricity usage time periods, receive value-added services, and provide feedback on electricity-related questions.

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