Data measuring and calculating method and device, computer equipment and storage medium

Abstract

The invention relates to a data measurement and calculation method. The method comprises the steps of obtaining service data corresponding to a target electricity price measurement and calculation service scene from a preset service system; determining target business data corresponding to a preset measurement and calculation data type and a measurement and calculation period from the business data; calling a target price calculation model corresponding to the target electricity price calculation business scene and the calculation data type; based on the target business data, performing price measurement and calculation on the target price measurement and calculation model by using a plurality of preset fitting price measurement and calculation rules to obtain a plurality of corresponding versions of electricity price measurement and calculation results; performing evaluation analysis on all the electricity price calculation results based on a preset evaluation rule to obtain corresponding evaluation results; and determining a target electricity price calculation result from all the electricity price calculation results based on the evaluation result. The invention further provides a data measuring and calculating device, computer equipment and a storage medium. According to the invention, the efficiency and flexibility of electricity price calculation are improved, and the accuracy of the obtained target electricity price calculation result is ensured.

Description

Technical Field

[0001] This application relates to the field of data processing technologies, and in particular, to data measurement methods, devices, computer devices, and storage media. Background Art

[0002] In the power grid industry, price measurement plays a crucial role. It is a key tool for power grid enterprises to respond to market fluctuations and policy adjustments, enabling enterprises to quickly adjust their business strategies and effectively reduce financial risks. Through accurate price measurement, power grid enterprises can reasonably allocate resources, optimize power generation and transmission and distribution costs, and thus maximize economic benefits while meeting user needs.

[0003] However, current power grid enterprises face the following problems in the process of price measurement. Usually, business personnel mainly rely on manual input of measurement formulas for price measurement. This method lacks a general systematic framework, resulting in a cumbersome and inefficient measurement process. More importantly, when the measurement formula needs to be updated or adjusted, the manual method is difficult to quickly adapt to these changes, thus affecting the accuracy and timeliness of the measurement. Summary of the Invention

[0004] The purpose of the embodiments of this application is to propose a data measurement method, device, computer device, and storage media to solve the technical problems of low efficiency and low accuracy in the existing price measurement methods used by power grid enterprises.

[0005] To solve the above technical problems, the embodiments of this application provide a data measurement method, which adopts the following technical solutions:

[0006] Obtain service data corresponding to the target electricity price measurement service scenario from a preset service system;

[0007] Determine target service data corresponding to the preset measurement data type and measurement period from the service data;

[0008] Invoke a target price measurement model corresponding to the target electricity price measurement service scenario and the measurement data type;

[0009] Based on the target service data, use a preset variety of fitting price measurement rules to measure the price of the target price measurement model respectively, and obtain corresponding multiple versions of electricity price measurement results;

[0010] Evaluate and analyze all the electricity price measurement results based on a preset evaluation rule to obtain corresponding evaluation results;

[0011] Determine a target electricity price measurement result that meets the requirements from all the electricity price measurement results based on the evaluation result.

[0012] Further, the step of determining target business data corresponding to a preset measurement data type and a measurement period from the business data specifically includes:

[0013] Preprocess the business data to obtain corresponding first business data;

[0014] Obtain the measurement data type and measurement period input by the user;

[0015] Determine second business data corresponding to the measurement data type and the measurement period from the first business data;

[0016] Use the second business data as the target business data.

[0017] Further, the step of using a preset variety of fitting price measurement rules to respectively perform price measurement on the target price measurement model based on the target business data to obtain corresponding multiple versions of electricity price measurement results specifically includes:

[0018] Obtain specified measurement parameters corresponding to a specified fitting price measurement rule; wherein, the specified fitting price measurement rule is any one of all the fitting price measurement rules;

[0019] Obtain a preset electricity price adjustment method;

[0020] Perform price measurement on the target price measurement model based on the target business data, the electricity price adjustment method, and the specified measurement parameters to obtain corresponding measurement processing results;

[0021] Use the measurement processing results as the specified electricity price measurement results corresponding to the specified fitting price measurement rule.

[0022] Further, the step of determining a target electricity price measurement result that meets the requirements from all the electricity price measurement results based on the evaluation result specifically includes:

[0023] Analyze and process the evaluation result to obtain a passing rate corresponding to each of the electricity price measurement results;

[0024] Compare the values of all the passing rates to determine a specific passing rate with the highest value;

[0025] Screen out a specific electricity price measurement result corresponding to the specific passing rate from all the electricity price measurement results;

[0026] Use the specific electricity price measurement result as the target electricity price measurement result.

[0027] Further, before the step of invoking the target price calculation model corresponding to the target electricity price calculation service scenario and the calculation data type, the following steps are also included:

[0028] Obtain a preset data source;

[0029] Obtain various preset electricity price calculation service scenarios;

[0030] Based on the data source, construct price calculation models respectively corresponding to various electricity price calculation service scenarios;

[0031] Perform a storage process on the price calculation models.

[0032] Further, after the step of respectively performing price calculations on the target price calculation model based on various preset fitting price calculation rules to obtain corresponding multiple versions of electricity price calculation results, the following steps are also included:

[0033] Obtain a preset target storage strategy;

[0034] Invoke a preset storage medium;

[0035] Based on the target storage strategy, store all the electricity price calculation results in the storage medium.

[0036] Further, after the step of respectively performing price calculations on the target price calculation model based on various preset fitting price calculation rules to obtain corresponding multiple versions of electricity price calculation results, the following steps are also included:

[0037] Construct a corresponding electricity price analysis report based on the electricity price calculation results;

[0038] Obtain a preset target display form;

[0039] Perform a display process on the electricity price analysis report based on the target display form.

[0040] To solve the above technical problems, an embodiment of the present application further provides a data calculation device, which adopts the following technical solutions:

[0041] A first acquisition module, configured to acquire service data corresponding to a target electricity price calculation service scenario from a preset service system;

[0042] A first determination module, configured to determine target service data corresponding to a preset calculation data type and a calculation period from the service data;

[0043] A first invocation module, configured to invoke a target price calculation model corresponding to the target electricity price calculation service scenario and the calculation data type;

[0044] A measurement module, configured to perform price measurement on the target price measurement model respectively based on a plurality of preset fitting price measurement rules, so as to obtain corresponding electricity price measurement results of multiple versions;

[0045] An evaluation module, configured to perform evaluation and analysis on all the electricity price measurement results based on a preset evaluation rule, so as to obtain corresponding evaluation results;

[0046] A second determination module, configured to determine a target electricity price measurement result meeting the requirements from all the electricity price measurement results based on the evaluation results.

[0047] To solve the above technical problems, an embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0048] Obtain service data corresponding to the target electricity price measurement service scenario from a preset service system;

[0049] Determine target service data corresponding to a preset measurement data type and a measurement period from the service data;

[0050] Invoke a target price measurement model corresponding to the target electricity price measurement service scenario and the measurement data type;

[0051] Perform price measurement on the target price measurement model respectively based on a plurality of preset fitting price measurement rules, so as to obtain corresponding electricity price measurement results of multiple versions;

[0052] Perform evaluation and analysis on all the electricity price measurement results based on a preset evaluation rule, so as to obtain corresponding evaluation results;

[0053] Determine a target electricity price measurement result meeting the requirements from all the electricity price measurement results based on the evaluation results.

[0054] To solve the above technical problems, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0055] Obtain service data corresponding to the target electricity price measurement service scenario from a preset service system;

[0056] Determine target service data corresponding to a preset measurement data type and a measurement period from the service data;

[0057] Invoke a target price measurement model corresponding to the target electricity price measurement service scenario and the measurement data type;

[0058] Perform price measurement on the target price measurement model respectively based on a plurality of preset fitting price measurement rules, so as to obtain corresponding electricity price measurement results of multiple versions;

[0059] Evaluate and analyze all the electricity price calculation results based on a preset evaluation rule to obtain corresponding evaluation results;

[0060] Determine the target electricity price calculation results that meet the requirements from all the electricity price calculation results based on the evaluation results.

[0061] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

[0062] The present application first obtains service data corresponding to the target electricity price calculation service scenario from a preset service system; then determines target service data corresponding to a preset calculation data type and a calculation period from the service data; then calls a target price calculation model corresponding to the target electricity price calculation service scenario and the calculation data type; subsequently, based on the target service data, uses a variety of preset fitting price calculation rules to perform price calculations on the target price calculation model respectively to obtain corresponding multiple versions of electricity price calculation results; further evaluates and analyzes all the electricity price calculation results based on a preset evaluation rule to obtain corresponding evaluation results; finally, determines the target electricity price calculation results that meet the requirements from all the electricity price calculation results based on the evaluation results. In this way, the present application abstracts and automates the electricity price calculation process by combining the use of target service data, target price calculation model, fitting price calculation rules and evaluation rules to obtain corresponding target electricity price calculation results, reduces manual intervention, effectively improves the efficiency and flexibility of electricity price calculation, and ensures the accuracy of the obtained target electricity price calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] To more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0064] Figure 1 is an exemplary system architecture diagram to which the present application can be applied;

[0065] Figure 2 is a flowchart of an embodiment of the data calculation method according to the present application;

[0066] Figure 3 is a schematic structural diagram of an embodiment of the data calculation device according to the present application;

[0067] Figure 4 is a schematic structural diagram of an embodiment of the computer device according to the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification, claims, and drawings of this application are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification, claims, or drawings of this application are used to distinguish different objects and not to describe a specific order.

[0069] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0070] To enable those skilled in the technical field to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings.

[0071] As Figure 1 shown, the system architecture 100 may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is a medium for providing a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0072] The user may use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications may be installed on the terminal device 101, such as a web browser application, a shopping application, a search application, an instant messaging tool, an email client, a social platform software, etc.

[0073] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop 1011, the tablet computer 1012, or the mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer, a desktop computer, and so on.

[0074] The server 103 can be a server that provides various services, such as a background server that supports the pages displayed on the terminal device 101.

[0075] It should be noted that the data measurement method provided by the embodiments of the present application is generally executed by the server / terminal device. Correspondingly, the data measurement device is generally set in the server / terminal device.

[0076] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0077] Continuing to refer to Figure 2 , a flowchart of an embodiment of the data measurement method according to the present application is shown. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0078] Step S201, obtain service data corresponding to the target electricity price measurement service scenario from a preset service system.

[0079] In this embodiment, the electronic device on which the data measurement method runs (such as Figure 1The server / terminal device shown can obtain service data corresponding to the target electricity price calculation service scenario through a wired connection method or a wireless connection method. It should be noted that the above wireless connection method may include, but is not limited to, 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future-developed wireless connection methods. The execution entity of this application can specifically be a data calculation system, which can be simply referred to as a system. The above service system can specifically be a marketing system and a trading system. The above target electricity price calculation service scenario can include any service scenario in the electricity price full-volume service classification price calculation scenarios such as power purchase type, power sale type, inter-network power purchase and sale type, transmission and distribution type, etc. The actual service data corresponding to the target electricity price calculation service scenario can be obtained from the above marketing system and trading system, and specifically can include information such as electricity quantity, electricity price, and electricity charge.

[0080] Step S202: Determine target service data corresponding to the preset calculation data type and calculation period from the service data.

[0081] In this embodiment, the above calculation data type is the data type selected by the user for calculation, including actual numbers or estimated numbers. The above calculation period is the time range specified by the user for calculation, which can be a specific month, quarter, year, or a custom time period. Among them, the specific implementation process of determining the target service data corresponding to the preset calculation data type and calculation period from the service data will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here too much.

[0082] Step S203: Invoke the target price calculation model corresponding to the target electricity price calculation service scenario and the calculation data type.

[0083] In this embodiment, the system integrates multiple data sources, obtains the actual service occurrence data of the electricity price full-category service scenario from the service system, and establishes price calculation models for multiple electricity price calculation service scenarios according to different data sources and different service classifications. Specifically, the constructed price calculation models can include a power purchase actual number calculation model, a power sale actual number calculation model, an inter-network power purchase actual number calculation model, an inter-network power sale actual number calculation model, a power purchase estimated number calculation model, a power sale estimated number calculation model, an inter-network power purchase estimated number calculation model, an inter-network power sale estimated number calculation model, and so on. Exemplarily, if the target electricity price calculation service scenario is of the power purchase type and the calculation data type is the actual number, the target price calculation model corresponding to the target electricity price calculation service scenario and the calculation data type is the above power purchase actual number calculation model.

[0084] Step S204, based on the target business data, use a plurality of preset fitting price calculation rules to perform price calculations on the target price calculation model respectively, and obtain corresponding multiple versions of electricity price calculation results.

[0085] In this embodiment, the above-mentioned fitting price calculation rules may at least include fitting price calculation rules for applying policy electricity prices, fitting price calculation rules for applying annual budgets, and fitting price calculation rules for applying historical data. Among them, the system supports switching price calculation processes of different time dimensions and business dimensions. In the time dimension, the actual business occurrence number and business report data are integrated according to the monthly, quarterly, annual and other time periods. On the one hand, the timeliness of the data is ensured, and the price execution situation can be calculated based on the monthly real-time data. On the other hand, the integrity of the data is ensured, and the system can identify the overall price change trend and the impact of seasonal changes on the business. In the business dimension, in addition to supporting the switching of price calculation scenarios for all types of electricity price business classifications such as electricity purchase, electricity sales, inter-grid purchase and sale, and power transmission and distribution, it also supports the integrated calculation of all types of business data, and can flexibly adjust the calculation rules and parameters and generate calculation results in real time. The support of multiple scenarios is used to improve comprehensive management capabilities. In addition, the above-mentioned specific implementation process of using a plurality of preset fitting price calculation rules to perform price calculation on the target price calculation model based on the target business data to obtain corresponding multiple versions of electricity price calculation results will be further described in detail in subsequent specific embodiments of the present application and will not be elaborated on here.

[0086] Step S205: evaluating and analyzing all the electricity price calculation results based on preset evaluation rules to obtain corresponding evaluation results.

[0087] In this embodiment, the system provides the ability to conduct in-depth comparative analysis of price calculation results of multiple scenario versions by referencing flexible and configurable evaluation rules. For the indicator evaluation method, the system automatically obtains the configured evaluation rules, views and selects analysis indicators, and evaluates the compliance status under the current indicators by selecting one or more versions of price calculation results, and obtains the corresponding evaluation results. Among them, each evaluation rule contains a set of clear standards and indicators, which are optimized to effectively measure various aspects of the electricity price calculation results. When referencing the evaluation rules, the system automatically matches the current analysis scenario and applies the corresponding rule set to ensure the efficiency and consistency of the evaluation process.

[0088] Step S206: determining a target electricity price calculation result that meets the demand from all the electricity price calculation results based on the evaluation result.

[0089] In this embodiment, the system deeply analyzes the electricity price calculation results of each version by automatically applying the set evaluation rules. Regarding the method for evaluating the optimal version, the system first parametrically analyzes the electricity price calculation results of each version, details the compliance rates of each key parameter under the evaluation rules, and quantitatively compares the compliance rates of each version. Specifically, the methods of visual charts and data comparison can be used to intuitively see the performance differences between versions in different indicators, rank them according to the comparative analysis of the electricity price calculation results of multi-scenario versions, and recommend an optimal version based on the comprehensive evaluation results to obtain the final target electricity price calculation result that meets the requirements. The automated system evaluation method improves the decision-making efficiency, and the optimization suggestions provided by the quantitative indicators reduce human bias. Among them, for the specific implementation process of determining the target electricity price calculation result that meets the requirements from all the electricity price calculation results based on the evaluation results, this application will further describe the details in subsequent specific embodiments and will not elaborate too much here.

[0090] This application first obtains the business data corresponding to the target electricity price calculation business scenario from a preset business system; then determines the target business data corresponding to the preset measurement data type and measurement period from the business data; then calls the target price calculation model corresponding to the target electricity price calculation business scenario and the measurement data type; subsequently, based on the target business data, uses a variety of preset fitting price calculation rules to calculate the price of the target price calculation model respectively, and obtains the electricity price calculation results of corresponding multiple versions; further evaluates and analyzes all the electricity price calculation results based on the preset evaluation rules to obtain the corresponding evaluation results; finally, determines the target electricity price calculation result that meets the requirements from all the electricity price calculation results based on the evaluation results. In this way, this application abstracts and automates the electricity price calculation process by combining the use of target business data, target price calculation model, fitting price calculation rules and evaluation rules to obtain the corresponding target electricity price calculation result, reduces manual intervention, effectively improves the efficiency and flexibility of electricity price calculation, and ensures the accuracy of the obtained target electricity price calculation result.

[0091] In some alternative implementation manners, step S202 includes the following steps:

[0092] Preprocess the business data to obtain the corresponding first business data.

[0093] In this embodiment, the above preprocessing may include data cleaning, format conversion and other processing to ensure that the processed data meets the requirements of the price calculation model.

[0094] Obtain the measurement data type and measurement period input by the user.

[0095] In this embodiment, there are no specific limitations on the selection of the above measurement data types and measurement periods, which can be determined according to the actual personal needs of the user. Among them, the above measurement data types are the data types selected by the user for measurement, including actual numbers or estimated numbers. The actual numbers are the data at the end of each month, and the estimated numbers are the predicted data at the beginning of the next month. Exemplarily, the actual numbers are the business data obtained from the marketing system and the transaction system at the end of each month, and the estimated numbers are the business data obtained from the marketing system and the transaction system at the beginning of the next month. In addition, the above measurement period is the time range specified by the user for measurement, which can be a specific month, quarter, year, or a custom time period, such as from January to December of this year.

[0096] Determine the second business data corresponding to the measurement data type and the measurement period from the first business data.

[0097] In this embodiment, relevant data such as electricity quantity, electricity price, and user information can be automatically extracted from the above first business data according to the measurement data type and measurement period selected by the user to obtain the above second business data.

[0098] Use the second business data as the target business data.

[0099] This application preprocesses the business data to obtain the corresponding first business data; then obtains the measurement data type and measurement period input by the user; then determines the second business data corresponding to the measurement data type and the measurement period from the first business data; and subsequently uses the second business data as the target business data. This application preprocesses the business data to obtain the corresponding first business data, and obtains the measurement data type and measurement period input by the user, so as to further determine the second business data corresponding to the measurement data type and the measurement period from the first business data and use it as the target business data, effectively ensuring the accuracy of the obtained target business data.

[0100] In some alternative implementation manners of this embodiment, step S204 includes the following steps:

[0101] Obtain the specified measurement parameters corresponding to the specified fitting price measurement rule; where the specified fitting price measurement rule is any one of all the fitting price measurement rules.

[0102] In this embodiment, the above-mentioned fitting price calculation rules may at least include the fitting price calculation rules for applying policy electricity prices, the fitting price calculation rules for applying annual budgets, and the fitting price calculation rules for applying historical data. The above-mentioned specified fitting price calculation rule is any one of all the fitting price calculation rules. Specifically, for the fitting price calculation rule for applying policy electricity prices, the system provides a functional application for calculating prices by fitting policy electricity prices. By selecting whether to fit the annual policy and which version of the policy electricity price to fit, the system automatically quotes the policy electricity price as a calculation parameter to calculate the price of the price calculation model. For the fitting price calculation rule for applying annual budgets, the system provides a functional application for calculating prices by fitting annual budget data. By selecting whether to fit the current year's budget electricity price, the system automatically quotes the budget electricity price as a calculation parameter to calculate the price of the price calculation model. For the fitting price calculation rule for applying historical data, the system provides a functional application for calculating prices by fitting historical data. By selecting whether to fit historical price data, the system automatically quotes the electricity prices and cost data of previous years as calculation parameters to calculate the price of the price calculation model.

[0103] Specifically, the method for calculating the fitting policy electricity price includes: the system automatically obtains the policy electricity price data of the corresponding business in the annual policy management function. And in the annual policy management, the system parses the content of the latest electricity price policy document, and automatically stores and quotes the policy electricity price parsed therefrom to the price calculation module based on the parsed policy electricity price. According to the policy electricity price parameters, calculate the expenses affected after implementing at the latest price, and compare the price and expense change trends before and after the adjustment, so as to analyze the impact of policy changes on operating benefits.

[0104] The method for calculating the fitting budget electricity price includes: the system automatically obtains the annual, quarterly, and monthly budget data such as the first submission, second submission, and mid-year adjustment of the corresponding business in the annual budget management function. According to the budget prices of each business category, calculate the expense difference affected between the current execution price and the budget price and the adjusted expenses. Through this difference analysis, it is possible to deeply understand the expense changes of each business category under different budget cycles, evaluate the effectiveness of budget execution, and make adjustments when necessary according to the business change trend.

[0105] The method for calculating the fitting historical electricity price includes: the system automatically integrates and obtains the price, cost, etc. data of the corresponding business in previous years. According to the calculation parameters such as the change ratio and change amount between the historical price and the current execution price, calculate the adjusted expenses and compare them with the existing expenses. Identify the long-term trends, seasonal fluctuations, and abnormal situations of prices and expenses based on the expense impact. In addition, the system also provides a visualization tool, which can intuitively view the historical trends and fluctuations of prices and expenses and their impact on the current operating status, compare the price and expense changes in different time periods, and use them to dynamically adjust the price strategy.

[0106] Obtain a preset electricity price adjustment method.

[0107] In this embodiment, the above-mentioned electricity price adjustment method can be to automatically adjust the electricity price by applying the electricity price policy, or it can also be to manually adjust the change factors, including adjusting the electricity quantity change factor and / or adjusting the electricity price change factor, supporting only adjusting one factor or both factors. Among them, for the method of automatically adjusting the electricity price by applying the electricity price policy, the current year's electricity price policy can be selected, or the previous year's electricity price policy can be selected. The electricity price policy described here refers to the one parsed from the official document, and the data can be manually input by the user or obtained from the policy parsing system. The present invention will not be introduced in detail here.

[0108] For the method of manually adjusting the change factors, the electricity quantity change factor can be adjusted by adjusting the electricity quantity change ratio (i.e., the percentage of increase or decrease) or the electricity quantity change amount (i.e., the value of increase or decrease) of each electricity type. The electricity price change factor can be adjusted by adjusting the electricity price change ratio or the electricity price change amount. Among them, after adjusting the electricity quantity change ratio or change amount, the system automatically calculates the adjusted electricity quantity value. After adjusting the electricity price change ratio or change amount, the system automatically calculates the adjusted electricity price value.

[0109] Based on the target business data, the electricity price adjustment method, and the specified measurement parameters, perform price measurement on the target price measurement model to obtain a corresponding measurement processing result.

[0110] In this embodiment, according to the obtained target business data, the selected target price measurement model, the parameters corresponding to the above-mentioned electricity price adjustment method, and the measurement parameters corresponding to the fitting price measurement rule, the corresponding price measurement process can be automatically executed, thereby generating a corresponding measurement processing result. Specifically, the system can load the corresponding algorithms and logics according to the selected target price measurement model, input the obtained target business data and the parameters corresponding to the above-mentioned electricity price adjustment method into the target price measurement model, and then input the measurement parameters corresponding to the fitting price measurement rule into the selected target price measurement model to perform measurement operations. The target price measurement model will calculate the impact of the electricity charge and the change in the electricity charge according to the built-in algorithms and logics and output the corresponding measurement processing result.

[0111] Use the measurement processing result as the specified electricity price measurement result corresponding to the specified fitting price measurement rule.

[0112] This application obtains specified measurement parameters corresponding to a specified fitting price measurement rule; then obtains a preset electricity price adjustment method; afterwards, based on the target business data, the electricity price adjustment method, and the specified measurement parameters, performs price measurement on the target price measurement model to obtain a corresponding measurement processing result; subsequently, uses the measurement processing result as the specified electricity price measurement result corresponding to the specified fitting price measurement rule. By obtaining the specified measurement parameters corresponding to the specified fitting price measurement rule and obtaining the preset electricity price adjustment method, and then performing price measurement on the target price measurement model based on the target business data, the electricity price adjustment method, and the specified measurement parameters, this application can quickly and accurately generate multiple versions of electricity price measurement results, improve the processing efficiency of electricity price measurement, and ensure the data accuracy of the obtained electricity price measurement results.

[0113] In some alternative implementation manners, step S206 includes the following steps:

[0114] Analyze and process the evaluation result to obtain the pass rates corresponding to each of the electricity price measurement results respectively.

[0115] In this embodiment, the system provides the ability to deeply compare and analyze the price measurement results of multiple scenarios by referring to flexible and configurable evaluation rules. For the method of index evaluation, the system will automatically obtain the configured evaluation rules, view and select the analysis indicators, evaluate the pass situation (pass rate) under the current indicators by selecting one or more versions of the price measurement results, and obtain the corresponding evaluation results. Subsequently, by sorting and analyzing the evaluation results corresponding to the price measurement results of various versions, the pass rates corresponding to each of the electricity price measurement results can be obtained.

[0116] Compare the values of all the pass rates to determine a specific pass rate with the highest value.

[0117] In this embodiment, the specific pass rate with the highest value can be screened out by comparing and sorting the values of all the obtained pass rates.

[0118] Screen out the specific electricity price measurement result corresponding to the specific pass rate from all the electricity price measurement results.

[0119] In this embodiment, the above-mentioned specific electricity price measurement result refers to the electricity price measurement result that has a data correspondence relationship with the specific pass rate among the above-mentioned electricity price measurement results.

[0120] Use the specific electricity price measurement result as the target electricity price measurement result.

[0121] Through analyzing and processing the evaluation results, the present application obtains the pass rates corresponding to the respective electricity price calculation results; then numerically compares all the pass rates to determine a specific pass rate with the highest value; thereafter, screens out the specific electricity price calculation result corresponding to the specific pass rate from all the electricity price calculation results; and subsequently uses the specific electricity price calculation result as the target electricity price calculation result. By analyzing and processing the evaluation results, the present application obtains the pass rates corresponding to the respective electricity price calculation results, numerically compares all the pass rates to determine a specific pass rate with the highest value, and then uses the specific electricity price calculation result screened out from all the electricity price calculation results and corresponding to the specific pass rate as the final target electricity price calculation result, effectively ensuring the accuracy of the obtained target electricity price calculation result and facilitating the use of the target electricity price calculation result to provide reasonable suggestions for subsequent electricity price policy adjustment.

[0122] Exemplarily, the operation process for the electricity price simulation calculation of grid power purchase and sale may include:

[0123] Calculation and analysis of power purchase motivation: Obtain the electricity quantity, electricity price, and electricity fee information of each power type for each month of grid power purchase, and calculate the proportion of electricity quantity composition (e.g., if the hydropower purchase quantity is 10, the wind power purchase quantity is 20, the thermal power purchase quantity is 30, and the total electricity quantity is 10 + 20 + 30 = 60, then the proportion of hydropower in the electricity quantity is 16.67%, the proportion of wind power in the electricity quantity is 33.33%, and the proportion of thermal power in the electricity quantity is 50%). Select the types of measurement data, including actual numbers and estimated numbers. The following descriptions are all based on actual numbers, and the estimated numbers are analogized.

[0124] Calculation and analysis of power sale motivation: Obtain the electricity quantity, electricity price, and electricity fee information of each electricity consumption category and voltage level for each month of grid power sale, and calculate the proportion of electricity quantity composition (e.g., if the electricity quantity of residential electricity at 1 - 10 kV is 10, the electricity quantity of general industrial and commercial electricity at 35 - 110 kV is 20, and the electricity quantity of large industrial electricity at 220 kV and above is 30, and the total electricity quantity is 10 + 20 + 30 = 60, then the proportion of residential electricity at 1 - 10 kV in the electricity quantity is 16.67%, the proportion of general industrial and commercial electricity at 35 - 110 kV in the electricity quantity is 33.33%, and the proportion of large industrial electricity at 220 kV and above in the electricity quantity is 50%). Select the types of measurement data, including actual numbers and estimated numbers. The following descriptions are based on actual numbers, and the estimated numbers are analogized.

[0125] Inter-network type driver calculation and analysis: Obtain the electricity quantity, electricity price, and electricity bill information of each channel for the purchase and sale of electricity between power grids each month, and calculate the proportion of electricity composition (e.g., the connected electricity quantity in region A is 10, the connected electricity quantity in region B is 20, and the total electricity quantity is 10 + 20 = 30. Then the proportion of the connected electricity quantity in region A is 33.33%, and the proportion of the connected electricity quantity in region B is 66.67%). Select the data types for calculation, including actual numbers and estimated numbers. The following description takes actual numbers as an example, and the estimated numbers are similar.

[0126] Taking the electricity purchase calculation operation as an example: Next, select the calculation period. Generally, the calculation is to use the data that has occurred to estimate the future. For example, use the electricity purchase cost budget amount set at the beginning of the year and the electricity purchase cost of the months that have occurred to estimate the electricity quantity to be controlled in the remaining months and the impact on the cost after the adjustment of the electricity price policy. If the selected calculation period includes months that have not occurred in the current year, e.g., the selected calculation period is from January to December of the current year, but currently it is July of the current year and an annual electricity price calculation for the current year is required, and the electricity purchase data for August to December is missing. At this time, the fitting method S3 can be selected: no fitting, fitting budget numbers, fitting historical data.

[0127] If no fitting is selected, the electricity purchase data from January to July will be used as the electricity purchase data for the current year. If fitting budget numbers are selected, the budget numbers for August to December made at the beginning or in the middle of the year will be used as the missing data for August to December during the calculation and fitted with the actual electricity purchase data from January to July to form the annual data. After the fitting method is confirmed, there are two options for the next step.

[0128] I. Select to apply the electricity price policy to automatically adjust the electricity price (the electricity price policy of the current year or the previous year can be selected. The electricity price policy described here and the data parsed from the official documents can be manually entered by the user or obtained from the policy parsing system. The present invention does not introduce the relevant content of policy parsing in detail here).

[0129] Taking the selection of the electricity price policy of last year as an example, assume that the hydropower price is X1, the wind power price is X2, and the thermal power price is X3 in the policy results of last year. According to the hydropower price X4, wind power price X5, and thermal power price X6 calculated from the data fitted in the current year, the system will calculate the electricity purchase cost after adjusting the electricity price according to the electricity price policy of last year and the hydropower, wind power, and thermal power purchase quantities in the current year, and compare it with the electricity purchase cost calculated without adjusting the electricity price. Analyze whether it is close to the target at the beginning of the current year.

[0130] II. Select to manually adjust the variable factors, including adjusting the electricity quantity variable factor S42 and the electricity price variable factor, supporting the adjustment of only one factor or both factors.

[0131] Finally, perform one-key calculation. The system calculates the electricity bill based on the adjusted electricity quantity and electricity price, and calculates the impact of the change in electricity quantity on the change in electricity bill and the impact of the change in electricity price on the electricity bill. Different adjustments can form different calculation schemes, which are uniformly queried in the calculation result management.

[0132] Overall category motivation calculation and analysis: One calculation scheme can be selected from the calculation results of power purchase, power sale, and grid-interconnection purchase and sale respectively to form a comprehensive calculation result, including power purchase, power sale fees, and grid-interconnection purchase and sale volume, price, and fee information. Different combinations generate different overall category calculation results, which are displayed in the calculation result management. After obtaining different calculation results according to different calculation schemes, the calculation index evaluation can be performed on the results. And the present invention supports setting indexes for each evaluated scheme, such as: -10% <= (

Total cost impact of power purchase calculation result table

Initial electricity bill of power purchase calculation result table

[0133] In some alternative implementation manners, before step S203, the above electronic device may further perform the following steps:

[0134] Obtain a preset data source.

[0135] In this embodiment, the above data source may include multiple data sources such as a marketing system and a trading system. Among them, the actual business data of grid power purchase, power sale, and grid-interconnection power purchase and sale, including information such as electricity quantity, electricity price, and electricity bill, can be obtained from the marketing system and the trading system.

[0136] Obtain various preset electricity price calculation business scenarios.

[0137] In this embodiment, the above electricity price calculation business scenarios may at least include electricity price full-volume business classification price calculation scenarios such as power purchase category, power sale category, grid-interconnection purchase and sale category, and transmission and distribution category.

[0138] Based on the data source, construct price calculation models respectively corresponding to various electricity price calculation business scenarios.

[0139] In this embodiment, the establishment of the price calculation model is the core foundation of the price calculation process. By using the model design module, the system integrates multiple data sources, obtains the actual business occurrence data of all types of electricity price business scenarios from the business occurrence module, and establishes price calculation models for multiple electricity price calculation business scenarios according to different data sources and different business classifications. Through modular design, it has high flexibility and scalability, and can be dynamically adjusted and expanded according to different business requirements.

[0140] By replacing the manual collation of electricity price business report data in the traditional method, the system supports automatically forming corresponding calculation models based on different report template data, ensuring the accuracy and consistency of the data, and reducing the interference of manual processing. In addition, the update of the report data supports multiple methods such as data interfaces, data imports, and online updates, and adjusts the data model in real time to ensure that it reflects the latest business dynamics and market changes. The automated and intelligent model establishment method is used to improve the efficiency and accuracy of electricity price calculation.

[0141] Perform storage processing on the price calculation model.

[0142] In this embodiment, the storage method of the above price calculation model is not specifically limited and can be determined according to actual storage requirements. For example, local database storage, cloud server storage, blockchain storage, disk storage, etc. can be used.

[0143] This application obtains a preset data source; then obtains various preset electricity price calculation business scenarios; then constructs price calculation models corresponding to various electricity price calculation business scenarios based on the data source; and subsequently performs storage processing on the price calculation models. This application obtains a preset data source and various preset electricity price calculation business scenarios; furthermore, based on the data source, it can quickly and accurately construct price calculation models corresponding to various electricity price calculation business scenarios, effectively improving the construction efficiency of the price calculation model, which is beneficial to improving the efficiency and accuracy of electricity price calculation based on the subsequent use of the price calculation model. And, by performing storage processing on the price calculation model, the security of the price calculation model can be improved and it is convenient for subsequent calls.

[0144] In some optional implementation manners of this embodiment, after step S204, the above electronic device may further perform the following steps:

[0145] Obtain a preset target storage strategy.

[0146] In this embodiment, the policy content of the above-mentioned target storage policy may include: creating and saving electricity price calculation results of multiple calculation versions according to different scenarios such as the measurement period, measurement data source, and measurement business classification, and storing the electricity price calculation results in the system for subsequent analysis and evaluation decision-making. Among them, the electricity price calculation results of each calculation version include detailed calculation models, calculation parameters, and corresponding price and cost results.

[0147] Invoke a preset storage medium.

[0148] In this embodiment, there is no specific limitation on the selection of the above-mentioned storage medium, which can be determined according to actual business needs. For example, any one of a local database, disk, cloud server, blockchain, etc. can be used.

[0149] Based on the target storage policy, store all the electricity price calculation results in the storage medium.

[0150] In this embodiment, the storage process of storing all the electricity price calculation results in the storage medium can be executed according to the policy content of the above-mentioned target storage policy. In addition, after storing the electricity price calculation results in the storage medium, different versions of the electricity price calculation results can be quickly located and retrieved through flexible screening and sorting functions, and the differences between different scenario versions and the potential impacts of these differences on the price strategy can be more clearly identified through comparative analysis.

[0151] This application obtains a preset target storage policy; then invokes a preset storage medium; subsequently, based on the target storage policy, stores all the electricity price calculation results in the storage medium. By obtaining a preset target storage policy and invoking a preset storage medium, and then based on the use of the target storage policy to store all the electricity price calculation results in the storage medium, it is beneficial for subsequent analysis and evaluation decision-making, improves the storage intelligence of the electricity price calculation results, and ensures the data security of the electricity price calculation results.

[0152] In some alternative implementation manners of this embodiment, after step S204, the above-mentioned electronic device may further execute the following steps:

[0153] Construct a corresponding electricity price analysis report based on the electricity price calculation results.

[0154] In this embodiment, the system can generate a corresponding electricity charge change impact report, that is, the above-mentioned electricity price analysis report, according to the electricity price calculation results obtained by performing price calculation based on the above-mentioned target price calculation model. The electricity price analysis report may include information such as the total electricity charge, the change range of the electricity charge, and the electricity charge distribution of different user categories.

[0155] Obtain a preset target display form.

[0156] In this embodiment, the selection of the above-mentioned target display form is not specifically limited and can be selected according to actual display requirements. For example, a graphical or tabular form can be adopted to facilitate users to intuitively understand the electricity price calculation results.

[0157] Perform display processing on the electricity price analysis report based on the target display form.

[0158] In this embodiment, the generated electricity price analysis report can be displayed to the user in the above-mentioned target display form to complete the display processing. In addition, a corresponding report file can also be generated for the user to download or print.

[0159] This application constructs a corresponding electricity price analysis report based on the electricity price calculation results; then obtains a preset target display form; and subsequently performs display processing on the electricity price analysis report based on the target display form. After obtaining multiple versions of electricity price calculation results, this application will automatically construct a corresponding electricity price analysis report based on the electricity price calculation results and perform display processing on the electricity price analysis report based on the obtained target display form, thereby providing users with convenient and accurate calculation services, facilitating users to intuitively understand the electricity price calculation results, and improving the user experience.

[0160] In some alternative implementation manners, the obtained user information has obtained the consent of the user and complies with the provisions of relevant laws and relevant policies.

[0161] In addition, the non-company software tools or components appearing in the embodiments of this application are only introduced by way of example and do not represent actual use.

[0162] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution is prior or posterior. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0163] In addition, this application realizes dynamic calculation of all types of business scenarios of electricity prices by establishing a comprehensive price calculation model, replacing the traditional manual sorting method and improving data accuracy and consistency. Through multi-dimensional calculation rules, flexible switching of time and business dimensions is realized to ensure the timeliness and integrity of data, and support price calculation in multiple scenarios to improve comprehensive management capabilities. By automatically applying policy electricity prices, annual budgets and historical data for fitting calculations, in-depth analysis of the impact of policy and budget changes on operating benefits can be achieved, and long-term trends in prices and costs can be identified. By saving the calculation results of multiple scenario versions, convenient subsequent analysis and decision-making can be achieved, and the differences between different versions can be quickly retrieved. By referencing flexible and configurable rules through the indicator evaluation module, in-depth comparative analysis of multi-scenario calculation results can be achieved, and the optimal version can be automatically evaluated and recommended. Through the overall optimization plan, the calculation efficiency and accuracy can be improved, human bias can be reduced, and scientific decision-making and lean management of enterprises can be supported.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0165] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0166] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a data calculation device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0167] like Figure 3As shown in the figure, the data measurement device 300 in this embodiment includes: a first acquisition module 301, a first determination module 302, a first call module 303, a measurement module 304, an evaluation module 305, and a second determination module 306. Among them:

[0168] The first acquisition module 301 is configured to acquire service data corresponding to a target electricity price measurement service scenario from a preset service system;

[0169] The first determination module 302 is configured to determine target service data corresponding to a preset measurement data type and a measurement period from the service data;

[0170] The first call module 303 is configured to call a target price measurement model corresponding to the target electricity price measurement service scenario and the measurement data type;

[0171] The measurement module 304 is configured to perform price measurement on the target price measurement model respectively based on a plurality of preset fitting price measurement rules to obtain corresponding multiple versions of electricity price measurement results;

[0172] The evaluation module 305 is configured to perform evaluation and analysis on all the electricity price measurement results based on a preset evaluation rule to obtain corresponding evaluation results;

[0173] The second determination module 306 is configured to determine a target electricity price measurement result that meets the requirements from all the electricity price measurement results based on the evaluation result.

[0174] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0175] In some optional implementation manners of this embodiment, the first determination module 302 includes:

[0176] A preprocessing sub-module, configured to preprocess the service data to obtain corresponding first service data;

[0177] A first acquisition sub-module, configured to acquire a measurement data type and a measurement period input by a user;

[0178] A first determination sub-module, configured to determine second service data corresponding to the measurement data type and the measurement period from the first service data;

[0179] A second determination sub-module, configured to use the second service data as the target service data.

[0180] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0181] In some alternative implementation manners of this embodiment, the measurement module 304 includes:

[0182] A second acquisition sub-module, configured to acquire specified measurement parameters corresponding to a specified fitting price measurement rule; wherein, the specified fitting price measurement rule is any one of all the fitting price measurement rules;

[0183] A third acquisition sub-module, configured to acquire a preset electricity price adjustment method;

[0184] A measurement sub-module, configured to perform price measurement on the target price measurement model based on the electricity price adjustment method and the specified measurement parameters, and obtain a corresponding measurement processing result;

[0185] A third determination sub-module, configured to use the measurement processing result as a specified electricity price measurement result corresponding to the specified fitting price measurement rule.

[0186] In this embodiment, the operations respectively performed by the foregoing modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0187] In some alternative implementation manners of this embodiment, the second determination module 306 includes:

[0188] An analysis sub-module, configured to perform analysis processing on the evaluation result to obtain a pass rate corresponding to each of the electricity price measurement results;

[0189] A comparison sub-module, configured to perform numerical comparison on all the pass rates to determine a specific pass rate with the highest value;

[0190] A screening sub-module, configured to screen out a specific electricity price measurement result corresponding to the specific pass rate from all the electricity price measurement results;

[0191] A fourth determination sub-module, configured to use the specific electricity price measurement result as the target electricity price measurement result.

[0192] In this embodiment, the operations respectively performed by the foregoing modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0193] In some alternative implementation manners of this embodiment, the data measurement device further includes:

[0194] A second acquisition module, configured to acquire a preset data source;

[0195] A third acquisition module, configured to acquire various preset electricity price measurement service scenarios;

[0196] A first construction module for constructing price calculation models corresponding to various electricity price measurement business scenarios based on the data sources.

[0197] A first storage module for storing and processing the price calculation models.

[0198] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0199] In some optional implementation manners of this embodiment, the data measurement device further includes:

[0200] A fourth acquisition module for acquiring a preset target storage policy.

[0201] A second invocation module for invoking a preset storage medium.

[0202] A second storage module for storing all the electricity price measurement results in the storage medium based on the target storage policy.

[0203] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0204] In some optional implementation manners of this embodiment, the data measurement device further includes:

[0205] A second construction module for constructing a corresponding electricity price analysis report based on the electricity price measurement results.

[0206] A fifth acquisition module for acquiring a preset target display form.

[0207] A display module for performing display processing on the electricity price analysis report based on the target display form.

[0208] In this embodiment, the operations respectively performed by the above modules or units correspond one by one to the steps of the data measurement method in the foregoing embodiment, and will not be elaborated herein.

[0209] To solve the above technical problems, an embodiment of the present application further provides a computer device. For details, please refer to Figure 4 , Figure 4 which is the basic structural block diagram of the computer device in this embodiment.

[0210] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are communicatively connected to each other via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0211] The computer device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can perform human-computer interaction with the user through means such as a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0212] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for the data calculation method. In addition, the memory 41 can also be used to temporarily store various data that have been output or will be output.

[0213] In some embodiments, the processor 42 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the data measurement method.

[0214] The network interface 43 may include a wireless network interface or a wired network interface, which is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0215] The present application also provides another implementation manner, that is, to provide a computer-readable storage medium storing computer-readable instructions, which can be executed by at least one processor to enable the at least one processor to execute the steps of the data measurement method as described above.

[0216] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions to enable a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present application.

[0217] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields shall be within the scope of the patent protection of the present application by the same token.

Claims

1. A data calculation method, characterized in that: The steps include: Obtain business data corresponding to the target electricity price calculation business scenario from the preset business system; Determine target business data corresponding to a preset calculation data type and calculation period from the business data; Calling a target price calculation model corresponding to the target electricity price calculation business scenario and the calculation data type; Based on the target business data, using a plurality of preset fitting price calculation rules to respectively perform price calculations on the target price calculation model, and obtaining corresponding multiple versions of electricity price calculation results; Based on the preset evaluation rules, all the electricity price calculation results are evaluated and analyzed to obtain corresponding evaluation results; Based on the evaluation result, a target electricity price calculation result that meets the demand is determined from all the electricity price calculation results.

2. The data calculation method according to claim 1, characterized in that: The step of determining target business data corresponding to a preset calculation data type and calculation period from the business data specifically includes: Preprocessing the service data to obtain corresponding first service data; Get the calculation data type and calculation period input by the user; Determining second business data corresponding to the calculated data type and the calculated period from the first business data; The second service data is used as the target service data.

3. The data calculation method according to claim 1, characterized in that: The step of performing price calculation on the target price calculation model based on the target business data using a plurality of preset fitting price calculation rules to obtain corresponding multiple versions of electricity price calculation results specifically includes: Obtaining a specified calculation parameter corresponding to a specified fitting price calculation rule; wherein the specified fitting price calculation rule is any one of all the fitting price calculation rules; Get the preset electricity price adjustment method; Based on the target business data, the electricity price adjustment method and the specified calculation parameters, the target price calculation model is used to calculate the price, and a corresponding calculation result is obtained; The calculation result is used as the specified electricity price calculation result corresponding to the specified fitting price calculation rule.

4. The data calculation method according to claim 1, characterized in that: The step of determining a target electricity price calculation result that meets the demand from all the electricity price calculation results based on the evaluation result specifically includes: Analyzing and processing the evaluation results to obtain compliance rates corresponding to the respective electricity price calculation results; Comparing all of the achievement rates numerically to determine the specific achievement rate with the highest numerical value; Filtering out a specific electricity price calculation result corresponding to the specific compliance rate from all the electricity price calculation results; The specific electricity price calculation result is used as the target electricity price calculation result.

5. The data calculation method according to claim 1, characterized in that: Before the step of calling the target price calculation model corresponding to the target electricity price calculation business scenario and the calculation data type, the method further includes: Get the preset data source; Obtain various preset electricity price calculation business scenarios; Based on the data source, construct price calculation models corresponding to various electricity price calculation business scenarios; The price estimation model is stored.

6. The data calculation method according to claim 1, characterized in that: After the step of respectively performing price calculation on the target price calculation model based on the preset multiple fitting price calculation rules to obtain corresponding multiple versions of electricity price calculation results, the method further includes: Get the preset target storage policy; Call the preset storage medium; Based on the target storage strategy, all the electricity price calculation results are stored in the storage medium.

7. The data calculation method according to claim 1, characterized in that: After the step of respectively performing price calculation on the target price calculation model based on the preset multiple fitting price calculation rules to obtain corresponding multiple versions of electricity price calculation results, the method further includes: Constructing a corresponding electricity price analysis report based on the electricity price calculation result; Get the preset target display format; The electricity price analysis report is displayed based on the target display format.

8. A data calculation device, characterized in that: include: A first acquisition module is used to acquire business data corresponding to the target electricity price calculation business scenario from a preset business system; A first determination module is used to determine target business data corresponding to a preset calculation data type and calculation period from the business data; A first calling module is used to call a target price calculation model corresponding to the target electricity price calculation business scenario and the calculation data type; A calculation module, used to perform price calculations on the target price calculation model based on a plurality of preset fitting price calculation rules, and obtain corresponding multiple versions of electricity price calculation results; An evaluation module, used to evaluate and analyze all the electricity price calculation results based on preset evaluation rules to obtain corresponding evaluation results; The second determination module is used to determine a target electricity price calculation result that meets the demand from all the electricity price calculation results based on the evaluation result.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the data calculation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the data calculation method according to any one of claims 1 to 7 are implemented.

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