Power retail package recommendation method, system and device and storage medium

Abstract

The invention discloses an electric power retail package recommendation method, system and device and a storage medium, and belongs to the technical field of electric power information processing. The method comprises the following steps: acquiring electricity consumption information of a user, acquiring a to-be-measured package specified by the user, and acquiring a current real-time electricity price; according to the electricity consumption information and the current real-time electricity price, the to-be-recommended package with the lowest price in the to-be-measured packages is determined through a preset measurement and calculation price comparison model, and the measurement and calculation price comparison model is constructed by a preset power retail standard package and a preset pricing algorithm; the power retail standard package is a unified package customized after integrating power retail packages in the power market; the related information of the to-be-recommended package is recommended to the user, so that the package with the lowest price is determined and provided to the user in a measurement and calculation mode through the preset measurement and calculation price comparison model according to the demand of the user, and the convenience when the user purchases the power retail package is greatly improved.

Description

Technical Field

[0001] The present application relates to the field of power information processing technology, and in particular to a method, system, device and storage medium for recommending power retail packages. Background Art

[0002] As domestic electricity applications continue to develop, the domestic electricity market is also constantly innovating. More and more power sales companies and retail users are joining the market and providing electricity retail packages to cope with different electricity usage scenarios. For example, fixed electricity prices and time-of-use fluctuating electricity prices based on users' peak and off-peak electricity usage periods are used to meet the electricity needs of different users.

[0003] However, due to the mixed quality of the current electricity market, the electricity retail packages provided by power sales companies and retail users have different prices and complicated package contents. In addition, there are problems such as opaque retail market information, difficulty in selecting power sales companies, a wide variety of transaction contracts, and difficult to understand price packages. When purchasing electricity retail packages, users need to spend a lot of time to select a suitable package from the complicated electricity retail packages. In other words, it is difficult for users to choose electricity retail packages in the current electricity market, and the time cost required is high. Summary of the Invention

[0004] The main purpose of this application is to provide a method, system, device and storage medium for recommending electricity retail packages, aiming to solve the technical problems that it is difficult for users to select electricity retail packages in the current electricity market and the time cost is high.

[0005] To achieve the above objectives, the present application provides a method for recommending electricity retail packages, which includes the following steps: Obtaining the user's electricity usage information, obtaining the package to be measured specified by the user from a preset electricity retail standard package, and obtaining the current real-time electricity price; Determining, based on the electricity usage information and the current real-time electricity price, the lowest-priced recommended package among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; The relevant information of the package to be recommended is recommended to the user, so that the user can select the corresponding package according to the recommended relevant information.

[0006] In one embodiment, the step of determining the lowest-priced recommended package among the packages to be calculated based on the electricity usage information and the current real-time electricity price using a preset price comparison model includes: The preset electricity retail standard packages include fixed price packages, proportional sharing packages and market price linkage packages; If the package to be measured is any one or more of a fixed price package, a proportional profit-sharing package, and a market price linkage package, and a capped price is set, then the electricity price in the package to be measured is compared with the current real-time electricity price; Based on the comparison results and the electricity consumption information, the expected prices for the user to purchase the fixed-price package, the proportional sharing package, and the market price linkage package are calculated respectively through a preset price comparison model, and the package with the lowest price among the expected prices is used as the package to be recommended.

[0007] In one embodiment, before the step of determining the lowest-priced recommended package among the packages to be calculated using a preset price comparison model based on the electricity usage information and the current real-time electricity price, the method further includes: Obtain historical information on electricity retail packages purchased by different users, obtain information on electricity retail packages provided by electricity sales companies, and obtain historical electricity price information; Determining electricity prices when different users purchase different electricity retail packages based on the historical information, the electricity retail package information, and the historical electricity price information; Based on the electricity price, determine an electricity consumption plan that meets preset requirements from the electricity retail package information, and generate, based on the electricity consumption plan and a preset capping mechanism algorithm, a fixed price package based on a fixed electricity price, a proportional sharing package based on a proportionate cost borne by the user and the power sales company, and a market price linkage package based on fluctuations in market electricity prices; A price comparison model is constructed based on a preset pricing algorithm, the fixed price package, the proportional sharing package and the market price linkage package.

[0008] In one embodiment, after the step of obtaining the user's electricity usage information, the method further includes: Before the user specifies the package to be measured, determining similar users with similar electricity demands as the user based on the electricity usage information; The package selection results of the similar users are obtained, and recommendation information is generated based on the package selection results, so that the user can specify the package to be measured based on the recommendation information.

[0009] In one embodiment, the step of determining similar users having similar electricity demand to the user based on the electricity usage information includes: The electricity usage information includes the user's electricity usage characteristics, the user's electricity retail package purchase tendency, and the user's electricity usage behavior characteristics in different time dimensions; determining, from a preset database, a first user group similar to the user based on the electricity usage characteristics and the electricity retail package purchase tendency; determining, from the preset database, a second user group having similar electricity usage behavior to the user based on the electricity usage behavior characteristics in the different time dimensions and the preset weights; Similar users having similar electricity demands to the user are determined based on the first user group and the second user group.

[0010] In one embodiment, after the step of recommending the relevant information of the to-be-recommended package to the user so that the user can select the corresponding package based on the recommended relevant information, the method further includes: After the user selects any electricity retail package based on the recommended relevant information, determining contract information between the user and the electricity sales company with which the user is to trade, wherein the contract information is relevant information used to generate an electronic contract; An electronic contract is generated based on the signing information.

[0011] In one embodiment, the step of generating an electronic contract based on the contract signing information through a preset digital retail platform includes: Randomly extracting unique identity information from the contract information provided by the user and the power sales company respectively, wherein the unique identity information is information composed of the user and / or the power sales company, and the unique identity information is information with a preset fixed number of digits; An electronic contract with unique transaction verification information is generated based on the unique identity identification information and the contract number information randomly generated according to the transaction behavior, wherein the contract number information is information in a preset fixed format containing multiple fixed information bits.

[0012] In addition, to achieve the above objectives, the present application also provides a power retail package recommendation system, which includes: An information collection module is used to obtain the user's electricity usage information, obtain the package to be measured specified by the user from the preset electricity retail standard package, and obtain the current real-time electricity price; a price calculation module, configured to determine, based on the electricity usage information and the current real-time electricity price, the lowest-priced recommended package among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; The information recommendation module is used to recommend the relevant information of the package to be recommended to the user, so that the user can select the corresponding package based on the recommended relevant information.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides an electricity retail package recommendation device, which includes: a memory, a processor, and an electricity retail package recommendation program stored on the memory and runnable on the processor, and the electricity retail package recommendation program is configured to implement the steps of the electricity retail package recommendation method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which a power retail package recommendation program is stored. When the power retail package recommendation program is executed by a processor, the steps of the power retail package recommendation method as described above are implemented.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: by obtaining the user's electricity consumption information, and obtaining the package to be measured specified by the user from the preset electricity retail standard package, and obtaining the current real-time electricity price; according to the electricity consumption information and the current real-time electricity price, through the preset calculation price comparison model, determining the lowest-priced recommended package among the packages to be measured, wherein the calculation price comparison model is constructed by the preset electricity retail standard package and the preset pricing algorithm, and the electricity retail standard package is a unified package customized after integrating various electricity retail packages in the electricity market; recommending the relevant information of the package to be recommended to the user, so that the user can Information, select the corresponding package, so that by obtaining the user's electricity consumption information and the package to be measured specified by the user, as well as the current real-time electricity price, the price of the package to be measured specified by the user can be calculated through the electricity retail standard package in the preset price calculation model, and the lowest-priced package among the electricity retail packages specified by the user can be determined, and it can be used as the package to be recommended, and the package to be recommended can be recommended to the user, so that the user can better choose the package that meets his needs and has the lowest price. That is, through the preset price calculation model, the various electricity retail packages in the market are summarized to meet the package calculation needs of different users, and the lowest-priced package can be recommended through price comparison, thereby improving the convenience and efficiency of users in selecting electricity retail packages. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart illustrating the first embodiment of the method for recommending a retail electricity package; Figure 2 A flowchart illustrating the second embodiment of the method for recommending electricity retail packages for this application; Figure 3 A flowchart illustrating the third embodiment of the method for recommending electricity retail packages for this application; Figure 4 This is a schematic diagram of the module structure of the electricity retail package recommendation system according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the electricity retail package recommendation method in the embodiment of this application.

[0019] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0021] It should be noted that the executing entity in this application is the electricity retail platform, which has a data interaction relationship with at least the marketing system and the electricity trading platform.

[0022] The electricity retail platform can obtain user basic profile data, electricity price data, and electricity consumption data from the marketing system, including the user's voltage level, time-of-use electricity price type, electricity consumption category, and monthly electricity consumption data. In addition, the electricity retail platform can push data such as retail contract information and deviation assessment information of the marketing system.

[0023] The electricity retail platform can obtain the registration information of power sales companies and retail users from the power trading platform, including the legal person information, contact information, and performance bond information of power sales companies, and the legal person information and contact information of retail users. In addition, the electricity retail platform can push the monthly retail finalized data of the electricity trading platform; and obtain data from the electronic signature platform including corporate credit codes, SMS verification codes, signature electronic documents and other data.

[0024] Specifically, the electricity retail platform adopts digital technologies such as big data and blockchain to build a one-stop retail transaction platform with core functions such as one-click screening of power sales companies, one-click price comparison of electricity purchase packages, one-click customization of individual needs, one-click signing of retail contracts, and one-click acquisition of market information. The platform can provide power sales companies with a window to display package contents like "Taobao", and provide retail users with a one-stop service of "information query, price comparison and electricity purchase", effectively reducing the difficulty for enterprises to enter the market, while greatly reducing the customer acquisition cost of power sales companies, and realizing the efficient operation of the market driven by digital means.

[0025] In addition, the main functional settings of the electricity retail platform include: 1. Power sales company display function: To address the problems faced by retail users, such as limited access to information about power sales companies and difficulty in selecting target companies, the power sales company display function displays basic information and operational information. The displayed information includes company registration time, registered capital, total assets, business locations, package sales, credit rating, etc., allowing retail users to intuitively understand the overall situation of the power sales company; 2. Retail Package Display: To address issues such as complex package information, incomplete product displays, and difficulty locating target packages, this function provides a categorized display of clearly marked and negotiated price packages, including detailed information such as package type, effective year, transaction electricity price, package duration, and package sales volume. This helps users intuitively understand the specific content of the packages and supports users to filter and locate the packages they need based on their needs. 3. Retail Package Price Comparison Function: To address the issues of limited channels for price comparison and price calculation for retail users and a lack of experience in selecting electricity price plans, a retail package price comparison function is provided. It conducts intelligent simulation calculations of electricity charges and uses deep learning algorithms to perform intelligent verification of detailed expenses, achieving full household electricity price calculation and comparison, assisting users in making decisions when entering the market and participating in retail transactions. 4. Retail transaction management function: In response to the personalized transaction needs of users, combined with the centralized management of key processes involving power purchase and sales between power sales companies and retail users during monthly and annual power transactions, a multi-layered transaction model with basic layer, platform layer, and application layer is established to realize package transactions, including retail monthly customized package transactions, retail monthly standard package transactions, retail annual customized package transactions, retail annual standard package transactions, offer management, user order management, and related contract management. 5. Digital Contract Management: To address issues such as lengthy transactions caused by offline paper contracts, the retail platform provides online electronic contract signing services, enabling contracts to be signed in seconds with just one click. Leveraging digital signature and encryption technologies, signatures or seals can be directly affixed to electronic documents, ensuring that the identity of the signatory is identifiable and the signed content cannot be tampered with. This enables electronic operations throughout the entire process, from generation, signing, storage, management, viewing, and downloading. VI. Market Information Display: To address issues such as insufficient visualization of market-based information and regulatory content, this platform utilizes technologies such as basic visual rendering and a data cockpit to clearly display market transaction information and operational dynamics through various configuration diagrams and charts. This platform promotes information integration and sharing, enhances data analysis capabilities, improves market information display mechanisms, strengthens information disclosure, and significantly reduces operational management costs in terms of time, manpower, material resources, and financial resources, thereby improving operational efficiency.

[0026] Reference Figure 1 , Figure 1 This is a flowchart of the first embodiment of the electricity retail package recommendation method of this application.

[0027] In a first embodiment, the electricity retail package recommendation method includes the following steps: S10, obtaining the user's electricity usage information, obtaining the package to be measured specified by the user from a preset electricity retail standard package, and obtaining the current real-time electricity price; It is understandable that when users want to choose to view or purchase a package through the electricity retail platform, the electricity retail platform will provide corresponding package calculation and price comparison services, and assist users in quickly finding electricity retail packages that meet their own electricity needs. In order to ensure the operation of the electricity retail package calculation and price comparison service, the platform will first obtain the user's previous electricity usage information records, such as the electricity retail packages that the user has purchased, the user's historical electricity usage behavior characteristics and other information (at least including electricity consumption, peak and valley electricity consumption time periods, etc.), and can provide a check-in service for the package to be calculated. Users can specify the electricity retail packages of existing power sales companies or customize package requirements, for example, negotiate with the power sales company to set a capped price, etc.

[0028] In addition, in order to ensure that the electricity retail platform can accurately calculate the electricity price of each electricity retail package, when the user requests to calculate the electricity bill required for the electricity retail package, it is necessary to obtain the current real-time electricity price.

[0029] It should be noted that the package to be measured that the user can specify is any of the standard electricity retail packages provided by the electricity retail platform. On this basis, the user can set other conditions, such as adding an electricity price cap mechanism, or choosing a flexible combination method. The user customizes the settings to select a fixed electricity price at certain times and a time-of-use electricity price at certain times.

[0030] S20, based on the electricity usage information and the current real-time electricity price, determining a recommended package with the lowest price among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; It is understandable that there are many types of electricity retail packages provided by power sales companies, and the charging standards within each package are not uniform. Therefore, users cannot directly choose a suitable package. At this time, users may calculate and compare prices based on the multiple electricity retail packages displayed on the electricity retail platform. When calculations need to be made for a certain package or multiple packages, different calculation methods need to be used for different packages, and calculations need to be made based on the customized rules of different users and on the basis of the standard electricity retail packages.

[0031] Therefore, on the basis of providing standard electricity retail packages, the electricity retail platform also needs to build a set of price calculation models suitable for different electricity retail packages based on various standard electricity retail packages and corresponding pricing algorithms, that is, a preset price calculation model.

[0032] It should be noted that before building this model, the electricity retail packages provided by various power sales companies will be integrated first, and standard electricity retail packages that meet user needs (low cost) and power sales company needs (guaranteed profitability) will be negotiated and formulated, and such packages will be provided on the electricity retail platform for users to choose.

[0033] It is understandable that after the electricity retail platform receives the user's demand for price comparison, it can use the preset price comparison model, combined with the user's electricity consumption information (for example, the electricity consumption and electricity consumption period of the previous month can be selected to predict the calculated electricity price under the current real-time electricity price) to calculate the packages to be calculated separately, calculate the price corresponding to each package, and select the package with the lowest price as the recommended package.

[0034] S30: Recommend the relevant information of the package to be recommended to the user, so that the user can select a corresponding package according to the recommended relevant information.

[0035] It is understandable that after the recommended package is determined, the recommended package can be recommended to the user, and the package content corresponding to the lowest price among the multiple packages currently calculated, the electricity sales company providing the package, the calculation rules involved in the package, etc. can be displayed in the form of a visual interface for users to review and select a suitable package.

[0036] In this embodiment, before the step of determining the lowest-priced recommended package among the packages to be calculated using a preset price comparison model based on the electricity usage information and the current real-time electricity price, the method further includes: Obtain historical information on electricity retail packages purchased by different users, obtain information on electricity retail packages provided by electricity sales companies, and obtain historical electricity price information; Determining electricity prices when different users purchase different electricity retail packages based on the historical information, the electricity retail package information, and the historical electricity price information; Based on the electricity price, determine an electricity consumption plan that meets preset requirements from the electricity retail package information, and generate, based on the electricity consumption plan and a preset capping mechanism algorithm, a fixed price package based on a fixed electricity price, a proportional sharing package based on a proportionate cost borne by the user and the power sales company, and a market price linkage package based on fluctuations in market electricity prices; A price comparison model is constructed based on a preset pricing algorithm, the fixed price package, the proportional sharing package and the market price linkage package.

[0037] It should be noted that, in this embodiment, before constructing the price comparison model, it is necessary to first integrate the electricity retail packages provided by different electricity sales companies. During the integration, it is necessary to consider the costs of the electricity sales companies, the costs of users purchasing electricity, and price fluctuations during electricity price transactions (pre-set requirements). Combined with the historical information of different users purchasing electricity retail packages, various types of electricity retail package information and historical electricity price information provided by different electricity sales companies, the electricity charges under various conditions are simulated, and the content with the highest economic cost-effectiveness is selected according to various situations to formulate the corresponding electricity retail standard package.

[0038] Specifically, considering the upper and lower limits of transaction prices, peak-to-valley ratios, and profit per kilowatt-hour, the system provides the following algorithm formula for customizing user packages: ; ; ; in, It represents the total cost of the user. represents the profit per kilowatt-hour of electricity sold by the electricity sales company. It represents the fixed cost of the electricity sales company. represents the fixed cost recovery period, It represents the electricity purchase cost of the electricity sales company under the forward market contract. It represents the calculation period of the forward power purchase. It represents the daily cost of electricity purchased by the electricity sales company in the spot market. It represents the amount of electricity purchased by retailers at time t in the forward market. It represents the price of electricity purchased by retailers in the spot market at time t. It represents the amount of electricity purchased by retailers in the spot market, T is the sales cycle of retail packages in the spot market, is a constant, To implement the electricity charges of users before the peak, flat and valley time-of-use electricity prices, They are the user electricity charges after implementing peak, flat and valley time-of-use electricity prices. To assess the electricity charges for deviation, For electricity consumption before the implementation of time-of-use electricity price, It refers to the electricity consumption after the implementation of time-of-use electricity price.

[0039] It is understood that in this embodiment, the standard electricity retail packages set mainly include: a fixed price package based on a fixed electricity price, a proportional sharing package based on the proportion of costs borne by users and power sales companies, and a market price linkage package based on the fluctuation of market electricity prices. Furthermore, based on the above different packages, a capping mechanism algorithm is also provided, which can further reduce the user's electricity costs. Among them, the capping mechanism algorithm mainly refers to setting the price used for a certain proportion of electricity consumption as the current real-time electricity price for monthly transactions when calculating electricity prices.

[0040] Among them, fixed-price packages are retail packages where the electricity sales company and the retail user agree on a fixed transaction settlement price. Both parties can set a cap as needed to prevent the fixed price from being set too high. Retail users who choose fixed-price packages are less affected by fluctuations in the average transaction price. This means that the electricity price remains fixed during the contract period. If wholesale prices rise, the user is not exposed to price increases, but if they fall, they are unable to benefit from price reductions.

[0041] 1. If no cap is set: Time-of-use electricity price users: Retail transaction electricity fee = ∑each period (time-of-use settlement electricity Time-of-use fixed electricity price); Fixed price users: Retail transaction electricity fee = Fixed price settlement electricity quantity Fixed electricity price; 2. If a cap is set: Users of time-of-use electricity prices: If the fixed time-of-use electricity price is greater than the monthly average time-of-use price for that period, then: Retail transaction electricity fee = time-of-use electricity consumption (Cap ratio Monthly average trading price + (100% - cap ratio) Time-of-use fixed electricity price); Otherwise: Retail transaction electricity fee = ∑Each time period (time-sharing settlement electricity Time-of-use fixed electricity price); Fixed price users: If the fixed price is greater than the average fixed price of monthly transactions, the calculation formula is: Retail transaction electricity fee = fixed price settlement electricity Cap ratio Average monthly transaction price + fixed price settlement electricity (100% - cap ratio) Fixed electricity price; Otherwise: Retail transaction electricity fee = fixed price settlement electricity Fixed electricity price.

[0042] Among them, the Proportional Share Plan: A retail package where the electricity sales company and the retail user agree on a base price and a share ratio, sharing profits and risks based on the monthly average time-of-use transaction price. For retail users who choose the Proportional Share Plan, their electricity bills are moderately affected by fluctuations in the average transaction price. During the contract period, electricity prices fluctuate with market prices. When wholesale prices rise, the retail user bears some of the risk of price increases; when they fall, the retail user benefits from price reductions.

[0043] 1. If no cap is set: Time-of-use electricity price users: Share settlement electricity price = Time-of-use transaction benchmark price - (Time-of-use transaction benchmark price - Monthly time-of-use transaction average price) Proportion of profit; Retail transaction electricity fee = ∑Each time period (time-sharing settlement electricity Share settlement electricity price); When the base price of intraday trading is less than the monthly average price of intraday trading, the profit sharing ratio shall be the loss ratio; When the base price of intraday trading is greater than the monthly average price of intraday trading, the profit ratio will be used as the profit ratio; When the time-sharing transaction base price = the monthly time-sharing transaction average price, the settlement electricity price shall be the fixed price transaction base price.

[0044] Fixed Price Users: Shared Settlement Electricity Price = Fixed Price Transaction Base Price + (Monthly Fixed Price Average Price - Fixed Price Transaction Base Price) Proportion of profit; Retail transaction electricity fee = fixed price settlement electricity Share settlement electricity price; When the fixed price trading base price is less than the monthly fixed price average price, the profit sharing ratio shall be the loss ratio; When the fixed price transaction base price is greater than the monthly fixed price average price, the profit ratio will be the profit ratio; When the base price of the fixed price transaction = the average price of the monthly fixed price transaction, the fixed price transaction base price shall be taken as the settlement electricity price.

[0045] 2. If a cap is set: Time-of-use electricity price users: If the time-of-use settlement electricity price of each period is greater than the time-of-use capped price of that period, then: Retail transaction electricity fee = ∑ each period (time-of-use settlement electricity price Time-sharing ceiling price); Otherwise: Retail transaction electricity fee = ∑Each time period (time-sharing settlement electricity Share of settlement electricity price).

[0046] Fixed price users: If the split settlement electricity price > fixed price capped price, then: Retail transaction electricity fee = fixed price settlement electricity volume Fixed price capped price; Otherwise: Retail transaction electricity fee = fixed price settlement electricity Share the settlement electricity price.

[0047] Among them, the market price linkage package: a retail package in which the power sales company and the retail user agree on a floating fee or a floating fee based on the monthly average time-of-use transaction price as the transaction settlement price. For retail users who choose the market price linkage package, their electricity bills are significantly affected by fluctuations in the average transaction price. The transaction price fluctuates with the market, and the retail user bears the full risk of market price fluctuations.

[0048] Example of calculation formula for market price linkage package: The calculation formula of time-of-use electricity price: Retail transaction electricity fee = time-of-use settlement electricity (Monthly transaction time-of-use average price + floating electricity price); The formula for calculating the fixed price is: Retail transaction electricity fee = Fixed price settlement electricity (Monthly transaction average price + floating electricity price).

[0049] It should also be noted that in this embodiment, it is necessary to determine an electricity consumption plan that meets the preset requirements from the electricity retail package information based on the electricity price. Specifically, based on the user's peak and valley electricity consumption and the corresponding electricity consumption ratio, it is inferred whether the user should choose a time-of-use electricity price or a flat price. The calculation process is mainly as follows: Assume that the user's peak and valley electricity consumption is x, y, z, and the electricity sales company's flat price to the household is A. Taking a general industrial and commercial 1-10kV user as an example, when the user chooses the time-of-use electricity price, the retail transaction electricity fee is 1.75A x+1.3A y+(1-0.47)A z, when the user chooses a fixed price, the retail transaction electricity fee is A (x+y+z); Among them, when the retail transaction electricity fee of the time-of-use electricity price is less than the retail transaction electricity fee of the fixed price, it is cost-effective to choose time-of-use electricity, that is: 1.75A x+1.3A y+(1-0.47)A z (x+y+z); Simplifying, 0.75x+0.3y-0.48z<0; Assuming that the user has no peak electricity consumption, if the proportion of the user's off-peak electricity consumption meets the conditions for choosing a more economical time-of-use electricity price, then regardless of the actual proportion of the user's peak electricity consumption, it is more economical to choose a time-of-use electricity price.​

[0050] Among them, let the total power x+y+z=n, the valley power ratio m=z / n, and the formula is 0.75 (nm n)-0.48m n<0, simplifying to get m>0.75 / (0.75+0.48)=60.98%, that is, the valley power ratio is greater than the peak floating ratio / (peak floating ratio + valley floating ratio); Assuming that the user has no peak electricity consumption, if the proportion of the user's off-peak electricity consumption meets the conditions for choosing a more economical price, then regardless of the actual proportion of the user's peak electricity consumption, it is more economical to choose the flat electricity price.

[0051] Among them, let the total power x+y+z=n, the valley power ratio m=z / n, and get the formula 0.3 (nm n)-0.48m n>0, simplifying to m<0.3 / (0.3+0.48)=38.46%. That is, the valley electricity ratio is less than the peak floating ratio / (peak floating ratio + valley floating ratio). In summary, when the proportion of off-peak electricity consumption is less than 38.46%, it is more economical to choose a flat price; when the proportion of off-peak electricity consumption is greater than 60.98%, it is more economical to choose a time-of-use electricity price; when the proportion of off-peak electricity consumption is between 38.46% and 60.98%, whether to choose a time-of-use electricity price is more economical requires comprehensive consideration of the proportion of peak and peak electricity consumption.

[0052] In this embodiment, the step of determining the lowest-priced recommended package among the packages to be calculated based on the electricity usage information and the current real-time electricity price using a preset price comparison model includes: The preset electricity retail standard packages include fixed price packages, proportional sharing packages and market price linkage packages; If the package to be measured is any one or more of a fixed price package, a proportional profit-sharing package, and a market price linkage package, and a capped price is set, then the electricity price in the package to be measured is compared with the current real-time electricity price; Based on the comparison results and the electricity consumption information, the expected prices for the user to purchase the fixed-price package, the proportional sharing package, and the market price linkage package are calculated respectively through a preset price comparison model, and the package with the lowest price among the expected prices is used as the package to be recommended.

[0053] It can be understood that when the preset calculation price model is used to calculate the user-specified to-be-calculated package, the current user-selected package type and whether the package is set with a ceiling price are mainly considered, and the price of each package under the ceiling condition is given. Among the user-specified to-be-calculated packages, the package with the lowest price is given as the to-be-recommended package.

[0054] Specifically, the calculation of various packages is as follows: Among them, the calculation example of the fixed price package is as follows: Among them, in the time-of-use electricity price, when the ceiling price is set, it is assumed that user B1 is a large industrial 10 kV time-of-use user, and signs a retail transaction fixed price package with electricity sales company A in June 2023. The package stipulates that B1 settles the market transaction price in 2023 according to the fixed peak price of 1 yuan / kWh, the peak price of 0.8 yuan / kWh, and the valley price of 0.15 yuan / kWh.

[0055] Both parties agree to set a ceiling price, and the ceiling price of 40% of the electricity in each period is the corresponding time-of-use monthly average transaction price. When the fixed price is higher than the corresponding ceiling price, it is settled according to the ceiling price.

[0056] The monthly time-of-use average transaction price in July is 1.1 yuan / kWh, the monthly time-of-use average transaction price in the peak period is 0.85 yuan / kWh, and the monthly time-of-use average transaction price in the valley period is 0.1 yuan / kWh. The peak power of B1 is 8000 kWh, the peak power is 12500 kWh, and the valley power is 10000 kWh. Then the retail transaction electricity fee of this household in the month is: Peak - retail transaction electricity fee = 1 8000 = 8000 yuan; High peak - retail transaction electricity fee = 12500 0.8 = 10000 yuan; Low valley - retail transaction electricity fee = 40% 10000 0.1 + 60% 10000 0.15 = 1300 yuan; Retail transaction electricity fee = peak - retail transaction electricity fee + high peak - retail transaction electricity fee + low valley - retail transaction electricity fee = 19300 yuan, the retail transaction electricity fee of this user in the month is 19300 yuan.

[0057] In a fixed-price scenario, when a capped price is set, user B2, a large industrial 10kV fixed-price user, signed a fixed-price retail transaction package with power retail company A in June 2023. The package stipulates that user B2 will be settled at a fixed price of 0.5 yuan / kWh based on the market-based electricity price in 2023. Parties A and B agreed to set a capped price. The capped price for 40% of the electricity volume is the average of the monthly fixed-price transactions. If the fixed price exceeds the capped price, settlement will be based on the capped price.

[0058] In June, the average monthly transaction price was 0.6 yuan / kWh, and the user's electricity consumption was 20,000 kWh. The retail transaction electricity fee for this user in July is: Retail transaction electricity fee = 0.5 20000=10000 yuan.

[0059] In July, the average monthly transaction price was 0.45 yuan / kWh, and the user's electricity consumption was 20,000 kWh. The user's electricity bill for this month is: Retail transaction electricity fee = 0.45 20000 40%+0.5 20000 60%=9,600 yuan.

[0060] Among them, the calculation example for the proportional sharing package is: When selecting time-of-use electricity prices and setting capped prices, user C1 is a general industrial and commercial 10 kV time-of-use user, and signed a retail transaction ratio sharing package with power sales company A in June 2023. The package stipulates that C1's peak transaction benchmark price in 2023 is 1 yuan / kWh, the peak transaction benchmark price is 0.7 yuan / kWh, and the valley transaction benchmark price is 0.15 yuan / kWh.

[0061] The electricity consumption for each period will be divided proportionally based on the monthly average time-of-use price for that period. For any positive difference between the monthly average time-of-use price and the benchmark price, 40% will be borne by C1, with the remaining portion borne by Power Sales Company A. For any negative difference, 50% will be borne by C1, with the remaining portion borne by Power Sales Company A. The two parties agreed to set a capped price: 1.1 yuan / kWh for peak, 0.8 yuan / kWh for high, and 0.2 yuan / kWh for low. If the final transaction settlement price resulting from the proportional sharing method is higher than the corresponding capped price, settlement will be based on the capped price.

[0062] In July, the monthly average transaction time-of-use price during peak hours was 1.5 yuan / kWh, the monthly average transaction time-of-use price during off-peak hours was 0.85 yuan / kWh, and the monthly average transaction time-of-use price during off-peak hours was 0.11 yuan / kWh. User C1's peak electricity consumption was 8,000 kWh, peak electricity consumption was 12,500 kWh, and off-peak electricity consumption was 10,000 kWh. The retail electricity bill for that month was: Peak electricity price = 1 + (1.5-1) 40% = 1.16 yuan / kWh > 1.1 yuan / kWh, settled at 1.1 yuan / kWh; Peak electricity price = 0.7 + (0.85-0.7) 40% = 0.76 yuan / kWh < 0.8 yuan / kWh, settled at 0.76 yuan / kWh; Off-peak electricity price = 0.15 + (0.11-0.15) 50% = 0.13 yuan / kWh < 0.2 yuan / kWh, settled at 0.13 yuan / kWh; Peak-retail transaction electricity price = 1.1 8000=8800 yuan; Peak-retail transaction electricity rate = 0.76 12500=9500 yuan; Low - Retail transaction electricity fee = 0.13 10000=1300 yuan; Retail transaction electricity fee = peak - retail transaction electricity fee + peak - retail transaction electricity fee + trough - retail transaction electricity fee = 19,600 yuan. The user's retail transaction electricity fee this month is 19,600 yuan.

[0063] When selecting a fixed price and setting a capped price, user C2 is a general industrial and commercial 10kV fixed price user. They sign a retail transaction share package with Power Sales Company A in June 2023. The package stipulates that C2 will receive a market-based transaction base price of 0.48 yuan / kWh in 2023. The fixed price electricity consumption is split proportionally based on the monthly average fixed price. For any positive difference between the monthly average fixed price and the base price, C2 will cover 40% of the difference, with Power Sales Company A covering the remainder. For any negative difference, C2 will receive 50% of the difference, with Power Sales Company A covering the remainder.

[0064] The two parties also agreed to set a capped price of 0.55 yuan per kilowatt-hour. If the final settlement price of electricity generated by the proportional sharing method is higher than the capped price, the capped price will be used for settlement.

[0065] In July, the average monthly transaction price was 0.43 yuan / kWh, and the user's electricity consumption was 20,000 kWh. The user's electricity bill for that month was: The electricity price of the fixed price = 0.48 + (0.43-0.48) 40% = 0.46 yuan / kWh < 0.55 yuan / kW, settled at 0.46 yuan / kWh; Market-based transaction - fixed price electricity fee = 0.46 20000=9200 yuan, the user’s retail transaction electricity bill in April was 9200 yuan.

[0066] In August, the average monthly transaction price was 0.68 yuan / kWh, and the user's electricity consumption was 40,000 kWh. The user's electricity bill for that month was: Electricity price of one price = 0.48 + (0.68-0.48) 40% = 0.56 yuan / kWh > 0.55 yuan / kW, settled at 0.55 yuan / kWh; Market-based transaction - fixed price electricity fee = 40,000 0.55=22,000 yuan. The user’s retail transaction electricity bill in August was 22,000 yuan.

[0067] Among them, the calculation example for the market price linkage package is: When choosing a time-of-use electricity price, user C1, a general commercial and industrial 10kV time-of-use user, signed a retail market price linkage package with power retailer A in June 2023. This package specifies the amount of electricity used by C1's account number at each metering point during each time period. The settlement cycle is monthly, and all electricity consumption is settled using a market price linkage method. The settlement price for each time period is 0.05 yuan / kWh higher than the monthly average time-of-use price for each time period (including the monthly average time-of-use price and the monthly average flat price).

[0068] In July, the monthly average transaction time-sharing price during peak hours was 1 yuan / kWh, the monthly average transaction time-sharing price during off-peak hours was 0.85 yuan / kWh, and the monthly average transaction time-sharing price during off-peak hours was 0.1 yuan / kWh. User C1's peak electricity consumption was 8,000 kWh, peak electricity consumption was 12,500 kWh, and off-peak electricity consumption was 10,000 kWh. The user's electricity bill for that month was: Peak-retail transaction electricity fee = (1 + 0.05) 8000=8400 yuan; Peak-retail transaction electricity fee = (0.85 + 0.05) 12500=11250 yuan; Low point - Retail transaction electricity cost = 10,000 (0.1+0.05)=1500 yuan; Retail transaction electricity fee = peak - retail transaction electricity fee + peak - retail transaction electricity fee + trough - retail transaction electricity fee = 21,150 yuan. The user's retail transaction electricity fee this month is 21,150 yuan.

[0069] When choosing the fixed price, user C2, a general industrial and commercial 10kV fixed price user, signed a standard retail transaction package with power sales company A in June 2023. The package specifies the electricity consumption of user C2's account number at each metering point, with a monthly settlement cycle, and all electricity consumption is settled in accordance with market price linkage. The transaction settlement price is 0.05 yuan / kWh higher than the average monthly fixed price.

[0070] In July, the average monthly transaction price was 0.52 yuan / kWh, and the user's electricity consumption was 20,000 kWh. The user's electricity bill for that month was: Market-based transaction - fixed price electricity fee = (0.52 + 0.05) 20000=10400 yuan, the user’s retail transaction electricity bill in July was 10400 yuan.

[0071] This embodiment obtains the user's electricity consumption information, obtains the package to be measured specified by the user from the preset electricity retail standard package, and obtains the current real-time electricity price; based on the electricity consumption information and the current real-time electricity price, the preset price comparison model is used to determine the lowest-priced recommended package among the packages to be measured, wherein the price comparison model is constructed by the preset electricity retail standard package and the preset pricing algorithm, and the electricity retail standard package is a unified package customized after integrating various electricity retail packages in the electricity market; the relevant information of the package to be recommended is recommended to the user, so that the user can select the corresponding package based on the recommended relevant information, thereby By obtaining the user's electricity consumption information and the package to be calculated specified by the user, as well as the current real-time electricity price, the price of the package to be calculated specified by the user can be calculated through the electricity retail standard package in the preset price calculation model, and the lowest-priced package among the electricity retail packages specified by the user can be determined and used as the package to be recommended. The package to be recommended can be recommended to the user so that the user can better choose the package that meets his needs and has the lowest price. That is, through the preset price calculation model, the various electricity retail packages in the market are summarized to meet the package calculation needs of different users, and the lowest-priced package can be recommended through price comparison, thereby improving the convenience and efficiency of users in selecting electricity retail packages.

[0072] like Figure 2 As shown, based on the first embodiment, a second embodiment of the method for recommending electricity retail packages of this application is proposed. In this embodiment, the method specifically further includes: S110, before the user specifies a package to be measured, determining similar users having similar electricity demands to the user based on the electricity usage information; S120 , obtaining the package selection results of the similar users, and generating recommendation information based on the package selection results, so that the user can specify the package to be measured based on the recommendation information.

[0073] It is understandable that there are many different forms of packages available in the electricity market, and the price and retail forms of each package are different. When users need to choose a suitable package for their electricity needs, they need to consider the cost-effectiveness of different application scenarios corresponding to each package. The electricity prices in various packages include fixed-price electricity prices, time-based fluctuating electricity prices, and dynamic electricity prices set according to market electricity price fluctuations. However, the same electricity price charging model will produce different charging results based on the electricity needs of different users. For example, there are peak sections for electricity consumption in shopping malls (lunch and evening meal times), and industrial users may have peak and valley electricity consumption differences during the day and night. Therefore, different electricity consumption patterns of users will lead to different preferences for electricity retail packages. However, there are many types of electricity retail packages, and users cannot quickly choose a package that suits their own electricity needs.

[0074] Therefore, in this embodiment, corresponding screening is performed for different users, and similar users are compared and aggregated for similarity, so that alternative packages that better meet their needs can be recommended to different users, so that users can quickly choose a package type that is more suitable for their electricity needs when calculating and comparing packages.

[0075] Specifically, before the user specifies the package to be measured, the user's electricity usage information will be used to determine the user's historical electricity demand. Based on this, similar users with similar electricity demand will be found, and based on the package purchase results of the similar users, corresponding recommendation information will be generated so that the user can quickly determine the package to be measured.

[0076] In this embodiment, the step of determining similar users having similar electricity demand to the user based on the electricity usage information includes: The electricity usage information includes the user's electricity usage characteristics, the user's electricity retail package purchase tendency, and the user's electricity usage behavior characteristics in different time dimensions; determining, from a preset database, a first user group similar to the user based on the electricity usage characteristics and the electricity retail package purchase tendency; determining, from the preset database, a second user group having similar electricity usage behavior to the user based on the electricity usage behavior characteristics in the different time dimensions and the preset weights; Similar users having similar electricity demands to the user are determined based on the first user group and the second user group.

[0077] It is understandable that the user's electricity consumption information will include the nature of the user's electricity consumption (industrial and commercial electricity consumption or residential electricity consumption, etc.), the user's purchase tendency when purchasing electricity retail packages in the past (package purchase records, transaction records with a certain electricity sales company, etc.), and the user's electricity consumption behavior characteristics in different time dimensions (peak and valley electricity consumption periods and peak electricity consumption in each period, electricity consumption change trends in peak and valley electricity consumption periods, total daily electricity consumption, total monthly electricity consumption, total quarterly electricity consumption, seasonal electricity consumption change cycle, etc.).

[0078] It is understandable that, based on the content recorded in the user's telecommunications, similar users with specific electricity demands similar to that of the user can be found from the database corresponding to the electricity retail platform. In this embodiment, the user groups that are similar to the user are mainly screened through secondary clustering methods with different tendencies.

[0079] Specifically, the first user group is determined from a preset database based on the nature of electricity consumption and the tendency to purchase electricity retail packages. This determination method mainly screens out user groups (industrial and commercial users) with similar user properties to the user based on the nature of electricity consumption. At the same time, further screening is performed in combination with the tendency to purchase electricity retail packages, that is, the first user group with similar electricity demand to the user is analyzed at the user intention level based on the purchasing tendency of users with the same electricity consumption nature.

[0080] Specifically, based on the analysis of a large number of data samples, it can be learned that when purchasing electricity, electricity users not only evaluate the prices of various packages, but also usually show a herd tendency. When purchasing packages, they will refer to the package purchase results of other users in similar industries to make decisions. Therefore, when designing the electricity retail platform, recommendation tags are added to the packages publicly available by various electricity sales companies, including " Industry sales ranked top 3", " %User Collection", "Repeat Orders ” and other labels.

[0081] Mainly considering the influence of k other users with high electricity consumption similarity on user i's package purchase decision, user i's electricity purchase utility evaluation result for the package can be further revised as follows: ; in, Purchase a package for user i The revised comprehensive electricity purchase utility; Select a package for user i Basic electricity purchase utility reflects the independent decision-making result of the user's own needs (such as electricity price, electricity consumption preference, etc.), and h is the coefficient measuring the degree of herd mentality of user i. The larger it is, the more easily user i is affected by the decisions of other users; is the set of k users whose electricity usage behavior is most similar to that of user i, is the influence weight of user j on user i, Select a package for user j Basic electricity purchasing utility.

[0082] The preset database includes relevant information on other users' electricity retail packages purchased that is stored in the electricity retail platform.

[0083] Furthermore, in this embodiment, based on the electricity consumption behavior characteristics and preset weights in different time dimensions, a second user group with similar electricity consumption behavior to the user is determined from the preset database. This is mainly through the peak and valley electricity consumption periods and the peak electricity consumption in each period, the trend of electricity consumption changes in the peak and valley electricity consumption periods, the total daily electricity consumption, the total monthly electricity consumption, the total quarterly electricity consumption, the seasonal electricity consumption change cycle and other characteristics. It is analyzed from the preset database that there is a user group with similar electricity consumption behavior to that of the user, and the differences in electricity consumption behavior characteristics of different users are taken into account.

[0084] Therefore, in this embodiment, corresponding preset weight values ​​are also assigned to the user's electricity consumption behavior characteristics in different time dimensions. For example, for users with seasonal electricity consumption, the weight corresponding to the seasonal electricity consumption change cycle will be increased. For users with large peak electricity demand during the day, the weights of the electricity consumption change trend during the peak and valley electricity consumption periods and the peak and valley electricity consumption periods and the electricity consumption peak in each period will be increased.

[0085] Specifically, the dynamic change of the preset weight is mainly based on the proportion of peak and valley periods contained in the electricity consumption information, the change in electricity consumption trend during peak and valley periods, and the change cycle of electricity demand. According to the above three characteristics, the main electricity consumption periods of users are divided. For example, commercial streets will be open all night in the summer, beverage companies will extend the operating time of equipment to increase production in the summer, and the processing plant equipment will concentrate on operating in the morning on weekdays, etc. Based on this, the size of the preset weight is adjusted, and the corresponding size of the weight is used to affect the proportion of various features in the electricity consumption behavior characteristics under different time dimensions as similarity analysis.

[0086] It should be noted that each user's needs are significantly different. Before assigning weights to the user's electricity usage behavior characteristics, it is necessary to analyze the user's key electricity demand trends based on the user's historical electricity usage characteristics, and dynamically adjust the size of the preset weight accordingly.

[0087] In summary, through the analysis of the above electricity consumption behavior characteristics, a second user group having similar electricity consumption demands to the user is screened out from the preset database.

[0088] It should be noted that there is an intersection between the first user group and the second user group, but the meanings represented by the two groups are different. Specifically, the first user group is mainly aimed at the purchasing tendencies of users with the same electricity usage characteristics and the same electricity usage characteristics, but the total electricity consumption and other electricity usage behavior characteristics of such user groups may be significantly different from those of the users. The first user group can only represent the purchasing tendencies of users with the same electricity usage characteristics within a large range, while the second user group is mainly a user group with similar electricity demand and electricity usage behavior to the user. The second user group is closer to the group divided according to electricity demand. Therefore, in this embodiment, it is necessary to combine the above two user groups and filter out the user groups with overlapping parts, which can be defined as similar users with similar electricity demand to the user.

[0089] This embodiment determines similar users with similar electricity demand as the user based on the electricity consumption information before the user specifies the package to be measured; obtains the package selection results of the similar users, and generates recommendation information based on the package selection results, so that the user can specify the package to be measured based on the recommendation information. This can reduce the time required for the user to select the package to be measured by directly recommending packages purchased by other users.

[0090] like Figure 3 As shown, based on the first embodiment, a third embodiment of the method for recommending electricity retail packages of this application is proposed. In this embodiment, the method further includes: S210, after the user selects any electricity retail package based on the recommended relevant information, determining contract information between the user and the electricity sales company with which the user is to trade, wherein the contract information is relevant information used to generate an electronic contract; S220: Generate an electronic contract based on the signing information.

[0091] It is understandable that the electricity retail platform can obtain relevant information of users and power sales companies through the marketing system and the electricity trading platform respectively. Therefore, after the user calculates the price comparison of various packages through the price comparison model in the electricity retail platform, selects the corresponding package and needs to purchase the package, the signing information of the two parties can be pulled in the electricity retail platform, and the user can directly sign a contract with the power sales company in the electricity retail platform to generate an electronic contract.

[0092] In this embodiment, the step of generating an electronic contract based on the contract signing information through a preset digital retail platform includes: Randomly extracting unique identity information from the contract information provided by the user and the power sales company respectively, wherein the unique identity information is information composed of the user and / or the power sales company, and the unique identity information is information with a preset fixed number of digits; An electronic contract with unique transaction verification information is generated based on the unique identity identification information and the contract number information randomly generated according to the transaction behavior, wherein the contract number information is information in a preset fixed format containing multiple fixed information bits.

[0093] It is understandable that users and power sales companies can sign contracts directly online through the power retail platform, thereby reducing the waiting time for users to purchase power packages, thereby improving the convenience of selling power retail packages. However, at the same time, electronic file changes or other forms of data changes are prone to occur in the online transaction model, which may affect the subsequent cooperation between users and power sales companies.

[0094] Therefore, in order to ensure the immutability and security of online signing, in this embodiment, corresponding security measures are provided for the online electronic signing process. When the electronic contract is generated, it is given a specific, unique and tamper-proof unique transaction verification information. If the unique transaction verification information is tampered with, it is determined that there is an abnormality in the electronic contract. If the unique transaction verification information is lost, it is determined that there is an abnormality in the electronic contract. This ensures the validity and security of the electronic contract. At the same time, both users and power sales companies can review and verify the electronic contract through the unique transaction verification information to prevent other people from arbitrarily viewing the electronic contract and causing information leakage risks.

[0095] Specifically, in this embodiment, in order to ensure the uniqueness of the unique transaction verification information, the specific method adopted is to randomly extract the unique identity information from the contract information of the user and the power sales company respectively, and the power retail platform randomly generates the contract number information according to the current transaction behavior of the user and the power sales company, and generates the unique transaction verification information through the unique identity information and contract number information group.

[0096] Among them, when generating unique transaction verification information, it can be achieved through simple permutations and combinations or information superposition and fusion.

[0097] Among them, when randomly generating unique identity identification information, the main method is to randomly extract a fixed number of digits of information from the contract information of the user and / or the power sales company. Specifically, for example, it can be randomly extracted from the address information of the user and / or the power sales company, the legal person information of the user's corresponding company, the tax number information provided by the user, etc. The extraction order and extraction content are all random, and it is only necessary to ensure that the unique identity identification information can represent the user and the power sales company as a whole under the current transaction.

[0098] Among them, when randomly generating the contract number information, the main consideration is the relevant information of the transaction behavior generated by the current user and the power sales company, such as the transaction package number information, transaction time information, contract duration information, etc., and randomly related information content is used to generate a preset fixed format information containing multiple fixed information bits.

[0099] It should be noted that the preset fixed format refers to an information format containing multiple fixed information bits, for example, AxxxBxxxCxxx, where ABC is the fixed bit information, and the information in the fixed bit information is fixed content. Specifically, in this implementation, A can be the user's information code, B is the information code of the power sales company, C is the transaction time information code, and the information of other bits can be randomly extracted from the relevant information of the transaction behavior.

[0100] This embodiment determines the contract information between the user and the power sales company with which the user is to transact after the user selects any electricity retail package based on recommended relevant information, wherein the contract information is relevant information used to generate an electronic contract; an electronic contract is generated based on the contract information, and the contract information between the corresponding user and the power sales company is quickly obtained online. A one-click electronic contract generation function is provided to improve contract signing efficiency.

[0101] In addition, the present application embodiment also proposes a power retail package recommendation system, referring to Figure 4 , the electricity retail package recommendation system includes: The information collection module 10 is used to obtain the user's electricity consumption information, obtain the package to be measured specified by the user from the preset electricity retail standard package, and obtain the current real-time electricity price; a price calculation module 20 for determining, based on the electricity usage information and the current real-time electricity price, the lowest-priced recommended package among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; The information recommendation module 30 is used to recommend the relevant information of the package to be recommended to the user, so that the user can select the corresponding package based on the recommended relevant information.

[0102] This embodiment obtains the user's electricity consumption information, obtains the package to be measured specified by the user from the preset electricity retail standard package, and obtains the current real-time electricity price; based on the electricity consumption information and the current real-time electricity price, the preset price comparison model is used to determine the lowest-priced recommended package among the packages to be measured, wherein the price comparison model is constructed by the preset electricity retail standard package and the preset pricing algorithm, and the electricity retail standard package is a unified package customized after integrating various electricity retail packages in the electricity market; the relevant information of the package to be recommended is recommended to the user, so that the user can select the corresponding package based on the recommended relevant information, thereby By obtaining the user's electricity consumption information and the package to be calculated specified by the user, as well as the current real-time electricity price, the price of the package to be calculated specified by the user can be calculated through the electricity retail standard package in the preset price calculation model, and the lowest-priced package among the electricity retail packages specified by the user can be determined and used as the package to be recommended. The package to be recommended can be recommended to the user so that the user can better choose the package that meets his needs and has the lowest price. That is, through the preset price calculation model, the various electricity retail packages in the market are summarized to meet the package calculation needs of different users, and the lowest-priced package can be recommended through price comparison, thereby improving the convenience and efficiency of users in selecting electricity retail packages.

[0103] It should be noted that each module in the above system can be used to implement each step in the above method and achieve corresponding technical effects, which will not be described in detail in this embodiment.

[0104] Reference Figure 5 , Figure 5 This is a schematic diagram of the structure of the equipment in the hardware operating environment involved in the embodiment of the present application.

[0105] like Figure 5 As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as a disk storage device. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0106] Those skilled in the art will understand that Figure 5The structure shown in the figure does not constitute a limitation on the device, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0107] As shown in Figure 5 The memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a power retail package recommendation program.

[0108] In the device shown in Figure 5 The network interface 1004 is mainly used for data communication with an external network; the user interface 1003 is mainly used for receiving user input instructions; the device calls the power retail package recommendation program stored in the memory 1005 through the processor 1001, and performs the following operations: Obtain the user's electricity information, and obtain the to-be-calculated package specified by the user from the preset power retail standard package, and obtain the current real-time electricity price; According to the electricity information and the current real-time electricity price, a preset calculation price comparison model is used to determine the to-be-recommended package with the lowest price in the to-be-calculated package, wherein the calculation price comparison model is constructed by the preset power retail standard package and a preset pricing algorithm, and the power retail standard package is a unified package customized after integrating each power retail package in the power market; The related information of the to-be-recommended package is recommended to the user, so that the user can select the corresponding package according to the recommended related information.

[0109] Further, the processor 1001 can call the power retail package recommendation program stored in the memory 1005, and further perform the following operations: The preset power retail standard package includes a fixed price package, a proportional sharing package, and a market price linkage package; If the to-be-calculated package is any one or any combination of the fixed price package, the proportional sharing package, and the market price linkage package, and a ceiling price is set, then compare the size between the electricity price in the to-be-calculated package and the current real-time electricity price; According to the comparison result and the electricity information, the expected price of the user purchasing the fixed price package, the proportional sharing package, and the market price linkage package is calculated through the preset calculation price comparison model, and the package with the lowest price in the expected price is taken as the to-be-recommended package.

[0110] Further, the processor 1001 can call the power retail package recommendation program stored in the memory 1005, and further perform the following operations: The preset power retail standard package includes a fixed price package, a proportional sharing package, and a market price linkage package; Obtain historical information on electricity retail packages purchased by different users, obtain information on electricity retail packages provided by electricity sales companies, and obtain historical electricity price information; Determining electricity prices when different users purchase different electricity retail packages based on the historical information, the electricity retail package information, and the historical electricity price information; Based on the electricity price, determine an electricity consumption plan that meets preset requirements from the electricity retail package information, and generate, based on the electricity consumption plan and a preset capping mechanism algorithm, a fixed price package based on a fixed electricity price, a proportional sharing package based on a proportionate cost borne by the user and the power sales company, and a market price linkage package based on fluctuations in market electricity prices; A price comparison model is constructed based on a preset pricing algorithm, the fixed price package, the proportional sharing package and the market price linkage package.

[0111] Furthermore, the processor 1001 may call the electricity retail package recommendation program stored in the memory 1005 and perform the following operations: Before the user specifies the package to be measured, determining similar users with similar electricity demands as the user based on the electricity usage information; The package selection results of the similar users are obtained, and recommendation information is generated based on the package selection results, so that the user can specify the package to be measured based on the recommendation information.

[0112] Furthermore, the processor 1001 may call the electricity retail package recommendation program stored in the memory 1005 and perform the following operations: The electricity usage information includes the user's electricity usage characteristics, the user's electricity retail package purchase tendency, and the user's electricity usage behavior characteristics in different time dimensions; determining, from a preset database, a first user group similar to the user based on the electricity usage characteristics and the electricity retail package purchase tendency; determining, from the preset database, a second user group having similar electricity usage behavior to the user based on the electricity usage behavior characteristics in the different time dimensions and the preset weights; Similar users having similar electricity demands to the user are determined based on the first user group and the second user group.

[0113] Furthermore, the processor 1001 may call the electricity retail package recommendation program stored in the memory 1005 and perform the following operations: After the user selects any electricity retail package based on the recommended relevant information, determining contract information between the user and the electricity sales company with which the user is to trade, wherein the contract information is relevant information used to generate an electronic contract; An electronic contract is generated based on the signing information.

[0114] Furthermore, the processor 1001 may call the electricity retail package recommendation program stored in the memory 1005 and perform the following operations: Randomly extracting unique identity information from the contract information provided by the user and the power sales company respectively, wherein the unique identity information is information composed of the user and / or the power sales company, and the unique identity information is information with a preset fixed number of digits; An electronic contract with unique transaction verification information is generated based on the unique identity identification information and the contract number information randomly generated according to the transaction behavior, wherein the contract number information is information in a preset fixed format containing multiple fixed information bits.

[0115] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, and the computer-readable program instructions are used to execute the electricity retail package recommendation method in the above-mentioned embodiment.

[0118] The computer readable storage medium provided in the application may be, for example, a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0119] The above computer readable storage medium may be contained in the electricity retail package recommendation device, or may exist separately without being assembled into the electricity retail package recommendation device.

[0120] The above computer readable storage medium carries one or more programs, which, when executed by the electricity retail package recommendation device, cause the electricity retail package recommendation device to: obtain electricity consumption information of a user, and obtain a to-be-calculated package specified by the user from a preset electricity retail standard package, and obtain a current real-time electricity price; determine, according to the electricity consumption information and the current real-time electricity price, a to-be-recommended package with the lowest price in the to-be-calculated package by a preset calculation price comparison model, wherein the calculation price comparison model is constructed by the preset electricity retail standard package and a preset pricing algorithm, and the electricity retail standard package is a unified package customized after integrating each electricity retail package in an electricity market; recommend relevant information of the to-be-recommended package to the user, so that the user selects a corresponding package according to the recommended relevant information.

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0123] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0124] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for recommending electricity retail packages. This computer-readable storage medium can address the technical issues surrounding electricity retail package recommendation. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for recommending electricity retail packages provided in the aforementioned embodiments, and are not further elaborated here.

[0125] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

[0126] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0127] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0129] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for recommending electricity retail packages, characterized in that: The electricity retail package recommendation method comprises the following steps: Obtaining the user's electricity usage information, obtaining the package to be measured specified by the user from a preset electricity retail standard package, and obtaining the current real-time electricity price; Determining, based on the electricity usage information and the current real-time electricity price, the lowest-priced recommended package among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; The relevant information of the package to be recommended is recommended to the user, so that the user can select the corresponding package according to the recommended relevant information.

2. The method according to claim 1, wherein The step of determining the lowest-priced recommended package among the packages to be calculated based on the electricity usage information and the current real-time electricity price using a preset price comparison model includes: The preset electricity retail standard packages include fixed price packages, proportional sharing packages and market price linkage packages; If the package to be measured is any one or more of the fixed price package, the proportional share package, and the market price linkage package, and a capped price is set, then the electricity price in the package to be measured is compared with the current real-time electricity price; Based on the comparison results and the electricity consumption information, the expected prices for the user to purchase the fixed-price package, the proportional sharing package, and the market price linkage package are calculated respectively through a preset price comparison model, and the package with the lowest price among the expected prices is used as the package to be recommended.

3. The method according to claim 2, wherein Before the step of determining the lowest-priced recommended package among the packages to be calculated using a preset price comparison model based on the electricity usage information and the current real-time electricity price, the method further includes: Obtain historical information on electricity retail packages purchased by different users, obtain information on electricity retail packages provided by various electricity sales companies, and obtain historical electricity price information; Determining electricity prices when different users purchase different electricity retail packages based on the historical information, the electricity retail package information, and the historical electricity price information; Based on the electricity price, determine an electricity consumption plan that meets preset requirements from the electricity retail package information, and generate, based on the electricity consumption plan and a preset capping mechanism algorithm, a fixed price package based on a fixed electricity price, a proportional sharing package based on a proportionate cost borne by the user and the power sales company, and a market price linkage package based on fluctuations in market electricity prices; A price comparison model is constructed based on a preset pricing algorithm, the fixed price package, the proportional sharing package and the market price linkage package.

4. The method according to claim 1, wherein After the step of obtaining the user's electricity usage information, the method further includes: Before the user specifies the package to be measured, determining similar users with similar electricity demands as the user based on the electricity usage information; The package selection results of the similar users are obtained, and recommendation information is generated based on the package selection results, so that the user can specify the package to be measured based on the recommendation information.

5. The method according to claim 4, wherein The step of determining similar users having similar electricity demand to the user based on the electricity consumption information includes: The electricity usage information includes the user's electricity usage characteristics, the user's electricity retail package purchase tendency, and the user's electricity usage behavior characteristics in different time dimensions; determining, from a preset database, a first user group similar to the user based on the electricity usage characteristics and the electricity retail package purchase tendency; determining, from the preset database, a second user group having similar electricity usage behavior to the user based on the electricity usage behavior characteristics in the different time dimensions and the preset weights; Similar users having similar electricity demands to the user are determined based on the first user group and the second user group.

6. The method according to claim 1, wherein After the step of recommending the relevant information of the to-be-recommended package to the user so that the user can select a corresponding package based on the recommended relevant information, the method further includes: After the user selects any electricity retail package based on the recommended relevant information, determining contract information between the user and the electricity sales company with which the user is to trade, wherein the contract information is relevant information used to generate an electronic contract; An electronic contract is generated based on the signing information.

7. The method according to claim 6, wherein The step of generating an electronic contract based on the contract signing information through a preset digital retail platform includes: Randomly extracting unique identity information from the contract information provided by the user and the power sales company respectively, wherein the unique identity information is information composed of the user and / or the power sales company, and the unique identity information is information with a preset fixed number of digits; An electronic contract with unique transaction verification information is generated based on the unique identity identification information and the contract number information randomly generated according to the transaction behavior, wherein the contract number information is information in a preset fixed format containing multiple fixed information bits.

8. A power retail package recommendation system, characterized in that: The electricity retail package recommendation system includes: An information collection module is used to obtain the user's electricity usage information, obtain the package to be measured specified by the user from the preset electricity retail standard package, and obtain the current real-time electricity price; a price calculation module, configured to determine, based on the electricity usage information and the current real-time electricity price, the lowest-priced recommended package among the packages to be calculated using a preset price comparison model, wherein the price comparison model is constructed using the preset standard electricity retail package and a preset pricing algorithm, and the standard electricity retail package is a unified package customized by integrating various electricity retail packages in the electricity market; The information recommendation module is used to recommend the relevant information of the package to be recommended to the user, so that the user can select the corresponding package based on the recommended relevant information.

9. An electricity retail package recommendation device, characterized in that: The electricity retail package recommendation device includes: a memory, a processor, and an electricity retail package recommendation program stored on the memory and executable on the processor, wherein the electricity retail package recommendation program is configured to implement the steps of the electricity retail package recommendation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: A program for implementing a method for recommending a power retail package is stored on a storage medium, and the program for implementing a method for recommending a power retail package is executed by a processor to implement the steps of the method for recommending a power retail package as described in any one of claims 1 to 7.

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