Carbon credit project recommendation method and device, terminal and storage medium

Abstract

The invention provides a carbon credit project recommendation method and device, a terminal and a storage medium, and the method comprises the steps: receiving a current account login instruction, obtaining user demand information, account information of a current account and a pre-constructed recommendation list, and enabling the recommendation list to comprise a plurality of feature scoring rules; according to the user demand information and the account information, determining a feature score of a disclosed carbon credit project under each feature scoring rule; and obtaining a recommended item based on each feature score. According to the embodiment of the invention, feature scoring is carried out through the user demand information, the account information of the current account and the pre-constructed recommendation list, and centralized data integration is carried out on the disclosed carbon credit project, so that the recommended project is obtained, automatic carbon credit project recommendation is realized, the recommendation efficiency is improved, and convenience is provided for the user.

Description

Technical Field

[0001] The present invention relates to the field of carbon credit technology, and in particular to a carbon credit project recommendation method, device, terminal and storage medium. Background Art

[0002] Carbon credit, also known as carbon rights, is a unit of measurement for carbon emissions that can be entered into the carbon trading market after reducing carbon emissions by increasing energy efficiency, reducing pollution or reducing fossil energy development. Carbon credit is a core element in the carbon trading market, which reflects the commodity attributes of carbon emission rights.

[0003] However, currently, information on carbon credit projects is scattered across different platforms and systems, lacking centralized data integration. When users search for carbon credit projects on demand, they rely on manual operations, which are inefficient and prone to errors.

[0004] Therefore, the prior art has defects and needs to be improved and developed. Summary of the invention

[0005] The present application provides a carbon credit project recommendation method, device, terminal and storage medium to solve the technical problems in the related technology that the information of carbon credit projects is scattered in different platforms and systems, lacks centralized data integration, and when users search for carbon credit projects on demand, they rely on manual operation, which is inefficient and prone to errors.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] The first embodiment of the present application provides a carbon credit project recommendation method, which includes:

[0008] Receive a current account login instruction, obtain user demand information, account information of the current account, and a pre-built recommendation list, wherein the recommendation list includes a number of feature scoring rules;

[0009] Determining the characteristic score of the disclosed carbon credit project under each of the characteristic scoring rules according to the user demand information and the account information;

[0010] Recommended items are obtained based on the scores of the respective features.

[0011] In one embodiment of the present application, before determining the characteristic score of the disclosed carbon credit project under each characteristic scoring rule according to the user demand information and the account information, the method further includes:

[0012] Obtain basic project information of all publicly disclosed carbon credit projects from the carbon credit project public website, and obtain transaction data of each carbon credit project within a predetermined period from the public trading website;

[0013] Performing data check on the basic information and transaction data of the project, discarding carbon credit projects that do not meet the predetermined specifications, and saving the basic information and transaction data corresponding to the carbon credit projects that meet the predetermined specifications into a relational database;

[0014] Based on the basic project information and transaction data of each carbon credit project in the relational database, the potential level and priority of each carbon credit project are determined.

[0015] In one embodiment of the present application, the basic project information includes: project name, type, location and carbon emission reduction, and the transaction data includes: price and transaction volume.

[0016] In one embodiment of the present application, based on the basic project information and transaction data of each carbon credit project in the relational database, determining the potential level and priority of each carbon credit project includes:

[0017] Selecting a first preset number of target carbon credit projects in the relational database, and marking a potential level and a priority for each of the target carbon credit projects;

[0018] According to the marked potential levels and priorities, a clustering algorithm is used to determine the potential levels and priorities corresponding to the carbon credit projects other than the target carbon credit project in the relational database.

[0019] In one embodiment of the present application, each of the target carbon credit projects is labeled with a potential level and priority, including:

[0020] The potential level is determined based on the carbon emission reduction amount of each of the target carbon credit projects, and the priority is determined based on the transaction volume of each of the target carbon credit projects.

[0021] In one embodiment of the present application, the user demand information includes: preferred project type, purpose of purchasing carbon credit projects and carbon credit unit price demand range; the account information includes: user's geographical location and account historical operation records;

[0022] Determining the characteristic scores of the disclosed carbon credit projects under each of the characteristic scoring rules according to the user demand information and the account information includes:

[0023] Determining the first characteristic score of the disclosed carbon credit project under the first characteristic scoring rule according to the preferred project type;

[0024] Determine the second characteristic score of the disclosed carbon credit project under the second characteristic scoring rule according to the purpose of the purchased carbon credit project;

[0025] Determine the third characteristic score of the disclosed carbon credit project under the third characteristic scoring rule according to the carbon credit unit price demand range;

[0026] Determining the fourth characteristic score of the disclosed carbon credit project under the fourth characteristic scoring rule according to the geographical location of the user;

[0027] According to the historical operation records of the account, the carbon credit projects browsed within a preset period are determined, and according to the potential level and priority of each browsed carbon credit project, the fifth feature score of the disclosed carbon credit project under the fifth feature scoring rule is determined.

[0028] In one embodiment of the present application, obtaining recommended items based on the scores of the respective features includes:

[0029] Based on the preset weights corresponding to the characteristic scores, weighted summing the characteristic scores is performed to obtain weighted scores of the carbon credit projects;

[0030] All the carbon credit projects are sorted in descending order of the weighted scores, and the second preset number of carbon credit projects arranged in the front are taken as recommended projects.

[0031] A second aspect of the present application provides a carbon credit project recommendation device, which includes:

[0032] An acquisition module, used to receive a current account login instruction, acquire user demand information, account information of the current account, and a pre-built recommendation list, wherein the recommendation list includes a number of feature scoring rules;

[0033] A scoring module, used to determine the characteristic score of the disclosed carbon credit project under each characteristic scoring rule according to the user demand information and the account information;

[0034] The recommendation module is used to obtain recommended items based on the scores of each feature.

[0035] The third aspect of the present application provides a terminal, wherein the terminal includes a memory, a processor, and a carbon credit project recommendation program stored in the memory and executable on the processor, and when the processor executes the carbon credit project recommendation program, the steps of the carbon credit project recommendation method described above are implemented.

[0036] The fourth aspect of the present application provides a computer-readable storage medium, wherein a carbon credit project recommendation program is stored on the computer-readable storage medium, and when the carbon credit project recommendation program is executed by a processor, the steps of the carbon credit project recommendation method as described above are implemented.

[0037] Beneficial effects of the present invention: The embodiment of the present invention receives the current account login instruction, obtains user demand information, account information of the current account and a pre-constructed recommendation list, wherein the recommendation list includes several feature scoring rules; determines the feature scores of the disclosed carbon credit projects under each of the feature scoring rules according to the user demand information and the account information; and obtains recommended projects based on each of the feature scores. The embodiment of the present application performs feature scoring through user demand information, account information of the current account and a pre-constructed recommendation list, and performs centralized data integration of disclosed carbon credit projects to obtain recommended projects, thereby realizing automated carbon credit project recommendations, improving recommendation efficiency, providing convenience for users, and solving the problem in the related technology that the information of carbon credit projects is scattered across different platforms and systems, lacks centralized data integration, and relies on manual operations when users search for carbon credit projects on demand, which is inefficient and prone to errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of a preferred embodiment of the carbon credit project recommendation method in the present invention.

[0039] Figure 2 It is a logic block diagram of the platform system in a preferred embodiment of the carbon credit project recommendation method of the present invention.

[0040] Figure 3 It is a logical schematic diagram of a preferred embodiment of the carbon credit project recommendation method in the present invention.

[0041] Figure 4 It is a functional principle block diagram of a preferred embodiment of the carbon credit project recommendation device in the present invention.

[0042] Figure 5 It is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] See also Figure 1 The carbon credit project recommendation method according to the embodiment of the present invention comprises the following steps:

[0045] Step S100: receiving a current account login instruction, obtaining user demand information, account information of the current account, and a pre-built recommendation list, wherein the recommendation list includes several feature scoring rules.

[0046] like Figure 1As shown, the carbon credit project recommendation method also includes the following steps:

[0047] Step S200: determining the characteristic scores of the disclosed carbon credit projects under each of the characteristic scoring rules according to the user demand information and the account information.

[0048] In the embodiment of the present application, the step before step S200 includes:

[0049] Step S10: Obtain basic project information of all published carbon credit projects from a carbon credit project disclosure website, and obtain transaction data of each carbon credit project within a predetermined period from a public trading website;

[0050] Step S20: Check the basic project information and transaction data, discard carbon credit projects that do not meet the predetermined specifications, and save the basic project information and transaction data corresponding to the carbon credit projects that meet the predetermined specifications into a relational database;

[0051] Step S30: Determine the potential level and priority of each carbon credit project based on the basic project information and transaction data of each carbon credit project in the relational database.

[0052] Specifically, the transaction data of each carbon credit project within a week can be obtained from the public trading website, and the above data can be obtained regularly in units of weeks. When performing data checks, multiple check items are included, such as data missing check, duplicate data check and outlier check. Data missing check refers to checking the project name, type, location, carbon emission reduction obtained from the project website and the price and transaction volume obtained from the trading website. If an empty string appears, the field is considered missing and discarded. Duplicate data check refers to determining whether the project name, type, location, and carbon emission reduction are the same as other projects. If they are the same, they are considered duplicate projects and discarded. Outlier check means that if the transaction volume of a project in a certain period of time (such as a week) is less than the preset number (such as 100 transactions), the liquidity of this project is considered insufficient and it is discarded. The standardized data is shown in Table 1.

[0053] Project Name String type The above type, string Location Latitude and longitude, positive and negative floating point numbers Carbon Emission Reduction Integer (unit: tons) price Positive floating point number (unit: yuan) Trading Volume Integer (unit: pen)

[0054] Table 1

[0055] The embodiment of the present application also establishes an index to facilitate searching for specific projects. The project information table is indexed by the project name, and the transaction table is indexed by the project name and transaction date, which facilitates searching for the price and transaction volume of the project on the corresponding date.

[0056] The embodiments of the present application utilize big data collection technology to obtain carbon credit projects in different platforms and integrate information from multiple parties, thereby solving the problem in the prior art that information on carbon credit projects is scattered across different platforms and systems and lacks centralized data integration.

[0057] In the embodiment of the present application, the basic information of the project includes: project name, type, location and carbon emission reduction, and the transaction data includes: price and transaction volume.

[0058] Specifically, the types include: renewable energy projects, avoided deforestation projects, afforestation and reforestation projects, industrial gas emission reduction projects and energy efficiency improvement projects.

[0059] The embodiment of the present application facilitates the calculation of feature scores by introducing project names, types, locations, carbon emission reductions, prices, and transaction volumes.

[0060] In the embodiment of the present application, the step S30 specifically includes:

[0061] Step S31, selecting a first preset number of target carbon credit projects in the relational database, and marking a potential level and priority for each of the target carbon credit projects;

[0062] Step S32: Based on the marked potential levels and priorities, a clustering algorithm is used to determine the potential levels and priorities corresponding to the carbon credit projects other than the target carbon credit project in the relational database.

[0063] Specifically, a clustering algorithm is used to model the data and identify the potential and priority of carbon credit projects. Clustering algorithm process: First, randomly select 10% of the projects as the target, mark the potential and priority of these projects, and the marking standard is: all are divided into 4 levels, 0 is the lowest, and 3 is the highest. Take 10% of the projects as the cluster center, and then take the remaining 90% of the projects as the data set. Use the clustering algorithm, take 10% of the projects as the initial center point, and use 4 data fields when calculating the distance: type, location, carbon emission reduction, transaction volume, and projects that belong to the same category as the marked center are considered to have the same potential and priority, and then obtain the potential and priority of all projects, and record the results in a relational database.

[0064] The embodiment of the present application adopts a clustering algorithm, and uses four data fields of type, location, carbon emission reduction, and transaction volume for clustering, thereby ensuring the comprehensiveness of determining potential levels and priorities.

[0065] In one embodiment of the present application, the potential level and priority are marked for each of the target carbon credit projects, including: determining the potential level based on the carbon emission reduction of each of the target carbon credit projects, and determining the priority based on the transaction volume of each of the target carbon credit projects.

[0066] Specifically, the potential level may be determined according to the rules in Table 2, and the priority may be determined according to the rules in Table 3.

[0067] Carbon Emission Reduction Less than 100 tons 100-1000 1000-10000 10000 and above potential 0 1 2 3

[0068] Table 2

[0069]

[0070]

[0071] Table 3

[0072] In the embodiment of the present application, the user demand information includes: preferred project type, purpose of purchasing carbon credit projects and carbon credit unit price demand range; the account information includes: user's geographical location and account historical operation records. The step S200 specifically includes:

[0073] Step S210: Determine the first characteristic score of the disclosed carbon credit project under the first characteristic scoring rule according to the preferred project type;

[0074] Step S220: Determine the second characteristic score of the disclosed carbon credit project under the second characteristic scoring rule according to the purpose of the purchased carbon credit project;

[0075] Step S230: Determine the third characteristic score of the disclosed carbon credit project under the third characteristic scoring rule according to the carbon credit unit price demand range;

[0076] Step S240: Determine the fourth characteristic score of the disclosed carbon credit project under the fourth characteristic scoring rule according to the user's geographical location;

[0077] Step S250: determine the carbon credit projects browsed within a preset period of time according to the historical operation records of the account, and determine the fifth feature score of the disclosed carbon credit projects under the fifth feature scoring rule according to the potential level and priority of each browsed carbon credit project.

[0078] Specifically, when a user logs into the system for the first time, a questionnaire pops up to ask about customer needs, including three questions: 1. What type of project do you prefer? The answer options are the above project types; 2. What is the purpose of purchasing carbon credit projects? Answer options: A. For personal carbon emission offset, B. For corporate carbon emission offset; 3. What is the acceptable unit price of carbon credits? A. Less than 10, B. 10-100, C. 100-1000, D. More than 1000. These questions can be expanded through subsequent version upgrades to be more diverse and detailed. These questions can be changed at any time, and the recommendation algorithm will be automatically re-executed after the change. The recommendation algorithm uses collaborative filtering to recommend projects based on user needs and historical behaviors. First, the answers to the above three questions are obtained, and then the operations performed by the user's current account are analyzed, including: 1. Obtaining the IP address of the user's login, and then deriving the user's geographical location; 2. Obtaining the project data that the user has recently browsed. Based on the above information, 5 user features are obtained, and a recommendation list is obtained based on these 5 features. As shown in Table 4, the scores of the 5 features are obtained using Table 4:

[0079]

[0080] Table 4

[0081] For the first feature scoring rule, since a carbon credit project may belong to one or two types, and the user can make multiple selections, the types selected by the user are compared with the type of the carbon credit project, and the number of matches is multiplied by 100. That is, if the user selects 3 types and a carbon credit project belongs to two of them, then the number of matches is 2, and the first feature score S1 is 200.

[0082] For the second characteristic scoring rule, for a carbon credit project, first confirm its carbon emission reduction. If the user chooses to use it for personal carbon emission offset, the carbon emission reduction will be subtracted by 100. If the user chooses to use it for corporate carbon emission offset, the carbon emission reduction will be subtracted by 1000. The remaining carbon emission reduction is the second characteristic score S2.

[0083] For the third feature scoring rule, if the carbon credit unit price of a carbon credit project is within the range selected by the user, the third feature score S3 is 100 points; if it is not within the range selected by the user, the third feature score S3 is 0 points.

[0084] For the fourth feature scoring rule, the user's geographical location is determined according to the user's login IP address, and the distance between the user's geographical location and the location of the carbon credit project is calculated, and the distance is used as the fourth feature score S4.

[0085] For the fifth feature scoring rule, calculate the average potential level of several (e.g., 5) projects that the user has recently browsed, calculate the difference between the potential level of the current carbon credit project and the average potential level, take the absolute value and multiply by 100 to obtain the potential score; then calculate the average priority of several (e.g., 5) projects that the user has recently browsed, calculate the difference between the priority of the current carbon credit project and the average priority, take the absolute value and multiply by 100 to obtain the priority score; calculate the sum of the potential score and the priority score to obtain the fifth feature score S5.

[0086] The embodiment of the present application utilizes user demand information and account information to perform feature analysis to facilitate targeted recommendations, thereby improving the accuracy of recommendations.

[0087] like Figure 1 As shown, the carbon credit project recommendation method also includes the following steps:

[0088] Step S300: obtaining recommended items based on the scores of the respective features.

[0089] In the embodiment of the present application, the step S300 specifically includes:

[0090] Step S310: Based on the preset weights corresponding to the characteristic scores, weighted sum is performed on the characteristic scores to obtain weighted scores of the carbon credit projects;

[0091] Step S320: sort all the carbon credit projects in descending order of the weighted scores, and take the second preset number of carbon credit projects at the front as recommended projects.

[0092] Specifically, the calculation formula of the weighted score is as follows:

[0093] Score=0.1*51+0.1*52+0.2*53+0.3*54+0.3*55;

[0094] Among them, each weight can be adjusted according to actual needs. The sorted carbon credit projects can be displayed to users, which improves the efficiency of customers browsing carbon credit projects.

[0095] The embodiment of the present application utilizes big data analysis technology to reduce manual dependence, and can automate the process of data collection, processing and analysis. By analyzing the data of carbon credit projects, the most suitable carbon credit projects can be recommended to users according to user needs to optimize the efficiency and accuracy of carbon credit transactions. In addition, by sorting all the carbon credit projects, users can fully understand all available projects, avoiding the problem that users cannot fully understand all available projects when selecting carbon credit projects, resulting in insufficient decision-making information.

[0096] In a specific embodiment, the technical architecture principle block diagram of the embodiment of the present application is as follows: Figure 2 As shown, the embodiment of the present application can be designed as a platform or system, which includes: an interface layer, an algorithm layer and a storage layer. The interface layer includes: a data collection module and a user interface; the algorithm layer includes: a data integration module, a data analysis module, and a recommendation algorithm module; the storage layer includes: a database index and a relational database.

[0097] like Figure 3 As shown in the figure, the interface layer obtains project data from the outside, organizes the project data and sends it to the algorithm layer. The algorithm layer analyzes the project data and stores the project data in the storage layer. Users submit their needs to the algorithm layer, the algorithm layer obtains project information from the storage layer, and the storage layer returns the project information to the algorithm layer. The algorithm layer recommends according to user needs and returns the recommended projects.

[0098] In one embodiment, if Figure 4 As shown, based on the above carbon credit project recommendation method, the present invention also provides a carbon credit project recommendation device, including:

[0099] The acquisition module 100 is used to receive the current account login instruction, obtain the user demand information, the account information of the current account and the pre-built recommendation list, wherein the recommendation list includes several feature scoring rules;

[0100] A scoring module 200, for determining a characteristic score of a disclosed carbon credit project under each characteristic scoring rule according to the user demand information and the account information;

[0101] The recommendation module 300 is used to obtain recommended items based on the scores of the respective features.

[0102] It should be noted that the above explanations of the embodiment of the carbon credit project recommendation method are also applicable to the carbon credit project recommendation device of this embodiment, and will not be repeated here.

[0103] The present invention discloses a carbon credit project recommendation device, which receives a current account login instruction, obtains user demand information, account information of the current account, and a pre-constructed recommendation list, wherein the recommendation list includes several feature scoring rules; determines the feature scores of the disclosed carbon credit projects under each of the feature scoring rules according to the user demand information and the account information; and obtains recommended projects based on each of the feature scores. The embodiment of the present application performs feature scoring through user demand information, account information of the current account, and a pre-constructed recommendation list, and performs centralized data integration on the disclosed carbon credit projects to obtain recommended projects, thereby realizing automated carbon credit project recommendations, improving recommendation efficiency, providing convenience for users, and solving the problem in the related art that the information of carbon credit projects is scattered across different platforms and systems, lacks centralized data integration, and relies on manual operations when users search for carbon credit projects on demand, which is inefficient and prone to errors.

[0104] Figure 5 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. The terminal may include:

[0105] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0106] When the processor 502 executes the program, the carbon credit project recommendation method provided in the above embodiment is implemented.

[0107] Furthermore, the terminal further includes:

[0108] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0109] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0110] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0111] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0112] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0113] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0114] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above carbon credit project recommendation method is implemented.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0116] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes additional implementations, in which the order shown or discussed may not be followed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can read instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary and then storing it in a computer memory.

[0119] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0121] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0122] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A carbon credit project recommendation method, characterized in that: include: Receive a current account login instruction, obtain user demand information, account information of the current account, and a pre-built recommendation list, wherein the recommendation list includes a number of feature scoring rules; Determining the characteristic scores of the disclosed carbon credit projects under each of the characteristic scoring rules according to the user demand information and the account information; Recommended items are obtained based on the scores of the respective features.

2. The carbon credit project recommendation method according to claim 1, characterized in that: Before determining the characteristic scores of the disclosed carbon credit projects under each of the characteristic scoring rules according to the user demand information and the account information, the method further includes: Obtain basic project information of all publicly disclosed carbon credit projects from the carbon credit project public website, and obtain transaction data of each carbon credit project within a predetermined period from the public trading website; Performing data check on the basic information and transaction data of the project, discarding carbon credit projects that do not meet the predetermined specifications, and saving the basic information and transaction data corresponding to the carbon credit projects that meet the predetermined specifications into a relational database; Based on the basic project information and transaction data of each carbon credit project in the relational database, the potential level and priority of each carbon credit project are determined.

3. The carbon credit project recommendation method according to claim 2, characterized in that: The basic project information includes: project name, type, location and carbon emission reduction; the transaction data includes: price and transaction volume.

4. The carbon credit project recommendation method according to claim 3, characterized in that: Based on the basic project information and transaction data of each carbon credit project in the relational database, the potential level and priority of each carbon credit project are determined, including: Selecting a first preset number of target carbon credit projects in the relational database, and marking a potential level and a priority for each of the target carbon credit projects; According to the marked potential levels and priorities, a clustering algorithm is used to determine the potential levels and priorities corresponding to the carbon credit projects other than the target carbon credit project in the relational database.

5. The carbon credit project recommendation method according to claim 4, characterized in that: The potential level and priority of each target carbon credit project are marked, including: The potential level is determined based on the carbon emission reduction amount of each of the target carbon credit projects, and the priority is determined based on the transaction volume of each of the target carbon credit projects.

6. The carbon credit project recommendation method according to claim 2, characterized in that: The user demand information includes: preferred project type, purpose of purchasing carbon credit projects and carbon credit unit price demand range; the account information includes: user's geographical location and account historical operation records; Determining the characteristic scores of the disclosed carbon credit projects under each of the characteristic scoring rules according to the user demand information and the account information includes: Determining the first characteristic score of the disclosed carbon credit project under the first characteristic scoring rule according to the preferred project type; Determine the second characteristic score of the disclosed carbon credit project under the second characteristic scoring rule according to the purpose of the purchased carbon credit project; Determine the third characteristic score of the disclosed carbon credit project under the third characteristic scoring rule according to the carbon credit unit price demand range; Determining the fourth characteristic score of the disclosed carbon credit project under the fourth characteristic scoring rule according to the geographical location of the user; According to the historical operation records of the account, the carbon credit projects browsed within a preset period are determined, and according to the potential level and priority of each browsed carbon credit project, the fifth feature score of the disclosed carbon credit project under the fifth feature scoring rule is determined.

7. The carbon credit project recommendation method according to claim 1, characterized in that: Recommended items are obtained based on the scores of each of the features, including: Based on the preset weights corresponding to the characteristic scores, weighted summing the characteristic scores is performed to obtain weighted scores of the carbon credit projects; All the carbon credit projects are sorted in descending order of the weighted scores, and the second preset number of carbon credit projects arranged in the front are taken as recommended projects.

8. A carbon credit project recommendation device, characterized in that: include: An acquisition module, used to receive a current account login instruction, acquire user demand information, account information of the current account, and a pre-built recommendation list, wherein the recommendation list includes several feature scoring rules; A scoring module, used to determine the characteristic score of the disclosed carbon credit project under each characteristic scoring rule according to the user demand information and the account information; The recommendation module is used to obtain recommended items based on the scores of each feature.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a carbon credit project recommendation program stored in the memory and executable on the processor. When the processor executes the carbon credit project recommendation program, the steps of the carbon credit project recommendation method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a carbon credit project recommendation program, and when the carbon credit project recommendation program is executed by a processor, the steps of the carbon credit project recommendation method according to any one of claims 1 to 7 are implemented.

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