CN111667317B - Service processing method, device and system - Google Patents
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- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
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Abstract
The embodiment of the application discloses a service processing method, a device and a system, wherein the method comprises the following steps: determining target customer service based on a reply message of a user, wherein the reply message is a response of the user to a recommended message; and adding the return visit task of the user into the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing a product corresponding to the recommendation message for the user. Therefore, after the business processing system determines that the user is interested in the product based on the reply message of the user aiming at the recommendation message, a return visit task is generated aiming at the user and is automatically distributed to proper customer service staff, the return visit task is displayed in a task list of the customer service staff, the customer service staff is instructed to quickly contact the user in a telephone return visit mode, high-quality product related service is timely and effectively provided for the user, and the success rate of marketing products of enterprises and the experience of the user on marketing are improved to a certain extent.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a service processing method, device, and system.
Background
It is desirable for an enterprise to introduce its own product to users in need of the product in as efficient a manner as possible. Common marketing approaches include: and the telephone marketing and the short message marketing are mainly characterized in that customer service personnel contact users acquired in various channels in a telephone way to introduce own products, and the telephone marketing and the short message marketing can bring trouble to the users and even give the users an objection, so that the effect is poor. Short message marketing is favored by many enterprises due to the characteristics of low cost, less time consumption and the like.
In the short message marketing, the number of words which can be carried in the short message for introducing the product is limited, and the marketed product cannot be introduced comprehensively and clearly for the user. Based on the above, a more reasonable and efficient service processing method is needed to be provided at present, which can more intelligently introduce relevant information of products to users comprehensively on the basis of short message marketing and improve service processing experience of enterprises and users.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the application provides a service processing method, a device and a system, which improve service processing efficiency and bring better experience to users and enterprises.
In a first aspect, a service processing method is provided, where the method includes:
determining target customer service based on a reply message of a user, wherein the reply message is a response of the user to a recommended message;
and adding the return visit task of the user into the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing a product corresponding to the recommendation message for the user.
Optionally, the determining the target customer service based on the reply message of the user includes:
determining user characteristic information according to the reply message, wherein the user characteristic information comprises at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user;
and determining the matched target customer service according to the user characteristic information.
Optionally, determining the matched target customer service according to the user characteristic information includes:
according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
Optionally, the determining the target customer service based on the reply message of the user includes:
determining the matching degree of each customer service and the reply message based on the reply message of the user;
and selecting the preset number of customer services with the largest matching degree as the target customer services.
Optionally, after the adding the return visit task of the user to the task list of the target customer service, the method further includes:
and responding to any one target customer service to complete the return visit task of the user, and deleting the return visit task from a task list of the target customer service with a preset number.
Optionally, the method further comprises:
performing big data analysis on the historical behavior information of the user, and adding a mark for the user;
determining a target product or target service for the user based on the indicia;
and sending the recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply.
In a second aspect, there is also provided a service processing apparatus, the apparatus comprising:
the first determining unit is used for determining target customer service based on a reply message of a user, wherein the reply message is a response of the user to the recommended message;
and the adding unit is used for adding the return visit task of the user to the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing the product corresponding to the recommendation message to the user.
Optionally, the first determining unit includes:
a first determining subunit, configured to determine, according to the reply message, user characteristic information, where the user characteristic information includes at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user;
and the second determining subunit is used for determining the matched target customer service according to the user characteristic information.
Optionally, the second determining subunit is specifically configured to:
according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
Optionally, the first determining unit includes:
a third determining subunit, configured to determine, based on the reply message of the user, a matching degree between each customer service and the reply message;
and the selecting subunit is used for selecting the preset number of customer services with the largest matching degree as the target customer services.
Optionally, the apparatus further comprises:
and the deleting unit is used for responding to any one target customer service to finish the return visit tasks of the user after the return visit tasks of the user are added to the task list of the target customer service, and deleting the return visit tasks from the task list of the target customer service with the preset number.
Optionally, the apparatus further comprises:
the marking unit is used for carrying out big data analysis on the historical behavior information of the user and adding marks for the user;
a second determining unit for determining a target product or a target service for the user based on the mark;
the sending unit is used for sending the recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply.
In a third aspect, a service processing system is provided, including a memory and a processor. Wherein the memory is configured to store a computer program or instructions, and the processor is configured to invoke the computer program or instructions stored in the memory, so that the service processing system performs the method provided in the first aspect.
In a fourth aspect, the present application also provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method provided in the first aspect above.
In a fifth aspect, the application also provides a computer program product comprising a computer program or computer readable instructions which, when run on a computer, cause the computer to perform the method provided in the first aspect.
Compared with the prior art, the embodiment of the application has the following beneficial effects:
in an embodiment of the present application, a service processing method is provided, where the method is applied to a service processing system, and the method specifically may include: firstly, determining target customer service based on a reply message of a user, wherein the reply message is a response of the user to a recommended message; and then, adding the return visit task of the user into the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing a product corresponding to the recommendation message for the user. Therefore, through the service processing method, when the service processing system receives the reply message of the recommended message of the user for a certain product, the user can be determined to be interested in the product, and the product requirement is further known, then the service processing system automatically distributes the return visit task generated by the user to a proper customer service staff for the return visit task, displays the return visit task in a task list of the customer service staff, instructs the customer service staff to quickly contact the user in a telephone return visit mode, timely and effectively provides high-quality product related service for the user, and improves the success rate of marketing products of enterprises and the experience of the user on marketing to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those of ordinary skill in the art.
Fig. 1 is a schematic structural diagram of a service processing system 100 according to an embodiment of the present application;
fig. 2 is a flow chart of a service processing method in an embodiment of the application;
fig. 3 is a schematic structural diagram of a service processing apparatus 300 according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a service processing system 400 according to an embodiment of the present application.
Detailed Description
At present, when an enterprise adopts short message marketing to recommend products, the main characteristics of the products are generally edited into characters, and the characters are used as the content of the short message to be sent to users. However, because the content of the short message is less, only the related information of the product is carried, the product cannot be introduced to the user in detail, and the marketing effect is often poor.
Based on this, the embodiment of the application provides an intelligent service processing method, so as to solve the problem that the marketing effect is poor due to the short content of a marketing short message (hereinafter also referred to as a recommended message), and by adding a reply indication in the recommended message, the user is prompted to reply to the recommended message if interested in the recommended product or related type of product, once the service processing system receives the reply message of the user, a target customer service is determined for the user who sends the reply message based on the reply message, and a return visit task of the user is added to a task list of the target customer service, so that the target customer service returns the user based on the task list, and the product corresponding to the recommended message is introduced to the user. Thus, by the method provided by the embodiment of the application, the business processing system can intelligently realize that a return visit task is generated for a user with requirements without human intervention, target customer service is distributed, and the return visit task is added to the task list of the target customer service, if the user is interested in related products in the received recommendation message, the business system can distribute proper target customer service for the user to provide more detailed business service for the user only by replying according to replying instructions, on one hand, the success rate of marketing products of enterprises is improved, and on the other hand, the experience of the user on marketing is improved.
Before introducing the service processing method provided by the embodiment of the application, the service processing system provided by the embodiment of the application is introduced.
Referring to fig. 1, the service processing system 100 may include: big data analysis module 110, marketing products library 120, text message module 130, and outbound module 140. The big data analysis module 110, the marketing product library 120 and the outbound module 140 are respectively connected with the short message module 130. The big data analysis module 110 is configured to analyze all user data of the user through big data technology, for example: analyzing transaction data of the user, purchase data of fund financial products and the like, formulating user portraits and marking, and determining products or product sets which are possibly interested by the user; the marketing products library 120 is maintained by business personnel for editing and saving corresponding recommended messages for various products; the short message module 130 is configured to send a recommended message of a related product in the marketing product library 120 to the user information determined by the big data analysis module 110, and the short message module 130 is further configured to analyze a received reply message of the user, determine that the reply message of the user corresponds to the recommended message, and determine that the user is interested in the recommended product, so as to send the reply message and related content (e.g., user information, interested product information, etc.) to the outbound module 140; the outbound module 140 is configured to determine a target customer service according to the reply message and related content sent by the short message module 130 and the current conditions of the customer service (e.g., the current idle conditions of each customer service seat, the overall service level of each customer service seat, the skills of each customer service seat, etc.), generate a return visit task, and add the return visit task to the task list of the target customer service. It can be seen that the business processing system 100 can provide a more intelligent product marketing mechanism, and provide a good marketing experience for users without causing trouble to the users, and can also effectively improve the success rate of marketing products for enterprises.
The following describes a specific implementation manner of a service processing method in the embodiment of the present application in detail by means of an embodiment with reference to the accompanying drawings.
Fig. 2 shows a flow chart of a service processing method according to an embodiment of the present application. Referring to fig. 2, the method is applied to a service processing system, for example: applied to the business processing system 100 described above. The method may for example comprise:
s201, determining target customer service based on a reply message of a user, wherein the reply message is a response of the user to a recommended message.
Prior to S201, the service processing method may further include: s11, carrying out big data analysis on the historical behavior information of each user, and adding marks for the users; s12, determining a target product or a target service for the user based on the mark; s13, sending the recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply.
For S11, specifically, the service processing system may use a big data technology to analyze big data of the historical behavior information of each user, and add a mark for the user. The historical behavior information of the user includes, but is not limited to: a record of the user's consumption, a record of the user's browsing of various products, a record of the user's shopping cart, etc. The indicia added to the user may be specifically a product of interest to the user, or a quantification of industry and degree of interest, such as: the labels added for user a may be "product 1, product 2", characterizing that user a is interested in product 1 and product 2; also for example: the labels added to user a may also be "product 1-85%, product 2-40%", indicating that user a is interested in product 1 to 85% and user a is also interested in product 2 to 40%. Wherein a greater degree of interest indicates that the more interested the user is in the product, the more desirable the user is to understand the product, and the more likely the product is purchased.
For S12, the business processing system may determine a target product or target service for each user based on the label of that user. For example: user a is labeled "product 1, product 2", then the target product determined for user a may be product 1 and/or product 2. Also for example: the user a may determine, as target products, a preset number of products with the greatest degree of interest for the user a, where the preset number=1, and the target product determined for the user a is product 1, if the user a is marked with "product 1-85% and product 2-40%".
For S13, the service processing system generates a recommendation message for one or a class of target products of each user, and sends the recommendation message for recommending the target products to the user. In order to enable the user to briefly indicate whether the user has a wish to continuously know the target product, the recommendation message carries an indication mark for indicating the user to reply. For example: the indication identity may specifically be "reply Y knows more".
It should be noted that, in order to further improve the experience of marketing to the user, the recommendation message may also prompt the user to reply to the appropriate return visit time, for example, the recommendation message indicates that the user indicates the appropriate return visit time in the reply message, for example, the recommendation message includes "reply appropriate return visit time".
When the service processing system receives the reply message of the user, it can be determined that the user has a need of continuing to know the related information of the target product, and at this time, S201 to S202 in the method can be executed.
As one example, S201 may include, for example: s21, determining user characteristic information according to the reply message, wherein the user characteristic information comprises at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user; s22, determining the matched target customer service according to the user characteristic information. Wherein, S22 may specifically include: according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
For example: the service processing system can determine the customer service with idle time or idle return visit time of the user based on the related information of the product of interest of the user, the historical browse or consumption record of the user, the grade of the user and the like, or determine the customer service with highest success rate of promoting the product for the user, or recommend the customer service matched with the grade of the user, or recommend the customer service with better historical service recommending capability for the user, and take the determined customer service as the target customer service.
As another example, S201 may also include, for example: s31, determining the matching degree of each customer service and the reply message based on the reply message of the user; s32, selecting the preset number of customer services with the largest matching degree as the target customer services. In this case, the service processing system may add a mark to each customer service based on the historical working condition of each customer service, for example, each customer service is good at recommended products, traffic, average time of success of recommendation, and grade; then, when receiving the reply message of the user, a mark of the customer service with the highest matching degree can be selected for the user and/or the product based on the relevant information of the user and/or the product corresponding to the reply message, and the customer service corresponding to the mark is determined as the target customer service.
It should be noted that, the service processing system automatically determines the target customer service based on the preset processing rule, the relevant information of the user, the relevant information of the customer service, and the like. The processing rule can be a data processing model designed by an enterprise according to actual products and customer service conditions, for example, the processing rule can be a machine learning model.
S202, adding the return visit task of the user to the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing the product corresponding to the recommendation message to the user.
It should be noted that, for each customer service, there is a task list of the customer service itself, and the task that the customer service needs to execute is displayed. The task list comprises a series of return visit tasks that the customer service needs to call for return visit.
After S201, the service processing system may generate a corresponding return visit task for the user, where the return visit task may include, but is not limited to, the following: user information and product information of interest. In addition, the return visit task can further comprise time for generating the return visit task, so that the target customer service can carry out sequential return visit according to the generation time of each return visit task in the morning and evening, and the user experience of marketing is improved; if the reply message includes the appropriate return visit time, the return visit task may also include the appropriate return visit time; the return visit task can also include historical behavior information of the user so that the target customer service knows the user in advance, thereby providing better marketing services for the user.
In some possible implementations, in order to improve the working efficiency of the customer service, the return visit is performed for the product interested by the user more timely, and the target customer service determined in S201 may also be a preset number (e.g. 6) of customer services, and then in S202, the return visit task of the user is added to the task list of the preset number of target customer services at the same time. In this case, the service processing method may further include: s203, responding to any target customer service to complete the return visit task of the user, and deleting the return visit task from the task list of the target customer service with the preset number. Therefore, on one hand, the return visit efficiency can be improved, and on the other hand, the trouble to the user caused by the fact that the same return visit task is returned by a plurality of target customer services is avoided, so that the service processing method provided by the service processing system is more intelligent and efficient.
It can be seen that in the service processing method provided in the embodiment of the present application, first, a target customer service is determined based on a reply message of a user, where the reply message is a response of the user to a recommendation message; and then, adding the return visit task of the user into the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing a product corresponding to the recommendation message for the user. Therefore, through the service processing method, when the service processing system receives the reply message of the recommended message of the user for a certain product, the user can be determined to be interested in the product, and the product requirement is further known, then the service processing system automatically distributes the return visit task generated by the user to a proper customer service staff for the return visit task, displays the return visit task in a task list of the customer service staff, instructs the customer service staff to quickly contact the user in a telephone return visit mode, timely and effectively provides high-quality product related service for the user, and improves the success rate of marketing products of enterprises and the experience of the user on marketing to a certain extent.
Correspondingly, the embodiment of the application also provides a service processing device, as shown in fig. 3, the device 300 includes:
a first determining unit 301, configured to determine a target customer service based on a reply message of a user, where the reply message is a response of the user to a recommendation message;
and the adding unit 302 is configured to add the return visit task of the user to the task list of the target customer service, so that the target customer service returns visit to the user based on the task list, and introduce the product corresponding to the recommendation message to the user.
Optionally, the first determining unit 301 includes:
a first determining subunit, configured to determine, according to the reply message, user characteristic information, where the user characteristic information includes at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user;
and the second determining subunit is used for determining the matched target customer service according to the user characteristic information.
Optionally, the second determining subunit is specifically configured to:
according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
Optionally, the first determining unit 301 includes:
a third determining subunit, configured to determine, based on the reply message of the user, a matching degree between each customer service and the reply message;
and the selecting subunit is used for selecting the preset number of customer services with the largest matching degree as the target customer services.
Optionally, the apparatus 300 further includes:
and the deleting unit is used for responding to any one target customer service to finish the return visit tasks of the user after the return visit tasks of the user are added to the task list of the target customer service, and deleting the return visit tasks from the task list of the target customer service with the preset number.
Optionally, the apparatus 300 further includes:
the marking unit is used for carrying out big data analysis on the historical behavior information of the user and adding marks for the user;
a second determining unit for determining a target product or a target service for the user based on the mark;
the sending unit is used for sending the recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply.
It should be noted that, the service processing apparatus 300 corresponds to the method shown in fig. 2, and the specific implementation and the achieved effect are described in connection with the method shown in fig. 2.
In addition, the embodiment of the application also provides a service processing system, and referring to fig. 4, the service processing system 400 includes a memory 401 and a processor 402. Wherein the memory 401 is configured to store a computer program or instructions, and the processor 402 is configured to invoke the computer program or instructions stored in the memory 401, so that the service processing system 400 performs the method provided in fig. 2.
Furthermore, the present application provides a computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method provided above in fig. 2.
Furthermore, the application provides a computer program product comprising a computer program or computer readable instructions which, when run on a computer, cause the computer to carry out the method provided in fig. 2 as described above.
The "first" in the names of the "first determining unit", "first determining subunit", and the like in the embodiments of the present application is only used for name identification, and does not represent the first in sequence. The rule applies equally to "second" etc.
From the above description of embodiments, it will be apparent to those skilled in the art that all or part of the steps of the above described example methods may be implemented in software plus general hardware platforms. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a read-only memory (ROM)/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network communication device such as a router) to perform the method according to the embodiments or some parts of the embodiments of the present application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments and the system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The apparatus and system embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
The foregoing is merely a preferred embodiment of the present application and is not intended to limit the scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from its scope.
Claims (10)
1. A method of service processing, the method comprising:
carrying out big data analysis on historical behavior information of a user, and adding a mark for the user;
determining a target product or target service for the user based on the indicia;
sending a recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply;
determining target customer service based on a data processing model, related information of each customer service and corresponding products of reply messages of the user and/or related information of the user, wherein the reply messages are responses of the user to the recommended messages;
adding the return visit task of the user to the task list of the target customer service so that the target customer service returns visit to the user based on the task list, and introducing the target product or the target service corresponding to the recommendation message to the user;
and responding to any one target customer service to complete the return visit task of the user, and deleting the return visit task from a task list of the target customer service with a preset number.
2. The method of claim 1, wherein the determining the target customer service comprises:
determining user characteristic information according to the reply message, wherein the user characteristic information comprises at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user;
and determining the matched target customer service according to the user characteristic information.
3. The method of claim 2, wherein said determining the matched target customer service based on the user characteristic information comprises:
according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
4. The method of claim 1, wherein the determining the target customer service comprises:
determining the matching degree of each customer service and the reply message based on the reply message of the user;
and selecting the preset number of customer services with the largest matching degree as the target customer services.
5. A service processing apparatus, the apparatus comprising:
the marking unit is used for carrying out big data analysis on the historical behavior information of the user and adding marks for the user;
a second determining unit for determining a target product or a target service for the user based on the mark;
the sending unit is used for sending a recommendation message for recommending the target product or the target service to the user, wherein the recommendation message carries an indication mark and is used for indicating the user to reply;
the first determining unit is used for determining target customer service based on a data processing model, relevant information of each customer service and products corresponding to reply messages of users and/or relevant information of the users, wherein the reply messages are responses of the users to the recommended messages;
the adding unit is used for adding the return visit task of the user to the task list of the target customer service so that the target customer service returns visit to the user based on the task list of the target customer service and introduces the target product or the target service corresponding to the recommendation message to the user;
and the deleting unit is used for responding to any one target customer service to finish the return visit tasks of the user after the return visit tasks of the user are added to the task list of the target customer service, and deleting the return visit tasks from the task list of the target customer service with the preset number.
6. The apparatus according to claim 5, wherein the first determining unit includes:
a first determining subunit, configured to determine, according to the reply message, user characteristic information, where the user characteristic information includes at least one of the following information: the corresponding information of the product or service recommended by the recommendation message of the reply message, the historical behavior information of the user, or the grade of the user;
and the second determining subunit is used for determining the matched target customer service according to the user characteristic information.
7. The apparatus of claim 6, wherein the second determining subunit is specifically configured to:
according to customer service characteristic information of each customer service, determining the target customer service matched with the customer service characteristic information, wherein the customer service characteristic information comprises at least one of the following information: customer service level, current service status, historical service recommendation record, or historical service recommendation capability.
8. The apparatus according to claim 5, wherein the first determining unit includes:
a third determining subunit, configured to determine, based on the reply message of the user, a matching degree between each customer service and the reply message;
and the selecting subunit is used for selecting the preset number of customer services with the largest matching degree as the target customer services.
9. A business processing system comprising a memory and a processor;
the memory is used for storing a computer program or instructions;
the processor is configured to invoke a computer program or instructions stored in the memory, causing the service processing system to perform the method of any of the preceding claims 1-4.
10. A computer readable storage medium having instructions stored therein which, when run on a computer, cause the computer to perform the method of any of the preceding claims 1-4.
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| CN111047076A (en) * | 2019-11-21 | 2020-04-21 | 国网甘肃省电力公司 | Photovoltaic short-term power forecasting method based on sunflower number 8 satellite cloud picture |
| CN115118822A (en) * | 2022-06-15 | 2022-09-27 | 马上消费金融股份有限公司 | Task processing method and device |
| CN115103073B (en) * | 2022-08-25 | 2022-12-13 | 深圳市星网信通科技有限公司 | Revisit task processing method and device and computer readable storage medium |
| CN116304007A (en) * | 2022-11-11 | 2023-06-23 | 蚂蚁财富(上海)金融信息服务有限公司 | Information recommendation method and device, storage medium and electronic equipment |
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