CN111339153A - Method and device for matching user information, storage medium and processor - Google Patents
Method and device for matching user information, storage medium and processor Download PDFInfo
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- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000010801 machine learning Methods 0.000 claims description 18
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- 238000001914 filtration Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 8
- 230000036632 reaction speed Effects 0.000 abstract description 4
- 230000008447 perception Effects 0.000 description 17
- 230000008569 process Effects 0.000 description 5
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- 238000005859 coupling reaction Methods 0.000 description 3
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- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
Abstract
The application discloses a method and device for matching user information, a storage medium and a processor. Wherein, the method comprises the following steps: collecting user information; matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; determining a matching scheme corresponding to the user information according to the matching result; and sending the matching scheme corresponding to the user information to the terminal equipment. The method and the device solve the technical problems that the existing user information sensing method needs manual operation, has data redundancy, cannot feed the sensing information back to the user in time, and has long service processing flow, high cost, low reaction speed and low timeliness.
Description
Technical Field
The present application relates to the field of user information perception, and in particular, to a method and an apparatus for matching user information, a storage medium, and a processor.
Background
The invention patent with publication number CN109815382A discloses a video sharing method based on demand sensing and resource caching in a wireless mobile network, which comprises the following steps: s1, modeling a video resource dissemination process by using an infectious disease model; s2, sensing the demand domain of the node according to the video resource watched by the node; s3, calculating the interaction frequency and the interaction success rate of two nodes according to the pushing and requesting process of the video resources between any two nodes; s4, calculating the mobility stability of the two nodes according to the times of the two nodes becoming one-hop neighbor nodes and the time for keeping the one-hop neighbor nodes; s5, calculating the contact compactness between the two nodes; s6, distributing the nodes into a plurality of node sets according to the contact compactness among the nodes; s7, calculating the distribution change degree of the video resource according to the transmission process parameter of the video resource; and S8, adjusting the cache state of the video resource in the node set based on the distribution change degree of the video resource. The invention can effectively improve the request success rate and the pushing success rate of the node for the video resource.
The existing user information sensing method needs manual operation, has redundant data, cannot feed the sensing information back to the user in time, and has long service processing flow, high cost, slow reaction speed and low timeliness.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method and a device for matching user information, a storage medium and a processor, and aims to at least solve the technical problems that the conventional user information sensing method needs manual operation, has data redundancy, cannot feed sensed information back to a user in time, and has long service processing flow, high cost, low reaction speed and low timeliness.
According to an aspect of an embodiment of the present application, there is provided a method for matching user information, including: collecting user information; matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; determining a matching scheme corresponding to the user information according to the matching result; and sending the matching scheme corresponding to the user information to the terminal equipment.
Optionally, determining a matching scheme corresponding to the user information according to the matching result includes: if the matching is successful, feeding back the matched first matching scheme to the user; and if the matching fails, inputting the user information into a preset machine learning model for learning to obtain a second matching scheme matched with the user information, and feeding back the second matching scheme to the user.
Optionally, before matching the user information according to a preset rule, the method further includes: respectively determining the weight of each kind of information contained in the user information; and filtering information with the weight lower than the first threshold value to obtain the filtered user information.
Optionally, matching the user information according to a preset rule, including: matching with the screened user information by utilizing a plurality of matching schemes respectively to obtain a plurality of matching accuracy rates; and taking the matching scheme with the matching precision rate higher than the second threshold value as the first matching scheme.
Optionally, the preset machine learning model is obtained by training in the following manner: acquiring a plurality of groups of training sample data for training a preset machine learning model, wherein each group of training sample data comprises: the user information and the matching scheme with the user information matching accuracy rate higher than a second threshold value; and training based on multiple groups of training sample data to obtain a preset machine learning model.
Optionally, if the matching is successful, after the matched first matching scheme is fed back to the user, the method further includes: and caching the corresponding relation between the first matching scheme and the user information to a preset rule.
Optionally, matching the user information according to a preset rule, including: matching the user information locally; the method further comprises the following steps: and if the matching fails, inputting the user information into a preset machine learning model running on the cloud server for learning to obtain a second matching scheme matched with the user information.
According to another aspect of the embodiments of the present application, there is also provided an apparatus for matching user information, including: the acquisition module is used for acquiring user information; the matching module is used for matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; the determining module is used for determining a matching scheme corresponding to the user information according to the matching result; and the communication module is used for sending the matching scheme corresponding to the user information to the terminal equipment.
According to still another aspect of the embodiments of the present application, there is provided a storage medium including a stored program, where the apparatus on which the storage medium is located is controlled to perform the above method of matching user information when the program runs.
According to still another aspect of the embodiments of the present application, there is also provided a processor for executing a program stored in a memory, wherein the program executes the above method for matching user information.
In the embodiment of the application, user information is collected; matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; determining a matching scheme corresponding to the user information according to the matching result; the method for sending the matching scheme corresponding to the user information to the terminal equipment comprises the steps of sensing operation, sensing receiving, sensing rule matching, sensing rule learning, sensing reply and the like of a user to timely, accurately and efficiently transmit the information to the user, so that the time cost of user information sensing is reduced, the labor cost is reduced, the technical effects of processing speed and production efficiency are improved, and the technical problems that the conventional user information sensing method needs manual operation, data redundancy and cannot timely feed the sensing information back to the user, the service processing flow is long, the cost is high, the reaction speed is low and the timeliness is low are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method of matching user information according to an embodiment of the present application;
FIG. 2 is a flow chart of another method of matching user information according to an embodiment of the present application;
fig. 3 is a block diagram of an apparatus for matching user information according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a method for matching user information, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a flowchart of a method for matching user information according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
and step S102, collecting user information.
According to an alternative embodiment of the present application, the information collecting module is triggered to collect the user information through a sensor or an operation while step S102 is executed.
And step S104, matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements.
For example, for the real estate sales industry, the system matches the appropriate sources and recommends them to the business clerk based on the area where the customer is located, the climate of the area where the customer is located, the sources that the customer has viewed, and the products (villas, high-rise, ocean houses, etc.) that have viewed. The area where the client is located, the climate of the area where the client is located, and the house source viewed by the client are all the user information mentioned in step S102.
In another optional application scenario of the embodiment of the present application, for example, in the software industry, through the operation of the user (very simple operation queries a type a customer or triggers a certain business action), the system automatically matches an appropriate rule (combination of multiple ways such as private subscription or degree of matching or ranking in a machine learning module), and then returns more efficient and accurate recommended business information used by the user.
In another optional application scenario of the embodiment of the application, for example, in logistics industry, according to information such as size/area of an article mailed by a user, the system automatically matches appropriate rules (climate, express company, express delivery mode (aviation/land/marine), site, personnel (evaluation degree), and the like) and returns more efficient and accurate recommended service information used by the user.
And step S106, determining a matching scheme corresponding to the user information according to the matching result.
And step S108, sending the matching scheme corresponding to the user information to the terminal equipment.
Matching a user requirement scheme according to the collected user information, and then sending the matched scheme to the terminal device, wherein the terminal device may be a computer or a mobile terminal device.
Through the steps, information is timely, accurately and efficiently transmitted to the user through the steps of perception operation, perception receiving, perception rule matching, perception rule learning, perception reply and the like of the user, so that the time cost of user information perception is reduced, the labor cost is reduced, and the technical effects of processing speed and production efficiency are improved.
According to an alternative embodiment of the present application, step S106 may be implemented by: if the matching is successful, feeding back the matched first matching scheme to the user; and if the matching fails, inputting the user information into a preset machine learning model for learning to obtain a second matching scheme matched with the user information, and feeding back the second matching scheme to the user.
In an optional embodiment of the application, if the matching is successful, the matching method corresponding to the matched user information is fed back to the user, and if the matching is unsuccessful, the collected user information is transmitted to the machine learning module for learning, a requirement scheme of the user is matched, and then the requirement scheme is fed back to the client.
In an alternative embodiment of the present application, before performing step S104, the weight of each type of information included in the user information is determined respectively; and filtering information with the weight lower than the first threshold value to obtain the filtered user information.
Before the requirement scheme of the user is matched according to the collected user information, the collected user information is preliminarily screened, specifically, the user information with lower weight is filtered by determining the weight of each type of user information, and the matching precision of the matching scheme can be improved through the preliminary screening step.
In an optional embodiment of the present application, when step S104 is executed, a plurality of matching schemes are respectively used to match with the filtered user information, so as to obtain a plurality of matching accuracy rates; and taking the matching scheme with the matching precision rate higher than the second threshold value as the first matching scheme.
After the collected user information is preliminarily screened, a plurality of matching schemes are matched, the matching precision rate of each matching scheme and the user information is respectively calculated, and the matching scheme with the matching precision rate higher than a preset threshold value is used as a final user demand scheme.
According to an alternative embodiment of the present application, the preset machine learning model is trained by: acquiring a plurality of groups of training sample data for training a preset machine learning model, wherein each group of training sample data comprises: the user information and the matching scheme with the user information matching accuracy rate higher than a second threshold value; and training based on multiple groups of training sample data to obtain a preset machine learning model.
In an optional embodiment of the present application, after the matching is successful and the matched first matching scheme is fed back to the user, the method further includes: and caching the corresponding relation between the first matching scheme and the user information to a preset rule. By caching the user information and the matching scheme corresponding to the user information, the response speed can be effectively improved.
In an optional embodiment of the present application, step S104 may also be implemented by the following method: matching the user information locally; and if the matching fails, inputting the user information into a preset machine learning model running on the cloud server for learning to obtain a second matching scheme matched with the user information.
The collected user information is matched locally, and the user information is sent to the machine learning model of the server side for learning after the matching fails, so that the problems of instability and low transmission speed in network transmission can be solved. Compared with the whole matching process which is carried out at the cloud server, the scheme can also reduce the overhead of the cloud server and improve the efficiency of local matching.
Fig. 2 is a flowchart of another method for matching user information according to an embodiment of the present application, as shown in fig. 2, the method includes the following steps:
s201, a user acquires user information through a sensor or an operation trigger information acquisition module.
S202, the information acquisition module directly transmits the user information to the information analysis module through the network.
S203, the information analysis module calls a perceptibility rule module to perform rule matching.
And S204, after receiving the preliminary screening information distributed by the information analysis module, the perceptibility rule module performs rule matching, if the matching is successful, the information corresponding to the matching is transmitted to the information feedback module, and if the matching is failed, the information is transmitted to the perception learning module, the perception learning module records and learns, a user requirement scheme is matched, and then the scheme is transmitted to the information feedback module. The perception learning module establishes a matching scheme, automatically sets a probability ratio and then sends the probability ratio to the information feedback module.
S205, the information feedback module directly transmits the transmitted sensing information to the response sensing triggering module in a network mode, triggers the user and timely reminds, issues information and updates other information. The information matching rule and response perception triggering module has a caching function and self-updating capability, and response speed is effectively improved.
Fig. 3 is a block diagram of an apparatus for matching user information according to an embodiment of the present application, as shown in fig. 3, the apparatus including:
and the acquisition module 30 is used for acquiring user information.
The matching module 32 is configured to match the user information according to a preset rule, where the preset rule is used to indicate a corresponding relationship between the user information and multiple matching schemes matched with the user information, and the matching schemes are used to represent user requirements.
And the determining module 34 is configured to determine a matching scheme corresponding to the user information according to the matching result.
And the communication module 36 is configured to send the matching scheme corresponding to the user information to the terminal device.
Through the device, information is timely, accurately and efficiently transmitted to the user through steps of perception operation, perception receiving, perception rule matching, perception rule learning, perception reply and the like of the user, so that time cost and labor cost of user information perception are reduced, and the technical effects of processing speed and production efficiency are improved.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 3, and details are not described here again.
The embodiment of the application also provides a storage medium, the storage medium comprises a stored program, and when the program runs, the device where the storage medium is located is controlled to execute the method for matching the user information.
The storage medium stores a program for executing the following functions: collecting user information; matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; determining a matching scheme corresponding to the user information according to the matching result; and sending the matching scheme corresponding to the user information to the terminal equipment.
The embodiment of the present application further provides a processor, where the processor is configured to run a program stored in a memory, where the program performs the above method for matching user information when running.
The processor is used for running a program for executing the following functions: collecting user information; matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing the user requirements; determining a matching scheme corresponding to the user information according to the matching result; and sending the matching scheme corresponding to the user information to the terminal equipment.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method of matching user information, comprising:
collecting user information;
matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing user requirements;
determining a matching scheme corresponding to the user information according to a matching result;
and sending the matching scheme corresponding to the user information to terminal equipment.
2. The method of claim 1, wherein determining the matching scheme corresponding to the user information according to the matching result comprises:
if the matching is successful, feeding back the matched first matching scheme to the user;
and if the matching fails, inputting the user information into a preset machine learning model for learning to obtain a second matching scheme matched with the user information, and feeding back the second matching scheme to the user.
3. The method of claim 2, wherein before matching the user information according to a preset rule, the method further comprises:
respectively determining the weight of each kind of information contained in the user information;
and filtering the information with the weight lower than the first threshold value to obtain the filtered user information.
4. The method of claim 3, wherein matching the user information according to a preset rule comprises:
matching the screened user information by using the multiple matching schemes respectively to obtain multiple matching precision rates;
and taking the matching scheme with the matching precision rate higher than a second threshold value as the first matching scheme.
5. The method of claim 4, wherein the pre-set machine learning model is trained by:
acquiring a plurality of groups of training sample data for training the preset machine learning model, wherein each group of training sample data comprises: user information and a matching scheme with the matching precision rate of the user information higher than the second threshold value;
and training based on the multiple groups of training sample data to obtain the preset machine learning model.
6. The method of claim 2, wherein if the matching is successful, after the matched first matching scheme is fed back to the user, the method further comprises:
and caching the corresponding relation between the first matching scheme and the user information to the preset rule.
7. The method of claim 2,
matching the user information according to a preset rule, comprising: matching the user information locally;
the method further comprises the following steps: and if the matching fails, inputting the user information into the preset machine learning model running on a cloud server for learning to obtain a second matching scheme matched with the user information.
8. An apparatus for matching user information, comprising:
the acquisition module is used for acquiring user information;
the matching module is used for matching the user information according to a preset rule, wherein the preset rule is used for indicating the corresponding relation between the user information and a plurality of matching schemes matched with the user information, and the matching schemes are used for expressing user requirements;
the determining module is used for determining a matching scheme corresponding to the user information according to a matching result;
and the communication module is used for sending the matching scheme corresponding to the user information to the terminal equipment.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the method for matching user information according to any one of claims 1 to 7.
10. A processor for executing a program stored in a memory, wherein the program when executed performs the method of matching user information according to any one of claims 1 to 7.
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