User information communication method and system based on sparse communication

Abstract

The user information communication method and system based on sparse communication provided in the present application load each local optimization requirement into the local network, and execute the corresponding local optimization requirement in the local network. Thus, the embodiment of the present disclosure can accurately determine different local optimization requirements by building important networks and local networks. In this way, user information communication can be accurately determined, thereby improving the accuracy and reliability of user information communication.

Description

Technical Field

[0001] The present application relates to the field of information communication technology, and in particular to a user information communication method and system based on sparse communication. Background Art

[0002] Information and communications technology (ICT) is an umbrella term covering any communications equipment or application software: for example, radio, television, mobile phones, computers, network hardware and software, satellite systems, etc.; as well as various related services and application software, such as video conferencing and distance learning.

[0003] At present, there may be some defects when users communicate information, which may lead to the problem of inaccurate communication information. Therefore, a technical solution is urgently needed to improve the above technical problems. Summary of the invention

[0004] In order to improve the technical problems existing in the related technologies, the present application provides a user information communication method and system based on sparse communication.

[0005] In a first aspect, a user information communication method based on sparse communication is provided, the method comprising at least: obtaining at least two user favorite descriptions of user interaction preference information in a user interaction indication; determining a local optimization requirement cluster for optimization in each of the communication descriptions based on at least two communication descriptions in a reference communication description and the at least two user favorite descriptions; building at least one local network through each important network in at least one important network; loading the completed local optimization requirements to each local network through each important network, and obtaining the optimization status of each local network; and determining the optimization status of the user interaction preference information in the sample information communication set in combination with the optimization status obtained for each important network.

[0006] In an independently implemented embodiment, the method of building at least one local network through each important network in at least one important network includes: obtaining an indication report of a communication description corresponding to the local optimization requirement in each important network, building a first matrix of the important network specifying the calculation information of at least one local network, a second matrix for obtaining data of at least one local network, and an interaction track between the important network and the at least one local network; and building the at least one local network in combination with the indication report, the first matrix, the second matrix and the interaction track.

[0007] In an independently implemented embodiment, the optimization status of each of the at least one local network is determined based on CNN network analysis to determine local optimization requirements; the construction of the important network specifies a first matrix of calculation information of the several local networks, a second matrix for obtaining data of the several local networks, and an interaction trajectory between the important network and the several local networks, including: determining the number of previously determined CNN networks as the number of local networks to be built; and building the first matrix, the second matrix and the interaction trajectory in combination with the number of local networks to be built.

[0008] In an independently implemented embodiment, the optimization situation of each local network in the at least one local network is the result obtained after determining the communication description in the corresponding local optimization requirement as a variable description, based on the user preference description in the corresponding local optimization requirement and the variable description; the optimization situation of the user interaction preference information in the sample information communication set is determined in combination with the optimization situation obtained from each important network, including: in each important network, determining the matching degree between the obtained optimization situation and the corresponding initial keyword in the sample information communication set; in the optimization situation obtained from each important network, screening the optimization situation with a number of first set variables, and determining the screened optimization situation with a number of first set variables as the output optimization situation of the important network; in combination with the output optimization situation of each important network, the optimization situation of the user interaction preference information in the sample information communication set is obtained.

[0009] In an independently implemented embodiment, the optimization situation of the user interaction preference information in the sample information communication set is obtained by combining the output optimization situation of each important network, including: in the output optimization situation of each important network, by fusing the optimization situation of each local optimization requirement in each communication description, to obtain the optimization situation of each local optimization requirement in the sample information communication set; the optimization situation of the user interaction preference information in the sample information communication set is obtained, and the optimization situation of the user interaction preference information in the sample information communication set includes: the optimization situation of each local optimization requirement in the sample information communication set.

[0010] In an independently implemented embodiment, the optimization situation of each local optimization requirement in each communication description is integrated to obtain the optimization situation of each local optimization requirement in the sample information communication set, including: integrating the optimization situation of each local optimization requirement in each communication description to obtain a fusion result corresponding to each local optimization requirement; among the fusion results corresponding to each local optimization requirement, the optimization situation whose number is a second set variable is screened, and the screened optimization situation whose number is the second set variable is determined as the optimization situation of each local optimization requirement in the sample information communication set.

[0011] In an independently implemented embodiment, the method also includes: determining the label of each decision in the sample information communication set; according to the label of each decision in the sample information communication set, cleaning the keywords in the sample information communication set that do not belong to the first set label constraint condition; before obtaining at least two user preference descriptions of the user interaction preference information in the user interaction indication, the method also includes: determining the label of each decision in the user interaction preference information; according to the label of each decision in the user interaction preference information, cleaning the keywords in the user interaction preference information that do not belong to the second set label constraint condition.

[0012] In a second aspect, a user information communication system based on sparse communication is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0013] The user information communication method and system based on sparse communication provided in the embodiments of the present application load each local optimization requirement into the local network, and execute the corresponding local optimization requirement in the local network. Thus, the embodiments of the present disclosure can accurately determine different local optimization requirements by building important networks and local networks. In this way, user information communication can be accurately determined, thereby improving the accuracy and reliability of user information communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A flowchart of a user information communication method based on sparse communication provided in an embodiment of the present application.

[0016] Figure 2 A block diagram of a user information communication device based on sparse communication provided in an embodiment of the present application.

[0017] Figure 3 An architectural diagram of a user information communication system based on sparse communication provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0019] See also Figure 1 , shows a user information communication method based on sparse communication, which may include the technical solutions described in the following steps 100-400.

[0020] Step 100: Obtain at least two user preference descriptions of user interaction preference information in the user interaction indication.

[0021] Step 200: Determine a local optimization requirement cluster to be optimized in each of the communication descriptions based on at least two communication descriptions in the reference communication description and the at least two user favorite descriptions.

[0022] Step 300, build at least one local network through each important network in at least one important network; load the completed local optimization requirements to each local network through each important network, and obtain the optimization status of each local network.

[0023] Step 400: Determine the optimization status of the user interaction preference information in the sample information communication set in combination with the optimization status obtained from each important network.

[0024] It can be understood that when executing the contents described in the above steps 100 to 400, each local optimization requirement is loaded into the local network, and the corresponding local optimization requirement is executed in the local network. Thus, the embodiment of the present disclosure can accurately determine different local optimization requirements by building important networks and local networks. In this way, user information communication can be accurately determined, thereby improving the accuracy and reliability of user information communication.

[0025] In a possible implementation example, the step of building at least one local network through each of at least one important network may specifically include the contents described in the following step q1 and step q2.

[0026] Step q1, in each of the important networks, obtain an indication report of the communication description corresponding to the local optimization requirements, build a first matrix for the important network to specify the computing information of at least one local network, a second matrix for obtaining data of at least one local network, and an interaction track between the important network and the at least one local network.

[0027] Step q2, combining the indication report, the first matrix, the second matrix and the interaction track to build at least one local network.

[0028] In a possible implementation embodiment, the optimization status of each of the at least one local network is based on CNN network analysis to determine local optimization requirements; the construction of the important network specifies a first matrix of the calculation information of the several local networks, a second matrix for obtaining data of the several local networks, and the interaction trajectory between the important network and the several local networks, which may specifically include the contents described in the following steps w1.

[0029] Step w1, determining the number of previously determined CNN networks as the number of local networks to be built; and building the first matrix, the second matrix and the interaction trajectory in combination with the number of local networks to be built.

[0030] In a possible implementation embodiment, the optimization status of each local network in the at least one local network is a result obtained by determining the communication description in the corresponding local optimization requirement as a variable description, based on the user preference description in the corresponding local optimization requirement and the variable description; the optimization status of the user interaction preference information in the sample information communication set is determined by combining the optimization status obtained from each important network, which may specifically include the contents described in the following steps e1 to e3.

[0031] Step e1, in each of the important networks, determining the matching degree between the obtained optimization situation and the corresponding initial keywords in the sample information communication set.

[0032] Step e2, among the optimization situations obtained for each important network, the optimization situations whose number is the first set variables are screened, and the screened optimization situations whose number is the first set variables are determined as the output optimization situations of the important network.

[0033] Step e3, combining the output optimization status of each important network, and obtaining the optimization status of the user interaction preference information in the sample information communication set.

[0034] In a possible implementation example, the optimization status of the user interaction preference information in the sample information communication set is obtained by combining the output optimization status of each important network, which may specifically include the contents described in the following steps r1 and r2.

[0035] Step r1, in the output optimization situation of each important network, by fusing the optimization situation of each local optimization requirement in each communication description, the optimization situation of each local optimization requirement in the sample information communication set is obtained.

[0036] If it is less than r2, the optimization situation of the user interaction preference information in the sample information communication set is obtained, and the optimization situation of the user interaction preference information in the sample information communication set includes: the optimization situation of each local optimization requirement in the sample information communication set.

[0037] In a possible implementation, the optimization situation of each local optimization requirement in each communication description is integrated to obtain the optimization situation of each local optimization requirement in the sample information communication set, including: integrating the optimization situation of each local optimization requirement in each communication description to obtain a fusion result corresponding to each local optimization requirement; among the fusion results corresponding to each local optimization requirement, screening the optimization situations whose number is a second set variable, and determining the screened optimization situations whose number is the second set variable as the optimization situation of each local optimization requirement in the sample information communication set.

[0038] In a possible implementation example, the process also includes the contents described in steps t1 to t3.

[0039] Step t1, determining the label of each decision in the sample information communication set.

[0040] Step t2: according to each decision label in the sample information communication set, the keywords in the sample information communication set that do not belong to the first set label constraint condition are cleaned.

[0041] Step t3, before obtaining at least two user preference descriptions of the user interaction preference information in the user interaction indication, the method also includes: determining the label of each decision of the user interaction preference information; according to the label of each decision in the user interaction preference information, cleaning the keywords in the user interaction preference information that do not belong to the second set label constraint condition.

[0042] Based on the above, please refer to Figure 2 , a user information communication device 200 based on sparse communication is provided, which is applied to a user information communication system based on sparse communication, and the device includes:

[0043] Description obtaining module 210, used to obtain at least two user-favorite descriptions of user interaction preference information in the user interaction indication;

[0044] A description optimization module 220, configured to determine a local optimization requirement cluster to be optimized in each of the communication descriptions based on at least two communication descriptions in the reference communication description and the at least two user favorite descriptions;

[0045] The result acquisition module 230 is used to build at least one local network through each important network in at least one important network; load the completed local optimization requirements to each local network through each important network, and obtain the optimization status of each local network.

[0046] The result optimization module 240 is used to determine the optimization status of the user interaction preference information in the sample information communication set in combination with the optimization status obtained from each important network.

[0047] Based on the above, please refer to Figure 3 , shows a user information communication system 300 based on sparse communication, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0048] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0049] In summary, based on the above scheme, each local optimization requirement is loaded into the local network, and the corresponding local optimization requirement is executed in the local network. Thus, the embodiment of the present disclosure can accurately determine different local optimization requirements by building important networks and local networks. In this way, user information communication can be accurately determined, thereby improving the accuracy and reliability of user information communication.

[0050] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0051] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0052] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0053] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0054] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0055] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0056] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0057] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0058] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0059] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0060] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0061] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0062] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A user information communication method based on sparse communication, It is characterized in that The method at least comprises: obtaining at least two user preference descriptions of the user interaction preference information in the user interaction indication; Based on at least two communication descriptions in the reference communication description and the at least two user favorite descriptions, determining a local optimization requirement cluster to be optimized in each of the communication descriptions; Building at least one local network through each important network in at least one important network; loading the completed local optimization requirements to each local network through each important network, and obtaining the optimization status of each local network; Determine the optimization status of the user interaction preference information in the sample information communication set in combination with the optimization status obtained from each important network; The optimization condition of each local network in the at least one local network is a result obtained by determining the communication description in the corresponding local optimization requirement as a variable description, based on the user preference description in the corresponding local optimization requirement and the variable description; the optimization condition of the user interaction preference information in the sample information communication set is determined by combining the optimization condition obtained from each important network, including: In each of the important networks, determining a matching degree between the obtained optimization situation and the corresponding initial keywords in the sample information communication set; Among the optimization situations obtained for each important network, the optimization situations whose number is the first set variable are screened, and the screened optimization situations whose number is the first set variable are determined as the output optimization situations of the important network; Combining the output optimization of each important network, deriving the optimization of the user interaction preference information in the sample information communication set; The step of combining the output optimization of each important network to obtain the optimization of the user interaction preference information in the sample information communication set includes: in the output optimization of each important network, by fusing the optimization of each local optimization requirement in each communication description, obtaining the optimization of each local optimization requirement in the sample information communication set; The optimization status of the user interaction preference information in the sample information communication set includes: the optimization status of each local optimization requirement in the sample information communication set; Among them, the method of fusing the optimization situation of each local optimization requirement in each communication description to obtain the optimization situation of each local optimization requirement in the sample information communication set includes: fusing the optimization situation of each local optimization requirement in each communication description to obtain a fusion result corresponding to each local optimization requirement; among the fusion results corresponding to each local optimization requirement, screening the optimization situations whose number is a second set variable, and determining the screened optimization situations whose number is a second set variable as the optimization situation of each local optimization requirement in the sample information communication set.

2. The method according to claim 1, It is characterized in that The step of building at least one local network through each of at least one important network includes: In each of the important networks, an indication report of a communication description corresponding to the local optimization requirement is obtained, and a first matrix for specifying the calculation information of the at least one local network, a second matrix for obtaining data of the at least one local network, and an interaction track between the important network and the at least one local network are constructed; In combination with the indication report, the first matrix, the second matrix and the interaction track, at least one local network is constructed.

3. The method according to claim 2, It is characterized in that The optimization condition of each local network in the at least one local network is based on CNN network analysis to determine the local optimization requirements; the construction of the important network specifies a first matrix of computing information of the at least one local network, a second matrix for obtaining data of the at least one local network, and an interaction track between the important network and the at least one local network, including: The number of previously determined CNN networks is determined as the number of local networks to be built; and the first matrix, the second matrix and the interaction trajectory are built in combination with the number of local networks to be built.

4. The method according to claim 1, It is characterized in that The method further comprises: Determining a label for each decision in the sample information communication set; According to each decision label in the sample information communication set, keywords in the sample information communication set that do not belong to the first set label constraint condition are cleaned; Before obtaining at least two user-preferred descriptions of the user interaction preference information in the user interaction indication, the method further includes: determining a label for each decision of the user interaction preference information; and cleaning keywords in the user interaction preference information that do not belong to a second set label constraint condition according to the label of each decision in the user interaction preference information.

5. A user information communication system based on sparse communication, It is characterized in that The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 4.

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