Information generation method and device, equipment, storage medium and program product

Abstract

The invention provides an information generation method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent recommendation. The method comprises the following steps: determining a demand type of demand information, determining a business attribute of the demand information in response to the demand type being a business demand, extracting a keyword of the demand information in response to the business attribute of the demand information being a target service class business, performing business matching on the keyword and collected service information of a service provider, and sending the service matching result to the service provider. And generating a service recommendation result. According to the method, accurate information matching can be realized.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the field of intelligent recommendation technology, and in particular to an information generation method, device, electronic device and computer-readable storage medium. Background Art

[0002] With the advent of the information age, information technology is gradually penetrating into all aspects of society and changing people's lifestyles. The Internet provides a vast amount of information resources and has become an important channel for people to obtain information, allowing people to access and obtain various types of information anytime and anywhere, greatly enriching people's ways and methods of obtaining information. Summary of the invention

[0003] The embodiments of the present disclosure provide an information generation method, device, electronic device, computer-readable storage medium, and computer program product, which can achieve accurate information matching.

[0004] In a first aspect, an embodiment of the present disclosure proposes an information generation method, including: determining a demand type of demand information; in response to the demand type being a business demand, determining a business attribute of the demand information; in response to the business attribute of the demand information being a target service type business, extracting keywords of the demand information, matching the keywords with the collected service information of the service provider, and generating a business recommendation result.

[0005] In a second aspect, an embodiment of the present disclosure proposes an information generation device, comprising: a first determination module, configured to determine a demand type of demand information. A second determination module, configured to determine a business attribute of the demand information in response to the demand type being a business demand. A first generation module, configured to extract keywords of the demand information in response to the business attribute of the demand information being a target service type business, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that when the at least one processor executes, it can implement the information generation method described in any implementation method in the first aspect.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, which are used to enable a computer to implement the information generation method described in any implementation manner in the first aspect when executed.

[0008] In a fifth aspect, an embodiment of the present disclosure provides a computer program product including a computer program, which, when executed by a processor, can implement the information generation method described in any implementation manner in the first aspect.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Other features, objects and advantages of the present disclosure will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0011] Figure 1 is an exemplary system architecture in which the present disclosure may be applied;

[0012] Figure 2 A flowchart of an information generation method provided by an embodiment of the present disclosure;

[0013] Figure 3 A flowchart of another information generation method provided by an embodiment of the present disclosure;

[0014] Figure 4 A flowchart of another information generation method provided in an embodiment of the present disclosure;

[0015] Figure 5 A flowchart of another information generation method provided in an embodiment of the present disclosure;

[0016] Figure 6 A structural block diagram of an information generating device provided in an embodiment of the present disclosure;

[0017] Figure 7 A schematic diagram of the structure of an electronic device suitable for executing an information generating method provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description. It should be noted that the embodiments in the present disclosure and the features in the embodiments may be combined with each other without conflict.

[0019] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0020] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the information generation method, apparatus, electronic device, and computer-readable storage medium disclosed herein can be applied.

[0021] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0022] The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various applications for realizing information communication between the terminal devices 101, 102, 103 and the server 105 can be installed, such as information matching applications, information generation applications or platforms, instant messaging applications, etc. Various client applications and intelligent interactive applications can be installed on the terminal devices 101, 102, 103, such as information generation applications, information matching applications, etc.

[0023] Terminal devices 101, 102, 103 and server 105 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, etc.; when terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above, which can be implemented as multiple software or software modules, or as a single software or software module, and no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server; when the server is software, it can be implemented as multiple software or software modules, or as a single software or software module, and no specific limitation is made here.

[0024] The server 105 can provide various services through various built-in applications. Taking the information matching application that can generate information as an example, the server 105 can at least implement the following steps when running the information matching application: determine the demand type of the demand information, respond to the demand type as a business demand, determine the business attribute of the demand information, respond to the business attribute of the demand information as a target service business, extract keywords of the demand information, match the keywords with the collected service provider's service information, and generate business recommendation results. This method can achieve accurate information matching.

[0025] It should be noted that the demand information and / or service information can be obtained from the terminal devices 101, 102, 103 through the network 104. In addition to obtaining the service information from the terminal devices 101, 102, 103 through the network 104, it can also be pre-stored in the server 105 in various ways. Therefore, when the server 105 detects that the data has been stored locally, it can choose to directly obtain the data locally.

[0026] Since generating business recommendation results, screening results, and query results requires more computing resources and stronger computing power, the information generation methods provided in the subsequent embodiments of the present disclosure are generally performed by the server 105 with stronger computing power and more computing resources, and accordingly, the information generation device is generally also set in the server 105. However, it should also be pointed out that when the terminal devices 101, 102, and 103 also have computing power and computing resources that meet the requirements, the terminal devices 101, 102, and 103 can also complete the above-mentioned various operations that are handed over to the server 105 through the information generation application or information matching application installed thereon, and then output the same results as the server 105. Especially in the case where there are multiple terminal devices with different computing powers at the same time, when the information generation application or the information matching application determines that the terminal device where it is located has stronger computing power and more remaining computing resources, it can let the terminal device perform the above-mentioned operations, thereby appropriately reducing the computing pressure of the server 105, and accordingly, the information generation device can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104 .

[0027] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0028] Please refer to Figure 2 , Figure 2 A flowchart of an information generation method provided by an embodiment of the present disclosure, wherein process 200 includes the following steps:

[0029] Step 201: Determine the requirement type of the requirement information.

[0030] Exemplarily, the demand information may include, but is not limited to, one or more of user demand, user information, and business cooperation requirements.

[0031] Optionally, user needs may refer to specific needs of users (individuals or enterprises, etc.). Business cooperation requirements may refer to specific business requirements in the needs for enterprises. User information may refer to basic information of users, such as name, contact number, email address, mailing address, business scope, enterprise code, qualification information and / or enterprise website, etc.

[0032] In this embodiment, the execution subject of the information generation method (for example Figure 1 The server 105 shown determines the demand type of the demand information and classifies the demands to provide matching results for different types of demands.

[0033] Step 202: In response to the requirement type being a business requirement, determine the business attribute of the requirement information.

[0034] Optionally, the demand type may include but is not limited to: business demand and query demand. Business demand may refer to scenario-based demands such as business cooperation, purchase of goods, service consultation, franchising, etc., and query demand may refer to information query, search, etc.

[0035] After the above-mentioned execution subject confirms that the demand type of the demand information is a business demand, it further determines the business attribute of the demand information to give a specific recommendation result.

[0036] Step 203: In response to the business attribute of the demand information being a target service type business, extract keywords from the demand information, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

[0037] Optionally, the target service business may refer to business such as purchasing goods, service consultation, etc.

[0038] For the demand information of the target service type business, the above-mentioned execution entity performs word slot extraction, extracts the keywords of the preset slots of the demand information, and performs business matching analysis on the extracted keywords and the collected service information of one or more service providers based on the artificial intelligence algorithm to generate business recommendation results. The business recommendation results may include but are not limited to matching service providers, and / or services that match the demand.

[0039] In some embodiments, the service provider may refer to an enterprise, an advertiser, an individual certified user, etc. The service information may include, but is not limited to, one or more of the following: service type, service scope, service price, advertising information, and attribute information of the service provider. Among them, the service type may include, but is not limited to, commodity sales, technical services, consulting services, franchise services, and / or logistics services, etc., and the attribute information of the service provider may include, but is not limited to, qualification information, experience, and / or previous customer evaluations, etc. The service information is rich, and more accurate business recommendation results can be provided based on the rich service information.

[0040] The information generation method provided by the disclosed embodiment not only connects the demand with the service information of the service provider and optimizes resource allocation, but also makes specific judgments on the demand type and business attributes of the demand information, and specifically matches the keywords extracted based on the demand information with the service information of the service provider to generate business recommendation results, which can achieve more accurate information matching, improve the efficiency and quality of service matching, promote the effective connection between market supply and demand, and enable both parties to reach cooperation or transactions accurately and efficiently.

[0041] Please refer to Figure 3 , Figure 3 A flowchart of an information generation method provided by an embodiment of the present disclosure, wherein process 300 includes the following steps:

[0042] Step 301: Determine the requirement type of the requirement information.

[0043] Step 302: In response to the requirement type being a business requirement, determine the business attribute of the requirement information.

[0044] Step 303: In response to the business attribute of the demand information being a target service type business, extract keywords from the demand information, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

[0045] The above steps 301 to 303 are consistent with the above steps 201 to 203. For the same contents, please refer to the corresponding parts above, which will not be repeated here.

[0046] Step 304: In response to the business attribute of the demand information being a non-target service type business, extracting keywords of the demand information.

[0047] Optionally, the business attributes of the demand information include target service type business and non-target service type business, wherein the non-target service type business may refer to business cooperation, franchising and the like.

[0048] Step 305: Based on the keywords, output prompt information for supplementing the required information.

[0049] In this embodiment, the execution subject may output prompt information for supplementing the demand information based on keywords and preset rules, so that the user can supplement the demand information based on the prompt information, so that the supplemented demand information is more detailed and comprehensive. If the demand information provided by the user meets the preset rules, the execution subject may not perform step 305, and directly determine the screening results based on the demand information and the enterprise information library.

[0050] Step 306: Determine the screening result based on the supplemented demand information and the enterprise information base.

[0051] Exemplarily, the enterprise information database may include various types of information about one or more enterprises, such as service type, service scope, service price, advertising information, attribute information of the service provider, name, contact number, email address, mailing address, business scope, enterprise code, qualification information and / or enterprise website, etc.

[0052] Exemplarily, assuming that the business attribute of the demand information is franchise demand, the screening results may include brands that the user wants to franchise, franchise rule information and / or franchise data information, etc.

[0053] The information generation method provided by the embodiment of the present disclosure, in response to the business attribute of the demand information being a non-target service business, outputs prompt information for supplementing the demand information, so that the supplemented demand information is more detailed and comprehensive, so that based on the supplemented demand information and the enterprise information base, the screening results can be obtained more accurately and efficiently.

[0054] Please refer to Figure 4 , Figure 4 A flowchart of another information generation method provided by an embodiment of the present disclosure, wherein process 400 includes the following steps:

[0055] Step 401: Determine the requirement type of the requirement information.

[0056] The above step 401 is consistent with the above step 201. For the same content, please refer to the corresponding part above, which will not be repeated here.

[0057] Step 402: In response to the requirement type being a query requirement, perform reference resolution on the requirement information, and perform information retrieval on the reference resolution result to generate a query result.

[0058] Exemplarily, the query demand may refer to information query, search, etc.

[0059] Exemplarily, coreference resolution may refer to the process of determining the specific object or entity referred to by a pronoun, noun phrase, etc. by analyzing text context and semantic information in natural language processing. For example, coreference resolution of demand information may include completing a missing convenience or subject.

[0060] In some embodiments, the above step 402 performs reference resolution on the demand information, and performs information retrieval on the reference resolution results to generate query results, which may include: based on the large language model, performing reference resolution on the demand information; based on the retrieval enhancement generation model and the knowledge graph library, performing information retrieval on the reference resolution results; inputting the retrieval results into the large language model to generate query results.

[0061] The above-mentioned execution subject can perform reference resolution on the demand information based on the large language model, combined with the vector example library and contextual prompt information, and obtain the reference resolution result. Based on the Retrieval-Augmented Generation (RAG) model and the knowledge graph library, information retrieval is performed on the reference resolution result to obtain the retrieval result, and the retrieval result is input into the large language model, and combined with the vector example library and contextual prompt information, the query result is generated. Among them, the knowledge graph library is a knowledge base that represents knowledge such as entities, relationships and attributes in a graphical form.

[0062] In this way, after the demand information is dereferenced, accurate search results can be obtained by searching based on the search enhancement generation model and the knowledge graph library. The search results are input into the large language model to generate more readable query results.

[0063] The information generation method provided by the embodiment of the present disclosure, in response to determining that the demand type is a query demand, performs reference resolution on the demand information, and performs information retrieval on the reference resolution result to generate a query result. In this way, the present disclosure can not only provide accurate business recommendation results for business needs by connecting the demand with the service information of the service provider, but also generate accurate and more readable query results for the query demand.

[0064] Optionally, the number of solutions in the business recommendation results, the solutions in the screening results, and the solutions in the query results can be one or more, which can be selected by the service demander. The service demander can refer to a user (individual or enterprise, etc.), such as a search user, a buyer, or a platform user.

[0065] Optionally, the service demander and the service provider execute the selected business recommendation results or solutions in the screening results. The above-mentioned execution entity can continuously interact with the service demander and the service provider to monitor the execution status of all links, and can also obtain feedback information from the service demander and / or service provider during the execution of the solution, and adjust or supplement the details of the implementation of the solution.

[0066] In some embodiments, the above step 201 of determining the demand type of the demand information may include: performing intent recognition on the demand information to obtain intent recognition results; performing cluster analysis on the intent recognition results based on clustering rules to determine the demand type of the demand information.

[0067] Optionally, the clustering rules include query requirement rules and business requirement rules. The query requirement rules may refer to clustering rules preset for query requirements, and the business requirement rules may refer to clustering rules preset for business requirements.

[0068] In this embodiment, the above-mentioned execution entity performs intent recognition on the demand information to obtain intent recognition results; cluster analysis is performed on the intent recognition results based on clustering rules. Cluster analysis refers to the analysis process of grouping a collection of physical or abstract objects into multiple classes composed of similar objects, so as to determine the specific demand type of the demand information, which can improve the accuracy of determining the demand type.

[0069] In some embodiments, the above step 203 (or step 303) matches the keywords with the collected service information of the service provider to generate a business recommendation result, including: matching the keywords with the service information to generate a first intermediate result; in response to obtaining the supplementary information of the demand, extracting the supplementary keywords of the preset slots of the supplementary information of the demand, matching the supplementary keywords with the service information to generate a second intermediate result; adjusting the first intermediate result based on the second intermediate result to generate a business recommendation result.

[0070] In this embodiment, the execution subject matches the extracted keywords with the service information to generate a first intermediate result; in response to determining that the user is continuing to supplement the demand information, the demand supplement information is obtained, the supplementary keywords of the preset slots of the demand supplement information are extracted, the supplementary keywords are matched with the service information to generate a second intermediate result; the first intermediate result is adjusted based on the second intermediate result to generate a business recommendation result. In this way, more accurate business recommendation results can be obtained through a more in-depth analysis of comprehensive and complete demand information.

[0071] Optionally, the service demander may also make modification suggestions based on these results, and the above-mentioned execution entity may adjust the business recommendation results, screening results, and query results based on the modification suggestions.

[0072] In some embodiments, the above step 203 (or step 303) matches the keywords with the collected service information of the service providers to generate business recommendation results, including: matching the keywords with the collected service information of the service providers to obtain matched service providers; sending demand information to the matched service providers, and prompting the matched service providers to confirm whether they have the ability to meet the demand information; generating business recommendation results based on the service information of the service providers that have the ability to meet the demand information.

[0073] In this embodiment, the above-mentioned execution entity matches the extracted keywords with the collected service information of the service providers, obtains the matched service providers, sends the demand information to the matched service providers, and prompts the matched service providers to confirm whether they have the ability to meet the demand information; matches the service information of the service providers that have the ability to meet the demand information with the keywords, and generates business recommendation results. In this way, by confirming whether the matched service providers have the ability to meet the demand information, service providers that currently do not meet the demand information can be screened out, avoiding recommending unsatisfactory service providers, and further improving the accuracy of the business recommendation results.

[0074] Optionally, the service provider may modify its own service information, and the above-mentioned execution entity obtains the modified service information, updates and stores the service information of the service provider for subsequent use.

[0075] In some embodiments, the method provided by the embodiments of the present disclosure may also include: determining recommendation statements to be prompted to the user based on historical behavior data, an enterprise information library and / or a conversation database, and conducting multiple rounds of conversations with the user based on the recommendation statements; integrating one or more of the user needs, user information and business cooperation requirements in the content of the multiple rounds of conversations to obtain demand information.

[0076] For example, the historical behavior data may include historical behaviors of one or more users on the statements they last selected based on multiple recommended statements.

[0077] Exemplarily, the enterprise information database may include various types of information about one or more enterprises, such as service type, service scope, service price, advertising information, attribute information of the service provider, name, contact number, email address, mailing address, business scope, enterprise code, qualification information and / or enterprise website, etc.

[0078] Exemplarily, the conversation database may include historical conversation data between one or more users and the subject executing the present disclosure, and / or conversation data between one or more users and devices other than the subject executing the present disclosure.

[0079] In this embodiment, the above-mentioned execution entity interacts with the user by prompting the user with recommendation statements, obtains and organizes user needs, user information and / or business cooperation requirements in multiple rounds of dialogue content, and can realize dynamic demand expression and obtain more comprehensive demand information.

[0080] Optionally, the execution entity may perform risk control on the content of the sentence provided by the user, such as limiting the use of sensitive words and prompting the user to re-enter the sentence. The execution entity may perform risk control on the content of the results (such as business recommendation results, screening results, and query results).

[0081] In some embodiments, the method provided by the embodiments of the present disclosure may further include: in response to determining that a conversation is ongoing, saving the content of the ongoing conversation in the memory; in response to determining that the conversation is interrupted, saving the content of the interrupted conversation in the cache; in response to determining that the conversation has ended, saving the content of the ended conversation in the conversation database. In this way, the storage of conversation content in different states can be achieved.

[0082] In some embodiments, the method provided by the embodiments of the present disclosure may also include: extracting clues from the conversation content in the conversation database to obtain demand clue information; in response to obtaining evaluation information of the business recommendation results, updating the business recommendation results based on the evaluation information and demand clue information.

[0083] In this embodiment, in response to determining that the conversation has ended, the execution subject extracts clues from the interactive conversation content between the execution subject and the current user in the conversation database, such as extracting and storing the user's basic information, such as name, contact number, email address, mailing address, quantity of purchased goods, etc. The user selects and implements a solution from the business recommendation results, screening results, or query results, and can evaluate the selected solution, such as satisfaction or detailed evaluation. The execution subject obtains evaluation information of the business recommendation results, and updates the business recommendation results based on the evaluation information and the stored demand clue information, such as proposing subsequent improvement measures or additional services, which can improve the user experience.

[0084] Optionally, the above-mentioned execution entity can also obtain the implementation process information of the plan recorded by the user for the selected business recommendation results, screening results or query results. After the implementation of the plan is completed, the execution entity can provide the final implementation information to the user. The user can confirm the implementation process information. The execution entity can also generate an evaluation report based on the implementation process information and display it to the user.

[0085] For ease of understanding, the following Figure 5 The process 500 shown provides an application scenario in which the information generating method of the embodiment of the present disclosure can be implemented.

[0086] Step 501: Determine a recommendation statement to be prompted to the user based on historical behavior data, an enterprise information database and / or a dialogue database in the recommendation system, and conduct multiple rounds of dialogue with the user based on the recommendation statement.

[0087] Step 502: Obtain user demand information based on multiple rounds of conversation content.

[0088] Step 503: Perform risk management on the conversation content and user account.

[0089] Step 504: perform intent recognition on the demand information to obtain intent recognition results, perform cluster analysis on the intent recognition results based on clustering rules, and determine the demand type of the demand information.

[0090] Step 505: In response to the requirement type being a query requirement, reference resolution is performed on the requirement information based on the large language model.

[0091] Step 506: Based on the retrieval enhancement generation model and the knowledge graph library, information retrieval is performed on the coreference resolution results, and the retrieval results are input into the large language model to generate query results.

[0092] Step 507: In response to the requirement type being a business requirement, determining whether the business attribute of the requirement information is a target service type business.

[0093] Step 508: In response to the business attribute of the demand information being a target service type business, extract keywords from the demand information, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

[0094] Step 509: In response to the business attribute of the demand information being a non-target service business, a screening result is determined based on the keywords of the demand information and the enterprise information database.

[0095] Step 510: transfer the interactive dialogue state and / or link, and manage the word slots of the dialogue content.

[0096] The state of the interactive dialogue can be interrupted or ongoing. A link refers to a dialogue between a user and the above-mentioned execution subject as a link. Managing the word slots of the dialogue content can refer to the slots for storing the keywords of the dialogue content and iteratively updating the preset slot information.

[0097] Step 511: Risk management may be performed on the content of the results.

[0098] Step 512: In response to determining that a conversation is ongoing, the content of the ongoing conversation is saved in the memory; in response to determining that the conversation is interrupted, the content of the interrupted conversation is saved in the cache; in response to determining that the conversation has ended, the content of the ended conversation is saved in the conversation database.

[0099] Step 513: extract clues from the conversation content in the conversation database to obtain demand clue information; in response to the evaluation information of the obtained result, update the result based on the evaluation information and the demand clue information.

[0100] Figure 5 The specific implementation of each step can refer to the corresponding description in the above method embodiment, which will not be repeated here.

[0101] Further references Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an information generating device. The device embodiment corresponds to the above method embodiment, and the device can be specifically applied to various electronic devices.

[0102] like Figure 6 As shown, the information generating device 600 of this embodiment may include: a first determining module 601, a second determining module 602 and a first generating module 603. The first determining module 601 is configured to determine the demand type of the demand information. The second determining module 602 is configured to determine the business attribute of the demand information in response to the demand type being a business demand. The first generating module 603 is configured to extract keywords of the demand information in response to the business attribute of the demand information being a target service class business, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

[0103] In this embodiment, the specific processing of the first determination module 601, the second determination module 602 and the first generation module 603 in the information generating device 600 and the technical effects brought about by them can refer to the relevant descriptions of the steps in the above embodiments respectively, and will not be repeated here.

[0104] In some optional implementations of this embodiment, the information generating device 600 further includes: an extraction module configured to extract keywords of the demand information in response to the business attribute of the demand information being a non-target service business. An output module configured to output prompt information for supplementing the demand information based on the keywords. A third determination module configured to determine the screening result based on the supplemented demand information and the enterprise information base.

[0105] In some optional implementations of this embodiment, the information generating device 600 further includes: a second generating module configured to perform reference resolution on the demand information in response to the demand type being a query demand, and perform information retrieval on the reference resolution result to generate a query result.

[0106] In some optional implementations of this embodiment, the first determination module 601 is further configured to: perform intent recognition on the demand information to obtain an intent recognition result. Perform cluster analysis on the intent recognition result based on clustering rules to determine the demand type of the demand information, and the clustering rules include query demand rules and business demand rules.

[0107] In some optional implementations of this embodiment, the information generating device 600 further includes: a second generating module, further configured to: perform reference resolution on the demand information based on the large language model. Perform information retrieval on the reference resolution result based on the retrieval enhancement generation model and the knowledge graph library. Input the retrieval result into the large language model to generate a query result.

[0108] In some optional implementations of this embodiment, the first generation module 603 is further configured to: perform business matching between the keyword and the service information to generate a first intermediate result. In response to obtaining the demand supplementary information, extract the supplementary keyword of the preset slot of the demand supplementary information, perform business matching between the supplementary keyword and the service information to generate a second intermediate result. Adjust the first intermediate result based on the second intermediate result to generate a business recommendation result.

[0109] In some optional implementations of this embodiment, the first generating module 603 is further configured to: match the keyword with the service information of the collected service provider to obtain a matched service provider, send the demand information to the matched service provider, and prompt the matched service provider to confirm whether it has the ability to meet the demand information, and generate a business recommendation result based on the service information of the service provider that has the ability to meet the demand information.

[0110] In some optional implementations of this embodiment, the information generating device 600 further includes: a dialogue module configured to determine a recommendation sentence to be prompted to the user based on the historical behavior data, the enterprise information database and the dialogue database, and to conduct multiple rounds of dialogues with the user based on the recommendation sentence. An acquisition module configured to integrate one or more of the user demand, user information and business cooperation requirements in the multiple rounds of dialogue content to obtain demand information.

[0111] In some optional implementations of this embodiment, the information generating device 600 further includes: a first saving module configured to save the content of the ongoing conversation in the memory in response to determining that the conversation is ongoing. A second saving module configured to save the content of the interrupted conversation in the cache in response to determining that the conversation is interrupted. A third saving module configured to save the content of the ended conversation in the conversation database in response to determining that the conversation is ended.

[0112] In some optional implementations of this embodiment, the information generating device 600 further includes: an extraction module configured to extract clues from the conversation content in the conversation database to obtain demand clue information; an update module configured to update the business recommendation result based on the evaluation information and the demand clue information in response to obtaining the evaluation information of the business recommendation result.

[0113] In some optional implementations of this embodiment, the service information includes one or more of the following: service type, service scope, service price, advertising information, and attribute information of the service provider.

[0114] This embodiment exists as a device embodiment corresponding to the above-mentioned method embodiment. The technical effects brought about by the embodiments involving the information generating device provided by this embodiment can be referred to the corresponding relevant descriptions in the above-mentioned method embodiments respectively, and will not be repeated here.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the information generation method described in any of the above embodiments when executing.

[0116] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the information generating method described in any of the above embodiments when executed.

[0117] According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, which, when executed by a processor, can implement the information generating method described in any of the above embodiments.

[0118] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0119] like Figure 7As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0120] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0121] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as the information generation method. For example, in some embodiments, the information generation method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the information generation method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the information generation method in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services. The server may also be a server of a distributed system, or a server combined with a blockchain.

[0128] The information generation method provided by the disclosed embodiment not only connects the demand with the service information of the service provider and optimizes resource allocation, but also makes specific judgments on the demand type and business attributes of the demand information, and specifically matches the keywords extracted based on the demand information with the service information of the service provider to generate business recommendation results, which can achieve more accurate information matching, improve the efficiency and quality of service matching, promote the effective connection between market supply and demand, and enable both parties to reach cooperation or transactions accurately and efficiently.

[0129] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0130] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for generating information, comprising: Determine the type of information required; In response to the requirement type being a business requirement, determining a business attribute of the requirement information; In response to the business attribute of the demand information being a target service type business, keywords of the demand information are extracted, and business matching is performed between the keywords and the collected service information of the service provider to generate a business recommendation result.

2. The method according to claim 1, further comprising: In response to the business attribute of the demand information being a non-target service business, extracting keywords of the demand information; Based on the keywords, output prompt information for supplementing the required information; Determine the screening results based on the supplemented demand information and enterprise information database.

3. The method according to claim 1 or 2, further comprising: In response to the requirement type being a query requirement, reference resolution is performed on the requirement information, and information retrieval is performed on the reference resolution result to generate a query result.

4. The method according to claim 1 or 2, wherein: The requirement type of the determined requirement information includes: Perform intent recognition on the demand information to obtain intent recognition results; The intention recognition result is clustered and analyzed based on clustering rules to determine the demand type of the demand information, wherein the clustering rules include query demand rules and business demand rules.

5. The method according to claim 3, wherein: The performing reference resolution on the demand information and performing information retrieval on the reference resolution result to generate a query result includes: Based on the large language model, the demand information is resolved; Based on the retrieval enhancement generation model and knowledge graph library, information retrieval is performed on the coreference resolution results; The search results are input into the large language model to generate the query results.

6. The method according to claim 1 or 2, wherein: The step of matching the keywords with the collected service information of the service provider to generate a service recommendation result includes: Performing business matching between the keyword and the service information to generate a first intermediate result; In response to obtaining the demand supplementary information, extracting the supplementary keyword of the preset slot of the demand supplementary information, performing business matching between the supplementary keyword and the service information, and generating a second intermediate result; The first intermediate result is adjusted based on the second intermediate result to generate a business recommendation result.

7. The method according to claim 1, wherein: The step of matching the keywords with the collected service information of the service provider to generate a service recommendation result includes: Performing business matching between the keyword and the collected service information of the service provider to obtain a matching service provider; Sending the demand information to the matched service provider, and prompting the matched service provider to confirm whether it has the ability to meet the demand information; The business recommendation result is generated based on the service information of the service provider having the ability to meet the demand information.

8. The method according to claim 1 or 2, further comprising: Determine a recommendation statement to be prompted to the user based on historical behavior data, the enterprise information database and the dialogue database, and conduct multiple rounds of dialogue with the user based on the recommendation statement; Integrate one or more of the user needs, user information, and business cooperation requirements in the content of multiple rounds of conversations to obtain the demand information.

9. The method according to claim 1 or 2, further comprising: In response to determining that a conversation is ongoing, saving content of the ongoing conversation in memory; In response to determining that the conversation is interrupted, storing the interrupted conversation content in a cache; In response to determining that the conversation has ended, the ended conversation content is saved in a conversation database.

10. The method according to claim 9, further comprising: Extracting clues from the conversation content in the conversation database to obtain demand clue information; In response to obtaining the evaluation information of the service recommendation result, the service recommendation result is updated based on the evaluation information and the demand clue information.

11. The method according to claim 1 or 2, wherein: The service information includes one or more of the following: service type, service scope, service price, advertising information and attribute information of the service provider.

12. An information generating device, comprising: A first determining module is configured to determine a demand type of the demand information; A second determination module is configured to determine a business attribute of the demand information in response to the demand type being a business demand; The first generating module is configured to extract keywords of the demand information in response to the business attribute of the demand information being a target service type business, perform business matching between the keywords and the collected service information of the service provider, and generate a business recommendation result.

13. The apparatus according to claim 12, further comprising: An extraction module is configured to extract keywords of the demand information in response to the business attribute of the demand information being a non-target service type business; An output module is configured to output prompt information for supplementing the required information based on the keyword; The third determination module is configured to determine the screening result based on the supplemented demand information and the enterprise information base.

14. The device according to claim 12 or 13, further comprising: The second generating module is configured to perform reference resolution on the requirement information in response to the requirement type being a query requirement, and perform information retrieval on the reference resolution result to generate a query result.

15. The device according to claim 12 or 13, wherein: The first determination module is further configured to: perform intent recognition on the demand information to obtain intent recognition results; perform cluster analysis on the intent recognition results based on clustering rules to determine the demand type of the demand information, and the clustering rules include query demand rules and business demand rules.

16. The device according to claim 14, wherein: The second generation module is further configured to: perform reference resolution on the demand information based on a large language model; perform information retrieval on the reference resolution results based on a retrieval enhancement generation model and a knowledge graph library; and input the retrieval results into the large language model to generate the query results.

17. The device according to claim 12 or 13, wherein: The first generating module is further configured to: perform business matching between the keyword and the service information to generate a first intermediate result; in response to obtaining the demand supplementary information, extract the supplementary keyword of the preset slot of the demand supplementary information, perform business matching between the supplementary keyword and the service information to generate a second intermediate result; The first intermediate result is adjusted based on the second intermediate result to generate a business recommendation result.

18. The device according to claim 12, wherein: The first generating module is further configured to: perform business matching between the keyword and the collected service information of the service provider to obtain a matching service provider; The demand information is sent to the matched service provider, and the matched service provider is prompted to confirm whether it has the ability to meet the demand information; based on the service information of the service provider that has the ability to meet the demand information, the business recommendation result is generated.

19. The apparatus according to claim 12 or 13, further comprising: A dialogue module is configured to determine a recommendation sentence to be prompted to the user based on the historical behavior data, the enterprise information database and the dialogue database, and to conduct multiple rounds of dialogue with the user based on the recommendation sentence; The obtaining module is configured to integrate one or more of the user needs, user information and business cooperation requirements in the content of multiple rounds of dialogues to obtain the demand information.

20. The apparatus according to claim 12 or 13, further comprising: A first saving module, configured to save the content of the ongoing conversation in a memory in response to determining that the conversation is ongoing; A second saving module is configured to save the interrupted conversation content in a cache in response to determining that the conversation is interrupted; The third saving module is configured to save the ended conversation content in the conversation database in response to determining that the conversation has ended.

21. The apparatus according to claim 20, further comprising: An extraction module is configured to extract clues from the conversation content in the conversation database to obtain demand clue information; The updating module is configured to update the business recommendation result based on the evaluation information and the demand clue information in response to obtaining the evaluation information of the business recommendation result.

22. The device according to claim 12 or 13, wherein: The service information includes one or more of the following: service type, service scope, service price, advertising information and attribute information of the service provider.

23. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the information generating method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the information generation method according to any one of claims 1 to 11.

25. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the information generating method according to any one of claims 1 to 11 are implemented.

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