Information processing method and device, electronic equipment and storage medium

Abstract

The invention provides an information processing method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of document processing and intelligent office. According to the specific implementation scheme, the method comprises the steps that target information is obtained, and the target information comprises at least part of content in a long document or is input information associated with the long document; generating a first candidate chart of the long document through the large model according to the target information; obtaining a second candidate chart from a chart database according to the target information; and associating the first candidate chart and the second candidate chart corresponding to the same document content to obtain an associated candidate chart group, and generating a target chart of the same document content according to the associated candidate chart group.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the field of document processing and smart office technology, and in particular to an information processing method, device, electronic device and storage medium. Background Art

[0002] When there are no charts that match the content in general long-text documents, the common method is to manually draw appropriate tables, curves, etc. through manual writing, and embed them in appropriate locations according to the text outline. The whole process requires repeated revisions and re-expression to finally form the complete document content. Due to the large amount of manual intervention, the entire process cannot be automated, the efficiency is low, and the information retrieval used to build charts is not necessarily comprehensive. Summary of the invention

[0003] The present disclosure provides an information processing method, device, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, there is provided an information processing method, comprising: acquiring target information, the target information including at least part of the content in the long document, or being input information associated with the long document; generating a first candidate graph of the long document according to the target information through a large model; acquiring a second candidate graph from a graph database according to the target information; associating the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generating a target graph of the same document content according to the associated candidate graph group.

[0005] According to another aspect of the present disclosure, an information processing device is provided, including: a first acquisition module, used to acquire target information, the target information including at least part of the content in the long document, or input information associated with the long document; a first generation module, used to generate a first candidate graph of the long document according to the target information through a large model; a second acquisition module, used to acquire a second candidate graph from a graph database according to the target information; a second generation module, used to associate the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generate a target graph of the same document content according to the associated candidate graph group.

[0006] According to another aspect of the present disclosure, an electronic device is provided, 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 the at least one processor can execute the information processing method described in the above-mentioned one aspect embodiment.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, on which a computer program / instructions are stored, and the computer instructions are used to enable the computer to execute the information processing method described in the above-mentioned embodiment.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the information processing method described in the above-mentioned first embodiment is implemented.

[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] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 A flowchart of an information processing method provided by an embodiment of the present disclosure;

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

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

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

[0015] Figure 5 A schematic diagram of a process for generating a target chart provided in an embodiment of the present disclosure;

[0016] Figure 6 A schematic diagram of the structure of an information processing device provided by an embodiment of the present disclosure;

[0017] Figure 7 The present invention is a block diagram of an electronic device for implementing the information processing method according to 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 the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0019] The information processing method, apparatus, and electronic device according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0020] Artificial Intelligence (AI) is a discipline that studies how computers can simulate certain thought processes and intelligent behaviors of human beings (such as learning, reasoning, thinking, planning, etc.). It includes both hardware-level technologies and software-level technologies. AI hardware technologies generally include computer vision technology, speech recognition technology, natural language processing technology, as well as learning / deep learning, big data processing technology, knowledge graph technology, and other aspects.

[0021] Figure 1 A flowchart of an information processing method provided by an embodiment of the present disclosure.

[0022] like Figure 1 As shown, the information processing method may include:

[0023] S101, obtaining target information, where the target information includes at least a portion of the content in the long document, or is input information associated with the long document.

[0024] It should be noted that the execution subject of the information processing method in the embodiment of the present disclosure may be a hardware device with data processing capabilities and / or the necessary software required to drive the hardware device to work. Optionally, the execution subject may include a server, a user terminal and other intelligent devices. Optionally, the user terminal includes but is not limited to a mobile phone, a computer, an intelligent voice interaction device, etc. Optionally, the server includes but is not limited to a network server, an application server, and may also be a server of a distributed system, or a server combined with a blockchain, etc. The embodiment of the present disclosure is not specifically limited.

[0025] In some implementations, a long document may be obtained from a search engine or a document database, and target information may be determined based on the long document. Alternatively, partial content in the long document may be intercepted as target information. For example, partial content for which a chart needs to be generated may be used as target information.

[0026] In some implementations, the user may determine input information based on the long document and use the input information as target information. That is, the input information refers to information related to the long document but not necessarily directly contained in the document. For example, the subject information of the long document may be used as input information associated with the long document.

[0027] S102, generating a first candidate graph of the long document according to the target information through the large model.

[0028] In some implementations, a key search term of the target information may be determined, and the large model may generate a chart of the long document as the first candidate chart based on the key search term. Alternatively, a search may be performed based on the key search term to obtain reference information that is effective for generating the chart, and the large model may fuse the reference information to obtain the first candidate chart.

[0029] Optionally, keyword recognition can be performed on the target information, and the recognized keywords can be used as key search terms for the target information. For example, the key search terms can be determined based on information such as the type and subject of the chart. If a bar chart appears, the bar chart is used as the key search term.

[0030] In some implementations, key search terms may be used to search in an external search engine to determine reference information related to chart generation. The reference information may include numerical data, text descriptions, images, and other information. Further, the reference information is fused using a large model. The large model processes and analyzes the reference information to determine the relationship and trend between the reference information, and generates a first candidate chart based on the relationship and trend of the reference information.

[0031] S103: Acquire a second candidate graph from a graph database according to the target information.

[0032] In some implementations, key search terms of target information are determined and vectorized to obtain vector representations corresponding to the key search terms, similarity matching is performed in a graph database based on the vector representation, and a second candidate graph is determined based on the similarity.

[0033] Optionally, multiple charts with a threshold value set for the vector representation similarity score can be obtained, and the multiple charts can be sorted based on the similarity score, and the sorted charts can be used as the second candidate charts. Optionally, the number of charts can be pre-set, and based on the similarity score, N charts with larger similarity scores with the vector representation are determined from the chart database, and then the N charts are sorted to obtain the second candidate chart. Wherein, N is a natural number greater than 1.

[0034] S104 , associating the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generating a target graph for the same document content according to the associated candidate graph group.

[0035] In some implementations, the associated candidate chart group may be obtained by determining the document content corresponding to each of the first candidate chart and the second candidate chart, and associating the first candidate chart and the second candidate chart with the same document content.

[0036] Furthermore, a target graph of the same document content can be determined based on the first candidate graph and the second candidate graph in the associated candidate graph group. Optionally, the first candidate graph and the second candidate graph can be weighted fused to obtain the target graph. Optionally, the confidence of each of the first candidate graph and the second candidate graph can be determined, and the candidate graph with a large confidence can be used as the target graph.

[0037] According to the information processing method provided by the embodiment of the present disclosure, the target information of the long document is determined, and the large model determines the first candidate chart of the long document according to the target information, and determines the second candidate chart from the chart database according to the target information. Then, the target chart corresponding to the long document can be determined based on the first candidate chart and the second candidate chart. The scheme provided by the present disclosure can quickly generate accurate, professional charts that fit the current document content, and in the process of generating document charts, greatly avoid human intervention, so as to improve the professionalism of document generation and shorten the time consumption of chart generation.

[0038] Figure 2 A flowchart of an information processing method provided by an embodiment of the present disclosure.

[0039] like Figure 2 As shown, the information processing method may include:

[0040] S201, obtaining target information, where the target information includes at least a portion of the content in the long document, or is input information associated with the long document.

[0041] The relevant contents of step S201 can be found in the above embodiment and will not be described again here.

[0042] S202, determining key search terms according to target information, and acquiring reference information for graph generation based on the key search terms.

[0043] In some implementations, the document content for which a chart needs to be generated can be determined from the target information, and keyword recognition can be performed on the document content to determine key search terms, thereby improving information retrieval efficiency and ensuring the quality of retrieved information.

[0044] Optionally, by performing content recognition on the target information, the document content for which the chart needs to be generated is obtained, and key search terms are determined based on the document content for generating the chart. The key search terms can be determined by identifying keywords related to the chart in the document content.

[0045] Furthermore, key search terms can be used to search for diagram generation information in an external search engine to determine reference information for diagram generation. Using key search terms can quickly find reference information related to diagram generation in the search engine, thereby improving search efficiency and accuracy.

[0046] Optionally, an external search engine may be used to search for key search terms. A search request is sent to the external search engine through the target interface, wherein the search request includes the key search terms. After receiving the search request, the external search engine uses the key search terms to search and obtain initial search information.

[0047] Further, by receiving the initial search information fed back by the search engine, the initial search information is obtained by the search engine through information retrieval according to the key search words, and then effective information is extracted from the initial search information to obtain reference information. Optionally, information related to the chart can be used as effective information, and then effective information is extracted to obtain reference information.

[0048] S203, fusing the reference information through the large model to obtain fused information, and generating a first candidate graph based on the fused information.

[0049] Optionally, fusion can be performed based on the features of the reference information to obtain fused information. Feature extraction is performed on the reference information to obtain a feature vector with semantic information, and the feature vector machine is fused using a large model to obtain fused information.

[0050] Further, the chart type may be determined based on the fusion information, and the fusion information may be visualized in the form of a chart to obtain a first candidate chart. For example, the chart type may be determined to be a line chart, and the fusion information may be visualized in the form of a line chart to obtain a first candidate chart.

[0051] S204: Obtain a second candidate graph from the graph database according to the target information.

[0052] S205 , associating the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generating a target graph for the same document content according to the associated candidate graph group.

[0053] The relevant contents of steps S204-S205 can be found in the above embodiment and will not be repeated here.

[0054] According to the information processing method provided by the embodiment of the present disclosure, the target information of the long document is determined, and the key search terms are determined based on the target information. Then, the reference information for chart generation is determined based on the key search terms, and the first candidate chart of the long document is generated by the large model based on the reference information. Further, according to the target information, the second candidate chart is determined from the chart database. Then, the target chart corresponding to the long document can be determined based on the first candidate chart and the second candidate chart. The scheme provided by the present disclosure can quickly generate accurate, professional charts that fit the current document content, and in the process of generating document charts, it can greatly avoid human intervention, so as to improve the professionalism of document generation and shorten the time of chart generation.

[0055] Figure 3 A flowchart of an information processing method provided by an embodiment of the present disclosure.

[0056] like Figure 3 As shown, the information processing method may include:

[0057] S301, obtaining target information, where the target information includes at least part of the content in the long document, or is input information associated with the long document.

[0058] S302, generating a first candidate graph of the long document according to the target information through the large model.

[0059] The relevant contents of steps S301 - S302 can be found in the above embodiment and will not be described again here.

[0060] S303: Determine a key search term according to the target information, and obtain a first vector representation of the key search term.

[0061] In some implementations, the document content for which a chart needs to be generated can be determined from the target information, and keyword recognition can be performed on the document content to determine key search terms, thereby improving information retrieval efficiency and ensuring the quality of retrieved information.

[0062] Optionally, by performing content recognition on the target information, the document content for which the chart needs to be generated is obtained, and key search terms are determined based on the document content for generating the chart. The key search terms can be determined by identifying keywords related to the chart in the document content.

[0063] Furthermore, by vectorizing the key search terms, a first vector representation of the key search terms can be obtained.

[0064] S304 , searching a second candidate graph from the graph database according to the first vector representation of the key search term.

[0065] In some implementations, similarity calculation can be performed based on vector representation to obtain candidate graphs in the graph database that match the key search terms as the second candidate graph. Using the vector similarity calculation method, the candidate graph that best matches the key search terms can be quickly found, thereby improving the flexibility and speed of retrieval.

[0066] Optionally, by obtaining a second vector representation of the candidate graph in the graph database, the first vector representation and the second vector representation can be matched, and at least one candidate graph matching the key search term can be obtained from the graph database based on the matching result. Further, the candidate graphs matching the key search term are sorted to obtain a second candidate graph.

[0067] Optionally, by calculating the similarity between the first vector representation and the second vector, and taking the top N candidate graphs with the highest similarity scores as candidate graphs matching the key search term, the candidate graphs are sorted according to the similarity scores, and the sorted candidate graphs are taken as the second candidate graphs, where N is a natural number greater than 1.

[0068] S305 , associating the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generating a target graph for the same document content according to the associated candidate graph group.

[0069] The relevant contents of associating the candidate graph in step S305 can be found in the above embodiment, and will not be described again here.

[0070] In some implementations, by determining the first candidate chart and the second candidate chart in the associated candidate chart group, and determining the target chart according to the confidence of the candidate chart, it is possible to more accurately determine whether the chart conforms to the content of the long document, so as to improve the accuracy of chart generation. That is, by obtaining the confidence of each of the first candidate chart and the second candidate chart, and selecting the candidate chart with a high confidence as the target chart.

[0071] In some implementations, the candidate graphs may be weightedly fused according to the respective confidence levels of the first candidate graph and the second candidate graph to obtain a target graph. Through weighted fusion, information of the first candidate graph and the second candidate graph may be comprehensively considered, thereby improving the accuracy and reliability of the target graph.

[0072] Optionally, the confidences of the first candidate graph and the second candidate graph are obtained, and the fusion weights of the first candidate graph and the second candidate graph are determined according to the confidences of the first candidate graph and the second candidate graph. Further, the first candidate graph and the second candidate graph are fused according to the fusion weights to obtain the target graph.

[0073] According to the information processing method provided by the embodiment of the present disclosure, the target information of the long document is determined, and the large model determines the first candidate chart of the long document based on the target information, and determines the key search terms based on the target information, and determines the second candidate chart from the chart database based on the key search terms. Then, the target chart corresponding to the long document can be determined based on the confidence of the first candidate chart and the second candidate chart. The solution provided by the present disclosure can quickly generate accurate, professional charts that fit the current document content, and in the process of generating document charts, greatly avoid human intervention, so as to improve the professionalism of document generation and shorten the time consumption of chart generation.

[0074] Figure 4 A flowchart of an information processing method provided by an embodiment of the present disclosure.

[0075] like Figure 4 As shown, the information processing method may include:

[0076] S401, obtaining target information, where the target information includes at least part of the content in the long document, or is input information associated with the long document.

[0077] S402, generating a first candidate graph of the long document according to the target information through the large model.

[0078] S403: Obtain a second candidate graph from a graph database according to the target information.

[0079] S404 , associating the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generating a target graph for the same document content according to the associated candidate graph group.

[0080] The relevant contents of steps S401 - S404 can be found in the above embodiment and will not be described again here.

[0081] S405, determining an insertion position of the target chart in the long document, and inserting the target chart at the insertion position.

[0082] In some implementations, after generating a target chart corresponding to a long document, the target chart can be inserted at the insertion position in the long document by determining the insertion position of the target chart, thereby obtaining complete, effective, and highly professional long document content.

[0083] In some implementations, the position of the key search term associated with the target graph can be determined and used as the insertion position of the target graph in the long document, which can ensure that the narrative logic of the document is more coherent and improve the readability and comprehension of the long document.

[0084] Optionally, the second candidate chart corresponding to the target chart can be obtained, and the key search terms associated with the target chart can be determined based on the second candidate chart. Optionally, the first vector representation matched by the second vector representation of the second candidate chart can be obtained, and the key search terms associated with the target chart can be determined based on the matched first vector representation. In other words, the key search terms used when determining the second candidate chart can be used as the key search terms associated with the target chart to enhance the relevance between the icon and the document, thereby improving the document quality.

[0085] Further, the insertion position of the target graph in the long document can be determined according to the key search terms associated with the target graph. Alternatively, the long document can be searched according to the key search terms to determine the position of the key search terms in the long document, and the position is used as the insertion position of the target graph in the long document.

[0086] According to the information processing method provided by the embodiment of the present disclosure, the target information of the long document is determined, and the large model determines the first candidate chart of the long document according to the target information, and determines the second candidate chart from the chart database according to the target information. Then, the target chart corresponding to the long document can be determined based on the first candidate chart and the second candidate chart. Furthermore, the generated target chart can be inserted into the long document to obtain the complete, effective and highly professional content of the long document. The solution provided by the present disclosure can quickly generate accurate, professional charts that fit the current document content, and in the process of generating document charts, it can greatly avoid human intervention to improve the professionalism of document generation and shorten the time of chart generation.

[0087] like Figure 5 The flowchart of generating the target graph is shown in FIG. By obtaining the key search terms in the target information and searching in an external search engine using the key search terms, initial search information related to graph generation can be obtained, and reference information can be extracted from the initial search information. Furthermore, the first candidate graph for the long document can be obtained by fusing the reference information with the large model.

[0088] By vectorizing the key search terms, a first vector representation corresponding to the key search terms can be obtained, and a second vector representation of the candidate graph in the graph database can be obtained, so as to match the first vector representation and the second vector representation to obtain a candidate graph matching the key search terms, and sort the candidate graphs to obtain a second candidate graph.

[0089] Furthermore, the confidences of the first candidate graph and the second candidate graph are determined respectively, so as to generate the target graph according to the confidences, the first candidate graph and the second candidate graph.

[0090] Corresponding to the information processing methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an information processing device. Since the information processing device provided in the embodiment of the present disclosure corresponds to the information processing methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned information processing methods are also applicable to the information processing device provided in the embodiment of the present disclosure and will not be described in detail in the following embodiments.

[0091] Figure 6 A schematic diagram of the structure of an information processing device provided in an embodiment of the present disclosure.

[0092] like Figure 6 As shown, the information processing device 600 of the embodiment of the present disclosure includes a first acquisition module 601 , a first generation module 602 , a second acquisition module 603 and a second generation module 604 .

[0093] A first acquisition module 601 is used to acquire target information, where the target information includes at least part of the content in the long document, or is input information associated with the long document;

[0094] A first generating module 602, configured to generate a first candidate graph of the long document according to the target information by using a large model;

[0095] A second acquisition module 603 is used to acquire a second candidate chart from a chart database according to the target information;

[0096] The second generating module 604 is used to associate the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generate a target graph of the same document content according to the associated candidate graph group.

[0097] In one embodiment of the present disclosure, the first generation module 602 is further used to: determine key search terms according to the target information, and obtain reference information for chart generation based on the key search terms; fuse the reference information through the large model to obtain fused information, and generate the first candidate chart based on the fused information.

[0098] In one embodiment of the present disclosure, the first generation module 602 is further used to: send a search request to an external search engine through a target interface, wherein the search request includes the key search term; receive initial search information fed back by the search engine, wherein the initial search information is obtained by the search engine performing information retrieval based on the key search term; and extract effective information from the initial search information to obtain the reference information.

[0099] In one embodiment of the present disclosure, the second acquisition module 603 is further used to: determine a key search term according to the target information, and obtain a first vector representation of the key search term; and retrieve the second candidate graph from the graph database according to the first vector representation of the key search term.

[0100] In one embodiment of the present disclosure, the second acquisition module 603 is further used to: obtain a second vector representation of the candidate graph in the graph database; match the first vector representation and the second vector representation, and obtain at least one candidate graph matching the key search term from the graph database according to the matching result; sort the candidate graphs matching the key search term to obtain the second candidate graph.

[0101] In one embodiment of the present disclosure, the first generating module 602 is further used to: perform content recognition on the target information to obtain the document content for which a chart needs to be generated; and determine the key search term based on the document content for generating the chart.

[0102] In an embodiment of the present disclosure, the second generating module 604 is further configured to: obtain the confidence of each of the first candidate chart and the second candidate chart, and select the candidate chart with a high confidence as the target chart.

[0103] In one embodiment of the present disclosure, the second generation module 604 is further used to: obtain the confidence of each of the first candidate chart and the second candidate chart; determine the fusion weight of each of the first candidate chart and the second candidate chart according to the confidence of each of the first candidate chart and the second candidate chart; and fuse the first candidate chart and the second candidate chart according to the fusion weight to obtain the target chart.

[0104] In one embodiment of the present disclosure, the second generating module 604 is further used to: determine an insertion position of the target chart in the long document, and insert the target chart at the insertion position.

[0105] In one embodiment of the present disclosure, the second generation module 604 is further used to: obtain a second candidate chart corresponding to the target chart; determine the key search terms associated with the target chart based on the second candidate chart; and determine the insertion position of the target chart in the long document based on the key search terms associated with the target chart.

[0106] In one embodiment of the present disclosure, the second generating module 604 is further configured to: obtain a first vector representation matched by the second vector representation of the second candidate graph, and determine a key search term associated with the target graph according to the matched first vector representation.

[0107] According to the information processing device provided by the embodiment of the present disclosure, the target information of the long document is determined, and the large model determines the first candidate chart of the long document according to the target information, and determines the second candidate chart from the chart database according to the target information. Then, the target chart corresponding to the long document can be determined based on the first candidate chart and the second candidate chart. The scheme provided by the present disclosure can quickly generate accurate, professional charts that fit the current document content, and in the process of generating document charts, greatly avoid human intervention, so as to improve the professionalism of document generation and shorten the time consumption of chart generation.

[0108] In the technical solution disclosed herein, the acquisition, storage and application 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.

[0109] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0110] 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.

[0111] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program / instruction stored in a read-only memory (ROM) 702 or a computer program / instruction loaded from a storage unit 706 to 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.

[0112] 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.

[0113] 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 information processing methods. For example, in some embodiments, the information processing method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 706. In some embodiments, part or all of the computer program / instructions may be loaded and / or installed on the device 700 via ROM 702 and / or communication unit 709. When the computer program / instructions are loaded into RAM 703 and executed by the computing unit 701, one or more steps of the information processing method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the information processing method in any other appropriate manner (e.g., by means of firmware).

[0114] 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 / instructions 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] The systems and techniques described herein can be implemented in a computing system that includes backend 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 frontend 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 backend components, middleware components, or frontend components. The components of the system can 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), the Internet, and a blockchain network.

[0119] 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 of client and server is generated by computer programs / instructions running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0120] 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 the disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in the disclosure can be achieved, and this document does not limit them here.

[0121] 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. An information processing method, wherein: The method comprises: Acquire target information, where the target information includes at least part of the content in the long document, or is input information associated with the long document; Generate a first candidate graph of the long document according to the target information by using a large model; According to the target information, obtaining a second candidate graph from a graph database; The first candidate graph and the second candidate graph corresponding to the same document content are associated to obtain an associated candidate graph group, and a target graph of the same document content is generated according to the associated candidate graph group.

2. The method according to claim 1, wherein: The step of generating a first candidate graph of the long document according to the target information by using the large model includes: Determining key search terms according to the target information, and acquiring reference information for chart generation based on the key search terms; The reference information is fused by using the large model to obtain fused information, and the first candidate graph is generated based on the fused information.

3. The method according to claim 2, wherein: The step of obtaining reference information for generating a chart based on the key search term includes: Sending a search request to an external search engine via a target interface, wherein the search request includes the key search term; Receiving initial search information fed back by the search engine, wherein the initial search information is obtained by the search engine through information search based on the key search term; Effective information is extracted from the initial search information to obtain the reference information.

4. The method according to claim 1, wherein: The step of acquiring a second candidate chart from a chart database according to the target information includes: Determine a key search term according to the target information, and obtain a first vector representation of the key search term; The second candidate graph is retrieved from the graph database according to the first vector representation of the key search term.

5. The method according to claim 4, wherein: The retrieving the second candidate graph from the graph database according to the first vector representation of the key search term comprises: Obtain a second vector representation of the candidate graph in the graph database; Matching the first vector representation and the second vector representation, and acquiring at least one candidate graph matching the key search term from the graph database according to the matching result; The candidate graphs matching the key search term are sorted to obtain the second candidate graph.

6. The method according to claim 2 or 4, wherein: Determining a key search term according to the target information includes: Performing content recognition on the target information to obtain document content for which a chart needs to be generated; The key search term is determined according to the document content of the generated chart.

7. The method according to any one of claims 1 to 4, wherein: The step of generating the target graph of the same document content according to the associated candidate graph group includes: The confidences of the first candidate graph and the second candidate graph are obtained, and the candidate graph with a high confidence is selected as the target graph.

8. The method according to any one of claims 1 to 4, wherein: The step of generating the target graph of the same document content according to the associated candidate graph group includes: Obtaining respective confidences of the first candidate graph and the second candidate graph; determining, according to the confidence levels of the first candidate graph and the second candidate graph, respective fusion weights of the first candidate graph and the second candidate graph; The first candidate graph and the second candidate graph are fused according to the fusion weight to obtain the target graph.

9. The method according to any one of claims 1 to 4, wherein: After generating the target graph of the same document content according to the associated candidate graph group, the method further includes: An insertion position of the target chart in the long document is determined, and the target chart is inserted at the insertion position.

10. The method according to claim 9, wherein: Determining the insertion position of the target chart in the long document includes: Obtaining a second candidate chart corresponding to the target chart; Determining a key search term associated with the target graph based on the second candidate graph; The insertion position of the target graph in the long document is determined according to the key search term associated with the target graph.

11. The method according to claim 9, wherein: The step of determining the key search term associated with the target graph according to the second candidate graph includes: The first vector representation matched by the second vector representation of the second candidate graph is obtained, and a key search term associated with the target graph is determined according to the matched first vector representation.

12. An information processing device, wherein: The device comprises: A first acquisition module, configured to acquire target information, wherein the target information includes at least part of the content in the long document, or is input information associated with the long document; A first generating module, configured to generate a first candidate graph of the long document according to the target information by using a large model; A second acquisition module, configured to acquire a second candidate chart from a chart database according to the target information; The second generating module is used to associate the first candidate graph and the second candidate graph corresponding to the same document content to obtain an associated candidate graph group, and generate a target graph of the same document content according to the associated candidate graph group.

13. The device according to claim 12, wherein: The first generating module is further used for: Determining key search terms according to the target information, and acquiring reference information for chart generation based on the key search terms; The reference information is fused by using the large model to obtain fused information, and the first candidate graph is generated based on the fused information.

14. The device according to claim 13, wherein: The first generating module is further used for: Sending a search request to an external search engine via a target interface, wherein the search request includes the key search term; Receiving initial search information fed back by the search engine, wherein the initial search information is obtained by the search engine through information search based on the key search term; Effective information is extracted from the initial search information to obtain the reference information.

15. The device according to claim 12, wherein: The second acquisition module is further used for: Determine a key search term according to the target information, and obtain a first vector representation of the key search term; The second candidate graph is retrieved from the graph database according to the first vector representation of the key search term.

16. The device according to claim 15, wherein: The second acquisition module is further used for: Obtain a second vector representation of the candidate graph in the graph database; Matching the first vector representation and the second vector representation, and acquiring at least one candidate graph matching the key search term from the graph database according to the matching result; The candidate graphs matching the key search term are sorted to obtain the second candidate graph.

17. The device according to claim 13 or 15, wherein: The first generating module is further used for: Performing content recognition on the target information to obtain document content for which a chart needs to be generated; The key search term is determined according to the document content of the generated chart.

18. The device according to any one of claims 12 to 15, wherein: The second generating module is further used for: The confidences of the first candidate graph and the second candidate graph are obtained, and the candidate graph with a high confidence is selected as the target graph.

19. The device according to any one of claims 12 to 15, wherein: The second generating module is further used for: Obtaining respective confidences of the first candidate graph and the second candidate graph; determining, according to the confidence levels of the first candidate graph and the second candidate graph, respective fusion weights of the first candidate graph and the second candidate graph; The first candidate graph and the second candidate graph are fused according to the fusion weight to obtain the target graph.

20. The device according to any one of claims 12 to 15, wherein: The second generating module is further used for: An insertion position of the target chart in the long document is determined, and the target chart is inserted at the insertion position.

21. The device according to claim 20, wherein: The second generating module is further used for: Obtaining a second candidate chart corresponding to the target chart; Determining a key search term associated with the target graph based on the second candidate graph; The insertion position of the target graph in the long document is determined according to the key search term associated with the target graph.

22. The device according to claim 20, wherein: The second generating module is further used for: The first vector representation matched by the second vector representation of the second candidate graph is obtained, and a key search term associated with the target graph is determined according to the matched first vector representation.

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 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 method according to any one of claims 1-11.

25. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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