Receiving and sending standard acquisition method and device, computer equipment and storage medium

Abstract

The invention relates to a receiving and sending standard acquisition method and device, computer equipment and a storage medium. The method comprises the following steps: performing text segmentation on a target text to obtain a plurality of text blocks; screening out a target text block from the plurality of text blocks; wherein the target text block is a text block related to a receiving and sending standard; performing feature extraction on the target text block to obtain feature attributes of the article; splicing the target text blocks with the same feature attributes to obtain feature text blocks; and carrying out text summarization on the feature text block to obtain a receiving and sending standard of the article. According to the method, the receiving and sending standard of the delivered article can be extracted from the huge and complex target text, and the receiving and sending standard comprises related materials which need to be prepared by enterprises or individuals in the delivery process, so that the efficiency of cross-border logistics is improved.

Description

Technical Field

[0001] The present application relates to the field of text processing technology, and in particular to a method, apparatus, computer equipment and storage medium for obtaining collection and delivery standards. Background Art

[0002] Cross-border logistics constantly face the question of whether an item can be sent from region A to region B. This requires a deep understanding of documents such as various customs laws and regulations. Therefore, how to extract the specific scope of prohibited items from this vast and complex document and summarize the relevant materials that businesses or individuals need to prepare during the delivery process is a pressing issue. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for obtaining the specific scope of prohibited items and the collection and delivery standards of relevant materials that need to be prepared in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for obtaining acceptance and delivery standards. The method comprises: segmenting a target text to obtain multiple text blocks; selecting a target text block from the multiple text blocks; wherein the target text block is a text block related to the acceptance and delivery standards; extracting features from the target text blocks to obtain characteristic attributes of an item; concatenating the target text blocks with the same characteristic attributes to obtain characteristic text blocks; and summarizing the characteristic text blocks to obtain the acceptance and delivery standards for the item.

[0005] In one embodiment, the step of performing text segmentation on the target text to obtain multiple text blocks includes: segmenting the target text based on a preset number of words and / or preset symbols to obtain multiple segmented texts; wherein the multiple segmented texts include: a first segmented text and a second segmented text; performing text summary on the first segmented text to obtain a first summary text; merging the first segmented text, the second segmented text and the first summary text to obtain a first merged text; wherein the second segmented text is adjacent to the first segmented text; performing semantic segmentation on the first merged text to obtain a first segmented text and a first segmented summary text; wherein the first segmented summary text is obtained by performing text summary on the first segmented text; and using the first segmented text and the first segmented summary text as the text blocks.

[0006] In one embodiment, the step of summarizing the first segmented text to obtain a first summary text includes: inputting summary prompt words into a large language model; wherein the summary prompt words include the first segmented text and summary requirement information; obtaining the first summary text output by the large language model based on the summary prompt words; wherein the first summary text is the text in the first segmented text that meets the summary requirement information.

[0007] In one embodiment, the step of semantically segmenting the first merged text to obtain a first segmented text and a first segmented summary text includes: inputting semantic segmentation prompt words into a large language model; wherein the semantic segmentation prompt words include the first merged text and semantic segmentation information; obtaining the first segmented text and the first segmented summary text output by the large language model based on the semantic segmentation prompt words; wherein the first segmented text is the text in the first merged text that meets the semantic segmentation information.

[0008] In one embodiment, the step of filtering out a target text block from the multiple text blocks includes: inputting a filtering prompt word into a large language model; wherein the filtering prompt word includes the multiple text blocks and filtering requirement information; obtaining the target text block output by the large language model based on the filtering prompt word; wherein the target text block is a text block among the multiple text blocks that meets the filtering requirement information.

[0009] In one embodiment, the step of extracting features from the target text block to obtain characteristic attributes of the item includes: inputting feature extraction prompt words into a large language model; wherein the feature extraction prompt words include the target text block and feature requirement information; obtaining the feature attributes output by the large language model based on the feature extraction prompt words; wherein the feature attributes are text in the target text block that meets the feature requirement information.

[0010] In one embodiment, the step of performing a text summary on the characteristic text block to obtain the acceptance and delivery standards of the item includes: inputting text summary prompt words into a large language model; wherein the text summary prompt words include the characteristic text block and text summary information; obtaining the acceptance and delivery standards output by the large language model based on the text summary prompt words; wherein the acceptance and delivery standards are the text in the characteristic text block that meets the text summary information.

[0011] In a second aspect, the present application also provides a device for obtaining acceptance and delivery standards. The device comprises: a text segmentation module for segmenting a target text to obtain multiple text blocks; a text screening module for screening a target text block from the multiple text blocks; wherein the target text block is a text block related to the acceptance and delivery standards; a feature extraction module for extracting features from the target text block to obtain characteristic attributes of the item; a text splicing module for splicing the target text blocks with the same characteristic attributes to obtain a characteristic text block; and a text summarization module for summarizing the characteristic text blocks to obtain the acceptance and delivery standards of the item.

[0012] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method for obtaining the collection and delivery standards described in the embodiment of the first aspect are implemented.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for obtaining the collection and delivery standards described in the embodiment of the first aspect.

[0014] The above-mentioned method, device, computer equipment and storage medium for obtaining the collection and delivery standards obtain multiple text blocks by performing text segmentation on the target text, and then screen out the target text blocks related to the collection and delivery standards from the multiple text blocks. At this time, the target text blocks include the collection and delivery standards of multiple items. Feature extraction is then performed on the target text blocks to obtain the characteristic attributes of the items corresponding to the target text blocks, and different characteristic attributes represent different items. The target text blocks with the same characteristic attributes are then spliced ​​to obtain characteristic text blocks, and one characteristic text block includes all the collection and delivery standards of an item. Finally, the characteristic text blocks are summarized to obtain the summarized and refined collection and delivery standards of the items. The present application can extract the collection and delivery standards of mailing items from a large and complex target text. The collection and delivery standards include the relevant materials that enterprises or individuals need to prepare during the mailing process, thereby improving the efficiency of cross-border logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a diagram of an application environment of a method for obtaining collection and delivery standards in one embodiment;

[0016] Figure 2 1 is a flow chart of a method for obtaining delivery and collection standards in one embodiment;

[0017] Figure 3 A schematic diagram of a process for obtaining a text block in one embodiment;

[0018] Figure 4A schematic diagram of the steps of obtaining a text block in one embodiment;

[0019] Figure 5 A schematic diagram of a process for obtaining a first summary text in one embodiment;

[0020] Figure 6 A schematic diagram of a process for obtaining a first segmented text and a first segmented summary text in one embodiment;

[0021] Figure 7 Schematic diagram of a process for obtaining a target text block in one embodiment;

[0022] Figure 8 A schematic diagram of a process for obtaining characteristic attributes in one embodiment;

[0023] Figure 9 A schematic diagram of a process for obtaining a collection and delivery standard in one embodiment;

[0024] Figure 10 This is a module diagram of a device for obtaining receiving and sending standards in one embodiment;

[0025] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0027] The method for obtaining the collection and delivery standards provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can obtain the target text from the server 104 through the communication network, and extract the collection and delivery standards of the items from the target text through the steps of text segmentation, screening, feature extraction, splicing and text summarization. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0028] In one embodiment, Figure 2As shown, a method for obtaining the collection and delivery standards is provided, and the method is applied to Figure 1 Taking the terminal 102 in FIG. 1 as an example, the method includes the following steps:

[0029] Step S100 , performing text segmentation on the target text to obtain multiple text blocks.

[0030] Specifically, after the terminal 102 obtains the target text from the server 104 through the communication network, it first performs text segmentation on the target text to obtain multiple text blocks. The purpose of this application is to refine the collection and delivery standards of items. Therefore, the target text includes laws and regulations related to cross-border logistics or logistics regulations of other scenarios (such as logistics regulations for fresh food). For ease of understanding, the following is an example of cross-border logistics. When performing text segmentation on the target text, it can be segmented by length, by keywords, by punctuation, by semantics, by paragraphs, etc., which can be determined according to subsequent text processing requirements. When segmenting the text, the target text can also be segmented by combining multiple segmentation forms. After text segmentation, multiple text blocks with different contents can be obtained.

[0031] Step S200: Filter out a target text block from multiple text blocks.

[0032] Specifically, after receiving multiple text blocks, terminal 102 needs to filter out text blocks related to the acceptance and delivery standards from the multiple text blocks and use them as target text blocks. When filtering text blocks, the following methods can be used: keyword filtering, i.e., filtering based on the keywords appearing in the text blocks and classifying text blocks containing specific keywords; semantic analysis filtering, i.e., using natural language processing technology to perform semantic analysis on the text to understand the meaning expressed in the text and thus perform filtering; text similarity matching, i.e., using a text similarity algorithm to calculate similarity between texts and clustering or filtering texts with high similarity; topic modeling, i.e., using a topic model to extract and classify texts and cluster or filter texts with similar topics. Through the above-mentioned filtering methods, the target text block can be filtered out from the multiple text blocks. It can be understood that the target text block includes multiple text blocks related to the acceptance and delivery standards.

[0033] Step S300: extract features from the target text block to obtain feature attributes of the object.

[0034] Specifically, when extracting features from a target text block, natural language processing techniques can be used to identify entities within the text, thereby obtaining characteristic attributes of the items. Each target text block corresponds to a characteristic attribute, and multiple target text blocks can have the same characteristic attributes. The characteristic attributes are used to identify the characteristics of the items included in the target text block. For example, characteristic attributes can be the names of specific items such as dogs, cats, toothpaste, and rice dumplings, or the names of broad categories such as food, electronic products, and pharmaceuticals.

[0035] Step S400: splicing target text blocks with the same characteristic attributes to obtain characteristic text blocks.

[0036] Specifically, after obtaining the characteristic attributes corresponding to each target text block, target text blocks with the same characteristic attributes are concatenated to obtain a characteristic text block. A characteristic text block includes all text blocks related to a certain characteristic attribute.

[0037] Step S500: Summarize the characteristic text blocks to obtain the acceptance and delivery standards of the items.

[0038] Specifically, after obtaining the characteristic text block, the characteristic text block includes all text blocks related to a certain characteristic attribute. At this time, by summarizing the text of the characteristic text block, the corresponding item acceptance and delivery standards can be obtained. It can be understood that each characteristic attribute corresponds to a characteristic text block, and at the same time, corresponds to the acceptance and delivery standards of an item. Through the above steps, the acceptance and delivery standards of all items included in the target text can be obtained, thereby forming a collection and delivery standard library. In the process of cross-border logistics, by searching in the collection and delivery standard library, the collection and delivery standards of the corresponding items can be determined, which makes it easier for users to view and prepare the corresponding information, thereby improving the efficiency of cross-border logistics.

[0039] The above-mentioned method for obtaining the collection and delivery standards performs text segmentation on the target text to obtain multiple text blocks, and then filters out the target text blocks related to the collection and delivery standards from the multiple text blocks. At this time, the target text blocks include the collection and delivery standards of multiple items. Feature extraction is then performed on the target text blocks to obtain the characteristic attributes of the items corresponding to the target text blocks, and different characteristic attributes represent different items. The target text blocks with the same characteristic attributes are then spliced ​​to obtain characteristic text blocks, and one characteristic text block includes all the collection and delivery standards of an item. Finally, the characteristic text blocks are summarized to obtain the summarized and refined collection and delivery standards of the items. The present application can extract the collection and delivery standards for mailing items from the huge and complex customs laws and regulations. The collection and delivery standards include the relevant materials that enterprises or individuals need to prepare during the mailing process, thereby improving the efficiency of cross-border logistics.

[0040] In one embodiment, Figure 3As shown, in step S100, the step of segmenting the target text to obtain multiple text blocks includes:

[0041] Step S110 , segmenting the target text based on a preset number of characters and / or preset symbols to obtain a plurality of segmented texts.

[0042] Specifically, when the target text is segmented in this embodiment, the target text can be segmented according to a preset number of words and / or according to preset symbols. The preset number of words (such as 500 words) can determine the length of the text, and the preset symbol can be a line break, a period, etc. In some embodiments, the target text can be segmented according to both the preset number of words and the preset symbol. When the preset number of words is 500 words and the preset symbol is a line break, the target text can be segmented into each paragraph, and if the number of words in a paragraph exceeds 500 words, it will be divided into multiple paragraphs according to the number of words. Figure 4 As shown, after the target text is segmented, the multiple segmented texts include: a first segmented text, a second segmented text, ..., an Nth segmented text.

[0043] Step S120 , performing text summary on the first segmented text to obtain a first summary text.

[0044] Specifically, after obtaining multiple segmented texts, a first segmented text, that is, the first segmented text, is subjected to text summary, thereby obtaining a first summary text. The text summary is used to summarize the main content of the segmented texts.

[0045] Step S130: Merge the first segmented text, the second segmented text, and the first summary text to obtain a first merged text.

[0046] Specifically, after obtaining the first summary text, the first segmented text, the second segmented text, and the first summary text obtained by segmentation are merged to obtain a first merged text, wherein the second segmented text is adjacent to the first segmented text.

[0047] Step S140 , semantic segmentation is performed on the first merged text to obtain a first segmented text and a first segmented summary text.

[0048] Specifically, after obtaining the first merged text, the main content of the first merged text also comes from the first segmented text and the second segmented text. Therefore, when performing semantic segmentation on the first merged text, it is divided into two parts to obtain the first segmented text and the second segmented text. At the same time, during semantic segmentation, the main contents of the two parts are summarized respectively to obtain the first segmentation summary text and the second segmentation summary text.

[0049] Step S150: The first segmented text and the first segmented summary text are taken as text blocks.

[0050] Specifically, the first segmented text obtained after semantic segmentation and the first segmented summary text are finally merged to obtain a text block. The second segmented summary text obtained after semantic segmentation is then merged with the original second segmented text and the third segmented text to obtain a new first merged text. Steps S140 and S150 are then repeated until all text blocks are obtained.

[0051] This embodiment processes the target text by adopting a semantic recursive slicing method, which pays more attention to the semantic integrity and can ensure the accuracy and completeness of the information.

[0052] In one embodiment, Figure 5 As shown, in step S120, the step of summarizing the first segmented text to obtain a first summary text includes:

[0053] Step S121, input summary prompt words into the large language model;

[0054] Step S122: obtaining a first summary text output by the large language model according to the summary prompt words.

[0055] Specifically, this embodiment utilizes a large language model to summarize the first segmented text, thereby generating a first summary text. The summary prompt includes the first segmented text and summary requirement information. The first summary text is the text in the first segmented text that meets the summary requirement information. Specifically, the first segmented text is first input into the large language model, followed by the summary requirement information. For example, if the summary requirement information is, for example, "Your task is to output a summary of the above content," the output of the large language model is the first summary text.

[0056] In one embodiment, Figure 6 As shown, in step S140, the step of performing semantic segmentation on the first merged text to obtain a first segmented text and a first segmented summary text includes:

[0057] Step S141, inputting semantic segmentation prompt words into the large language model;

[0058] Step S142 : obtaining the first segmentation text and the first segmentation summary text output by the large language model according to the semantic segmentation prompt words.

[0059] Specifically, this embodiment uses a large language model to perform semantic segmentation on the first merged text, thereby obtaining a first segmented text and a first segmented summary text. The semantic segmentation prompt word includes the first merged text and the semantic segmentation information, and the first segmented text is the text in the first merged text that meets the semantic segmentation information. As a specific example, the first merged text is first input into the large language model, and then the semantic segmentation information is input. In some embodiments, since the first merged text includes the first segmented text, the second segmented text and the first summary text, at this time, the first segmented text and the second segmented text are used as "partial original text", and the first summary text is used as auxiliary information of the "partial original text". The semantic segmentation information includes: text designation information, which is used to specify the text data processed by the large language model; text segmentation information, which is used to instruct the large language model to perform text segmentation; and text summary information, which is used to instruct the large language model to perform text summary. For a specific example, the semantic segmentation information is: "Your task: 1. Understand the auxiliary information of "partial original text" and "partial original text". 2. Analyze the structure of "partial original text" and divide it into two parts at the appropriate place. 3. Summarize the main content of the first part and give the starting line number and ending line number of the content of the first part. 4. For the second part, summarize its main content based on the context and the content of the first part." At this time, step 1 is text designation information, step 2 is text segmentation information, steps 3 and 4 are text summary information, the first part after segmentation is the first segmentation text, the second part is the second segmentation text, the main content of the summarized first part is the first segmentation summary text, and the main content of the summarized second part is the second segmentation summary text.

[0060] In one embodiment, Figure 7 As shown, in step S200, the step of selecting a target text block from multiple text blocks includes:

[0061] Step S210: inputting screening prompt words into the large language model;

[0062] Step S220 , obtaining a target text block output by the large language model according to the screening prompt words.

[0063] Specifically, this embodiment utilizes a large language model to filter multiple text blocks to obtain a target text block. The filtering prompt includes multiple text blocks and filtering requirement information, and the target text block is the text block among the multiple text blocks that meets the filtering requirement information. In some embodiments, the filtering requirement information includes: output requirement information indicating the output result of the large language model; and output condition information indicating the conditions under which the large language model performs output. For a specific example, the output requirement information is: "Based on the given title, original text summary, and original content, think about and judge each example to determine whether it is related to import and export, and whether it involves specific requirements for imported and exported items or delivery entities (such as individuals or companies). Finally, give a result to determine whether the content meets the requirements of the task, and indicate the answer with yes or no." The output condition information is: "If the content meets the following conditions, the judgment is "yes": 1. The text clearly states that a certain item cannot be imported or exported; 2. Specific conditions are set for the import or export of a certain item, and these conditions are clearly listed in the text; 3. The text sets specific requirements for companies or individuals delivering specific items, such as requiring certain licenses or qualifications. If the content meets the following conditions, the judgment is "no": 1. The text content is completely unrelated to imported and exported items, such as regulations on pharmacists writing prescriptions; 2. The text mentions the conditions for the import and export of a certain item, but does not specify the specific conditions or requirements; 3. The content is indeed related to import and export, but focuses mainly on departmental regulations, procedures, and punishment measures, rather than specific requirements for delivery items or delivery entities." In this setting, the output of the large language model is whether each text block is related to import and export. For example, a text block containing the following content: "Pharmacists must perform "four checks and ten comparisons" when dispensing prescriptions" is not related to import and export, so the corresponding output result is "no". In this case, the text block with the output result of "yes" is used as the target text block.

[0064] In one embodiment, Figure 8 As shown, in step S300, the step of extracting features from the target text block to obtain feature attributes of the object includes:

[0065] Step S310, inputting feature extraction prompt words into the large language model;

[0066] Step S320: Obtain the feature attributes output by the large language model based on the feature extraction prompt word.

[0067] Specifically, this embodiment uses a large language model to extract features from the target text block, thereby obtaining the characteristic attributes of the item. The feature extraction prompt word includes the target text block and feature requirement information, and the feature attribute is the text in the target text block that meets the feature requirement information. In some embodiments, the feature requirement information includes: extraction requirement information, which is used to indicate the extraction requirements of the large language model; and association judgment information, which is used to indicate the large language model to determine whether the current text is associated with a certain item. For example, the input extraction requirement information is: "Your task is to extract the names of items related to the collection and delivery standards from the text. The requirements are as follows: 1. Minimization: For items that can be disassembled, try to disassemble them. For example, live animals are prohibited from being mailed (except dogs and cats). Here, dogs and cats can be used as common product name categories, so they need to be split into dogs, cats, and live animals (except dogs and cats); 2. No need to split attributes: Different attributes of the same item do not need to be disassembled. For example, bird's nests are prohibited from being mailed (except for sterile treatment). Here, sterile treatment is just an attribute of bird's nests, not a product name itself, so it does not need to be disassembled. The bird's nest itself has been minimized; 3. It should not be an abstract item, for example For example, "other" and "items recorded in XX" must be specific items in real life, such as toothpaste, rice dumplings, cakes, etc., or they can be broad categories, such as food, electronic products, and pharmaceuticals. 4. "Any" can be used to represent any item, but it is not appropriate to write "any food" or "any electronic product". The latter can be directly expressed as "food" or "electronic products." The input association judgment information is: "Judge whether each item is related to food at the same time: 1. Food in the customs sense includes normal food, beverages, drinks, tea, tea bags, dried goods, seasonings, and health products; 2. When it is impossible to judge, it is assumed to be related to food. If the extracted item is any, it is also considered to be related to food." Under this setting, the output result of the large language model is the feature attribute corresponding to each target text block, that is, the name of the item. For example, a target text block contains the following: "(14) Animal carcasses, animal specimens, and animal-derived waste. (15) Soil and organic growing media." After feature extraction using a large language model, the resulting feature attributes are: "Animal carcasses, no; Animal specimens, no; Animal-derived waste, no; Soil, no; Organic growing media, no." The "no" indicates that the corresponding feature attributes are not related to food. It will be appreciated that in some embodiments, the feature requirement information only includes extraction requirement information, and the feature attributes of the item corresponding to the target text block can be obtained by extracting the requirement information.

[0068] In one embodiment, Figure 9 As shown, in step S500, the step of summarizing the characteristic text block to obtain the acceptance and delivery standards of the item includes:

[0069] Step S510: inputting text summary prompt words into the large language model;

[0070] Step S520: Obtain the collection and delivery standards output by the large language model based on the text summary prompt words.

[0071] Specifically, this embodiment utilizes a large language model to summarize the feature text blocks, thereby obtaining the acceptance and delivery standards for items. The text summary prompt includes the feature text block and text summary information, and the acceptance and delivery standards are the text in the feature text block that meets the text summary information. Specifically, the feature text block is first input into the large language model, and then the text summary information is input. For example, the text summary information is as follows: "Your task is to extract the export or import requirements for a certain item from the text. When extracting requirements, please note: 1. Only output the most important requirements. The shorter the better, the more intuitive and simple. 2. Only focus on the requirements for the item itself, or the sender or delivery company. There is no need to include abstract content such as processes, procedures, responsibilities, declarations, and penalties." Under this setting, the output result of the large language model is the acceptance and delivery standards for the item corresponding to each feature text block. For example, the content of the feature text block is: "This section mainly contains the relevant regulations on personal inbound and outbound mailings. It includes the definitions of personal use and reasonable quantity, as well as the value limits of items sent to different regions (RMB 800 for area A and RMB 1,000 for area B). It also explains how to handle items that exceed the prescribed limits." After summarizing the text through the large language model, the obtained standard for the acceptance of items is: "Any items sent by individuals out of the country must meet the following requirements: 1. Reasonable personal use; 2. Value limit requirements: For items sent to area A, the value limit is RMB 800 per time; for items sent to area B, the value limit is RMB 1,000 per time. For single and indivisible items, the value limit may be exceeded."

[0072] In a specific embodiment, in the method for obtaining the collection and delivery standards of the present application, text segmentation, screening, feature extraction, and text summarization are all processed using a large language model. Since the large language model is trained on a large amount of corpus, it can learn various language knowledge and patterns, and therefore has a strong generalization ability. It can adapt to various languages ​​and contexts, process various text data, and can process text data more naturally and accurately when performing tasks such as text classification and summary generation.

[0073] In one embodiment, in step S500, after obtaining the acceptance and delivery standards of the items, the contents can be manually modified, and some manually provided general rules can be added, or some acceptance and delivery standards can be deleted, so that the contents of the acceptance and delivery standard library are more accurate.

[0074] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0075] Based on the same inventive concept, embodiments of the present application also provide a device for obtaining receiving and sending standards for implementing the aforementioned method for obtaining receiving and sending standards. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for obtaining receiving and sending standards provided below can be found in the aforementioned limitations of the method for obtaining receiving and sending standards, and will not be further elaborated here.

[0076] In one embodiment, Figure 10 As shown, a device for obtaining a collection and delivery standard is provided, comprising: a text segmentation module 610, a text screening module 620, a feature extraction module 630, a text splicing module 640 and a text summarization module 650, wherein:

[0077] A text segmentation module 610 is used to segment the target text into multiple text blocks;

[0078] The text screening module 620 is used to screen out a target text block from multiple text blocks; wherein the target text block is a text block related to the collection and delivery standards;

[0079] Feature extraction module 630, used to extract features from the target text block to obtain feature attributes of the object;

[0080] The text splicing module 640 is used to splice target text blocks with the same characteristic attributes to obtain characteristic text blocks;

[0081] The text summarizing module 650 is used to perform text summarization on the characteristic text blocks to obtain the acceptance and delivery standards of the items.

[0082] The above-mentioned receiving and sending standards acquisition device obtains multiple text blocks by performing text segmentation on the target text, and then filters out the target text blocks related to the receiving and sending standards from the multiple text blocks. At this time, the target text blocks include the receiving and sending standards of multiple items. The target text blocks are then feature extracted to obtain the characteristic attributes of the items corresponding to the target text blocks, and different characteristic attributes represent different items. The target text blocks with the same characteristic attributes are then spliced ​​to obtain characteristic text blocks, and one characteristic text block includes all the receiving and sending standards of an item. Finally, the characteristic text blocks are summarized to obtain the summarized and refined receiving and sending standards of the items. The present application can extract the receiving and sending standards for mailing items from the huge and complex customs laws and regulations. The receiving and sending standards include the relevant materials that enterprises or individuals need to prepare during the mailing process, thereby improving the efficiency of cross-border logistics.

[0083] In one embodiment, the text segmentation module 610 is also used to segment the target text based on a preset number of words and / or preset symbols to obtain multiple segmented texts; wherein the multiple segmented texts include: a first segmented text and a second segmented text; performing a text summary on the first segmented text to obtain a first summary text; merging the first segmented text, the second segmented text and the first summary text to obtain a first merged text; wherein the second segmented text is adjacent to the first segmented text; performing semantic segmentation on the first merged text to obtain a first segmented text and a first segmented summary text; wherein the first segmented summary text is obtained by performing a text summary on the first segmented text; and treating the first segmented text and the first segmented summary text as text blocks.

[0084] In one embodiment, the text segmentation module 610 is further configured to input summary prompt words into the large language model; wherein the summary prompt words include the first segmented text and summary requirement information; and obtain a first summary text output by the large language model based on the summary prompt words; wherein the first summary text is the text in the first segmented text that meets the summary requirement information.

[0085] In one embodiment, the text segmentation module 610 is further used to input semantic segmentation prompt words into the large language model; wherein the semantic segmentation prompt words include the first merged text and semantic segmentation information; obtain the first segmented text and the first segmentation summary text output by the large language model based on the semantic segmentation prompt words; wherein the first segmented text is the text in the first merged text that meets the semantic segmentation information.

[0086] In one embodiment, the text filtering module 620 is further configured to input a filtering prompt word into the large language model; wherein the filtering prompt word includes multiple text blocks and filtering requirement information; and obtain a target text block output by the large language model based on the filtering prompt word; wherein the target text block is a text block among the multiple text blocks that meets the filtering requirement information.

[0087] In one embodiment, the feature extraction module 630 is further used to input feature extraction prompt words into the large language model; wherein the feature extraction prompt words include the target text block and feature requirement information; obtain feature attributes output by the large language model based on the feature extraction prompt words; wherein the feature attributes are text in the target text block that meets the feature requirement information.

[0088] In one embodiment, the text summary module 650 is further used to input text summary prompt words into the large language model; wherein the text summary prompt words include characteristic text blocks and text summary information; and obtain the acceptance and delivery standards output by the large language model based on the text summary prompt words; wherein the acceptance and delivery standards are the text in the characteristic text blocks that meets the text summary information.

[0089] Each module in the aforementioned receiving and delivery standard acquisition device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0090] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for obtaining receiving and sending standards. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0091] Those skilled in the art will understand that Figure 11The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0092] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0093] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented.

[0094] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0095] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0096] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for obtaining collection and delivery standards, characterized in that: The method comprises: Perform text segmentation on the target text to obtain multiple text blocks; Filtering out a target text block from the plurality of text blocks; wherein the target text block is a text block related to the collection and delivery standards; Performing feature extraction on the target text block to obtain feature attributes of the item; splicing the target text blocks with the same characteristic attributes to obtain characteristic text blocks; The characteristic text blocks are summarized to obtain the acceptance and delivery standards of the items.

2. The method for obtaining the collection and delivery standards according to claim 1, characterized in that: The step of segmenting the target text to obtain multiple text blocks includes: Segmenting the target text based on a preset number of words and / or preset symbols to obtain a plurality of segmented texts; wherein the plurality of segmented texts include: a first segmented text and a second segmented text; Performing text summarization on the first segmented text to obtain a first summary text; Merging the first segmented text, the second segmented text, and the first summary text to obtain a first merged text; wherein the second segmented text is adjacent to the first segmented text; Performing semantic segmentation on the first merged text to obtain a first segmented text and a first segmented summary text; wherein the first segmented summary text is obtained by performing text summarization on the first segmented text; The first segmented text and the first segmented summary text are used as the text block.

3. The method for obtaining the collection and delivery standards according to claim 2, characterized in that: The step of summarizing the first segmented text to obtain a first summary text includes: Inputting summary prompt words into the large language model; wherein the summary prompt words include the first segmented text and summary requirement information; The first summary text output by the large language model according to the summary prompt word is obtained; wherein the first summary text is the text in the first segmented text that meets the summary requirement information.

4. The method for obtaining the collection and delivery standards according to claim 2, characterized in that: The step of semantically segmenting the first merged text to obtain a first segmented text and a first segmented summary text includes: Inputting semantic segmentation prompt words into the large language model; wherein the semantic segmentation prompt words include the first merged text and semantic segmentation information; The first segmented text and the first segmented summary text output by the large language model according to the semantic segmentation prompt word are obtained; wherein the first segmented text is the text in the first merged text that meets the semantic segmentation information.

5. The method for obtaining the collection and delivery standards according to claim 1, characterized in that: The step of selecting a target text block from the plurality of text blocks comprises: Inputting a screening prompt word into the large language model; wherein the screening prompt word includes a plurality of the text blocks and screening requirement information; The target text block output by the large language model according to the screening prompt word is obtained; wherein the target text block is a text block that meets the screening requirement information among the multiple text blocks.

6. The method for obtaining the collection and delivery standards according to claim 1, characterized in that: The step of extracting features from the target text block to obtain feature attributes of the item includes: Inputting feature extraction prompt words into the large language model; wherein the feature extraction prompt words include the target text block and feature requirement information; The feature attribute output by the large language model according to the feature extraction prompt word is obtained; wherein the feature attribute is the text in the target text block that meets the feature requirement information.

7. The method for obtaining the collection and delivery standards according to claim 1, characterized in that: The step of summarizing the characteristic text block to obtain the acceptance and delivery standards of the item includes: Inputting text summary prompt words into the large language model; wherein the text summary prompt words include the feature text block and text summary information; The receiving and sending standard output by the large language model according to the text summary prompt word is obtained; wherein the receiving and sending standard is the text in the feature text block that meets the text summary information.

8. A device for obtaining receiving and sending standards, characterized in that: The device comprises: The text segmentation module is used to segment the target text into multiple text blocks; A text screening module, configured to screen out a target text block from the plurality of text blocks; wherein the target text block is a text block related to the collection and delivery standards; A feature extraction module is used to extract features from the target text block to obtain feature attributes of the object; A text splicing module, configured to splice the target text blocks with the same characteristic attributes to obtain characteristic text blocks; The text summarizing module is used to perform text summarizing on the characteristic text blocks to obtain the acceptance and delivery standards of the items.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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