Data transmission method and apparatus, nonvolatile storage medium, and processor

Abstract

The application discloses a data sending method and device, a nonvolatile storage medium and a processor. The method comprises the following steps: obtaining a message sending request of target message data, wherein the target key service information indicated by the target message data is carried in the sending request; determining a target message sending mode matched with the target key service information in a preset message mode set, wherein the target message sending mode is a preset message sending mode corresponding to a target message type to which the target key service information belongs; querying a target semantic template corresponding to the target message sending mode in a preset message template library, wherein the preset message template library records preset semantic templates corresponding to a plurality of preset message sending modes; and filling the target key service information into the target semantic template to generate the target message data. The application solves the technical problem that the prior art cannot automatically send data according to semantics.

Description

Technical Field

[0001] This invention relates to the field of computers, and more specifically, to a data transmission method, apparatus, non-volatile storage medium, and processor. Background Technology

[0002] With the continuous development of information technology, the types of data generated in business operations are becoming increasingly diverse. To improve the efficiency of business information processing, many organizations have begun to introduce various message middleware systems for information processing. As the scale of system users grows, a large number of frequent messages will inevitably be generated, requiring further optimization of the system. The various modules of the system also need to be more complete, and the interaction between systems needs to be more flexible. Various interfaces need to cooperate to execute tasks, which is where message middleware comes in. Message middleware uses the message receiving function to temporarily store message data and sends it when the user needs it, allowing multiple systems to move beyond synchronous calls and better decouple through asynchronous calls.

[0003] Currently, message middleware systems mostly process data transmitted by producers in the traditional way, and cannot automatically process information based on semantic similarity.

[0004] There is currently no effective solution to the problem that existing technologies cannot automatically send data based on semantics. Summary of the Invention

[0005] This invention provides a data transmission method, apparatus, non-volatile storage medium, and processor to at least solve the technical problem that the prior art cannot achieve automatic data transmission based on semantics.

[0006] According to one aspect of the present invention, a data transmission method is provided, comprising: acquiring a message transmission request for target message data, wherein the transmission request carries target key business information indicated by the target message data; determining a target message transmission pattern matching the target key business information in a preset message pattern set, wherein the preset message pattern set records a correspondence between multiple preset message transmission patterns and preset message types, and the target message transmission pattern is a preset message transmission pattern corresponding to the target message type to which the target key business information belongs; querying a target semantic template corresponding to the target message transmission pattern in a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to the preset message transmission patterns; and filling the target key business information into the target semantic template to generate the target message data.

[0007] Optionally, before determining the target message sending pattern matching the key business information from the preset message pattern set, the method further includes: acquiring multiple historical message data that have been sent; determining the historical message ontology of each historical message data, wherein the historical message ontology is used to represent the message terminal, message source, message type, message-related business, and message topic of the corresponding historical message data; performing frequent pattern mining on the multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are at least used to represent the message sending period and message content features of the corresponding historical message data; determining the preset message sending pattern of the corresponding historical message data based on the historical message ontology and historical message features, wherein the preset message sending pattern is used to represent the message sending period, message content features, message terminal, message source, message type, and message-related business of the corresponding historical message data; and determining the preset message pattern set based on the preset message sending patterns corresponding to the multiple historical message data.

[0008] Optionally, determining the historical message ontology for each historical message data includes: preprocessing the historical message data using text analysis techniques to generate a first message dataset, wherein the first message dataset includes multiple first message texts; clustering the first message texts in the first message dataset using a text clustering algorithm to generate a second message dataset, wherein the second message dataset includes multiple second message texts, the second message texts being determined based on the clustering results of at least one first message text; performing feature analysis on the second message dataset to determine a preset feature word set, wherein the preset feature word set includes multiple preset feature words, the preset feature words being determined based on the second message texts; and defining the historical message ontology for each historical message data based on the preset feature words.

[0009] Optionally, determining the historical message ontology for each historical message data includes: decomposing the historical message data into at least one historical feature word to obtain a set of historical feature words; vectorizing the historical feature words to determine the historical word vector for each historical feature word; weighting multiple historical word vectors in the set of historical feature words to determine the historical feature vector of the historical message data; determining the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent a feature of a preset feature type; determining the preset feature type with the highest feature similarity as the target feature type of the historical message data object; and determining the historical message ontology corresponding to the target feature type in a preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologs.

[0010] Optionally, determining the target message sending pattern that matches the target key business information from the preset message pattern set includes: identifying the target message type of the target key business information; and determining the target message sending pattern corresponding to the target message type from the preset message pattern set.

[0011] Optionally, determining the target message sending pattern matching the target key business information in a preset message pattern set includes: obtaining historical key business information used to determine the preset message sending pattern in the preset message pattern set, wherein the preset message sending pattern is determined based on historical message data carrying the historical key business information; calculating the information similarity between the target key business information and each piece of historical key business information; determining the historical key business information with the highest information similarity as the preset key business information; and determining the preset message sending pattern determined based on the preset key business information as the target message sending pattern.

[0012] Optionally, the preset message template library includes: a preset message ontology library and a preset ontology template library. Querying the target semantic template corresponding to the target message sending mode in the preset message template library includes: querying the target message ontology corresponding to the target message sending mode in the preset message ontology library. The preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode. The preset message sending mode is pre-generated based on the preset message ontology.

[0013] The target semantic template corresponding to the target message ontology is queried in the preset ontology template library. The preset ontology template library records multiple preset semantic templates corresponding to the preset message ontology. The preset semantic templates are generated in advance based on the preset message ontology.

[0014] According to another aspect of the present invention, a data sending apparatus is also provided, comprising: an acquisition module, configured to acquire a message sending request for target message data, wherein the sending request carries target key business information indicated by the target message data; a determination module, configured to determine a target message sending pattern matching the target key business information in a preset message pattern set, wherein the preset message pattern set records a correspondence between multiple preset message sending patterns and preset message types, and the target message sending pattern is a preset message sending pattern corresponding to the target message type to which the target key business information belongs; a query module, configured to query a target semantic template corresponding to the target message sending pattern in a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to the preset message sending patterns; and a generation module, configured to fill the target key business information into the target semantic template to generate the target message data.

[0015] According to another aspect of the present invention, a non-volatile storage medium is also provided, wherein a program is stored in the non-volatile storage medium, wherein the program controls the device where the non-volatile storage medium is located to execute the data transmission method described above when the program is executed.

[0016] According to another aspect of the present invention, an electronic device is also provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the data transmission method described above.

[0017] In this embodiment of the invention, a message sending request for target message data is obtained, wherein the sending request carries target key business information indicated by the target message data; a target message sending mode matching the target key business information is determined from a preset message mode set, wherein the preset message mode set records the correspondence between multiple preset message sending modes and preset message types, and the target message sending mode is the preset message sending mode corresponding to the target message type to which the target key business information belongs; a target semantic template corresponding to the target message sending mode is queried from a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to preset message sending modes; the target key business information is filled into the target semantic template to generate target message data. By selecting the target semantic template of the target key business information and filling the target key business information into the target semantic template, the target message data of the target key business information can be automatically generated and sent, realizing the technical effect of automatically generating and sending target message data based on semantics, thereby solving the technical problem that the prior art cannot achieve automatic data sending based on semantics. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of a data transmission method according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a mainstream technical process for data transmission according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of a multi-terminal message middleware system based on text mining and semantic templates according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of a message middleware execution flow according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of a message middleware system according to an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of a data transmission device according to an embodiment of the present invention;

[0025] Figure 7 This is a structural block diagram of a computer terminal according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of the present invention, a data transmission method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a data transmission method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Obtain a message sending request for the target message data, wherein the sending request carries target key business information indicated by the target message data;

[0031] Step S104: Determine the target message sending mode that matches the target key business information in the preset message mode set. The preset message mode set records the correspondence between multiple preset message sending modes and preset message types. The target message sending mode is the preset message sending mode corresponding to the target message type to which the target key business information belongs.

[0032] Step S106: Query the target semantic template corresponding to the target message sending mode in the preset message template library. The preset message template library records multiple preset semantic templates corresponding to preset message sending modes.

[0033] Step S108: Fill the target key business information into the target semantic template to generate target message data.

[0034] In this embodiment of the invention, a message sending request for target message data is obtained, wherein the sending request carries target key business information indicated by the target message data; a target message sending mode matching the target key business information is determined from a preset message mode set, wherein the preset message mode set records the correspondence between multiple preset message sending modes and preset message types, and the target message sending mode is the preset message sending mode corresponding to the target message type to which the target key business information belongs; a target semantic template corresponding to the target message sending mode is queried from a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to preset message sending modes; the target key business information is filled into the target semantic template to generate target message data. By selecting the target semantic template of the target key business information and filling the target key business information into the target semantic template, the target message data of the target key business information can be automatically generated and sent, realizing the technical effect of automatically generating and sending target message data based on semantics, thereby solving the technical problem that the prior art cannot achieve automatic data sending based on semantics.

[0035] In step S106 above, the preset message pattern set includes multiple preset message sending patterns, and the preset message sending pattern is represented as a six-tuple <d o f i , t i s o c i p i >, d i Indicates the message sending period, f i t represents the characteristics of message content. i Indicates the message terminal, s i Indicates the source of the information, c i Indicates the message type, p i This indicates message-related business.

[0036] As an optional embodiment, before determining the target message sending pattern matching the key business information from the preset message pattern set, the method further includes: acquiring multiple historical message data that have been sent; determining the historical message ontology of each historical message data, wherein the historical message ontology is used to represent the message terminal, message source, message type, message-related business, and message topic of the corresponding historical message data; performing frequent pattern mining on the multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are at least used to represent the message sending period and message content features of the corresponding historical message data; determining the preset message sending pattern of the corresponding historical message data based on the historical message ontology and historical message features, wherein the preset message sending pattern is used to represent the message sending period, message content features, message terminal, message source, message type, and message-related business of the corresponding historical message data; and determining the preset message pattern set based on the preset message sending patterns corresponding to the multiple historical message data.

[0037] In the above embodiments of the present invention, during the process of generating a preset message pattern set, multiple historical message data that have been sent can be analyzed to determine the historical message body of each historical message data. By performing frequent pattern mining on multiple historical message data, the historical message characteristics of each historical message data can be determined. Then, based on the combination of the historical message body and the historical message characteristics, a preset message sending pattern can be determined. Furthermore, based on the preset message sending patterns of multiple historical message data, a preset message pattern set can be determined, thereby realizing the generation of the preset message pattern set.

[0038] As an optional embodiment, determining the historical message ontology for each historical message data includes: preprocessing the historical message data using text analysis techniques to generate a first message dataset, wherein the first message dataset includes multiple first message texts; clustering the first message texts in the first message dataset using a text clustering algorithm to generate a second message dataset, wherein the second message dataset includes multiple second message texts, the second message texts being determined based on the clustering results of at least one first message text; performing feature analysis on the second message dataset to determine a preset set of feature words, wherein the preset set of feature words includes multiple preset feature words, the preset feature words being determined based on the second message texts; and defining the historical message ontology for each historical message data based on the preset feature words.

[0039] In the above embodiments of the present invention, in the process of determining the historical message ontology based on historical message data, text analysis technology can be used to preprocess the historical message data to generate a first message dataset; then, a text clustering algorithm is used to cluster the first message texts in the first message dataset to generate a second message dataset; then, feature analysis is performed on the second message dataset to determine a preset set of feature words, and the preset feature words in the preset set of feature words are used to define the historical message ontology of each historical message data, thereby realizing the determination of the historical message ontology.

[0040] As an optional embodiment, determining the historical message ontology for each historical message data includes: decomposing the historical message data into at least one historical feature word to obtain a set of historical feature words; vectorizing the historical feature words to determine the historical word vector for each historical feature word; weighting the multiple historical word vectors in the set of historical feature words to determine the historical feature vector of the historical message data; determining the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent the features of a preset feature type; determining the preset feature type with the highest feature similarity as the target feature type of the historical message data object; and determining the historical message ontology corresponding to the target feature type in a preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologs.

[0041] In the above embodiments of the present invention, by decomposing and analyzing the historical message data, the historical feature vector of each historical message data can be determined. Then, based on the historical feature vector of each historical message data, feature classification can be performed to determine the target feature type corresponding to the historical message data and the historical message ontology corresponding to the historical message data, thus achieving the purpose of determining the historical message ontology based on the target feature type corresponding to the historical message data.

[0042] As an optional embodiment, determining the target message sending pattern that matches the target key business information in the preset message pattern set includes: identifying the target message type of the target key business information; and determining the target message sending pattern corresponding to the target message type in the preset message pattern set.

[0043] In the above embodiments of the present invention, the target message sending mode is determined according to the target message type, and each target message type uses a specific target message sending mode to send target message data.

[0044] As an optional embodiment, determining the target message sending pattern that matches the target key business information in a preset message pattern set includes: obtaining historical key business information used to determine the preset message sending pattern in the preset message pattern set, wherein the preset message sending pattern is determined based on historical message data carrying historical key business information; calculating the information similarity between the target key business information and each piece of historical key business information; determining the historical key business information with the highest information similarity as the preset key business information; and determining the preset message sending pattern determined based on the preset key business information as the target message sending pattern.

[0045] In the above embodiments of the present invention, during the process of determining the target message sending mode that matches the target key business information, the historical key business information with the highest similarity can be determined as the preset key business information based on the information similarity between the target key business information and historical key business information. The preset message sending mode determined based on the preset key business information is then compared with the target message sending mode that matches the target key business information, thereby achieving the purpose of determining the target message sending mode based on similarity.

[0046] As an optional embodiment, the preset message template library includes: a preset message ontology library and a preset ontology template library. Querying the target semantic template corresponding to a target message sending mode in the preset message template library includes: querying the target message ontology corresponding to the target message sending mode in the preset message ontology library, wherein the preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode, and the preset message sending mode is pre-generated based on the preset message ontology; and querying the target semantic template corresponding to the target message ontology in the preset ontology template library, wherein the preset ontology template library records multiple preset semantic templates corresponding to preset message ontologs, and the preset semantic templates are pre-generated based on the preset message ontology.

[0047] In the above embodiments of the present invention, the preset message template library includes a preset message ontology library and a preset ontology template library. Through the preset message ontology library and the preset ontology template library, it is possible to query the target semantic template according to the target message sending mode.

[0048] The present invention also provides an optional embodiment that provides a multi-terminal messaging middleware system based on text mining and semantic templates.

[0049] The technical solution provided by this invention achieves automatic processing of heterogeneous messages in a multi-terminal environment by defining semantic templates for business messages and using text mining-based algorithms. Based on this, a message middleware supporting multiple terminals is designed to achieve more efficient and flexible message processing.

[0050] The purpose of this invention is to collect message data generated by various business operations, process the message text based on semantic analysis and text mining techniques, and achieve automatic processing of heterogeneous messages in a multi-terminal environment by defining semantic templates for business messages and algorithms based on natural language understanding. Based on this, a message middleware supporting multiple terminals is designed to achieve more efficient and flexible message processing. Therefore, this invention proposes a multi-terminal message middleware system based on text mining and semantic templates. This system includes multiple functional modules, which can improve the efficiency and flexibility of message processing.

[0051] Figure 2 This is a schematic diagram of a mainstream technical process for data transmission according to an embodiment of the present invention, such as... Figure 2 As shown, the steps are as follows:

[0052] S21, Message data reception.

[0053] S22, classify the received message data.

[0054] S23, Message Processing.

[0055] S24, message generation.

[0056] S25, message sent.

[0057] Figure 3 This is a schematic diagram of a multi-terminal message middleware system based on text mining and semantic templates according to an embodiment of the present invention, such as... Figure 3 As shown, different types of data are received from different messaging channels; a semantic similarity model is established through semantic analysis and text mining; information is automatically organized according to the model; and mobile and PC clients view and process the information.

[0058] The message middleware based on this invention, which supports both mobile and PC clients, can effectively process business messages in the following ways:

[0059] Step 1: Construct a message domain ontology (such as a preset message ontology or a historical message ontology) based on existing message data and domain knowledge.

[0060] Step 2: The message middleware collects message data generated by various business processes (such as historical message data).

[0061] Step 3: Implement automatic message classification based on text mining methods, including two levels of classification: one is based on the message topic, and the other is based on the message type or source, including SMS messages, Zhejiang Government DingTalk work notifications and DingTalk messages, system messages, email messages, etc.

[0062] Step 4: Perform subsequent operations on the messages based on the classification results. Users can query and process messages in the message center.

[0063] Step 5: Record the historical message data generated by users and perform frequent pattern mining on the data.

[0064] Step 6: Generate a semantic template based on the message ontology, and automatically generate messages by filling in the template.

[0065] Step 7: Send messages automatically.

[0066] Figure 4 This is a schematic diagram of a message middleware execution flow according to an embodiment of the present invention, such as... Figure 4 As shown, the steps are as follows:

[0067] S41, Message data reception.

[0068] S42, Message Text Processing.

[0069] S43, messages are automatically categorized.

[0070] S44, Message request initiated.

[0071] S45, based on steps S44 and S48, perform automatic message filling.

[0072] S46, Message sent.

[0073] S47, Message ontology modeling.

[0074] S48, Semantic Template.

[0075] As an optional embodiment, the specific implementation process of the above message middleware execution flow is as follows:

[0076] 1. Collect and organize message data generated during business operations, and preprocess the message text using text analysis techniques to obtain a message text dataset (such as the first message dataset) S = {s1, s2, ..., s}. n}

[0077] 2. Use a text clustering algorithm to cluster the message texts (such as the first message text) in S, obtaining a cluster set C = {C1, C2, ..., C...} k For any of the clusters C... i Merge the text in the cluster into a new text object s i ′.

[0078] 3. Transform the message text dataset (e.g., the first message dataset) S into a new message text dataset (e.g., the second message dataset) S′={s′1,s′2,…,s′ k}, and any text element s within it. i ′ are all merged and expanded message texts (such as the second message text).

[0079] 4. Perform high-frequency word analysis, word co-occurrence analysis, and PMI feature filtering on the elements of S′ to obtain the final feature word set (such as the preset feature word set) D.

[0080] 5. Based on in-depth analysis of business domain information, define a message ontology O for business message processing, using the feature words in D (such as a pre-defined feature word set) as the core. msg .

[0081] Optionally, the message body O msg (For example, the core concept of a historical message ontology is represented by a quintuple: <T i S i C i P i M i >; where T i S represents the message terminal. i Indicates the source of the information, C i Indicates the message type, P i M indicates message-related services. i It represents the message topic and generates the conceptual hierarchy of core elements, as well as the sum and formal expression of elements with nodes and edges as carriers and the semantic relationships between elements.

[0082] 6. Automatic classification of business messages based on text mining methods, the specific process is as follows:

[0083] (1) For any message text (such as historical message data) s i This is decomposed into a set of feature words (such as a set of historical feature words). i ={w1, w2, ..., w m}, where w i This refers to a characteristic word in the text.

[0084] (2) For s i The feature words (such as historical feature words) are vectorized, vec(w i ) represents the word vector (such as historical word vector) corresponding to the feature word.

[0085] (3) s i The word vector of each feature word (such as historical word vectors) is weighted using the corresponding TF-IDF value, then s i The corresponding vector (such as historical word vectors) is represented as:

[0086] (4) Vector-based representation of message text (e.g., historical word vectors) s i The message is categorized and automatically assigned to the category with the highest similarity. Message categorization can be based on the message ontology O. msg Pre-generated.

[0087] 8. For messages that have been automatically categorized, further processing can be performed by the middleware's message processing module.

[0088] 9. Record user-sent message data (such as historical message data), and mine frequent patterns in user historical messages (such as historical message data) based on message ontology O. msg The concepts and relationships (such as the historical message ontology) are used to obtain a series of user message sending patterns (such as a set of preset message patterns) PTN = {ptn1, ptn2, ... ptn} n}; where, message sending mode (such as preset message sending mode) ptn i It can be represented as a six-tuple <d i f i , t i s i c i p i >, d i Indicates the message sending period, f i t represents the characteristics of message content. i Indicates the message terminal, s i Indicates the source of the information, c i Indicates the message type, p i This indicates message-related business.

[0089] 10. For user message sending requests, implement automatic message generation and sending based on semantic templates. The specific process is as follows:

[0090] (1) Assume that the user's message sending request RQ = <d, info>, where d represents time, info represents the target key business information of the message, and info can be represented as a set of keywords {w1, w2, ..., w...} m}

[0091] (2) Match the info part of the request RQ with the user's pattern set PTN, and find the preset message sending pattern PTN with the highest similarity. i Based on message ontology O msg (e.g., historical message ontology) automatically generates semantic templates for messages (e.g., target semantic templates).

[0092] (3) Fill the target key business information in the RQ into the semantic template (such as the target semantic template), automatically generate the final business message that the user needs to send (such as the target message data), and send it automatically through the message middleware.

[0093] Figure 5 This is a schematic diagram of a message middleware system according to an embodiment of the present invention, such as... Figure 5 As shown, Figure 4 The message middleware execution flow shown is further encapsulated into a runnable message middleware system, while providing APIs for third-party applications to call. The system mainly includes: a message center personalization module, a message receiving module, a message classification module, a message processing module, a message customization module, and a message sending module.

[0094] Optionally, the message center personalization module is used to set message channel creation conditions, build multiple message channels, and implement message interface services; obtain user personalized needs, and set message receiving channel configurations according to user personalized needs.

[0095] Optionally, the message receiving module is used to obtain user information and channel basic data, and to receive tasks and messages from multiple message channels; the received tasks and messages are respectively stored in the task queue and message queue.

[0096] Optionally, the message classification module is used to classify tasks into pending tasks and completed tasks; and messages into notifications, announcements, etc.

[0097] Optionally, the message processing module is used to view or process tasks to be processed, view or cancel completed tasks, and automatically pop up notifications and announcements according to their importance.

[0098] Optionally, a message customization module is used to first use text analysis to mine messages that users are interested in, and then store messages that are relevant to the user's interests in the message middleware for easy sending according to the user's needs.

[0099] Optionally, the message sending module is designed to be able to send multiple messages to multiple clients simultaneously, encrypt important information, and improve information security.

[0100] The technical solution provided by this invention improves upon traditional message middleware, forming a highly efficient information system. First, text mining technology is used to analyze the business information stored in the information center. Then, semantic similarity is calculated to automatically classify messages, grouping similar information into one category, facilitating viewing and processing of relevant information on mobile and PC clients. Simultaneously, the message center provides rich API interfaces for businesses to send, retrieve, and process different types of message data, and offers message channels, message types, and message templates, enabling message configuration and improving message usage flexibility.

[0101] According to an embodiment of the present invention, a data transmission device embodiment is also provided. It should be noted that the data transmission device can be used to execute the data transmission method in the embodiment of the present invention, and the data transmission method in the embodiment of the present invention can be executed in the data transmission device.

[0102] Figure 6 This is a schematic diagram of a data transmission device according to an embodiment of the present invention, such as... Figure 6 As shown, the device may include: an acquisition module 62, used to acquire a message sending request for target message data, wherein the sending request carries target key business information indicated by the target message data; a determination module 64, used to determine a target message sending pattern matching the target key business information in a preset message pattern set, wherein the preset message pattern set records the correspondence between multiple preset message sending patterns and preset message types, and the target message sending pattern is the preset message sending pattern corresponding to the target message type to which the target key business information belongs; a query module 66, used to query a target semantic template corresponding to the target message sending pattern in a preset message template library, wherein the preset message template library records preset semantic templates corresponding to multiple preset message sending patterns; and a generation module 68, used to fill the target key business information into the target semantic template to generate target message data.

[0103] It should be noted that the acquisition module 62 in this embodiment can be used to execute step S102 in this application embodiment, the determination module 64 in this embodiment can be used to execute step S104 in this application embodiment, the query module 66 in this embodiment can be used to execute step S106 in this application embodiment, and the generation module 68 in this embodiment can be used to execute step S108 in this application embodiment. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments.

[0104] In this embodiment of the invention, a message sending request for target message data is obtained, wherein the sending request carries target key business information indicated by the target message data; a target message sending mode matching the target key business information is determined from a preset message mode set, wherein the preset message mode set records the correspondence between multiple preset message sending modes and preset message types, and the target message sending mode is the preset message sending mode corresponding to the target message type to which the target key business information belongs; a target semantic template corresponding to the target message sending mode is queried from a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to preset message sending modes; the target key business information is filled into the target semantic template to generate target message data. By selecting the target semantic template of the target key business information and filling the target key business information into the target semantic template, the target message data of the target key business information can be automatically generated and sent, realizing the technical effect of automatically generating and sending target message data based on semantics, thereby solving the technical problem that the prior art cannot achieve automatic data sending based on semantics.

[0105] As an optional embodiment, it further includes: an acquisition submodule, which acquires multiple historical message data that have been sent before determining the target message sending pattern matching the key business information in the preset message pattern set; a first determination submodule, which is used to determine the historical message body of each historical message data, wherein the historical message body is used to represent the message terminal, message source, message type, message-related business, and message topic of the corresponding historical message data; a second determination submodule, which is used to perform frequent pattern mining on the multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are used to represent at least the message sending period and message content features of the corresponding historical message data; a third determination submodule, which is used to determine the preset message sending pattern of the corresponding historical message data based on the historical message body and historical message features, wherein the preset message sending pattern is used to represent the message sending period, message content features, message terminal, message source, message type, and message-related business of the corresponding historical message data; and a fourth determination submodule, which is used to determine the preset message pattern set based on the preset message sending patterns corresponding to the multiple historical message data.

[0106] As an optional embodiment, the first determining submodule includes: a preprocessing submodule, used to preprocess historical message data using text analysis technology to generate a first message dataset, wherein the first message dataset includes: multiple first message texts; a clustering submodule, used to cluster the first message texts in the first message dataset using a text clustering algorithm to generate a second message dataset, wherein the second message dataset includes: multiple second message texts, the second message texts being determined based on the clustering results of at least one first message text; an analysis submodule, used to perform feature analysis on the second message dataset to determine a preset feature word set, wherein the preset feature word set includes multiple preset feature words, the preset feature words being determined based on the second message texts; and a definition submodule, used to define the historical message ontology of each historical message data, with the preset feature words as the core.

[0107] As an optional embodiment, the first determining submodule includes: a decomposition submodule, used to decompose historical message data into at least one historical feature word to obtain a set of historical feature words; a fifth determining submodule, used to vectorize the historical feature words to determine the historical word vector of each historical feature word; a sixth determining submodule, used to perform weighted calculation on multiple historical word vectors in the set of historical feature words to determine the historical feature vector of the historical message data; a seventh determining submodule, used to determine the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent the feature of a preset feature type; an eighth determining submodule, used to determine the preset feature type with the highest feature similarity as the target feature type of the historical message data object; and a ninth determining submodule, used to determine the historical message ontology corresponding to the target feature type in a preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologies.

[0108] As an optional embodiment, the determining module includes: an identification unit, used to identify the target message type of the target key business information; and a first determining unit, used to determine the target message sending mode corresponding to the target message type in a preset message mode set.

[0109] As an optional embodiment, the determining module includes: an acquisition unit, configured to acquire historical key business information used to determine a preset message sending mode from a preset message mode set, wherein the preset message sending mode is determined based on historical message data carrying historical key business information; a calculation unit, configured to calculate the information similarity between the target key business information and each piece of historical key business information; a second determining unit, configured to determine the historical key business information with the highest information similarity as the preset key business information; and a third determining unit, configured to determine the preset message sending mode determined based on the preset key business information as the target message sending mode.

[0110] As an optional embodiment, the preset message template library includes: a preset message ontology library and a preset ontology template library. The query module includes: a first query unit, used to query the target message ontology corresponding to the target message sending mode in the preset message ontology library, wherein the preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode, and the preset message sending mode is pre-generated based on the preset message ontology; and a second query unit, used to query the target semantic template corresponding to the target message ontology in the preset ontology template library, wherein the preset ontology template library records multiple preset semantic templates corresponding to preset message ontology, and the preset semantic templates are pre-generated based on the preset message ontology.

[0111] Embodiments of the present invention can provide a computer terminal, which can be any computer terminal device in a group of computer terminals. Optionally, in this embodiment, the computer terminal can also be replaced by a mobile terminal or other terminal device.

[0112] Optionally, in this embodiment, the computer terminal may be located in at least one of a plurality of network devices in a computer network.

[0113] In this embodiment, the computer terminal described above can execute the program code for the following steps in the data transmission method: obtaining a message transmission request for target message data, wherein the transmission request carries target key business information indicated by the target message data; determining a target message transmission pattern matching the target key business information in a preset message pattern set, wherein the preset message pattern set records the correspondence between multiple preset message transmission patterns and preset message types, and the target message transmission pattern is the preset message transmission pattern corresponding to the target message type to which the target key business information belongs; querying a target semantic template corresponding to the target message transmission pattern in a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to preset message transmission patterns; filling the target key business information into the target semantic template to generate target message data.

[0114] Optionally, Figure 7 This is a structural block diagram of a computer terminal according to an embodiment of the present invention. Figure 7 As shown, the computer terminal 70 may include: one or more (only one is shown in the figure) processors 72 and memory 74.

[0115] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the data transmission method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned data transmission method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal 70 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The processor can invoke information and application programs stored in the memory through the transmission device to perform the following steps: obtaining a message sending request for target message data, wherein the sending request carries target key business information indicated by the target message data; determining a target message sending pattern that matches the target key business information in a preset message pattern set, wherein the preset message pattern set records the correspondence between multiple preset message sending patterns and preset message types, and the target message sending pattern is the preset message sending pattern corresponding to the target message type to which the target key business information belongs; querying a target semantic template corresponding to the target message sending pattern in a preset message template library, wherein the preset message template library records preset semantic templates corresponding to multiple preset message sending patterns; and filling the target key business information into the target semantic template to generate target message data.

[0117] Optionally, the processor may also execute program code for the following steps: before determining the target message sending pattern matching the key business information from the preset message pattern set, acquire multiple historical message data that have been sent; determine the historical message body of each historical message data, wherein the historical message body is used to represent the message terminal, message source, message type, message-related business, and message topic of the corresponding historical message data; perform frequent pattern mining on the multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are used to represent at least the message sending period and message content features of the corresponding historical message data; determine the preset message sending pattern of the corresponding historical message data based on the historical message body and historical message features, wherein the preset message sending pattern is used to represent the message sending period, message content features, message terminal, message source, message type, and message-related business of the corresponding historical message data; and determine the preset message pattern set based on the preset message sending patterns corresponding to the multiple historical message data.

[0118] Optionally, the processor may also execute program code for the following steps: preprocessing historical message data using text analysis techniques to generate a first message dataset, wherein the first message dataset includes multiple first message texts; clustering the first message texts in the first message dataset using a text clustering algorithm to generate a second message dataset, wherein the second message dataset includes multiple second message texts, the second message texts being determined based on the clustering results of at least one first message text; performing feature analysis on the second message dataset to determine a preset set of feature words, wherein the preset set of feature words includes multiple preset feature words, the preset feature words being determined based on the second message texts; and defining the historical message ontology for each historical message data based on the preset feature words.

[0119] Optionally, the processor may also execute program code for the following steps: decomposing historical message data into at least one historical feature word to obtain a set of historical feature words; vectorizing the historical feature words to determine the historical word vector for each historical feature word; weighting the multiple historical word vectors in the set of historical feature words to determine the historical feature vector of the historical message data; determining the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent the features of a preset feature type; determining the preset feature type with the highest feature similarity as the target feature type of the historical message data object; and determining the historical message ontology corresponding to the target feature type in the preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologs.

[0120] Optionally, the processor may also execute program code that performs the following steps: identifying the target message type of the target key business information; and determining the target message sending mode corresponding to the target message type in a preset message mode set.

[0121] Optionally, the processor may also execute program code for the following steps: obtaining historical key business information from a set of preset message patterns used to determine a preset message sending pattern, wherein the preset message sending pattern is determined based on historical message data carrying historical key business information; calculating the information similarity between the target key business information and each piece of historical key business information; determining the historical key business information with the highest information similarity as the preset key business information; and determining the preset message sending pattern determined based on the preset key business information as the target message sending pattern.

[0122] Optionally, the preset message template library includes a preset message ontology library and a preset ontology template library. The processor can also execute program code that performs the following steps: querying the target message ontology corresponding to the target message sending mode in the preset message ontology library, wherein the preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode, and the preset message sending mode is pre-generated based on the preset message ontology; querying the target semantic template corresponding to the target message ontology in the preset ontology template library, wherein the preset ontology template library records multiple preset semantic templates corresponding to preset message ontology, and the preset semantic templates are pre-generated based on the preset message ontology.

[0123] This invention provides a data transmission scheme. It involves obtaining a message transmission request for target message data, wherein the transmission request carries target key business information indicated by the target message data; determining a target message transmission pattern matching the target key business information from a preset message pattern set, wherein the preset message pattern set records the correspondence between multiple preset message transmission patterns and preset message types, and the target message transmission pattern is the preset message transmission pattern corresponding to the target message type to which the target key business information belongs; querying a target semantic template corresponding to the target message transmission pattern from a preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to preset message transmission patterns; filling the target key business information into the target semantic template to generate target message data. By selecting the target semantic template for the target key business information and filling the target key business information into the target semantic template, the target message data for the target key business information can be automatically generated and transmitted, achieving the technical effect of automatically generating and transmitting target message data based on semantics, thereby solving the technical problem that existing technologies cannot achieve automatic data transmission based on semantics.

[0124] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. The computer terminal can also be a smartphone (such as an Android phone, an iOS phone, etc.), a tablet computer, a mobile internet device (MID), a PAD, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, the computer terminal 70 may also include components that are more advanced than those described above. Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0125] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0126] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the data transmission method provided in the above embodiments.

[0127] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0128] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a message sending request for target message data, wherein the sending request carries target key business information indicated by the target message data; determining a target message sending pattern matching the target key business information in a preset message pattern set, wherein the preset message pattern set records the correspondence between multiple preset message sending patterns and preset message types, and the target message sending pattern is the preset message sending pattern corresponding to the target message type to which the target key business information belongs; querying a target semantic template corresponding to the target message sending pattern in a preset message template library, wherein the preset message template library records preset semantic templates corresponding to multiple preset message sending patterns; filling the target key business information into the target semantic template to generate target message data.

[0129] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: before determining the target message sending pattern matching the key business information in the preset message pattern set, acquiring multiple historical message data that have been sent; determining the historical message body of each historical message data, wherein the historical message body is used to represent the message terminal, message source, message type, message-related business, and message topic of the corresponding historical message data; performing frequent pattern mining on the multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are at least used to represent the message sending period and message content features of the corresponding historical message data; determining the preset message sending pattern of the corresponding historical message data based on the historical message body and historical message features, wherein the preset message sending pattern is used to represent the message sending period, message content features, message terminal, message source, message type, and message-related business of the corresponding historical message data; and determining the preset message pattern set based on the preset message sending patterns corresponding to the multiple historical message data.

[0130] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: preprocessing historical message data using text analysis techniques to generate a first message dataset, wherein the first message dataset includes: multiple first message texts; clustering the first message texts in the first message dataset using a text clustering algorithm to generate a second message dataset, wherein the second message dataset includes: multiple second message texts, the second message texts being determined based on the clustering results of at least one first message text; performing feature analysis on the second message dataset to determine a preset feature word set, wherein the preset feature word set includes multiple preset feature words, the preset feature words being determined based on the second message texts; and defining the historical message ontology for each historical message data based on the preset feature words.

[0131] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: decomposing historical message data into at least one historical feature word to obtain a set of historical feature words; vectorizing the historical feature words to determine the historical word vector of each historical feature word; weighting the multiple historical word vectors in the set of historical feature words to determine the historical feature vector of the historical message data; determining the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent the feature of a preset feature type; determining the preset feature type with the highest feature similarity as the target feature type of the historical message data object; determining the historical message ontology corresponding to the target feature type in a preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologs.

[0132] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: identifying the target message type of the target key business information; and determining the target message sending mode corresponding to the target message type in a preset message mode set.

[0133] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining historical key business information in a preset message pattern set for determining a preset message sending pattern, wherein the preset message sending pattern is determined based on historical message data carrying historical key business information; calculating the information similarity between the target key business information and each historical key business information; determining the historical key business information with the highest information similarity as the preset key business information; and determining the preset message sending pattern determined based on the preset key business information as the target message sending pattern.

[0134] Optionally, in this embodiment, the preset message template library includes: a preset message ontology library and a preset ontology template library. The non-volatile storage medium is configured to store program code for performing the following steps: querying the target message ontology corresponding to the target message sending mode in the preset message ontology library, wherein the preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode, and the preset message sending mode is pre-generated based on the preset message ontology; querying the target semantic template corresponding to the target message ontology in the preset ontology template library, wherein the preset ontology template library records multiple preset semantic templates corresponding to preset message ontology, and the preset semantic templates are pre-generated based on the preset message ontology.

[0135] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0136] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0141] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data transmission method, characterized in that, include: A message sending request to obtain target message data, wherein the sending request carries target key business information indicated by the target message data; Acquire data from multiple historical messages that have been sent, wherein the historical message data is collected through a message middleware; The historical message ontology for each historical message data is determined. This involves preprocessing the historical message data using text analysis techniques to generate a first message dataset, which includes multiple first message texts. A text clustering algorithm is then used to cluster the first message texts in the first message dataset to generate a second message dataset, which includes multiple second message texts determined based on the clustering results of at least one first message text. The second message texts are obtained by merging first message texts clustered into a single cluster. Feature analysis is performed on the second message dataset to determine a preset set of feature words, which includes multiple preset feature words determined based on the second message texts. Using the preset feature words as the core, a historical message ontology for each historical message data is defined. This historical message ontology represents the corresponding historical message data's message terminal, message source, message type, message-related services, and message topic. The historical message ontology also determines a preset set of message patterns. A target message sending pattern matching the target key business information is determined from the preset message pattern set. The preset message pattern set records the correspondence between multiple preset message sending patterns and preset message types. The target message sending pattern is the preset message sending pattern corresponding to the target message type to which the target key business information belongs. Query the target semantic template corresponding to the target message sending mode in the preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to the preset message sending mode; The target key business information is filled into the target semantic template to generate the target message data.

2. The method according to claim 1, characterized in that, Before determining the target message sending pattern that matches the key business information from the preset message pattern set, the method further includes: Frequent pattern mining is performed on multiple historical message data to determine the historical message features of each historical message data, wherein the historical message features are used to represent at least the message sending time period and message content features of the corresponding historical message data; Based on the historical message ontology and historical message characteristics, a preset message sending mode is determined for the corresponding historical message data. The preset message sending mode is used to represent the message sending period, message content characteristics, message terminal, message source, message type, and message-related services of the corresponding historical message data. A preset message pattern set is determined based on the preset message sending patterns corresponding to multiple historical message data.

3. The method according to claim 2, characterized in that, The historical message ontology for each of the historical message data includes: The historical message data is decomposed into at least one historical feature word to obtain a set of historical feature words; The historical feature words are vectorized to determine the historical word vector for each historical feature word; The historical feature vector of the historical message data is determined by weighting and calculating multiple historical word vectors in the historical feature word set. Determine the feature similarity between the historical feature vector and each preset message feature vector, wherein the preset message feature vector is used to represent features of a preset feature type; The preset feature type with the highest feature similarity is determined as the target feature type of the historical message data object; The historical message ontology corresponding to the target feature type is determined in a preset feature ontology library, wherein the preset feature ontology library is used to record the correspondence between multiple preset feature types and preset message ontologs.

4. The method according to claim 1, characterized in that, Determining the target message sending pattern that matches the target key business information from the preset message pattern set includes: Identify the target message type of the target key business information; Determine the target message sending mode corresponding to the target message type from the preset message pattern set.

5. The method according to claim 1, characterized in that, Determining the target message sending pattern that matches the target key business information from the preset message pattern set includes: Obtain historical key business information from a set of preset message patterns used to determine the preset message sending pattern, wherein the preset message sending pattern is determined based on historical message data carrying the historical key business information; Calculate the information similarity between the target key business information and each of the historical key business information; The historical key business information with the highest similarity to the information is identified as the preset key business information; The preset message sending mode determined based on the preset key business information is determined as the target message sending mode.

6. The method according to claim 1, characterized in that, The preset message template library includes: a preset message ontology library and a preset ontology template library. Querying the target semantic template corresponding to the target message sending pattern in the preset message template library includes: The target message ontology corresponding to the target message sending mode is queried in the preset message ontology library. The preset message ontology library pre-records multiple preset message sending modes and a preset message ontology corresponding to each preset message sending mode. The preset message sending mode is generated in advance based on the preset message ontology. The target semantic template corresponding to the target message ontology is queried in the preset ontology template library. The preset ontology template library records multiple preset semantic templates corresponding to the preset message ontology. The preset semantic templates are generated in advance based on the preset message ontology.

7. A data transmission device, characterized in that, include: The acquisition module is used to acquire a message sending request for target message data, wherein the sending request carries target key business information indicated by the target message data; Acquire data from multiple historical messages that have been sent, wherein the historical message data is collected through a message middleware; The historical message ontology for each historical message data is determined. This involves preprocessing the historical message data using text analysis techniques to generate a first message dataset, which includes multiple first message texts. A text clustering algorithm is then used to cluster the first message texts in the first message dataset to generate a second message dataset, which includes multiple second message texts determined based on the clustering results of at least one first message text. The second message texts are obtained by merging first message texts clustered into a single cluster. Feature analysis is performed on the second message dataset to determine a preset set of feature words, which includes multiple preset feature words determined based on the second message texts. Using the preset feature words as the core, a historical message ontology for each historical message data is defined. This historical message ontology represents the corresponding historical message data's message terminal, message source, message type, message-related services, and message topic. The historical message ontology also determines a preset set of message patterns. The determining module is used to determine a target message sending mode that matches the target key business information from the preset message mode set, wherein the preset message mode set records the correspondence between multiple preset message sending modes and preset message types, and the target message sending mode is the preset message sending mode corresponding to the target message type to which the target key business information belongs; The query module is used to query the target semantic template corresponding to the target message sending mode in the preset message template library, wherein the preset message template library records multiple preset semantic templates corresponding to the preset message sending mode; The generation module is used to fill the target key business information into the target semantic template and generate the target message data.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores a program, wherein when the program is executed, it controls the device containing the non-volatile storage medium to perform the data transmission method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when executed, performs the data transmission method according to any one of claims 1 to 6.

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