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CN-121980168-A - Business activity information processing method, device, electronic equipment and medium

CN121980168ACN 121980168 ACN121980168 ACN 121980168ACN-121980168-A

Abstract

The application relates to the technical fields of data processing, artificial intelligence and the like, and discloses a business activity information processing method, a business activity information processing device, electronic equipment and a medium. The business activity information processing method comprises the steps of receiving multi-mode data fed back for a business activity site, extracting semantic features of the multi-mode data to obtain semantic feature data of different modes, carrying out event aggregation processing on the semantic feature data to obtain business activity events, and generating business activity feedback results based on the business activity events. By the method, valuable business activity information can be extracted from complex and huge information flows, and the processing efficiency of the business activity information is improved.

Inventors

  • YANG HAI

Assignees

  • 北京千丁智能技术有限公司

Dates

Publication Date
20260505
Application Date
20251224

Claims (10)

  1. 1. A business activity information processing method, characterized in that the method comprises: receiving multi-mode data fed back for a business activity site; extracting semantic features of the multi-mode data to obtain semantic feature data of different modes; performing event aggregation processing on the semantic feature data to obtain a business activity event; and generating a business activity feedback result based on the business activity event.
  2. 2. The method according to claim 1, wherein the performing event aggregation on the semantic feature data to obtain a business activity event includes: And aggregating a plurality of first information points in the semantic feature data to obtain the business activity event, wherein the time interval of any two information points in the plurality of first information points is smaller than a preset interval, and the topic similarity of the plurality of first information points is larger than the preset similarity.
  3. 3. The method according to claim 1, wherein the performing event aggregation on the semantic feature data to obtain a business activity event includes: And in the event aggregation process of the semantic feature data, carrying out mutual authentication and information enhancement processing on the semantic feature data of different modes to obtain the business activity event.
  4. 4. The method as recited in claim 1, further comprising: performing conflict event detection on a plurality of second information points in the semantic feature data to obtain a conflict detection result; And marking the plurality of second information points in the case that the conflict detection result indicates that the conflict exists among the plurality of second information points.
  5. 5. The method of claim 1, wherein the business activity event comprises a plurality of business activity events, and wherein generating a business activity feedback result based on the business activity events comprises: And sequencing the business activity events according to the time information to generate the business activity feedback result.
  6. 6. The method as recited in claim 4, further comprising: Generating at least one of activity traffic data, activity problem hotspot data, user emotion data during activity, and/or based on the business activity feedback result and/or the conflict detection result And generating activity early warning information under the condition that the user emotion data meets the preset condition.
  7. 7. The method according to any one of claims 1-6, wherein the multi-modal data includes at least one of original text data, original speech data, and original visual data, and the performing semantic feature extraction on the multi-modal data to obtain semantic feature data of different modalities includes: Converting the original voice data into text information with a time stamp, and carrying out natural language understanding processing on the text information and the original text data to obtain first semantic feature data, wherein the first semantic feature data comprises at least one of key objects, core events, user emotion tendencies and activity intention classifications; and carrying out visual recognition on the original visual data to obtain second semantic feature data, wherein the second semantic feature data comprises at least one of key entities, active scenes and user moods.
  8. 8. A business activity information processing apparatus, characterized in that the apparatus comprises: the receiving module is used for receiving multi-mode data fed back for the service activity site; The extraction module is used for extracting semantic features of the multi-mode data to obtain semantic feature data of different modes; the aggregation module is used for carrying out event aggregation processing on the semantic feature data to obtain a business activity event; And the generating module is used for generating a business activity feedback result based on the business activity event.
  9. 9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-7 when the computer program is executed.
  10. 10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-7.

Description

Business activity information processing method, device, electronic equipment and medium Technical Field The present application relates to the technical field of data processing, artificial intelligence, and the like, and in particular, to a method, an apparatus, an electronic device, and a medium for processing business activity information. Background The success of large business activities such as celebration, music festival, product release meeting, etc. in a market year depends not only on planning but also on real-time management and quick response capability of on-site execution. The manager needs to timely and comprehensively master information such as on-site passenger flow dynamics, material consumption, customer emotion, emergency and the like so as to make accurate resource allocation and emergency treatment decisions. If the key information of the scene cannot be timely collected and accurately processed, not only a great deal of precious data assets are wasted, but also errors are easy to occur in the strenuous activity scene. Disclosure of Invention Embodiments of the present application aim to solve, at least to some extent, one of the technical problems in the related art. For this reason, the embodiment of the application provides a business activity information processing method, a business activity information processing device, electronic equipment and a medium. The embodiment of the application provides a business activity information processing method which comprises the steps of receiving multi-mode data fed back for a business activity site, extracting semantic features of the multi-mode data to obtain semantic feature data of different modes, carrying out event aggregation processing on the semantic feature data to obtain business activity events, and generating a business activity feedback result based on the business activity events. In some embodiments, the event aggregation processing is performed on the semantic feature data to obtain a business activity event, which includes aggregating a plurality of first information points in the semantic feature data to obtain the business activity event, wherein a time interval of any two information points in the plurality of first information points is smaller than a preset interval, and a subject similarity of the plurality of first information points is larger than a preset similarity. In some embodiments, the event aggregation processing is performed on the semantic feature data to obtain a business activity event, which includes performing mutual authentication and information enhancement processing on the semantic feature data of different modes in the event aggregation process of the semantic feature data to obtain the business activity event. In some embodiments, the method further comprises performing conflict event detection on the plurality of second information points in the semantic feature data to obtain a conflict detection result, and marking the plurality of second information points if the conflict detection result indicates that a conflict exists among the plurality of second information points. In some embodiments, the business activity event comprises a plurality of business activity events, and generating a business activity feedback result based on the business activity events comprises ordering the plurality of business activity events according to time information to generate the business activity feedback result. In some embodiments, the method further comprises generating at least one of activity traffic data, activity problem hot spot data, user emotion data during an activity, and/or generating activity early warning information if the user emotion data meets a preset condition based on the business activity feedback result and/or the conflict detection result. In some embodiments, the multi-modal data comprises at least one of original text data, original voice data and original visual data, semantic feature extraction is carried out on the multi-modal data to obtain semantic feature data of different modes, the method comprises the steps of converting the original voice data into text information with a time stamp, carrying out natural language understanding processing on the text information and the original text data to obtain first semantic feature data, wherein the first semantic feature data comprises at least one of key objects, core events, user emotion tendencies and activity intention classifications, and visual recognition is carried out on the original visual data to obtain second semantic feature data, and the second semantic feature data comprises at least one of key entities, activity scenes and user emotion. The embodiment of the application provides a business activity information processing device which comprises a receiving module, an extracting module, an aggregation module and a generating module, wherein the receiving module is used for receiving multi-mode data fed back for a busine