Audio and video acquisition equipment data management system based on artificial intelligence

Abstract

The invention relates to the technical field of data processing, in particular to a video and audio acquisition equipment data management system based on artificial intelligence, which comprises a receiving module used for receiving video and audio data acquired by video and audio acquisition equipment and storing the received video and audio data; the preprocessing module is used for acquiring the audio and video data stored in the receiving module and extracting audio data from the acquired audio and video data; according to the method and the device, the audio data in the audio-video data is extracted to serve as a reference of the tracking target in the audio-video data, the characteristic frequency band is captured in the audio data, and the database is constructed, so that the query target is tracked by selecting the characteristic frequency band corresponding to the query target and combining with a calculation mode of the similarity of the characteristic frequency band; different from the existing machine vision technology for tracking the target, the method has more possibilities of tracking the specified target in the audio and video data, and the precision and comprehensiveness of tracking the target in the audio and video data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an audio and video acquisition equipment data management system based on artificial intelligence. Background Art

[0002] Audio and video acquisition equipment is a tool used to record sound and images. Common ones include cameras, which can shoot high-quality videos, capture light through optical lenses, and store images in built-in storage or memory cards. There are also voice recorders that use highly sensitive microphones to accurately capture sound. They play a key role in many fields such as film and television production, interviews, and meeting records.

[0003] The invention patent with application number 202011241905.X discloses a video behavior analysis and management system based on artificial intelligence, including several mobile terminals, data acquisition modules, positioning modules, scene switching modules, alarm modules, display modules, behavior analysis modules and task processing modules; several mobile terminals are used for users to register and log in to the video behavior analysis and management system, and send the personal information of successful registration to the online platform, and several mobile terminals are also used for users to shoot video images of multiple scenes and upload the video images to the online platform: the data acquisition module is used to collect video images of multiple scenes, and the data acquisition module includes high-definition cameras installed in multiple scenes and several mobile terminals; the positioning module is used to locate the video images and mobile terminals, and send the positioning information to the online platform: the scene switching module is used to switch the video images between multiple scenes; the display module is used to display the video images of multiple scenes; the alarm module is used to alarm abnormal video behavior; the behavior analysis module is used to analyze the behavior in the video images of multiple scenes, and the specific analysis process is as follows.

[0004] This application aims to solve the following problems: "In current technology, most video surveillance systems are still in the traditional mode, that is, "only recording but not judging", so abnormal situations and evidence can only be investigated through subsequent video playback. There is a disadvantage of being unable to judge abnormal behavior and alarm in real time, and staff are required to monitor activities in the scene continuously, which is a heavy workload and is easily affected by human sensory fatigue, resulting in missed detections and false detections, which loses the meaning of the monitoring system to conduct real-time on-site monitoring; the current video surveillance system cannot avoid the deterioration of the situation, and the camera cannot automatically "discover the alarm situation" and actively "analyze" the surveillance targets in the field of view in real time, nor can it determine whether the behavior of these monitored targets poses a security threat. It is impossible to promptly issue an alarm to security defense personnel for security threats that have appeared or are about to appear, and the security prevention capabilities of the monitoring area need to be improved: as the scale of the monitoring system expands, the number of videos increases massively, and it is becoming increasingly difficult to obtain useful information or intelligence from them, and the search efficiency is low, making it difficult to meet the needs of the monitoring system."

[0005] Based on the above and existing technologies, the current technology for continuously tracking a specified target in a video image still remains at the visual technology level, and does not consider the auditory technology to track the target.

[0006] Therefore, an artificial intelligence-based data management system for audio and video acquisition equipment is proposed. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an audio and video acquisition equipment data management system based on artificial intelligence, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: An artificial intelligence-based audio and video acquisition equipment data management system, comprising: The receiving module is used to receive the audio and video data collected by the audio and video acquisition device and store the received audio and video data; the preprocessing module is used to obtain the audio and video data stored in the receiving module and extract the audio data from the obtained audio and video data; the marking module is used to receive the audio data extracted by the preprocessing module and distinguish the characteristic frequency bands in the audio data; the selection module is used to select the characteristic frequency bands marked in the marking module and forward the characteristic frequency bands to the query module; the query module is used to receive the characteristic frequency bands selected in the selection module and query similar characteristic frequency bands in the remaining unselected characteristic frequency bands; the output module is used to receive the characteristic frequency bands queried in the query module and output the characteristic frequency bands.

[0009] Furthermore, the audio and video acquisition device is a monitoring device deployed in any public scene. When the receiving module stores the audio and video data, it is synchronously marked with the location coordinates of the audio and video acquisition device in the public scene where the audio and video data is located. After the preprocessing module extracts the audio data from the audio and video data, it synchronously iterates the extracted audio data to the receiving module, and the iteration target is the audio and video data corresponding to the audio data. The preprocessing module is provided with submodules at the lower level, including: A copy unit, used to receive the audio data extracted by the pre-processing module from the audio and video data, and to back up the audio data; The audio data copied by the copying unit and the audio data extracted from the audio and video data by the pre-processing module are respectively used for iteration to the receiving module and forwarding to the marking module.

[0010] Furthermore, after receiving the audio data, the marking module synchronously evaluates the quality of the audio data and decides whether to perform noise reduction processing on the audio data based on the audio data quality evaluation result; The audio data quality assessment logic is expressed as: ; Where: Q is the audio data quality performance value; P signal is the signal power of the audio data; P noise is the noise power of the audio data; n is the set of frequency points in the audio data; G i is the gain at the ith frequency point; G ideal is the ideal gain; P q is the qth harmonic power in the audio data; P1 is the fundamental wave power; Among them, the ideal gain G ideal The value of is customized by the system user. The larger the audio data quality performance value Q is, the higher the audio data quality is. When the quality performance value of the audio data is less than 0.95, noise reduction processing is performed on the corresponding audio data.

[0011] Furthermore, the noise reduction processing logic of the audio data is expressed as: ; Where: y(n) is the audio data after noise reduction; N is the number of discrete Fourier transform points; P s (k) is the original audio data; M is the number of noise samples; N m (k) is the discrete value of the mth noise sample in the frequency domain; S(k) is the result of discrete Fourier transform of the original audio data; j is the imaginary unit; k is the index of the frequency discrete point; t is the index of the time domain; The audio data after the noise reduction processing is synchronously outputted based on the corresponding audio spectrum of Adobe Audition, and the audio spectrum corresponding to the audio data after the noise reduction processing is provided to the lower-level sub-module of the marking module to run the application.

[0012] Furthermore, the marking module is provided with submodules at a lower level, including: A segmentation unit, used to obtain an audio spectrum corresponding to the audio data received by the marking module, and perform segmentation processing on the audio spectrum; A cloud database is used to receive the characteristic frequency bands marked by the marking module and store the marked characteristic frequency bands; Among them, during the operation stage of the segmentation unit, the system user customizes the segmentation time domain, and the segmentation unit segments the audio spectrum based on the segmentation time domain to obtain several groups of audio segments. The marking module uses the audio segment as the characteristic frequency band identification target and performs the characteristic frequency band identification operation. The marking module distinguishes the characteristic frequency bands with the following contents: the location coordinates of the audio and video acquisition device to which the audio data from the characteristic frequency band source belongs and the timestamp corresponding to the characteristic frequency band. When the cloud database stores the characteristic frequency bands, it distinguishes and stores them based on the audio data from the characteristic frequency band source.

[0013] Furthermore, the recognition logic of the characteristic frequency band is expressed as: Set a characteristic frequency band screening threshold, calculate the average sound intensity of each characteristic frequency band, and screen the audio band that meets the characteristic frequency band screening threshold; Arrange the filtered audio segments in descending order based on their respective average sound intensities, and obtain the audio segments in the first half of the arrangement queue as feature frequency bands; Arrange the filtered audio segments in descending order based on their average sound intensities, obtain the audio segments in the second half of the arrangement queue, take the audio segments in the second half as capture targets, identify whether there are audio segments with a similarity greater than 95% for each capture target in the first half of the audio segments, and take the corresponding capture targets of the identified audio segments with a similarity greater than 95% as feature frequency bands.

[0014] Furthermore, when selecting a characteristic frequency band, the selection module uses the characteristic frequency band stored in the cloud database as a selection target, and when the query module queries similar characteristic frequency bands, the remaining unselected characteristic frequency bands are derived from the cloud database.

[0015] Furthermore, during the operation phase of the query module, the timestamp of the selected characteristic frequency band mark is synchronously read, and based on the read timestamp in the cloud database, the characteristic frequency band after the timestamp is selected as the query target; The similarity of the characteristic frequency bands in the query module is calculated by the following formula: ; Where: SIMM (α, β) is the similarity between the characteristic frequency band α and the characteristic frequency band β; G is the sampling point in the characteristic frequency band; α g is the energy of the g-th sampling point in the characteristic frequency band α; β g is the energy of the g-th sampling point in the characteristic frequency band β; ε is the correction factor; in, Express The similarity calculation logic of the audio segment is the same as the above formula. The correction factor ε takes a value of 1 or -1. When the numerator of the fraction where the correction factor ε is located is less than or equal to the denominator, the correction factor ε takes a value of 1. Otherwise, the correction factor ε takes a value of -1. When SIMM(α, β) ≥ 95%, it indicates that the two feature frequency bands are feature frequency bands of similarity.

[0016] Furthermore, when the characteristic frequency band output by the output module is output, characteristic evaluation mark information is synchronously acquired, and the characteristic frequency band is synchronously output together with its mark information; The output module is connected to a mobile computer device held by a system end user via a wireless network, and the system end user reads the output content of the output module on the mobile computer device.

[0017] Furthermore, the receiving module is interactively connected to a preprocessing module via a wireless network, the preprocessing module is interactively connected to a copy unit via a wireless network, the preprocessing module is interactively connected to a marking module via a wireless network, the marking module is interactively connected to a segmentation unit and a cloud database via a wireless network, the marking module is interactively connected to a selection module, a query module and an output module via a wireless network, and the selection module and the query module are interactively connected to the cloud database via a wireless network.

[0018] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects: The present invention provides an artificial intelligence-based data management system for audio and video acquisition equipment. During operation, the system extracts audio data from audio and video data as a reference for tracking targets in the audio and video data, captures characteristic frequency bands in the audio data, and constructs a database. The system then tracks the query target based on the selection of characteristic frequency bands corresponding to the query target and the calculation of the characteristic frequency band similarity. This is different from the existing machine vision technology for tracking targets, so that the means for tracking designated targets in audio and video data have more possibilities, thereby improving the accuracy and comprehensiveness of tracking targets in audio and video data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 The figure is a structural diagram of an audio and video acquisition equipment data management system based on artificial intelligence. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The present invention will be further described below in conjunction with the embodiments.

[0023] Example: An artificial intelligence-based audio and video acquisition equipment data management system of this embodiment, such as Figure 1 As shown, including: The receiving module is used to receive the audio and video data collected by the audio and video collection device and store the received audio and video data; A preprocessing module, used for acquiring the audio and video data stored in the receiving module, and extracting audio data from the acquired audio and video data; The audio and video acquisition device is a monitoring device deployed in any public scene. When the receiving module stores the audio and video data, it is synchronously marked with the location coordinates of the audio and video acquisition device where the audio and video data is located in the public scene. After the preprocessing module extracts the audio data from the audio and video data, it synchronously iterates the extracted audio data to the receiving module, and the iteration target is the audio and video data corresponding to the audio data; The preprocessing module is provided with submodules at the lower level, including: A copy unit, used to receive the audio data extracted by the pre-processing module from the audio and video data, and to back up the audio data; The audio data copied by the copy unit and the audio data extracted from the audio and video data by the pre-processing module are respectively used for iteration to the receiving module and forwarding to the marking module; The marking module is used to receive the audio data extracted by the preprocessing module and distinguish and mark the characteristic frequency bands in the audio data; After receiving the audio data, the marking module synchronously evaluates the quality of the audio data and decides whether to perform noise reduction processing on the audio data based on the audio data quality evaluation result; The audio data quality assessment logic is expressed as: ; Where: Q is the audio data quality performance value; P signal is the signal power of the audio data; P noise is the noise power of the audio data; n is the set of frequency points in the audio data; G i is the gain at the ith frequency point; G ideal is the ideal gain; P q is the qth harmonic power in the audio data; P1 is the fundamental wave power; Among them, the ideal gain G ideal The value of is defined by the system user. The larger the audio data quality performance value Q is, the higher the audio data quality is. When the audio data quality performance value is less than 0.95, noise reduction processing is performed on the corresponding audio data. The quality of the audio data is evaluated through the above audio data quality evaluation logic formula, thereby providing a decision for the noise reduction processing of the audio data.

[0024] The noise reduction processing logic of audio data is expressed as: ; Where: y(n) is the audio data after noise reduction; N is the number of discrete Fourier transform points; P s (k) is the original audio data; M is the number of noise samples; N m (k) is the discrete value of the mth noise sample in the frequency domain; S(k) is the result of discrete Fourier transform of the original audio data; j is the imaginary unit; k is the index of the frequency discrete point; t is the index of the time domain; The audio data after the noise reduction process is synchronously outputted based on the corresponding audio spectrum of Adobe Audition, and the audio spectrum corresponding to the audio data after the noise reduction process is provided to the lower-level sub-module of the marking module for running the application; Through the above logic formula, the specified noise reduction processing logic is provided for the noise reduction processing of the audio data.

[0025] The marking module is provided with submodules, including: A segmentation unit, used to obtain an audio spectrum corresponding to the audio data received by the marking module, and perform segmentation processing on the audio spectrum; A cloud database is used to receive the characteristic frequency bands marked by the marking module and store the marked characteristic frequency bands; Among them, during the operation stage of the segmentation unit, the system end user customizes the segmentation time domain, and the segmentation unit segments the audio spectrum based on the segmentation time domain to obtain a number of audio segments. The marking module uses the audio segment as the characteristic frequency band identification target and performs the characteristic frequency band identification operation. The marking module distinguishes the characteristic frequency bands with the following contents: the location coordinates of the audio and video acquisition device to which the audio data from the characteristic frequency band source belongs and the timestamp corresponding to the characteristic frequency band. When the cloud database stores the characteristic frequency bands, it distinguishes and stores them based on the audio data from the characteristic frequency band source. The recognition logic of the characteristic frequency band is expressed as: Set a characteristic frequency band screening threshold, calculate the average sound intensity of each characteristic frequency band, and screen the audio band that meets the characteristic frequency band screening threshold; Arrange the filtered audio segments in descending order based on their respective average sound intensities, and obtain the audio segments in the first half of the arrangement queue as feature frequency bands; Arrange the screened audio segments in descending order based on their respective average sound intensities, obtain the audio segments in the second half of the arrangement queue, use the audio segments in the second half as capture targets, identify whether there are audio segments with a similarity greater than 95% for each capture target in the first half of the audio segments, and use the capture targets corresponding to the identified audio segments with a similarity greater than 95% as feature frequency bands; A selection module, used for selecting the characteristic frequency band marked in the marking module and forwarding the characteristic frequency band to the query module; A query module, used for receiving the characteristic frequency band selected by the selection module, and querying similar characteristic frequency bands in the remaining unselected characteristic frequency bands; During the query module operation phase, the timestamp of the selected characteristic frequency band is synchronously read, and based on the read timestamp in the cloud database, the characteristic frequency band after the timestamp is selected as the query target; The similarity of the characteristic frequency bands in the query module is calculated by the following formula: ; Where: SIMM (α, β) is the similarity between the characteristic frequency band α and the characteristic frequency band β; G is the sampling point in the characteristic frequency band; α g is the energy of the g-th sampling point in the characteristic frequency band α; β g is the energy of the g-th sampling point in the characteristic frequency band β; ε is the correction factor; in, Express The similarity calculation logic of the audio segment is the same as the above formula. The correction factor ε takes a value of 1 or -1. When the numerator of the fraction where the correction factor ε is located is less than or equal to the denominator, the correction factor ε takes a value of 1. Otherwise, the correction factor ε takes a value of -1. When SIMM (α, β) ≥ 95%, it indicates that the two characteristic frequency bands are characteristic frequency bands of similarity. Through the above logic formula, the similarity calculation logic of the characteristic frequency band is further limited, ensuring that the necessary operation data support is provided for the operation of the output module in the above system.

[0026] An output module, used to receive the characteristic frequency band queried in the query module and output the characteristic frequency band; The receiving module is interactively connected to the preprocessing module through a wireless network, the preprocessing module is interactively connected to the copy unit through a wireless network, the preprocessing module is interactively connected to the marking module through a wireless network, the marking module is interactively connected to the segmentation unit and the cloud database through a wireless network, the marking module is interactively connected to the selection module, the query module and the output module through a wireless network, and the selection module and the query module are interactively connected to the cloud database through a wireless network.

[0027] In this embodiment, the receiving module operates to receive the audio and video data collected by the audio and video acquisition device, and stores the received audio and video data. The preprocessing module synchronously obtains the audio and video data stored in the receiving module, extracts audio data from the obtained audio and video data, the copying unit receives the audio data extracted by the preprocessing module in the audio and video data in real time, and backs up the audio data. The marking module post-operates to receive the audio data extracted by the preprocessing module, and distinguishes and marks the characteristic frequency bands in the audio data. The segmentation unit synchronously obtains the audio spectrum corresponding to the audio data received by the marking module, and performs segmentation processing on the audio spectrum. The cloud database receives the characteristic frequency bands marked in the marking module in real time, stores the marked characteristic frequency bands, and then the selection module selects the marked characteristic frequency bands in the marking module, and forwards the characteristic frequency bands to the query module. The query module further receives the characteristic frequency bands selected in the selection module, and queries similar characteristic frequency bands in the remaining unselected characteristic frequency bands. Finally, the output module receives the characteristic frequency bands queried in the query module and outputs the characteristic frequency bands.

[0028] Through the operation of the system in the above embodiment, another new tracking method is provided for tracking the specified target in the audio and video data in the prior art. It is completely different from the existing machine vision tracking technology. In actual application scenarios, it can be combined with machine vision technology to implement more accurate and effective tracking of the specified tracking target.

[0029] like Figure 1 As shown, when the selection module selects the characteristic frequency band, the characteristic frequency band stored in the cloud database is used as the selection target, and when the query module queries similar characteristic frequency bands, the remaining unselected characteristic frequency bands are derived from the cloud database; When the characteristic frequency band output by the output module is output, the characteristic evaluation mark information is synchronously obtained, and the characteristic frequency band is synchronously output together with its mark information; The output module is connected to a mobile computer device held by a system end user via a wireless network, and the system end user reads the output content of the output module on the mobile computer device.

[0030] Through the above settings, further operation data support is provided for the operation of the system in the above embodiments, ensuring a more stable operation of the system in the above embodiments.

[0031] In summary, during operation, the system in the above embodiment extracts audio data from the audio and video data as a reference for tracking the target in the audio and video data, captures characteristic frequency bands in the audio data, and builds a database, thereby tracking the query target based on selecting the characteristic frequency band corresponding to the query target and combining the calculation of the characteristic frequency band similarity. This is different from the existing machine vision technology for tracking targets, and makes the means of tracking specified targets in audio and video data have more possibilities, thereby improving the accuracy and comprehensiveness of tracking targets in audio and video data.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence-based audio and video acquisition equipment data management system, characterized in that: include: The receiving module is used to receive the audio and video data collected by the audio and video collection device and store the received audio and video data; A preprocessing module, used for acquiring the audio and video data stored in the receiving module, and extracting audio data from the acquired audio and video data; The marking module is used to receive the audio data extracted by the preprocessing module and distinguish and mark the characteristic frequency bands in the audio data; A selection module, used for selecting the characteristic frequency band marked in the marking module and forwarding the characteristic frequency band to the query module; A query module, used for receiving the characteristic frequency band selected by the selection module, and querying similar characteristic frequency bands in the remaining unselected characteristic frequency bands; The output module is used to receive the characteristic frequency band queried in the query module and output the characteristic frequency band.

2. According to the artificial intelligence-based audio and video acquisition equipment data management system of claim 1, it is characterized in that: The audio and video acquisition device is a monitoring device deployed in any public scene. When the receiving module stores the audio and video data, it is synchronously marked with the location coordinates of the audio and video acquisition device in the public scene where the audio and video data is located. After the preprocessing module extracts the audio data from the audio and video data, it synchronously iterates the extracted audio data to the receiving module, and the iteration target is the audio and video data corresponding to the audio data; The preprocessing module is provided with submodules at the lower level, including: A copy unit, used to receive the audio data extracted by the pre-processing module from the audio and video data, and to back up the audio data; The audio data copied by the copying unit and the audio data extracted from the audio and video data by the pre-processing module are respectively used for iteration to the receiving module and forwarding to the marking module.

3. According to the artificial intelligence-based audio and video acquisition equipment data management system of claim 1, it is characterized in that: After receiving the audio data, the marking module synchronously evaluates the quality of the audio data and decides whether to perform noise reduction processing on the audio data based on the audio data quality evaluation result; The audio data quality assessment logic is expressed as: ； Where: Q is the audio data quality performance value; P signal is the signal power of the audio data; P noise is the noise power of the audio data; n is the set of frequency points in the audio data; G i is the gain at the ith frequency point; G ideal is the ideal gain; P q is the qth harmonic power in the audio data; P1 is the fundamental wave power; Among them, the ideal gain G ideal The value of is customized by the system user. The larger the audio data quality performance value Q is, the higher the audio data quality is. When the quality performance value of the audio data is less than 0.95, noise reduction processing is performed on the corresponding audio data.

4. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 3, characterized in that: The noise reduction processing logic of the audio data is expressed as: ； Where: y(n) is the audio data after noise reduction; N is the number of discrete Fourier transform points; P s (k) is the original audio data; M is the number of noise samples; N m (k) is the discrete value of the mth noise sample in the frequency domain; S(k) is the result of discrete Fourier transform of the original audio data; j is the imaginary unit; k is the index of the frequency discrete point; t is the index of the time domain; The audio data after the noise reduction processing is synchronously outputted based on the corresponding audio spectrum of Adobe Audition, and the audio spectrum corresponding to the audio data after the noise reduction processing is provided to the lower-level sub-module of the marking module to run the application.

5. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 4, characterized in that: The marking module is provided with submodules at the lower level, including: A segmentation unit, used to obtain an audio spectrum corresponding to the audio data received by the marking module, and perform segmentation processing on the audio spectrum; A cloud database is used to receive the characteristic frequency bands marked by the marking module and store the marked characteristic frequency bands; Among them, during the operation stage of the segmentation unit, the system user customizes the segmentation time domain, and the segmentation unit segments the audio spectrum based on the segmentation time domain to obtain several groups of audio segments. The marking module uses the audio segment as the characteristic frequency band identification target and performs the characteristic frequency band identification operation. The marking module distinguishes the characteristic frequency bands with the following contents: the location coordinates of the audio and video acquisition device to which the audio data from the characteristic frequency band source belongs and the timestamp corresponding to the characteristic frequency band. When the cloud database stores the characteristic frequency bands, it distinguishes and stores them based on the audio data from the characteristic frequency band source.

6. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 5, characterized in that: The recognition logic of the characteristic frequency band is expressed as: Set a characteristic frequency band screening threshold, calculate the average sound intensity of each characteristic frequency band, and screen the audio band that meets the characteristic frequency band screening threshold; Arrange the filtered audio segments in descending order based on their respective average sound intensities, and obtain the audio segments in the first half of the arrangement queue as feature frequency bands; Arrange the filtered audio segments in descending order based on their average sound intensities, obtain the audio segments in the second half of the arrangement queue, take the audio segments in the second half as capture targets, identify whether there are audio segments with a similarity greater than 95% for each capture target in the first half of the audio segments, and take the corresponding capture targets of the identified audio segments with a similarity greater than 95% as feature frequency bands.

7. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 1, characterized in that: When selecting a characteristic frequency band, the selection module uses the characteristic frequency band stored in the cloud database as a selection target, and when the query module queries similar characteristic frequency bands, the remaining unselected characteristic frequency bands are derived from the cloud database.

8. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 1, characterized in that: The query module synchronously reads the timestamp of the selected characteristic frequency band mark during the running phase, and based on the read timestamp in the cloud database, selects the characteristic frequency band after the timestamp as the query target; The similarity of the characteristic frequency bands in the query module is calculated by the following formula: ； Where: SIMM (α, β) is the similarity between the characteristic frequency band α and the characteristic frequency band β; G is the sampling point in the characteristic frequency band; α g is the energy of the g-th sampling point in the characteristic frequency band α; β g is the energy of the g-th sampling point in the characteristic frequency band β; ε is the correction factor; in, Express The similarity calculation logic of the audio segment is the same as the above formula. The correction factor ε takes a value of 1 or -1. When the numerator of the fraction where the correction factor ε is located is less than or equal to the denominator, the correction factor ε takes a value of 1. Otherwise, the correction factor ε takes a value of -1. When SIMM(α, β) ≥ 95%, it indicates that the two feature frequency bands are feature frequency bands of similarity.

9. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 1, characterized in that: When the characteristic frequency band output by the output module is output, characteristic evaluation mark information is synchronously acquired, and the characteristic frequency band is synchronously output together with its mark information; The output module is connected to a mobile computer device held by a system end user via a wireless network, and the system end user reads the output content of the output module on the mobile computer device.

10. The artificial intelligence-based audio and video acquisition equipment data management system according to claim 1, characterized in that: The receiving module is interactively connected to the preprocessing module via a wireless network, the preprocessing module is interactively connected to the copying unit via a wireless network, the preprocessing module is interactively connected to the marking module via a wireless network, the marking module is interactively connected to the segmentation unit and the cloud database via a wireless network, the marking module is interactively connected to the selection module, the query module and the output module via a wireless network, and the selection module and the query module are interactively connected to the cloud database via a wireless network.

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