Communication data governance analysis system based on artificial intelligence

Abstract

The invention discloses a communication data governance analysis system based on artificial intelligence, which relates to the technical field of data management and comprises a data pipeline optimization module, a multi-modal information fusion module, a data governance engine module, a communication security management module, an emotion analysis module, a data collaboration and sharing module and a deep semantic retrieval module. According to the communication data governance analysis system based on artificial intelligence, the communication data flow distribution model and the quantum encryption model are introduced, transmission resources can be intelligently and dynamically distributed according to actual flow and service requirements, encryption strength is optimized, and efficient and safe communication data governance is achieved. Compared with the prior art, the system has stronger self-regulation and real-time protection capabilities, ensures that the communication system can still run efficiently and stably under the conditions of sharp flow increase and security requirement improvement, and provides quantum-level security guarantee which cannot be cracked.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a communication data management and analysis system based on artificial intelligence. Background Art

[0002] In modern communication networks, the surge and diversification of data traffic has put communication systems under tremendous pressure, especially during peak hours or when processing large amounts of sensitive information. Traditional data transmission and encryption technologies are difficult to effectively cope with. Existing communication systems mainly rely on fixed bandwidth allocation and static encryption strategies, which leads to waste of resources or inability to handle sudden high traffic situations in a timely manner, thereby increasing communication delays and security risks. In particular, in terms of data security, traditional encryption technology relies on computational complexity and is vulnerable to the threat of future quantum computers, and cannot provide sufficient long-term security. In addition, existing systems generally lack intelligent dynamic adjustment capabilities and cannot make real-time optimizations based on actual traffic loads or business needs.

[0003] Disadvantages of existing technology

[0004] Fixed bandwidth allocation and encryption policies: Resource allocation cannot be adjusted dynamically based on actual traffic fluctuations or business sensitivity, resulting in low resource utilization or performance bottlenecks.

[0005] Traditional encryption technology is not secure enough: Traditional encryption methods are difficult to provide long-term and effective security protection.

[0006] Lack of real-time monitoring and optimization mechanisms: Existing systems have difficulty detecting and responding to potential security threats in real time, and are unable to dynamically adjust encryption strength or processing strategies. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a communication data governance and analysis system based on artificial intelligence to solve the problems raised in the above background technology.

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a communication data governance and analysis system based on artificial intelligence, comprising a data pipeline optimization module, a multimodal information fusion module, a data governance engine module, a communication security management module, a sentiment analysis module, a data collaboration and sharing module, and a deep semantic retrieval module;

[0009] The data pipeline optimization module dynamically optimizes the transmission and processing efficiency of the data pipeline according to the communication data flow and complexity, and adjusts the processing order and strategy in the data stream in real time through the reinforcement learning algorithm;

[0010] The multimodal information fusion module integrates different types of communication data including text, voice, video, and image through multimodal fusion technology and deep learning models to perform data analysis;

[0011] The data governance engine module automatically generates new governance policies and updates the data governance rule base by continuously monitoring and analyzing the changing trends of communication data and user behaviors;

[0012] The communication security management module forms a communication security solution based on the quantum encryption model, which is used to process sensitive messages and is applied to communication scenarios with high security requirements;

[0013] The sentiment analysis module simulates human emotions and cognitive processes to understand the emotions and intentions in communication data. Based on cognitive technology with cognitive enhancement, sentiment analysis combines multiple factors such as context, tone, and historical behavior to analyze more complex emotional states.

[0014] The data collaboration and sharing module uses secure multi-party computing and privacy enhancement technology to achieve collaborative analysis and governance of multi-party data without directly sharing data;

[0015] The deep semantic retrieval module realizes accurate semantic retrieval function by deeply understanding the semantic relationship in communication data.

[0016] Further optimizing the technical solution, the data pipeline optimization module has a built-in communication data flow distribution model, and the communication data flow distribution model is provided with a state variable ( )、action variables( ) and the reward function ( ), strategy function ( ), automatically allocates more resources to handle critical communication data, such as customer complaints or sensitive messages, when traffic surges during peak hours, and adopts latency optimization strategies when the data volume is small to reduce the waste of system resources;

[0017] It also supports priority identification of different types of communication data and targeted optimization; for example, critical communications are given relatively high transmission speeds and relatively low delays, and non-real-time communications are temporarily buffered.

[0018] Further optimizing the technical solution, the communication data flow distribution model assumes that the communication data is in different time periods ( ) face different loads ( ), dynamically adjust the communication load to maximize overall performance;

[0019] The state variable ( ):

[0020] Indicates that at time ( ) The current status of the system, including communication data flow ( )、Network delay( ), resource usage ( ) and communication type priority ( ), then:

[0021] ;

[0022] in,

[0023] ( ) is the system in ( ) traffic load at the moment;

[0024] ( ) is the average transmission delay of the current communication data;

[0025] ( ) is the utilization rate of current system resources such as bandwidth and computing resources, ranging from 0 to 1;

[0026] ( ) is the priority distribution of communication data, reflecting the priority of different types of communications, such as customer complaints and daily communications;

[0027] The action variable ( ):

[0028] Indicates that at time ( ) The optimization actions taken by the system include the adjustment of data flow routing and the reallocation of resource allocation. The action variables are as follows:

[0029] ;

[0030] in,

[0031] ( ) means in ( )The system always performs different data types ( )’s routing optimization strategy determines whether the data is transmitted through different paths;

[0032] ( ) is for different data types ( ) bandwidth allocation ratio, satisfying ( ).

[0033] Further optimizing the technical solution, the communication data flow allocation model is based on the reward function ( ) Through reinforcement learning, we find the action strategy that maximizes the long-term reward. The reward function is defined based on traffic load, delay, and resource utilization. The function is as follows:

[0034] ;

[0035] in,

[0036] ( ) represents the transmission delay at the current moment, and the reward function is expressed by ( ) multiplied by the delay value to control the penalty for delay;

[0037] ( ) is the resource utilization rate, ( ) is the corresponding weight coefficient;

[0038] ( ) reflects the incentive to allocate sufficient bandwidth to high-priority communication data, ( ) is the control weight, which encourages the system to prioritize important communications;

[0039] The strategy function ( ) Continuously optimize through reinforcement learning and select actions that maximize long-term rewards ( ), using reinforcement learning algorithm to find the optimal strategy ( ), the optimal strategy is as follows:

[0040] ;

[0041] in,( ) is the discount factor, which is used to measure the impact of future earnings.

[0042] To further optimize the technical solution, the multimodal information fusion module maps the communication data of text, voice, video, and image into a unified high-dimensional vector space through joint embedding, thereby integrating the data into the same analysis framework, specifically including the following joint embedding process:

[0043] Text embedding: text is converted into text vectors through natural language models such as BERT and GPT to represent its semantic features;

[0044] Speech embedding: Speech is converted into emotion vectors through acoustic models such as MFCC and emotion models to capture emotional features;

[0045] Video embedding, the video generates video feature vectors through facial expression and posture detection models;

[0046] Image embedding, images are converted into image vectors through deep convolutional neural networks, representing object, scene or situation features.

[0047] To further optimize the technical solution, the data governance engine module uses artificial intelligence-based meta-learning technology, which allows the module to quickly adapt to new rules and new environments;

[0048] When a new type of communication data is detected, a new set of data classification, labeling and processing rules is automatically generated and evaluated for effectiveness; if the new strategy shows better performance, the old strategy is automatically replaced to form a continuously optimized data governance closed loop.

[0049] To further optimize this technical solution, the communication security management module dynamically adjusts the quantum key through the quantum encryption model to adapt to the security requirements of different scenarios, and in the process of quantum key distribution, AI automatically detects and blocks any abnormal situation;

[0050] The quantum encryption model assumes that the system is based on the amount of data transmitted ( ) and business sensitivity ( ) Dynamically adjust the quantum key length ( ), and based on the AI ​​real-time monitoring mechanism, detect abnormal events in the quantum channel, quantum key length ( ) is dynamically adjusted as follows:

[0051] ;

[0052] in,( )and( ) is a weight coefficient, which is used to balance the impact of communication traffic and business sensitivity on key length, and to control the response speed of quantum key to data volume and business sensitivity respectively. The weight coefficient is dynamically adjusted by the system through historical data analysis and AI learning mechanism;

[0053] ( ) indicates the real-time size of the communication data volume, which is obtained through network bandwidth monitoring;

[0054] ( ) represents the business sensitivity score, which ranges from [0,1] and is automatically generated through AI risk assessment of business scenarios.

[0055] Further optimizing this technical solution, in the quantum encryption model, AI monitors the quantum state disturbance in the process of quantum key distribution, assuming that the quantum state ( ) may be attacked or interfered during transmission. The disturbance detection formula ( )Monitor any anomalies:

[0056] ;

[0057] in,( ) is the theoretically expected quantum state, representing an ideal state without interference;

[0058] ( ) is the actual received quantum state;

[0059] ( ) is the number of sampling times, which is used to improve the accuracy of detection.

[0060] Further optimizing the technical solution, in the quantum encryption model, the quantum key length ( ) After the adjustment is confirmed and distributed to both parties, data encryption is performed using a symmetric quantum encryption algorithm to ensure that each bit of information is encrypted using a unique quantum key; the encryption formula is as follows:

[0061] ;

[0062] in,( ) is the first ( ) encrypted ciphertext bits;

[0063] ( ) is the first ( ) plaintext bits;

[0064] ( ) is the first ( ) bits;

[0065] ( ) represents the exclusive OR operation (XOR), which ensures that each plaintext bit is encrypted with a unique quantum key bit.

[0066] To further optimize the technical solution, the system also includes an intelligent question-answering module, which combines NLP technology to interactively answer questions based on communication data;

[0067] Users ask questions using natural language, and the system gives accurate answers based on historical communication records and information in the knowledge base.

[0068] Compared with the prior art, the present invention provides a communication data management and analysis system based on artificial intelligence, which has the following beneficial effects:

[0069] This AI-based communication data management and analysis system, by introducing a communication data traffic allocation model and a quantum encryption model, can intelligently and dynamically allocate transmission resources according to actual traffic and business needs, optimize encryption strength, and achieve efficient and secure communication data management. Compared with traditional technologies, the system has stronger self-regulation and real-time protection capabilities, ensuring that the communication system can still operate efficiently and stably in the case of a surge in traffic and increased security requirements, and providing quantum-level security that cannot be cracked. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a schematic diagram of the structure of a communication data management and analysis system based on artificial intelligence proposed by the present invention;

[0071] Figure 2 A schematic diagram of the use of a communication data flow distribution model in a communication data management and analysis system based on artificial intelligence proposed by the present invention;

[0072] Figure 3 This is a schematic diagram of the use of a quantum encryption model in an artificial intelligence-based communication data governance and analysis system proposed by the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0074] Example:

[0075] See also Figure 1 , an artificial intelligence-based communication data governance and analysis system, including a data pipeline optimization module, a multimodal information fusion module, a data governance engine module, a communication security management module, a sentiment analysis module, a data collaboration and sharing module, and a deep semantic retrieval module.

[0076] The data pipeline optimization module dynamically optimizes the transmission and processing efficiency of the data pipeline according to the communication data flow and complexity, and adjusts the processing order and strategy in the data stream in real time through the reinforcement learning algorithm.

[0077] Not only can it dynamically learn the best processing methods in different situations based on historical data, but it can also analyze future communication load changes based on real-time data traffic and respond in advance. For example, when the system predicts a large number of communication requests, it will prepare caches and temporary data pipelines in advance to respond, maximizing the response speed and stability of the communication system.

[0078] In this embodiment, the data pipeline optimization module has a built-in communication data flow distribution model, and the communication data flow distribution model is provided with a state variable ( )、action variables( ) and the reward function ( ), strategy function ( ), automatically allocates more resources to handle critical communication data, such as customer complaints or sensitive messages, when traffic surges during peak hours, and adopts latency optimization strategies when the data volume is small to reduce the waste of system resources;

[0079] It also supports priority identification of different types of communication data and targeted optimization; for example, critical communications are given relatively high transmission speeds and relatively low delays, and non-real-time communications are temporarily buffered.

[0080] The communication data flow distribution model assumes that the communication data is distributed in different time periods ( ) face different loads ( ), dynamically adjust the communication load to maximize overall performance;

[0081] The state variable ( ):

[0082] Indicates that at time ( ) The current status of the system, including communication data flow ( )、Network delay( ), resource usage ( ) and communication type priority ( ), then:

[0083] ;

[0084] in,

[0085] ( ) is the system in ( ) traffic load at the moment;

[0086] ( ) is the average transmission delay of the current communication data;

[0087] ( ) is the utilization rate of current system resources such as bandwidth and computing resources, ranging from 0 to 1;

[0088] ( ) is the priority distribution of communication data, reflecting the priority of different types of communications, such as customer complaints and daily communications;

[0089] The action variable ( ):

[0090] Indicates that at time ( ) The optimization actions taken by the system include the adjustment of data flow routing and the reallocation of resource allocation. The action variables are as follows:

[0091] ;

[0092] in,

[0093] ( ) means in ( )The system always performs different data types ( )’s routing optimization strategy determines whether the data is transmitted through different paths;

[0094] ( ) is for different data types ( ) bandwidth allocation ratio, satisfying ( ).

[0095] The communication data traffic allocation model is based on the reward function ( ) Through reinforcement learning, we find the action strategy that maximizes the long-term reward. The reward function is defined based on traffic load, delay, and resource utilization. The function is as follows:

[0096] ;

[0097] in,

[0098] ( ) represents the transmission delay at the current moment, and the reward function is expressed by ( ) multiplied by the delay value to control the penalty for delay;

[0099] ( ) is the resource utilization rate, ( ) is the corresponding weight coefficient;

[0100] ( ) reflects the incentive to allocate sufficient bandwidth to high-priority communication data, ( ) is the control weight, which encourages the system to prioritize important communications;

[0101] The strategy function ( ) Continuously optimize through reinforcement learning and select actions that maximize long-term rewards ( ), using reinforcement learning algorithm to find the optimal strategy ( ), the optimal strategy is as follows:

[0102] ;

[0103] in,( ) is the discount factor, which is used to measure the impact of future earnings.

[0104] like Figure 2 As shown, the model, when used:

[0105] Data flow monitoring and status input:

[0106] At fixed intervals (e.g. 5 seconds), the system collects the current communication load data, including the current traffic ( ),Delay( ), Resource Usage( ) and priority distribution ( ). This information constitutes the current state ( ).

[0107] Action selection and resource allocation:

[0108] The system uses the strategy function ( ) selects the optimal action ( For example, when the system detects a surge in customer complaint traffic, it will allocate more bandwidth ( ) assigned to complaint traffic and optimize its routing ( ) to reduce latency.

[0109] Feedback and Reinforcement Learning Updates:

[0110] After each optimization action is completed, the system will calculate the reward value based on feedback information such as communication delay and resource consumption ( ). The reinforcement learning algorithm will adjust the strategy according to the reward value and gradually learn the optimal resource allocation plan under different traffic conditions.

[0111] Future load prediction and advance optimization:

[0112] The system combines historical communication data with current trends and uses a time series forecasting model to predict future traffic changes. When the system predicts that future traffic will increase significantly (for example, customer complaints are expected to increase significantly within a certain period of time), the system will prepare more cache resources in advance and optimize pipeline configuration to cope with the upcoming traffic peak.

[0113] The multimodal information fusion module integrates different types of communication data including text, voice, video, and image through multimodal fusion technology combined with a deep learning model to perform data analysis.

[0114] Different from traditional independent processing of modalities, this module uses joint embedding technology to map data from different modalities into a unified high-dimensional space to ensure lossless combination of information. This enables the system to gain more comprehensive insights when processing complex, multi-source data and reduce the risk of information loss or misunderstanding.

[0115] In this embodiment, the multimodal information fusion module maps the communication data of text, voice, video, and image into a unified high-dimensional vector space through joint embedding, thereby integrating the data into the same analysis framework, which specifically includes the following joint embedding process:

[0116] Text embedding: text is converted into text vectors through natural language models such as BERT and GPT to represent its semantic features;

[0117] Speech embedding: Speech is converted into emotion vectors through acoustic models such as MFCC and emotion models to capture emotional features;

[0118] Video embedding, the video generates video feature vectors through facial expression and posture detection models;

[0119] Image embedding, images are converted into image vectors through deep convolutional neural networks, representing object, scene or situation features.

[0120] These different modal embedding vectors are combined in high-dimensional space and fused through deep neural networks to generate a final feature vector containing multimodal information. This fused vector can more comprehensively represent user feedback information, allowing the system to make more accurate sentiment analysis and problem judgment when facing complex, multi-source data.

[0121] The data governance engine module automatically generates new governance policies and updates the data governance rule base by continuously monitoring and analyzing the changing trends of communication data and user behaviors.

[0122] In this embodiment, the data governance engine module uses artificial intelligence-based meta-learning technology, and the module quickly adapts to new rules and new environments;

[0123] When a new type of communication data is detected, a new set of data classification, labeling and processing rules is automatically generated and evaluated for effectiveness; if the new strategy shows better performance, the old strategy is automatically replaced to form a continuously optimized data governance closed loop.

[0124] It is not limited to updating rules, but can also actively generate data quality improvement strategies. For example, when it detects that the quality of a certain type of communication data has been lower than expected for a long time, the system will automatically analyze the reasons (such as data collection problems, transmission noise, or business process deviations) and make optimization suggestions. The engine can feed these suggestions back to users or directly apply them to the system to further improve the efficiency and accuracy of data governance.

[0125] The communication security management module forms a communication security solution based on a quantum encryption model, is used to process sensitive messages, and is applied to communication scenarios with high security requirements.

[0126] In this embodiment, the communication security management module dynamically adjusts the quantum key through the quantum encryption model to adapt to the security requirements of different scenarios, and in the process of quantum key distribution, AI automatically detects and blocks any abnormal situation;

[0127] The quantum encryption model assumes that the system is based on the amount of data transmitted ( ) and business sensitivity ( ) Dynamically adjust the quantum key length ( ), and based on the AI ​​real-time monitoring mechanism, detect abnormal events in the quantum channel, quantum key length ( ) is dynamically adjusted as follows:

[0128] ;

[0129] in,( )and( ) is a weight coefficient, which is used to balance the impact of communication traffic and business sensitivity on key length, and to control the response speed of quantum key to data volume and business sensitivity respectively. The weight coefficient is dynamically adjusted by the system through historical data analysis and AI learning mechanism;

[0130] ( ) indicates the real-time size of the communication data volume, which is obtained through network bandwidth monitoring;

[0131] ( ) represents the business sensitivity score, which ranges from [0,1] and is automatically generated through AI risk assessment of business scenarios.

[0132] The quantum key length not only changes in real time according to the amount of data transmission, but can also be adaptively adjusted according to the security requirements of the business scenario, ensuring that encryption strength is improved in highly sensitive scenarios and computing overhead is reduced in low-risk scenarios.

[0133] In the quantum encryption model, AI monitors the quantum state disturbance during quantum key distribution. Assuming that the quantum state ( ) may be attacked or interfered during transmission. The disturbance detection formula ( )Monitor any anomalies:

[0134] ;

[0135] in,( ) is the theoretically expected quantum state, representing an ideal state without interference;

[0136] ( ) is the actual received quantum state;

[0137] ( ) is the number of sampling times, which is used to improve the accuracy of detection.

[0138] when( ) When a certain threshold is exceeded, AI will immediately identify it as a potential attack and take the following actions:

[0139] Block communication: Immediately interrupt the quantum key distribution process to prevent data leakage.

[0140] Reconstructing quantum channels: Identify the best alternative channels through AI algorithms and redistribute quantum states.

[0141] In the quantum encryption model, the quantum key length ( ) After the adjustment is confirmed and distributed to both parties, data encryption is performed using a symmetric quantum encryption algorithm to ensure that each bit of information is encrypted using a unique quantum key; the encryption formula is as follows:

[0142] ;

[0143] in,( ) is the first ( ) encrypted ciphertext bits;

[0144] ( ) is the first ( ) plaintext bits;

[0145] ( ) is the first ( ) bits;

[0146] ( ) represents the exclusive OR operation (XOR), which ensures that each plaintext bit is encrypted with a unique quantum key bit.

[0147] like Figure 3 As shown, the model, when used:

[0148] Dynamic quantum key distribution: Dynamically adjust the key length according to data traffic and business sensitivity, so that the system can ensure high security without wasting computing resources.

[0149] AI monitoring and quantum state disturbance detection: By using AI to monitor quantum state changes in the process of quantum key distribution in real time, possible attacks can be identified and blocked in advance, ensuring the security of key distribution.

[0150] Adaptive key optimization: Through AI learning and data monitoring, the system is continuously optimized ( )and( ) value in order to adapt the encryption strength in different scenarios and improve the flexibility and security of the system.

[0151] This enables absolutely secure encrypted communications in complex and ever-changing communication environments.

[0152] The sentiment analysis module simulates human emotions and cognitive processes to understand the emotions and intentions in communication data. Based on cognitive technology with cognitive enhancement, sentiment analysis combines multiple factors such as context, tone, and historical behavior to analyze more complex emotional states.

[0153] In this embodiment, enterprises can gain in-depth understanding of customers’ potential emotional fluctuations, demand changes, and communication intentions. For example, the system can identify subtle changes in customers’ tone and determine whether they have negative emotions or potential complaint risks, thereby helping customer service staff intervene in advance and improve customer experience.

[0154] The data collaboration and sharing module, through secure multi-party computing and privacy enhancement technology, realizes collaborative analysis and governance of multi-party data without directly sharing data.

[0155] In this embodiment, an efficient cross-domain collaboration function is provided for scenarios where communication data needs to be shared between different business departments or enterprises.

[0156] The deep semantic retrieval module realizes accurate semantic retrieval function by deeply understanding the semantic relationship in communication data.

[0157] In this embodiment, the system also includes an intelligent question-answering module, which combines NLP technology to provide interactive answers based on communication data.

[0158] Users ask questions using natural language, and the system gives accurate answers based on historical communication records and information in the knowledge base.

[0159] The beneficial effects of the present invention are:

[0160] This AI-based communication data management and analysis system, by introducing a communication data traffic allocation model and a quantum encryption model, can intelligently and dynamically allocate transmission resources according to actual traffic and business needs, optimize encryption strength, and achieve efficient and secure communication data management. Compared with traditional technologies, the system has stronger self-regulation and real-time protection capabilities, ensuring that the communication system can still operate efficiently and stably in the case of a surge in traffic and increased security requirements, and providing quantum-level security that cannot be cracked.

[0161] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0162] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication data management and analysis system based on artificial intelligence, characterized in that: It includes data pipeline optimization module, multimodal information fusion module, data governance engine module, communication security management module, sentiment analysis module, data collaboration and sharing module and deep semantic retrieval module; The data pipeline optimization module dynamically optimizes the transmission and processing efficiency of the data pipeline according to the communication data flow and complexity, and adjusts the processing order and strategy in the data stream in real time through the reinforcement learning algorithm; The multimodal information fusion module integrates different types of communication data including text, voice, video, and image through multimodal fusion technology and deep learning models to perform data analysis; The data governance engine module automatically generates new governance policies and updates the data governance rule base by continuously monitoring and analyzing the changing trends of communication data and user behaviors; The communication security management module forms a communication security solution based on the quantum encryption model, which is used to process sensitive messages and is applied to communication scenarios with high security requirements; The sentiment analysis module simulates human emotions and cognitive processes to understand the emotions and intentions in communication data. Based on cognitive technology with cognitive enhancement, sentiment analysis combines multiple factors such as context, tone, and historical behavior to analyze more complex emotional states. The data collaboration and sharing module uses secure multi-party computing and privacy enhancement technology to achieve collaborative analysis and governance of multi-party data without directly sharing data; The deep semantic retrieval module realizes accurate semantic retrieval function by deeply understanding the semantic relationship in communication data.

2. According to claim 1, a communication data management and analysis system based on artificial intelligence is characterized in that: The data pipeline optimization module has a built-in communication data flow distribution model, and the communication data flow distribution model is provided with a state variable ( )、action variables( ) and the reward function ( ), strategy function ( ), automatically allocates more resources to handle critical communication data, such as customer complaints or sensitive messages, when traffic surges during peak hours, and adopts latency optimization strategies when the data volume is small to reduce the waste of system resources; It also supports priority identification of different types of communication data and targeted optimization; for example, critical communications are given relatively high transmission speeds and relatively low delays, and non-real-time communications are temporarily buffered.

3. According to claim 2, a communication data management and analysis system based on artificial intelligence is characterized in that: The communication data flow distribution model assumes that the communication data is distributed in different time periods ( ) face different loads ( ), dynamically adjust the communication load to maximize overall performance; The state variable ( ): Indicates that at time ( ) The current status of the system, including communication data flow ( )、Network delay( ), resource usage ( ) and communication type priority ( ), then: ; in, ( ) is the system in ( ) traffic load at the moment; ( ) is the average transmission delay of the current communication data; ( ) is the utilization rate of current system resources such as bandwidth and computing resources, ranging from 0 to 1; ( ) is the priority distribution of communication data, reflecting the priority of different types of communications, such as customer complaints and daily communications; The action variable ( ): Indicates that at time ( ) The optimization actions taken by the system include the adjustment of data flow routing and the reallocation of resource allocation. The action variables are as follows: ; in, ( ) means in ( )The system always performs different data types ( )’s routing optimization strategy determines whether the data is transmitted through different paths; ( ) is for different data types ( ) bandwidth allocation ratio, satisfying ( ).

4. According to claim 2, a communication data management and analysis system based on artificial intelligence is characterized in that: The communication data traffic allocation model is based on the reward function ( ) Through reinforcement learning, we find the action strategy that maximizes the long-term reward. The reward function is defined based on traffic load, delay, and resource utilization. The function is as follows: ; in, ( ) represents the transmission delay at the current moment, and the reward function is expressed by ( ) multiplied by the delay value to control the penalty for delay; ( ) is the resource utilization rate, ( ) is the corresponding weight coefficient; ( ) reflects the incentive to allocate sufficient bandwidth to high-priority communication data, ( ) is the control weight, which encourages the system to prioritize important communications; The strategy function ( ) Continuously optimize through reinforcement learning and select actions that maximize long-term rewards ( ), using reinforcement learning algorithm to find the optimal strategy ( ), the optimal strategy is as follows: ; in,( ) is the discount factor, which is used to measure the impact of future earnings.

5. According to the artificial intelligence-based communication data management and analysis system of claim 1, it is characterized in that: The multimodal information fusion module maps the communication data of text, voice, video, and image into a unified high-dimensional vector space through joint embedding, thereby integrating the data into the same analysis framework, which specifically includes the following joint embedding process: Text embedding: text is converted into text vectors through natural language models such as BERT and GPT to represent its semantic features; Speech embedding: Speech is converted into emotion vectors through acoustic models such as MFCC and emotion models to capture emotional features; Video embedding, the video generates video feature vectors through facial expression and posture detection models; Image embedding, images are converted into image vectors through deep convolutional neural networks, representing object, scene or situation features.

6. The communication data management and analysis system based on artificial intelligence according to claim 1 is characterized in that: The data governance engine module uses artificial intelligence-based meta-learning technology to quickly adapt to new rules and new environments; When a new type of communication data is detected, a new set of data classification, labeling and processing rules is automatically generated and evaluated for effectiveness; if the new strategy shows better performance, the old strategy is automatically replaced to form a continuously optimized data governance closed loop.

7. The communication data management and analysis system based on artificial intelligence according to claim 1 is characterized in that: The communication security management module dynamically adjusts the quantum key through the quantum encryption model to adapt to the security requirements of different scenarios, and in the process of quantum key distribution, AI automatically detects and blocks any abnormal situation; The quantum encryption model assumes that the system is based on the amount of data transmitted ( ) and business sensitivity ( ) Dynamically adjust the quantum key length ( ), and based on the AI ​​real-time monitoring mechanism, detect abnormal events in the quantum channel, quantum key length ( ) is dynamically adjusted as follows: ； in,( )and( ) is a weight coefficient, which is used to balance the impact of communication traffic and business sensitivity on key length, and to control the response speed of quantum key to data volume and business sensitivity respectively. The weight coefficient is dynamically adjusted by the system through historical data analysis and AI learning mechanism; ( ) indicates the real-time size of the communication data volume, which is obtained through network bandwidth monitoring; ( ) represents the business sensitivity score, which ranges from [0,1] and is automatically generated through AI risk assessment of business scenarios.

8. The communication data management and analysis system based on artificial intelligence according to claim 7 is characterized in that: In the quantum encryption model, AI monitors the quantum state disturbance during quantum key distribution. Assuming that the quantum state ( ) may be attacked or interfered during transmission. The disturbance detection formula ( )Monitor any anomalies: ； in,( ) is the theoretically expected quantum state, representing an ideal state without interference; ( ) is the actual received quantum state; ( ) is the number of sampling times, which is used to improve the accuracy of detection.

9. The communication data management and analysis system based on artificial intelligence according to claim 7 is characterized in that: In the quantum encryption model, the quantum key length ( ) After the adjustment is confirmed and distributed to both parties, data encryption is performed using a symmetric quantum encryption algorithm to ensure that each bit of information is encrypted using a unique quantum key; the encryption formula is as follows: ； in,( ) is the first ( ) encrypted ciphertext bits; ( ) is the first ( ) plaintext bits; ( ) is the first ( ) bits; ( ) represents the exclusive OR operation (XOR), which ensures that each plaintext bit is encrypted with a unique quantum key bit.

10. The communication data management and analysis system based on artificial intelligence according to claim 1, characterized in that: The system also includes an intelligent question-answering module that combines NLP technology to provide interactive answers based on communication data; Users ask questions using natural language, and the system gives accurate answers based on historical communication records and information in the knowledge base.

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