Multi-dimensional police data intelligent search system based on NLP semantic analysis

Abstract

The present application discloses a multi-dimensional police data intelligent search system based on NLP semantic analysis, including a data collection and preprocessing system, a language processing and semantic analysis system, a multimodal data fusion system, a result display and feedback system, and an update and optimization system, wherein the data collection and preprocessing system is used to collect multi-source information to generate a corpus and process it, the language processing and semantic analysis system analyzes and retrieves the corpus according to the terms queried by the user, the multimodal data fusion system is used to integrate the data, and the result display and feedback system displays the search results and obtains user feedback. As a result, the search system can deeply understand the semantics of the query statement, including the context, intent, and potential meaning, and keyword matching can significantly improve the accuracy and relevance of the search results, so that the search system can comprehensively consider multiple factors and provide more comprehensive and in-depth search results, which helps police officers make more accurate decisions.

Description

Technical Field

[0001] The present application relates to the technical field of police intelligent search, and in particular to a multi-dimensional police data intelligent search system based on NLP semantic analysis. Background Art

[0002] Conventional technical content of the industry at home and abroad at this stage:

[0003] 1. Semantic Search:

[0004] Query understanding: NLP technology can understand the intent and context of query statements, thereby providing more accurate search results.

[0005] Query Expansion: Expand your query and improve the comprehensiveness of your search results by identifying synonyms, related concepts, and contextually relevant terms.

[0006] Entity recognition: Identify specific entities in the text (such as names of people, places, organizations, etc.) and use this entity information for more accurate searches.

[0007] Concept Matching: Goes beyond exact keyword matching to identify conceptually related content, increasing the depth of search results.

[0008] 2. Information extraction:

[0009] Automatically extract key information from police data, such as the names of the people involved, the time of the crime, and case descriptive keywords, to form structured data. Use this structured data for further analysis and mining to support police decision-making.

[0010] 3. Sentiment Analysis:

[0011] In some cases, sentiment analysis is performed using NLP technology to determine the emotional tendency of text content, such as positive, negative or neutral. This is of great significance in analyzing the public's attitude towards police work and evaluating the impact of police situations.

[0012] The main disadvantages of current conventional technologies include:

[0013] 1. Data sparseness problem:

[0014] Natural language is a discrete symbol system, and each character and word is a discrete random variable. When converting text into vectors, we face the problem of data sparsity, which makes it difficult for the model to learn effective language rules from limited training data.

[0015] Limited semantic understanding: Traditional NLP technology can only process the surface information of the text, but cannot deeply understand the semantics of the text. This limits the application of NLP technology in complex police tasks, such as in-depth event analysis and sentiment analysis.

[0016] 2. Error propagation and feature engineering are cumbersome

[0017] Error propagation: In the traditional NLP processing flow, multiple steps are required (such as word segmentation, part-of-speech tagging, etc.). Each step may introduce errors and be amplified in subsequent steps, leading to error propagation problems.

[0018] Cumbersome feature engineering: Traditional NLP techniques rely on manually formulated feature templates, which are complex to design and require deep expertise and experience, while lacking versatility.

[0019] 3. Limitations and computational complexity of conversation analysis

[0020] Limitations of conversation analysis: The preset labeling method of traditional NLP technology is one-sided and inaccurate, and it is difficult to fully cover the complex content of police conversations. In addition, analysts may be affected by personal bias and subjective judgment during the label assignment process.

[0021] Computational complexity: Traditional NLP techniques can be computationally intensive and resource intensive, especially when processing large-scale police data.

[0022] 4. Lack of common sense and background knowledge

[0023] Lack of common sense and background knowledge: When processing police data, NLP systems often need to combine common sense and background knowledge to accurately understand user intent and text content. However, current NLP systems are still insufficient in this regard.

[0024] 5. Multimodal understanding and personalized needs

[0025] Insufficient multimodal understanding: Humans understand things through a combination of multiple senses such as vision and hearing, but current NLP systems mainly rely on text information and lack multimodal understanding capabilities.

[0026] Personalized needs are difficult to meet: Different police officers or different police scenarios have different personalized needs for search results, and the current NLP system needs to be improved in this regard. Summary of the invention

[0027] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0028] To this end, one purpose of this application is to propose a multi-dimensional police data intelligent search system based on NLP semantic analysis, which significantly improves the accuracy and relevance of search results, comprehensively considers multiple factors, provides more comprehensive and in-depth search results, reduces manual intervention, improves search efficiency and accuracy, and also reduces operating costs, processes and updates police data in real time, ensures the timeliness and accuracy of search results, finds required information more quickly, reduces the waste of search time and energy, and provides police officers with more comprehensive and in-depth information support, which helps them better understand the case, analyze trends and formulate strategies, has good scalability and flexibility, and can adapt to police data needs of different scales and types. As the amount of police data continues to increase and change, this technology can be continuously optimized and upgraded to maintain its advancement and competitiveness.

[0029] To achieve the above objectives, the first embodiment of the present application proposes a multi-dimensional police data intelligent search system based on NLP semantic analysis, including a data collection and preprocessing system, a language processing and semantic analysis system, a multimodal data fusion system, a result display and feedback system, and an update and optimization system, wherein:

[0030] The data collection and preprocessing system is used to collect police databases, surveillance videos and network information from multiple sources to generate a corpus and process it;

[0031] The language processing and semantic analysis system is used to process the data integrated by the data collection and preprocessing system, and to analyze and retrieve the corpus according to the terms queried by the user;

[0032] The multimodal data fusion system is used to integrate different types of data;

[0033] The multimodal data fusion system includes data fusion and feature fusion;

[0034] The result display and feedback system displays search results in a visual form and obtains user feedback;

[0035] The updating and optimizing system is used to regularly update and optimize the system;

[0036] The speech processing and semantic analysis system includes a word segmentation and part-of-speech tagging module, a named entity recognition module, a dependency syntax analysis module, a semantic understanding module and a semantic matching module, wherein:

[0037] The word segmentation and part-of-speech tagging module is used to segment the data text information into words and tag the part of speech of each word;

[0038] The named entity recognition module is used to identify and extract system-related entities;

[0039] The dependency syntax analysis module is used to analyze the dependency relationship between words and understand the sentence structure and semantics;

[0040] The semantic understanding module is used to understand the intention and context of the user's query semantics;

[0041] The semantic matching module is used to match user queries with system data to search for relevant information.

[0042] In one embodiment of the present application, the data collection and preprocessing system includes a data collection module, a data cleaning module, a data formatting module and a data labeling module, wherein:

[0043] The data collection module is used to integrate data from multiple sources including police databases, surveillance videos and network information;

[0044] The data cleaning module is used to remove noise data and duplicate data;

[0045] The data formatting module converts data in different formats into a unified format;

[0046] The data annotation module is used to manually or automatically annotate the data.

[0047] In one embodiment of the present application, the multimodal data fusion system includes a feature extraction module, a feature fusion module, a cross-modal retrieval module and a search and sorting module, wherein:

[0048] The feature extraction module is used to extract information from text, images and videos, and the feature extraction module includes image feature extraction and text feature extraction;

[0049] The feature fusion module is used to fuse features of different modalities, and the feature fusion module includes additive fusion, multiplicative fusion and weighted average fusion;

[0050] The cross-modal retrieval module is used to uniformly search for data of different modalities;

[0051] The search and sorting module uses an optimized search algorithm to retrieve relevant police data and sorts the data according to the relevance of the data to the search semantics.

[0052] In one embodiment of the present application, the result display and feedback system includes a result display module, a result update module and a user feedback module, wherein:

[0053] The result display module is used to display search results;

[0054] The result updating module is used to update the optimization search algorithm and model;

[0055] The user feedback module is used for user feedback.

[0056] In one embodiment of the present application, the updating and optimization system includes a model training module, a model optimization module, a sorting algorithm module and a security protection module, wherein:

[0057] The model training module is used to train the machine learning model, and the model training module includes regression model training, classification model training and clustering model training;

[0058] The model optimization module is used to adjust model parameters and improve algorithm structure;

[0059] The sorting algorithm module sorts the search results;

[0060] The security protection module is used to protect data security and user privacy.

[0061] In one embodiment of the present application, the semantic matching module includes a vector representation unit and a similarity calculation unit, wherein:

[0062] The vector representation unit is used to convert data text information into vector representation;

[0063] The similarity calculation unit is used to calculate the semantic similarity between the user query and the police data and calculate the ranking.

[0064] In one embodiment of the present application, the multi-dimensional police data intelligent search system operates as follows:

[0065] S1. Constructing the corpus: Integrate data from various sources such as police databases, surveillance videos, and network information through the data collection and preprocessing system, clean the integrated data, remove noise data and duplicate data, and convert data in different formats into a unified processing format;

[0066] S2. Optimize the corpus: The language processing and semantic analysis system and the multimodal data fusion system segment the integrated data, segment the integrated data text into words, and manually or automatically annotate the keywords, extract entity data from the corpus for annotation, and annotate words with dependency relationships;

[0067] S3, Semantic Analysis: The language processing and semantic analysis system uses semantic analysis technology to understand the intent and context of the user's query, matches the user's query terms with the corpus, finds the most relevant text information, converts the text information into a vector representation, and obtains the search results through similarity calculation;

[0068] S4. Similarity calculation: The language processing and semantic analysis system parses the query terms input by the user, generates structured query requests, optimizes the search algorithm, and cooperates with the multimodal data fusion system to retrieve relevant police data in the corpus, compares the relevance of the annotated data and the terms input by the user, and sorts the search results;

[0069] S5. Result display and feedback: The result display and feedback system displays search results in a visual form and obtains user feedback. It displays search results in the form of charts, texts, and videos, collects user feedback on search results, optimizes search algorithms and models, and updates search results and data in real time based on user feedback.

[0070] S6. Search algorithm and model optimization: The update and optimization system uses machine learning and deep learning, and uses a large amount of police data and query statements to train machine learning models so that they can accurately query intent and return relevant results. The performance of the search model is optimized by adjusting model parameters and improving algorithm structure.

[0071] Compared with the prior art, the multi-dimensional police data intelligent search system based on NLP semantic analysis in the embodiment of the present application has the following advantages:

[0072] 1. Deep semantic understanding ability:

[0073] NLP technology enables search systems to deeply understand the semantics of query statements, including context, intent, and potential meaning. This deep understanding capability far exceeds the keyword matching of conventional technologies and can significantly improve the accuracy and relevance of search results.

[0074] 2. Multi-dimensional data fusion capability:

[0075] This technology can integrate police data from different sources, formats and dimensions into a unified data view. This capability enables the search system to comprehensively consider multiple factors and provide more comprehensive and in-depth search results, helping police officers make more accurate decisions.

[0076] 3. Intelligence and automation:

[0077] The intelligent search algorithm based on NLP can automatically adjust the search strategy and parameters to obtain the best search results. This intelligent and automated capability reduces manual intervention, improves search efficiency and accuracy, and also reduces operating costs.

[0078] 4. Real-time dynamic updates:

[0079] The technology can process and update police data in real time, ensuring the timeliness and accuracy of search results. This is crucial for rapid response to police situations and incidents, helping police officers to obtain the latest information and respond accordingly.

[0080] 5. Improve user experience:

[0081] Through deep semantic understanding and intelligent search, the technology can provide search results that are more in line with user intent, thereby improving user experience. Users can find the information they need faster and reduce the waste of search time and energy.

[0082] 6. Enhanced decision support:

[0083] Intelligent search of multi-dimensional police data provides police officers with more comprehensive and in-depth information support, helping them to better understand cases, analyze trends and formulate strategies. This enhanced decision-making support capability is of great significance to improving the efficiency and effectiveness of police work.

[0084] 7. Scalability and flexibility:

[0085] NLP-based search technology has good scalability and flexibility, and can adapt to the needs of police data of different sizes and types. As the amount of police data continues to increase and change, the technology can be continuously optimized and upgraded to maintain its advancement and competitiveness.

[0086] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0088] Figure 1 This is a flowchart of the operation of the intelligent search system for data according to this application;

[0089] Figure 2 It is a principle block diagram of the data intelligent search system according to this application;

[0090] Figure 3 It is a structural schematic diagram of the data collection and preprocessing system according to the present application;

[0091] Figure 4 It is a principle block diagram of the language processing and semantic analysis system according to the present application;

[0092] Figure 5 is a principle block diagram of a multimodal data fusion system according to the present application;

[0093] Figure 6 This is a block diagram of the system for displaying and providing feedback based on the results of this application;

[0094] Figure 7 The following is a block diagram of the system for updating and optimizing according to this application. DETAILED DESCRIPTION

[0095] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limitations on the present application. On the contrary, the embodiments of the present application include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0096] The following describes the multi-dimensional police data intelligent search system based on NLP semantic analysis in accordance with an embodiment of the present application in conjunction with the accompanying drawings.

[0097] The multi-dimensional police data intelligent search system based on NLP semantic analysis provided in the embodiment of the present application can be applied to.

[0098] like Figure 1-7 As shown, the multi-dimensional police data intelligent search system based on NLP semantic analysis of an embodiment of the present application includes a data collection and preprocessing system, a language processing and semantic analysis system, a multimodal data fusion system, a result display and feedback system, and an update and optimization system.

[0099] The data collection and preprocessing system is used to collect and process multi-source information from police databases, surveillance videos, and network information, and the language processing and semantic analysis system is used to process the data integrated by the data collection and preprocessing system, and analyze and retrieve the corpus according to the user's query terms. The multimodal data fusion system is used to integrate different types of data, and the multimodal data fusion system includes data fusion and feature fusion;

[0100] The raw data from different modalities are integrated together through data fusion.

[0101] After extracting features, the feature vectors of different modalities are integrated together to form a higher-dimensional and more expressive feature space.

[0102] Furthermore, the multimodal data fusion system also includes a front-end fusion module, a back-end fusion module and an intermediate fusion module. The front-end fusion module fuses the multimodal data before feature extraction; the back-end fusion module fuses after each modality makes a decision; and the intermediate fusion module fuses after feature extraction and before decision making.

[0103] The result display and feedback system displays search results in a visual form and obtains user feedback. The update and optimization system is used to regularly update and optimize the system.

[0104] It should be noted that the data collection and preprocessing system described in this embodiment collects police data from different data sources for different types of multiple data sources. Police data includes text, images, and videos, etc. The police data comes from multiple sources such as police databases, surveillance videos, and network information. The multimodal data fusion system uses different types of data (such as text, images, and videos) in police data to improve the comprehensiveness and accuracy of search results. The result display and feedback system displays search results in a visual form, displays search results in the form of charts, texts, and videos, and collects user feedback results to optimize and update the database.

[0105] The speech processing and semantic analysis system includes word segmentation and part-of-speech tagging module, named entity recognition module, dependency syntax analysis module, semantic understanding module and semantic matching module.

[0106] Among them, the word segmentation and part-of-speech tagging module is used to segment the data text information into words and mark the part of speech of each word. The named entity recognition module is used to identify and extract system-related entities. The dependency syntax analysis module is used to analyze the dependency relationship between words and understand the sentence structure and semantics. The semantic understanding module is used to understand the semantic intent and context of user queries. The semantic matching module is used to match user queries with system data to search for relevant information.

[0107] It should be noted that the word segmentation and part-of-speech tagging module described in this embodiment focuses on the parts of speech in the text, phrases, sentences and paragraphs. The named entity recognition module is used to identify entities in the text (such as names of people, places and institutions, etc.), and associate the entity names with the corresponding police data. The marked parts of speech and entity names are associated together, and through concept matching, in the search process, not only the precise keywords are matched, but also the basic concepts and relationships between words and phrases are considered to achieve conceptual matching. The dependency syntax analysis module uses the intelligent search engine technology of semantic analysis to understand the user's true intentions through in-depth analysis of user sentences, thereby improving the accuracy and relevance of search results. The semantic matching module deeply understands the semantics of the query statement through the search system, including context, intent and potential meaning, so that the ability of deep understanding far exceeds the keyword matching of conventional technology.

[0108] Furthermore, text representation is the process of converting text data into a form that can be processed by computers (such as vectors). In police data search, text representation technology is used to convert query statements and police data into a unified vector format for similarity calculation and matching.

[0109] In one embodiment of the present application, the data collection and preprocessing system includes a data collection module, a data cleaning module, a data formatting module and a data labeling module.

[0110] The data collection module is used to integrate data from multiple sources such as police databases, surveillance videos and network information. The data cleaning module is used to remove noise data and duplicate data. The data formatting module converts data in different formats into a unified format. The data annotation module is used to manually or automatically annotate data.

[0111] It should be noted that the data collection module collects police data from different sources, formats and dimensions, such as police databases, surveillance videos and network information, and then removes noise data and duplicate data through the data cleaning module to ensure the accuracy and integrity of the data. The data formatting module then converts the data in different formats into a unified processing format, and the data annotation module manually or automatically annotates the data to improve the accuracy of subsequent processing.

[0112] In one embodiment of the present application, a multimodal data fusion system includes a feature extraction module, a feature fusion module, a cross-modal retrieval module, and a search and sorting module.

[0113] Among them, the feature extraction module is used to extract information from text, images and videos. The feature extraction module includes image feature extraction and text feature extraction;

[0114] Text feature extraction mainly uses NLP technology to extract features from text. For example, TF-IDF vectorizer is used to extract features from text, and the obtained feature vector is used for subsequent fusion.

[0115] The feature fusion module is used to fuse the features of different modalities. The feature fusion module includes additive fusion, multiplicative fusion and weighted average fusion;

[0116] The eigenvectors of different modes are directly added together.

[0117] Multiplicative fusion multiplies the feature vectors of different modalities element-wise.

[0118] Weighted average fusion assigns weights to the feature vectors of each modality and then performs weighted averaging.

[0119] The cross-modal retrieval module is used to perform unified retrieval of data in different modalities, and the search and sorting module uses an optimized search algorithm to retrieve relevant police data and sort the data according to the relevance to the retrieval semantics.

[0120] It should be noted that the feature extraction module is connected to the data collection and preprocessing system. Through feature extraction, police data from different sources, different formats and different dimensions are extracted and collected together. After the data is cleaned by the data cleaning module, the police data is fused through the feature fusion module and a unified data view is formed through the data formatting module. The data in the data view is identified through the data labeling module, and the police data is searched and the search results are sorted.

[0121] In one embodiment of the present application, the result display and feedback system includes a result display module, a result update module and a user feedback module.

[0122] Among them, the result display module is used to display search results, the result update module is used to update and optimize search algorithms and models, and the user feedback module is used for user feedback.

[0123] It should be noted that the result display module displays the search results in the form of charts, texts, videos, etc. for the convenience of users to view. After the user queries and completes the search, the user feedback module will provide feedback on the problems encountered during the search process. The system will update the search results and database in real time based on the feedback results.

[0124] In one embodiment of the present application, the update and optimization system includes a model training module, a model optimization module, a sorting algorithm module and a security protection module.

[0125] Among them, the model training module is used to train the machine learning model, and the model training module includes regression model training, classification model training and clustering model training;

[0126] The task is to predict a continuous value (such as age, score, etc.) using regression model training;

[0127] The task is to predict a category (such as object recognition, data analysis, etc.) using classification model training;

[0128] The task is to divide the input data into multiple clusters (such as user grouping, image segmentation, etc.) and use clustering model training to find the center point of the cluster so that the sum of the distances from each data point to the center point of the cluster to which it belongs is minimized.

[0129] The model optimization module is used to adjust model parameters and improve the algorithm structure, the sorting algorithm module sorts the search results, and the security protection module is used to protect data security and user privacy.

[0130] It should be noted that the model training module uses machine learning and deep learning to learn and improve algorithms from the database based on artificial intelligence, and uses a large amount of police data and query statements to train the machine learning model so that it can accurately understand the query intent and return relevant results. The model optimization module continuously updates the database according to the model training module, gradually adjusts the model parameters and improves the algorithm structure, and then continuously optimizes the performance of the search model. Advanced technologies such as deep learning are used to realize the automatic extraction and selection of features, reduce the dependence on manual templates, and introduce more advanced dialogue analysis models, such as dialogue systems based on deep learning, to improve the accuracy and comprehensiveness of dialogue analysis, and optimize algorithms and hardware resources to improve computing efficiency to meet the needs of large-scale police data processing. The sorting algorithm module uses search algorithms and indexing technologies to quickly locate police data related to query statements, establish indexes from keywords to document lists, and realize fast keyword searches. Through similarity calculation, the similarity between query statements and police data is calculated using algorithms such as cosine similarity and Jaccard similarity. The search results are sorted according to factors such as similarity, timestamps, and relevance through sorting algorithms, and the most relevant data is displayed first.

[0131] In one embodiment of the present application, the semantic matching module includes a vector representation unit and a similarity calculation unit.

[0132] Among them, the vector representation unit is used to convert data text information into vector representation, and the similarity calculation unit is used to calculate the semantic similarity between user queries and police data and calculate the ranking.

[0133] Semantic analysis is based on probability model algorithms, such as support vector machine (SVM), naive Bayes, decision tree, etc. It captures the semantic features of text in a way that computers can understand by training a large number of samples.

[0134] Furthermore, a deep neural network model is constructed through convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), etc. to automatically learn the hierarchical semantic representation of text.

[0135] It should be noted that the vector representation unit converts words or phrases into high-dimensional vectors through word vector technology, such as Word2Vec, BERT, etc., so that semantically similar words are close in distance in the vector space; at the same time, through TF-IDF vectorization: the TF-IDF algorithm is used to calculate the weight of each word in the text, and the text is represented as a vector of word weights. Finally, the similarity calculation unit and the sorting algorithm module automatically adjust the search strategy and parameters to obtain the best search results.

[0136] The representation of vector units is usually based on the Vector Space Model (VSM). In this model, text is considered to be composed of a series of independent words. Each text can be represented as a vector, and each dimension of the vector corresponds to the weight of a feature word (usually a word).

[0137] For a text D, if it contains words t1, t2, ..., tN, the text D can be represented as a vector D(w1, w2, ..., wN), where wk is the weight of word tk, 1≤k≤N.

[0138] Example:

[0139] Assume there are three texts D1, D2 and D3, and the feature words and weights they contain are as follows:

[0140] D1:(w1=0.5,w2=0.3,w3=0.2)

[0141] D2: (w1 = 0.4, w2 = 0.6, w3 = 0)

[0142] D3: (w1=0.2, w2=0.2, w3=0.6)

[0143] Here, w1, w2, and w3 represent the weights of feature words t1, t2, and t3, respectively.

[0144] Similarity calculation is used to measure the similarity between two texts. Commonly used similarity calculation methods include cosine similarity, Jaccard similarity, etc.

[0145] Cosine similarity: Cosine similarity measures the similarity between two vectors by measuring the cosine of the angle between them.

[0146] In one embodiment of the present application, the multi-dimensional police data intelligent search system operates as follows:

[0147] S1. Constructing a corpus: Integrate police data from various sources, formats and dimensions such as police databases, surveillance videos and network information through the data collection and preprocessing system, and build a large-scale training and testing corpus by collecting more and more comprehensive police data. Clean the integrated data, remove noise data and duplicate data, and convert data in different formats into a unified processing format to form a unified data view, and manually or automatically annotate the data. Collect and organize common sense and background knowledge in the field of policing, build a specialized knowledge base, and use it in conjunction with the NIP system to improve the system's understanding ability and accuracy.

[0148] Furthermore, by using natural language understanding technology, computers can understand and interpret texts written in natural language used by humans. In the process of intelligent search of police data, natural language understanding technology is used to parse query statements entered by users and understand their intent and context, including part-of-speech tagging, named entity recognition and syntactic analysis, which helps to extract key information from query statements, such as the persons involved, time and place, etc.

[0149] S2. Optimize the corpus: The language processing and semantic analysis system and the multimodal data fusion system will segment the integrated data, classify the data collected and unified in the corpus, classify the data of different categories in text form, and then mark the part of speech of each word in the text. Name the entities such as names of people, places and specific event names in the text, and establish an index from keywords to document lists. Then analyze the relevant dependencies between each sentence and word in the text, the system learns and understands the sentence structure and semantics, and gives the corresponding search results according to the words and sentences retrieved by the user.

[0150] S3. Semantic analysis: The language processing and semantic analysis system uses semantic analysis technology to understand the intent and context of user queries. Semantic analysis is one of the core technologies of NLP (natural language understanding technology), focusing on the meaning of words, phrases, sentences and paragraphs in the text. In police data search, semantic analysis technology is used to understand the deep relationship between query statements and police data. According to the user's query terms, the corpus is matched to find the most relevant text information, and synonyms, related concepts and context-related terms are identified through query expansion to expand the scope of the query statement and improve the accuracy of search results. The text information is converted into a vector representation and the text is converted into a vector representation.

[0151] Word vector technology: such as Word2Vec and BERT, which converts words or phrases into high-dimensional vectors so that semantically similar words are close to each other in the vector space.

[0152] TF-IDF vectorization: Use the TF-IDF algorithm to calculate the weight of each word in the text and represent the text as a vector of word weights.

[0153] The semantic similarity between the user query and the police data is then calculated through similarity, and the search results are ranked according to relevance and user needs.

[0154] S4. Similarity calculation: The language processing and semantic analysis system parses the query terms entered by the user and generates a structured query request. The user's query terms are compared with the terms in the index. The search algorithm is optimized and the multimodal data fusion system is used to retrieve relevant police data in the corpus. The relevance is compared based on the annotated data and the user's input terms, and the search results are sorted. The similarity between the query statement and the police data is calculated using similarity calculation methods such as cosine similarity and Jaccard similarity. The search results are sorted based on similarity, timestamp, relevance and other factors, and the most relevant data is displayed first.

[0155] S5. Result display and feedback: The result display and feedback system displays search results in a visual form and obtains user feedback. It displays search results in the form of charts, texts, and videos, collects user feedback on search results, optimizes search algorithms and models, and updates search results and data in real time based on user feedback. Combined with multimodal information such as images and voice, the multimodal understanding ability of the NLP system is improved, and the personalized needs of users are understood through user portraits, historical search records, etc., and the search results are optimized accordingly to improve the user experience.

[0156] S6. Search algorithm and model optimization: The update and optimization system uses machine learning and deep learning, and uses a large amount of police data and query statements to train machine learning models so that they can accurately query intent and return relevant results. The performance of the search model is optimized by adjusting model parameters and improving algorithm structure.

[0157] Machine learning and deep learning are important technologies in the field of artificial intelligence. They can automatically learn from data and improve the performance of algorithms. In police data search, these technologies are used to optimize search algorithms and improve the accuracy and efficiency of search results.

[0158] Model training: Use a large amount of police data and query statements to train machine learning models so that they can accurately understand query intent and return relevant results.

[0159] Model optimization: Continuously optimize the performance of the search model by adjusting model parameters, improving algorithm structure, etc.

[0160] In summary, the multi-dimensional police data intelligent search system based on NLP semantic analysis in the embodiment of the present application significantly improves the accuracy and relevance of search results, comprehensively considers multiple factors, provides more comprehensive and in-depth search results, reduces manual intervention, improves search efficiency and accuracy, and also reduces operating costs, processes and updates police data in real time, ensures the timeliness and accuracy of search results, finds required information more quickly, reduces the waste of search time and energy, and provides police officers with more comprehensive and in-depth information support, which helps them better understand the case, analyze trends and formulate strategies, has good scalability and flexibility, and can adapt to police data needs of different scales and types. As the amount of police data continues to increase and change, the technology can be continuously optimized and upgraded to maintain its advancement and competitiveness.

[0161] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and deform the above embodiments within the scope of the present application.

Claims

1. A multi-dimensional police data intelligent search system based on NLP semantic analysis, characterized in that: It includes data collection and preprocessing system, language processing and semantic analysis system, multimodal data fusion system, result display and feedback system and update and optimization system, among which: The data collection and preprocessing system is used to collect police databases, surveillance videos and network information from multiple sources to generate a corpus and process it; The language processing and semantic analysis system is used to process the data integrated by the data collection and preprocessing system, and to analyze and retrieve the corpus according to the terms queried by the user; The multimodal data fusion system is used to integrate different types of data; The multimodal data fusion system includes data fusion and feature fusion; The result display and feedback system displays search results in a visual form and obtains user feedback; The updating and optimizing system is used to regularly update and optimize the system; The speech processing and semantic analysis system includes a word segmentation and part-of-speech tagging module, a named entity recognition module, a dependency syntax analysis module, a semantic understanding module and a semantic matching module, wherein: The word segmentation and part-of-speech tagging module is used to segment the data text information into words and tag the part of speech of each word; The named entity recognition module is used to identify and extract system-related entities; The dependency syntax analysis module is used to analyze the dependency relationship between words and understand the sentence structure and semantics; The semantic understanding module is used to understand the intention and context of the user's query semantics; The semantic matching module is used to match user queries with system data to search for relevant information.

2. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The data collection and preprocessing system includes a data collection module, a data cleaning module, a data formatting module and a data labeling module, wherein: The data collection module is used to integrate data from multiple sources including police databases, surveillance videos and network information; The data cleaning module is used to remove noise data and duplicate data; The data formatting module converts data in different formats into a unified format; The data annotation module is used to manually or automatically annotate the data.

3. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The multimodal data fusion system includes a feature extraction module, a feature fusion module, a cross-modal retrieval module and a search and sorting module, wherein: The feature extraction module is used to extract information from text, images and videos, and the feature extraction module includes image feature extraction and text feature extraction; The image feature extraction is to extract features from the image using a convolutional neural network (CNN); The feature fusion module is used to fuse features of different modalities, and the feature fusion module includes additive fusion, multiplicative fusion and weighted average fusion; The cross-modal retrieval module is used to uniformly search for data of different modalities; The search and sorting module uses an optimized search algorithm to retrieve relevant police data and sorts the data according to the relevance of the data to the search semantics.

4. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The result display and feedback system includes a result display module, a result update module and a user feedback module, wherein: The result display module is used to display search results; The result updating module is used to update the optimization search algorithm and model; The user feedback module is used for user feedback.

5. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The updating and optimization system includes a model training module, a model optimization module, a sorting algorithm module and a security protection module, wherein: The model training module is used to train the machine learning model, and the model training module includes regression model training, classification model training and clustering model training; The model optimization module is used to adjust model parameters and improve algorithm structure; The sorting algorithm module sorts the search results; The security protection module is used to protect data security and user privacy.

6. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The semantic matching module includes a vector representation unit and a similarity calculation unit, wherein: The vector representation unit is used to convert data text information into vector representation; The similarity calculation unit is used to calculate the semantic similarity between the user query and the police data and calculate the ranking.

7. The multi-dimensional police data intelligent search system based on NLP semantic analysis according to claim 1 is characterized in that: The operation steps of the multi-dimensional police data intelligent search system are as follows: S1. Constructing the corpus: Integrate data from various sources such as police databases, surveillance videos, and network information through the data collection and preprocessing system, clean the integrated data, remove noise data and duplicate data, and convert data in different formats into a unified processing format; S2. Optimize the corpus: The language processing and semantic analysis system and the multimodal data fusion system segment the integrated data, segment the integrated data text into words, and manually or automatically annotate the keywords, extract entity data from the corpus for annotation, and annotate words with dependency relationships; S3, Semantic Analysis: The language processing and semantic analysis system uses semantic analysis technology to understand the intent and context of the user's query, matches the user's query terms with the corpus, finds the most relevant text information, converts the text information into a vector representation, and obtains the search results through similarity calculation; S4. Similarity calculation: The language processing and semantic analysis system parses the query terms input by the user, generates structured query requests, optimizes the search algorithm, and cooperates with the multimodal data fusion system to retrieve relevant police data in the corpus, compares the relevance of the annotated data and the terms input by the user, and sorts the search results; S5. Result display and feedback: The result display and feedback system displays search results in a visual form and obtains user feedback. It displays search results in the form of charts, texts, and videos, collects user feedback on search results, optimizes search algorithms and models, and updates search results and data in real time based on user feedback. S6. Search algorithm and model optimization: The update and optimization system uses machine learning and deep learning, and uses a large amount of police data and query statements to train machine learning models so that they can accurately query intent and return relevant results. The performance of the search model is optimized by adjusting model parameters and improving algorithm structure.

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